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Customer Experience Research: Steps, Methods, Best Practices

Have you ever wondered what sets successful businesses apart? The answer often lies in their commitment to understanding and enhancing the customer experience. How do industry leaders consistently deliver exceptional service? The key lies in strategic customer experience research.

Customer experience research is a systematic process of gathering and analyzing data to understand and evaluate the interactions between a customer and a company throughout the entire customer journey. 

It involves studying customer perceptions, expectations, and satisfaction levels to enhance and optimize the customer experience.

In this blog post, we will explore the essential steps, methods, and best practices for conducting effective customer experience research.

What is a Customer Experience Research?

Customer experience research is a systematic and strategic process of collecting, analyzing, and interpreting data related to customers’ interactions with a brand, product, or service. The objective of this research is to gain a comprehensive understanding of the overall customer journey, perceptions, preferences, and satisfaction levels. 

Through various research methods such as surveys, interviews, focus groups, and observational studies, businesses seek to uncover insights that can inform improvements in products, services, and customer interactions. 

The ultimate goal is to enhance customer satisfaction, loyalty, and the overall quality of the customer experience, contributing to the business’s long-term success. 

Importance of Customer Experience (CX) Research

The significance of customer experience (CX) research cannot be overstated, as it plays a pivotal role in various aspects of a business’s success. Here is a more detailed exploration of the importance:

Customer Retention and Loyalty Building

Customer experience research dives into understanding the intricate nuances of customer needs and expectations. Businesses can tailor their products, services, and interactions to create meaningful and positive experiences by gaining insights into what truly matters to customers. 

This, in turn, increases customer loyalty, as they feel understood and valued and are more likely to continue their association with the brand. Retaining existing customers is often more cost-effective than acquiring new ones, making customer retention a key focus for sustainable business growth.

Competitive Advantage in a Crowded Market

In a fiercely competitive marketplace, where products and services may be similar, the quality of positive customer experience emerges as a powerful differentiator. 

Companies that invest in understanding their customers and consistently deliver exceptional experiences gain a distinct competitive advantage. Positive customer interactions become the brand’s trademark, setting it apart from competitors and attracting a loyal customer base.

Driving Revenue Growth through Customer Satisfaction

Satisfied customers are likely to make repeat purchases and become brand advocates. Customer experience research helps identify the touchpoints that leave a lasting positive impression, encouraging customers to choose the brand repeatedly. 

Satisfied customers are more inclined to recommend the brand to their networks, effectively becoming brand ambassadors. This word-of-mouth marketing can significantly contribute to organic growth and increased revenue streams.

Operational Efficiency and Cost Savings

CX Research provides valuable insights beyond enhancing customer satisfaction by pinpointing pain points in the customer journey. It can lead to operational improvements within the organization. 

Streamlining processes, eliminating bottlenecks, and resolving pain points can increase operational efficiency and cost savings. This dual benefit of enhancing customer experience while optimizing internal operations is a strategic advantage that can positively impact the bottom line.

Steps Customer Experience (CX) Research

Conducting practical customer experience (CX) research involves a series of well-defined steps to ensure that you gather meaningful insights that can drive improvements in your products, services, and overall customer interactions. 

Here are the key steps for conducting customer experience research:

1. Define Objectives

At the outset of any customer experience research initiative, it is imperative to outline and define the goals and objectives meticulously. These should serve as the guiding principles throughout the research process, helping to maintain focus and relevance in enhancing the overall customer experience.

2. Identify Touchpoints

To comprehensively understand the customer journey, mapping out each touchpoint where customers interact with the brand is essential. This involves a detailed exploration of various phases, from initial awareness to post-purchase engagement. 

Identifying these touchpoints provides a holistic view of the customer experience, highlighting crucial moments that significantly impact satisfaction and loyalty.

3. Select Metrics

Choosing the right metrics is important to measure customer satisfaction, loyalty, and overall experience accurately. Metrics should align with the defined objectives and touchpoints, encompassing quantitative and qualitative aspects. 

Relevant metrics may include Net Promoter Score (NPS), customer satisfaction scores, and key performance indicators (KPIs) specific to each touchpoint.

4. Collect Data

Employing a multifaceted approach, customer data is collected through various methods, such as surveys, interviews, and analytics tools. Surveys offer structured insights, interviews provide in-depth qualitative information, and analytics tools offer quantitative data on customer behavior. 

This comprehensive data collection process ensures a well-rounded understanding of customer preferences and sentiments.

5. Analyze Data

Once the data is collected, a rigorous analysis is undertaken to discern patterns, identify trends, and pinpoint areas for improvement. Advanced analytical techniques may be applied to extract actionable insights. 

This phase transforms raw data into meaningful information that can guide decision-making and strategy formulation.

6. Implement Changes

With the insights from data analysis, strategic improvements are implemented in the customer experience. 

This phase involves making necessary adjustments to processes, communication channels, or any other touchpoints identified as potential areas for enhancement. The objective is to align the customer experience more closely with the defined goals and objectives.

7. Monitor and Iterate

The customer experience journey is an evolving process that necessitates continuous monitoring. Customer feedback, both solicited and unsolicited, is consistently reviewed. 

This iterative approach allows organizations to adapt swiftly to changing customer expectations, ensuring the customer experience strategy remains dynamic and responsive. Regular reviews and refinements based on ongoing feedback contribute to the sustained improvement of the overall customer experience.

Customer Experience Research Methods

Customer experience (CX) research employs various methods to gather insights into customers’ perceptions, expectations, and interactions with a brand. The choice of methods often depends on the research’s specific goals and the business’s nature. 

Here are some common customer experience research methods:

• Structured Questionnaires: Design surveys with clear and concise questions to collect quantitative data on specific aspects of the customer experience, such as satisfaction levels, ease of use, and overall impressions.
• Scale Utilization: Implement rating scales, Likert scales, or Net Promoter Score (NPS) scales to quantify responses and measure the degree of customer satisfaction or loyalty.
• In-Depth Exploration: Conduct one-on-one or group interviews to dive deeply into customer experiences, emotions, and perceptions, allowing for a nuanced understanding of their thoughts and motivations.
• Open-Ended Questions: Open-ended questions encourage customers to express themselves freely, providing rich qualitative data beyond predefined categories.

Observation

• Ethnographic Research: Immerse researchers in the customer’s environment, whether physical or digital, to observe natural behaviors and interactions, revealing insights that may not emerge through traditional surveys or interviews.
• Task Analysis: Break down customer interactions into specific tasks to identify pain points, bottlenecks, or areas where improvements can be made.

Social Media Monitoring

• Sentiment Analysis: Employ sentiment analysis tools to gauge the overall sentiment of customer conversations on social media platforms, helping identify positive and negative trends.
• Engagement Metrics: Track engagement metrics, such as likes, shares, and comments, to understand which aspects of the customer experience resonate most with the audience.

Usability Testing

• Task-Based Testing: Design usability tests with specific tasks for participants to complete, assessing how easily they can navigate products or services.
• Iterative Testing: Conduct iterative usability testing throughout development to identify and address usability issues early on.

Net Promoter Score (NPS)

• Standardized Scoring System: Use the NPS scale to categorize customers as promoters, passives, or detractors based on their likelihood to recommend the product or service.
• Follow-up Qualitative Questions: Supplement NPS surveys with open-ended questions to gather additional insights into the reasons behind customers’ scores and their suggestions for improved customer satisfaction. 

Best Practices for Customer Experience (CX) Research

Practical customer experience (CX) research requires careful planning and adherence to best practices to ensure the insights gained are meaningful and actionable. Here are some best practices for CX Research:

Customer-Centric Approach

• Understanding Customer Personas: Develop detailed customer personas to comprehend different customer segments’ diverse needs, preferences, and behaviors.
• Journey Mapping: Create comprehensive customer journey maps that outline every touchpoint, from initial awareness to post-purchase support, ensuring a holistic understanding of the customer experience.
• Empathy Building: Encourage customer service teams to adopt an empathetic mindset to see the world from the customer’s perspective and better anticipate and meet their needs.

Multi-Channel Analysis

• Integrated Data Systems: Implement integrated data systems that consolidate information from various channels, including online and offline interactions, social media, and customer support, providing a unified and comprehensive view of the customer journey.
• Omni-Channel Strategy: Develop an omni-channel strategy that ensures a seamless and consistent experience across all customer touchpoints, regardless of their chosen channel.

Regular Feedback

• Real-Time Feedback Mechanisms: Implement real-time feedback mechanisms, such as post-purchase surveys, online reviews, and social media listening, to capture immediate customer sentiments and preferences.
• Periodic Surveys: Conduct routine surveys to dive deeper into specific aspects of the customer experience, allowing for more in-depth insights into identifying evolving trends.

Employee Involvement

• Training and Awareness Programs: Provide employees with comprehensive training on the importance of customer experience and equip them with the skills to understand and respond to customer needs effectively.
• Employee Feedback Loops: Establish feedback loops where employees can share insights from customer interactions, fostering a collaborative approach to improving the overall customer experience.
• Recognition and Rewards: Recognize and reward employees who contribute positively to the customer experience, reinforcing a customer-centric culture.

Data Security

• Compliance Measures: Implement robust data security measures to ensure compliance with privacy regulations, such as GDPR or HIPAA, and build customer trust in handling sensitive information.
• Transparent Data Practices: Communicate openly with customers about data collection and usage, providing clear information on how their data is stored, protected, and utilized.

Continuous Improvement

• Agile Implementation of Findings: Adopt an agile approach to implementing research findings, allowing quick adjustments to products, services, or processes based on customer feedback.
• Key Performance Indicators (KPIs): Establish KPIs to measure the impact of changes implemented due to customer experience research, ensuring that improvements align with business goals.
• Benchmarking: Regularly benchmark against industry standards and competitors to identify areas for differentiation and innovation, fostering a commitment to continuous improvement beyond immediate customer feedback.

How QuestionPro CX Can Help in Customer Experience Research

QuestionPro is a survey and research platform that offers various tools for conducting customer experience (CX) research. It provides a range of features to help businesses gather feedback, analyze data, and make informed decisions based on customer insights.

Here’s a general overview of how QuestionPro CX can be used for customer experience research:

NPS & Churn Risk

• The NPS Survey Dashboard provides an advanced analytics platform for measuring Net Promoter Score (NPS) and predicting churn risk.
• Isolate, identify, and predict customer churn based on NPS data, allowing businesses to address issues and retain customers proactively.
• Leverage customer interactions to make informed decisions for improving products and services.

Sentiment Analysis

• Sentiment analysis helps classify text feedback as positive, negative, or neutral, offering more profound insights into the quality of interactions between customers and the organization.
• Move beyond numerical ratings to understand the emotional tone and sentiment behind customer feedback.
• Identify areas for improvement based on sentiment trends and patterns.

Advanced Dashboards

• Access customizable dashboards with various widget configurations, enabling you to tailor your dashboard to specific needs.
• Customize filters, chart types, labels, and month-tracking widgets to effectively visualize and analyze customer feedback.
• Gain a holistic view of customer experience data through visually appealing and insightful dashboards.

Workflow Setup

• CX Workflow allows you to assign and send surveys to customer segments within the same data file.
• Automate survey reminders to improve response rates and gather more comprehensive feedback.
• Streamline survey processes for efficient data collection and analysis.

Disposition Metrics

• Monitor emails sent continually to collect valuable data at every engagement point.
• Track changes in customer behavior over time and identify key touchpoints influencing customer satisfaction.
• Use disposition metrics to refine communication strategies and enhance customer engagement.

Closed Loop

• Capture the customer journey at various touchpoints in real time.
• Share feedback with different teams to foster collaboration and implement organizational improvements.
• Implement a closed-loop system to address customer issues promptly and enhance the overall customer experience.

Incorporating customer experience research into your business strategy is a proactive approach to building strong, lasting customer relationships. By following these steps, employing effective research methods, and embracing best practices, you can gain valuable insights that drive positive change and elevate the overall customer experience. 

Remember, a satisfied customer is not just a one-time buyer but a potential brand advocate who can contribute to the long-term success of your business.

QuestionPro CX empowers customer experience research through advanced NPS analytics, sentiment analysis, customizable dashboards, workflow automation, disposition metrics monitoring, and closed-loop feedback. 

This comprehensive toolset enables businesses to proactively identify issues, understand the sentiment, and continuously enhance customer interactions, ensuring a superior and informed customer experience.

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Researching Customer Satisfaction and Loyalty: How to Find out What People Really Think

Journal of Consumer Marketing

ISSN : 0736-3761

Article publication date: 1 April 2006

• Customer satisfaction
• Customer loyalty
• Market research methods

Goncalves, K.P. (2006), "Researching Customer Satisfaction and Loyalty: How to Find out What People Really Think", Journal of Consumer Marketing , Vol. 23 No. 3, pp. 173-173. https://doi.org/10.1108/07363760610663349

Emerald Group Publishing Limited

Copyright © 2006, Emerald Group Publishing Limited

Paul Szwarc's Researching Customer Satisfaction and Loyalty: How to Find out What People Really Think is a hybrid between the rigor and quantitative orientation of a textbook, and the “lightness” of a trade book. It is easy to read, well‐organized, easy to follow, and contains many helpful hints for practitioners new to commercial consumer research. The case studies throughout the book are likely to be especially interesting to new researchers. Senior researchers are not likely to find great value in this book.

Part I . Introduction and Theory (four chapters; 70 pages).

Part II . Getting Started (four chapters; 72 pages).

Part III . ‘Touching’ the Customer (one chapter; 20 pages).

Part IV . Outputs (two chapters; 45 pages).

Part V . What Lies Ahead? (one chapter; ten pages).

Part I provides useful background for anyone new to consumer satisfaction research. For example, Chapter 1 reminds readers that “customers” are really a wide array of stakeholders ranging from “external customers” to employees, stockholders, and prospective and lost customers. In chapter 2 the author reviews the important differences between strategic and operational research. He also takes the time to describe several well‐known customer service awards, as well as what some familiar terms mean (e.g. ISO 9002; Six Sigma).

“Instant feedback” must be the greatest concern of all moderators. Having just spent a couple of hours running a group, the moderator is asked to produce an instant summary of the “key findings” that emerged from the session. This does not allow any time for the moderator to reflect on all that has happened. Neither does it allow him or her to determine how different this group was from others her or she (or his/her colleagues) has conducted on the subject. Meanwhile, there is a risk that the client has drawn his or her own conclusions, and is keen to see if the moderator has similar “findings” (pp. 45‐6).

Chapter 4, on quantitative research, is where I had difficulty, because the author missed key points that may lead inexperienced researchers astray. For example, in the discussion of disadvantages of face‐to‐face interviewing, there is no mention of interviewer bias! Clearly, interviewer bias is a potential concern any time there is a live interviewer – telephone, in‐person, focus group moderation, etc. – so it should be included. In fact, bias is ignored or downplayed throughout the chapter, and experienced researchers know that bias can discredit any findings.

Aside from my disagreements with some of Chapter 4's content, it is easy to read, even for those who avoid the quantitative world of statistics, reliability levels, and sample size decisions. This alone, would make the chapter worth reading for new researchers, because it might help them overcome “numbers phobia”.

Part II addresses the research design process from when the research sponsor first develops its research objectives, until the formal research instrument is pre‐tested and ready for fieldwork. Chapters 5 and 6 provide both the “client” and “researcher” organizational perspectives – illuminating for those new to the field. These chapters also provide details such as who completes various tasks, how to handle budgets, and what to do when there are conflicts over methodology.

Chapter 7 moves on to sampling – who to reach, how to reach them, issues associated with certain types of samples, how many people to include, response rates, and other practical aspects of sampling that are hard to grasp until one has had to construct a sample. The author even includes a section on longitudinal research and how the samples, questionnaires, and research processes differ for one‐off projects versus those designed to be continuous or repeated at intervals.

Chapter 8 is a good overview of the questionnaire design process, from what to ask, to the role of order bias and how to handle sensitive questions. Szwarc's comments and advice are sound, and to a large degree, reflect what I have seen in my own practice. The sub‐headings he uses and some of the content are not exactly “purist” from an academic perspective, but they are very useful when designing commercial surveys.

What to do when you learn something confidential and time‐sensitive from a respondent, which should be shared with the client, but which is difficult (or impossible) to share given standard confidentiality rules.

Addressing misperceptions on the part of clients who have listened to or observed a small portion of the fieldwork, and then feel that anything which does not agree with their “knowledge” must be wrong.

Part IV (Chapters 10 and 11) are written in the same format as earlier sections but feel more like “checklists”, because they cover data cleaning, coding, entry, analysis and reporting. This is where many researchers seem to get lost, and these two chapters could easily be used to guide the data analysis and reporting process in an objective, logical fashion.

Part V, Chapter 12 shares the author's view of major global environmental shifts from demographics (the “aging” of the population in several countries) to technological change (internet, consumer electronics) to psychographics (consumer attitudes toward work, leisure and to the process of change itself). He also addresses how these shifts are affecting the market research process and industry. As he notes, everything is changing so rapidly, it is hard to keep up, and this chapter is a good example. No matter how recently the book was written, readers will find parts of this chapter sound dated – evidence that Szwarc is right!

Overall, this book is worthwhile for anyone new to market research. Junior staffers at research firms, as well as those who work for the companies that sponsor commercial research can benefit, and they may find that this becomes a reference work. It is easier to read than their marketing research textbook, and when in doubt about anything the author says, they can always refer to their textbook for a “purer”, more academic view of the world.

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Customer satisfaction, loyalty behaviors, and firm financial performance: what 40 years of research tells us

• Open access
• Published: 03 March 2023
• Volume 34 , pages 171–187, ( 2023 )

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• Vikas Mittal 1 ,
• Kyuhong Han 2 ,
• Carly Frennea 3 ,
• Markus Blut 4 ,
• Muzeeb Shaik 5 ,
• Narendra Bosukonda 6 &
• Shrihari Sridhar 6  

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The authors synthesize research on the relationship of customer satisfaction with customer- and firm-level outcomes using a meta-analysis based on 535 correlations from 245 articles representing a combined sample size of 1,160,982. The results show a positive association of customer satisfaction with customer-level outcomes (retention, WOM, spending, and price) and firm-level outcomes (product-market, accounting, and financial-market performance). A moderator analysis shows the association varies due to many contextual factors and measurement characteristics. The results have important theoretical and managerial implications.

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Customer satisfaction and firm performance: insights from over a quarter century of empirical research

Measuring Customer Satisfaction and Customer Loyalty

Avoid common mistakes on your manuscript.

1 Introduction

Oliver ( 2014 , p. 8) defines customer satisfaction (CS) as “a judgment that a product/service feature or the product or service itself provided (or is providing) a pleasurable level of consumption-related fulfillment, including levels of under- or over-fulfillment.” Similarly, Anderson and Sullivan ( 1993 , p. 126) characterize CS as a “post-purchase evaluation of product quality given repurchase expectations.” Thus, CS is a customer’s evaluative summary judgment of consumption experiences that is associated with customer- and firm-level outcomes.

Although we may theoretically know and expect that CS will have a positive association with many outcomes such as retention, WOM, and sales, a systematic and large-scale meta-analysis can provide important insights. First, it is important to compare differences in the strength of relationship across different customer- and firm-level outcomes (e.g., CS-retention vs. CS-sales). Second, it is important to examine the considerable variation in the magnitude of these relationships across studies. For example, some studies find the CS-retention correlation to be nonsignificant (e.g., van Birgelen, de Jong, and de Ruyter 2006 ) while others find a strong positive association (e.g., Anderson and Sullivan 1993 ).

Understanding the reasons behind these systematic differences can yield new and important research questions and insights. For example, is the association between CS and customer-level consequences stronger (or weaker) for business-to-consumer (B2C) markets relative to business-to-business (B2B) markets? What is the theoretical reason behind this difference, and what are its practical implications? Answering these questions can suggest more nuanced testable hypotheses and guide practitioners as well.

This study investigates the association of CS with 14 outcomes in a meta-analytic framework (see Fig. 1 , panel A). These outcomes include customer outcomes, product-market performance, accounting performance, and financial-market performance. These outcomes are of great importance to a firm’s chief marketing officer (CMO), chief sales officer (CSO), chief financial officer (CFO), and chief executive officer (CEO) (see Fig. 1 , panel B).

Customer satisfaction and its outcomes

As shown in Table 1 , there have been three meta-analyses of CS published in marketing journals. Szymanski and Henard ( 2001 ) conducted the first meta-analysis including 50 studies. Among them, 15 studies examined three CS outcomes (complaining, negative WOM, and repurchase) while 35 examined antecedents of CS. No studies investigated CS and firm-level outcomes.

Curtis et al. ( 2011 ) focused on CS and three customer-level outcomes, retention behavior, retention intention, and loyalty, with no firm-level outcomes. They showed that the positive association of CS with retention and loyalty varies across exchanges (goods vs. services), markets (B2C vs. B2B), and locations of study (North America vs. Europe vs. others).

The most recent meta-analysis by Otto, Szymanski, and Varadarajan ( 2020 ) did not examine any customer-level outcomes and included only five out of ten firm-level outcomes examined in the current study. While they included moderators such as goods vs. services and ACSI vs. non-ACSI metrics, factors such as location of study and scale points were not included.

This meta-analysis uses 535 effect sizes from 245 articles representing a combined sample size of 1,160,982 units, examines 14 effects, and includes nine moderators. It is the most comprehensive meta-analysis to date with a much larger number of articles, customer- and firm-level outcomes, and moderators (see Table 1 ).

2 Theoretical framework

Within the attitude-intentions-behavior framework (Fishbein and Ajzen 1975 ), satisfaction judgments are a function of expectations, disconfirmation, and performance (see, for a review, Oliver 2014 ). Satisfaction judgments drive customers’ behavioral intentions, which in turn guide subsequent actions such as WOM, repurchase, and spending. As customers repeatedly engage in these behaviors, their satisfaction judgments, intentions, and action are reinforced. The result of this process is a cumulative satisfaction judgment (Anderson, Fornell, and Lehmann 1994 ) and associated outcomes. This general process undergirds the framework in Fig. 1 , panel A. Note the current meta-analysis examines CS and its outcomes (and not antecedents).

2.1 Customer- and firm-level outcomes of customer satisfaction

Extant research has linked CS to four customer-level outcomes (retention, WOM, price outcomes, and spending outcomes) and ten firm-level outcomes (e.g., sales, cash flow, stock returns, and Tobin’s q ). Their definition, measures, and respective calculations are shown in Table 2 , panel A.

2.2 Moderators of the CS-outcomes relationship

Table 2 , panel B reports the nine moderators examined in this meta-analysis. These include (1) contextual factors such as type of exchange and location of study and (2) measurement characteristics including the number of items and the number of scale points in the CS measure, the source of CS measure (e.g., ACSI), the calculation of CS score (e.g., top-box score), and the measurement of outcome (e.g., behavior). Footnote 1

3 Methodology

3.1 literature search.

We identified studies using computerized searches of Web of Knowledge, ScienceDirect, and EBSCO with the keywords “customer satisfaction” and “consumer satisfaction.” We examined each issue of the major marketing journals in the USA and Europe starting from 1980. Footnote 2 Prior to 1980, CS research focused on its antecedents. We also reviewed and included pertinent articles from the three meta-analyses in Table 1 .

3.2 Criteria for inclusion/exclusion

A study was excluded if it: (1) measured satisfaction with specific attributes but not overall satisfaction, (2) used a composite measure of multiple outcomes (e.g., latent construct of repurchase and recommendation), and (3) did not report correlations or information that could be converted to correlations. Footnote 3 When a study provided multiple effect sizes, either for separate samples or relationships, we treated effects as independent. When a study provided multiple effect sizes for the same relationship (e.g., for subsets of the same sample), we calculated the average effect size. The final analyses use 535 correlations from 245 articles ( N = 1,160,982).

3.3 Approach to analysis

We calculate inverse-variance-weighted reliability-adjusted correlations between CS and each outcome (Hunter and Schmidt 2004 ). To adjust for reliability, we use Cronbach’s alpha (Nunally 1978 ) as a reliability measure and divide the raw correlations by the square root of the product of reliabilities of CS and the outcome. We are unable to correct for reliability for firm-level outcomes because they use a single metric based on archival financial data. We then transform the reliability-adjusted correlations to Fisher’s z coefficients and weight them by the inverse variance (i.e., 1/[ N  – 3]). Finally, we transform the Fisher’s z coefficients back to correlations to arrive at the weighted reliability-adjusted correlations. Footnote 4 The analyses use a random effects approach for effect size integration.

3.3.1 Publication bias

To address the file-drawer problem, we report the fail-safe N (FSN). This calculates the number of studies that would have to be missing from the analysis to nullify an effect or reduce it to a level that is not theoretically or practically significant (Orwin 1983 ). A funnel plot shows minimal publication bias (Fig. A 1 in Web Appendix A).

3.3.2 Homogeneity and moderator analysis

The Q test assesses between-study variability in the population effect size estimated by the individual studies. Footnote 5 In Table 3 , a statistically significant Q statistic suggests the need for subgroup analysis (e.g., Pick and Eisend 2014 ). Thus, we compare effect sizes across different levels of each moderator.

4.1 CS and customer-level outcomes

Table 3 , panel A reports that CS has a strong association with retention ( r = 0.60, p < 0.01) and WOM ( r = 0.68, p < 0.01) and is moderately correlated with spending ( r = 0.28, p < 0.01) and price outcomes ( r = 0.39, p < 0.01). Footnote 6 The statistically significant Q tests ( p s < 0.01) for all four outcomes indicate that effect sizes may vary based on exchange type, market type, location of study, measurement of outcome, scale items, and scale points. Disaggregated results are shown in panel A of Table A 2 in Web Appendix A and discussed next.

4.2 Moderator analysis for customer-level outcomes

4.2.1 exchange.

For retention, the association with CS is stronger for mixed exchanges ( r MIXED = 0.69) than for services ( r SERVICES = 0.56) but not for goods ( r GOODS = 0.57); the association does not differ between goods and services. The association between CS and WOM is statistically not different among goods ( r GOODS = 0.66), services ( r SERVICES = 0.64), and mixed exchanges ( r MIXED = 0.74). For spending outcomes, the association with CS is statistically similar for goods ( r GOODS = 0.38), services ( r SERVICES = 0.22), and mixed exchanges ( r MIXED = 0.27). Finally, the association of CS and price outcomes is also not statistically different across goods ( r GOODS = 0.08), services ( r SERVICES = 0.41), and mixed exchanges ( r MIXED = 0.34). Footnote 7

4.2.2 Market

The CS-retention association is statistically stronger in B2B ( r B2B = 0.66) than in B2C ( r B2C = 0.55) but not in mixed markets ( r MIXED = 0.63). The CS-WOM relationship is stronger in B2B markets than in others ( r B2C = 0.61 vs. r B2B = 0.74 vs. vs. r MIXED = 0.42). The CS-spending outcomes relationship is not statistically different across B2C ( r B2C = 0.33), B2B ( r B2B = 0.16), and mixed markets ( r MIXED = 0.23). Finally, the CS-price outcomes association is statistically similar in B2C and B2B markets ( r B2C = 0.41 vs. r B2B = 0.18).

4.2.3 Location of study

Relative to Europe, North American samples exhibit a stronger association of CS with retention ( r NORTH.AMERICA = 0.63 vs. r EUROPE = 0.51 vs. r ASIA = 0.64 vs. r AFRICA = 0.82), WOM ( r NORTH.AMERICA = 0.71 vs. r EUROPE = 0.57 vs. r ASIA = 0.65 vs. r AFRICA = 0.41), and price outcomes ( r NORTH.AMERICA = 0.75 vs. r EUROPE = 0.35). For spending outcomes, the association with CS does not statistically differ among samples from North America ( r NORTH.AMERICA = 0.25), Europe ( r EUROPE = 0.30), and Asia ( r ASIA = 0.50).

4.2.4 Measurement of outcome

The association with CS is stronger when the outcome is measured as intentions than as behaviors for retention ( r BEHAVIOR = 0.21 vs. r INTENTION = 0.65) and WOM ( r BEHAVIOR = 0.50 vs. r INTENTION = 0.71) but not for spending outcomes ( r BEHAVIOR = 0.24 vs. r INTENTION = 0.41).

4.2.5 Scale items

The association with CS is stronger when a single- vs. a multiple-item CS scale is used for retention ( r SINGLE = 0.66 vs. r MULTI = 0.55) and WOM ( r SINGLE = 0.73 vs. r MULTI = 0.59) but statistically not different for spending outcomes ( r SINGLE = 0.22 vs. r MULTI = 0.31).

4.2.6 Scale points

The association of CS with outcomes is statistically similar for 5-, 7-, 10-, and 100-point scales ( r 5-POINT = 0.62 vs. r 7-POINT = 0.60 vs. r 10-POINT = 0.50 vs. r 100-POINT = 0.54 for retention; r 5-POINT = 0.65 vs. r 7-POINT = 0.71 vs. r 10-POINT = 0.50 vs. r 100-POINT = 0.65 for WOM; r 5-POINT = 0.28 vs. r 7-POINT = 0.33 vs. r 10-POINT = 0.21 vs. r 100-POINT = 0.23 for spending outcomes; and r 5-POINT = 0.24 vs. r 7-POINT = 0.41 for price outcomes).

4.3 CS and firm-level outcomes

The CS-outcomes correlation is smaller at the firm level than at the customer level (see Table 3 , panel B) potentially because firm-level outcomes are more distal than customer-level outcomes. Different than the association of CS with customer-level outcomes, the magnitude of the association of CS with firm-level outcomes can be classified as small to moderate. Footnote 8

Specifically, CS has a positive and statistically significant association with sales ( r = 0.15, p < 0.01), profit ( r = 0.10, p < 0.01), ROA ( r = 0.22, p < 0.01), Tobin’s q ( r = 0.29, p < 0.01), and stock returns ( r = 0.08, p < 0.05); a negative and statistically significant association with cash flow variability ( r = –0.10, p < 0.01), risk ( r = –0.23, p < 0.01), and cost of debt financing ( r = –0.14, p < 0.01). CS has a nonsignificant association with market share ( r = 0.05, p > 0.10) and a weak positive association with cash flow ( r = 0.09, p < 0.10), which may occur because they likely represent multiple subgroups with large between-group variability in the association (Whitener 1990 ). Footnote 9

The Q statistics for all outcomes, except for cost of debt financing, indicate a statistically significant heterogeneity among studies (see Table 3 , panel B). Yet, with a small number of exceptions, the association between CS and firm-level outcomes is not statistically different across subgroups based on different levels of moderators (see panel B of Table A 2 in Web Appendix A). There are several potential reasons for the statistically nonsignificant results. First, for several moderator levels, each outcome has been investigated by a small number of studies (i.e., k in panel B of Table A 2 in Web Appendix A). Second, most of the firm-level studies include samples from multiple industries and preclude us from isolating correlations based on specific industry settings. Finally, published studies typically do not report correlations disaggregated by firm-level moderators such as firm size, advertising and R&D intensity, and industry concentration. Therefore, we report means by subgroups for firm-level outcomes but do not discuss them further.

5 Implications

5.1 research implications.

First, the moderator analysis shows that there is substantial and systematic heterogeneity in the positive association between CS and customer-level outcomes. Yet, we do not understand the different patterns of variability and their implications. As an example, the association of CS with price outcomes is more heterogeneous than its association with spending outcomes across markets, exchange types, and locations of study. Is it because firms have more control on price outcomes but not on spending outcomes? These issues need further research.

Second, studies that simultaneously examine and compare the association of CS with multiple customer-level outcomes under different contexts are needed. Specifically, attention to differences in effect sizes among subgroups as well as their causes and implications is a key research direction.

Third, the association of CS is strongest for WOM, followed by retention, and is the weakest for spending and price outcomes. Future research should develop a conceptual and theoretical framework to understand these relative differences. Thus, is it the case that higher CS is more beneficial for growing new customers than retaining current customers? To the extent that WOM affects the cost of attracting new customers, customer equity research can be expanded by including CS as a contributing factor for retaining current customers and attracting new customers. Third, a wider set of potential moderators including psychological constructs such as trust and commitment as well as structural factors such as company size, industry growth, and competitive intensity should be investigated.

Fourth, these results make a very strong case that consumer behavior scholars should use CS as a consequential dependent variable in their studies. CS has a clear association with actual consumer behaviors and firm-level financial outcomes. Thus, consumer behavior scholars can be reasonably assured that differences in CS are consequential, i.e., predictive of actual consumer behaviors and firm financial outcomes.

Fifth, these results call into question the long-standing insistence on using multi-item scales for measuring CS. The CS-outcomes linkage is impervious to single- vs. multiple-item scales or number of scale points (i.e., 5- vs. 7- vs. 10- vs. 100-point scale). Simple and single item scales suffice; this is an important insight for practitioners who value simplicity to reduce the cost of customer surveys.

Sixth, at the firm level, the mean association of CS with market share ( p > 0.10) and cash flow ( p < 0.10) is nonsignificant to weak (Table 3 , panel B). This may be the case if the association of CS with these outcomes is nonlinear and/or contingent on factors such as firms’ ability to standardize or customize their offerings, the heterogeneity in consumer preferences, and the nature of the offering (e.g., goods vs. services; Anderson, Fornell, and Rust 1997 ). In the same vein, CS has a stronger association with ROA than with cash flow. While we can speculate on the potential reasons for this, more studies are needed to better estimate the effects and explain the differences. Finally, the small number of studies for subgroups within different levels of moderators precluded specific conclusions; clearly, more studies on CS-firm outcomes are needed.

5.2 Implications for firm strategy and senior executives

Figure 1 , panel B organizes the outcomes of CS based on their relevance to CMOs, CSOs, CFOs, and CEOs and board members. CMOs who organize their efforts around CS and make CS as their key metric should be able to make a case for their relevance and contribution to customer retention, WOM, spending, and price outcomes. While CMOs are free to focus on other constructs such as net promoter, this research provides clear, strong, and convincing evidence for using CS as a metric to measure marketing and sales performance and relate it to firm performance. Specifically, CS can provide the basis for CMOs and CSOs to collaboratively grow the current customer base organically as well as expand it through additional sales. The positive association of CS with ROA and cash flow and its negative association with cash flow variability speak to CFOs.

Finally, our work makes a clear case for CEOs and board members to utilize CS as an organizing framework for strategy planning and execution. By making customer value, as measured through CS, the central mechanism for creating and implementing strategy, CEOs can reliably increase Tobin’s q and stock returns while decreasing risk, outcomes for which CEOs are most responsible.

In summary, a focus on CS can align C-suite members (CEO, CFO, CMO, and CSO) using a theoretically sound, conceptually consistent, and empirically validated approach. We hope that senior leaders in firms embrace a satisfaction-based approach to strategy planning and execution based on these results.

6 Concluding comments

CS is a core construct for guiding strategy research and a consequential outcome for consumer-behavior research. This meta-analysis of 535 effect sizes from 245 articles shows that the positive outcomes of CS at the customer- and firm-level vary across different outcomes and across different study characteristics. The results provide guidance for research scholars and show how senior executives can adopt a CS-based framework to develop, guide, and implement firm strategy.

The current research has limitations. First, the results are limited by data availability, which precluded a larger number of outcomes or additional moderators. Second, variation in effect sizes remained even after accounting for contextual and measurement factors, suggesting that sources of variation still exist. Finally, our analysis was based on traditional meta-analytic framework and could not capture nonlinearity in the relationship between two constructs. Studies reporting correlations at different levels of moderators and boundary conditions in the association of CS with its consequences can be helpful in this regard.

Data Availability

Please contact authors for data availability.

We calculated the proportion of studies for each combination of levels in different moderators. Table A1 in Web Appendix A reports the proportions showing adequate variation in study settings.

Journals include Journal of Marketing , Journal of Marketing Research , Marketing Science , Journal of Consumer Research , Journal of Service Research , Journal of Retailing , Journal of the Academy of Marketing Science , Journal of Services Marketing , Journal of Service Industry Management , Journal of Consumer Satisfaction, Dissatisfaction, and Complaining Behavior , Journal of Business Research , and International Journal of Service Industry Management. The list of papers included in the meta-analysis is provided in Web Appendix B.

We contacted 44 authors to request missing correlations for studies, and 17% of them provided the correlations.

We use the Fisher’s z transformation due to potential issues associated with using raw correlations. Specifically, different than Fisher’s z scores, raw correlations may be highly skewed and have a problematic standard error formulation: the standard error is used to compute the inverse variance weight in the meta-analysis (Lipsey and Wilson 2001 ). Still, we computed results using raw correlations. Reassuringly, most of the results remained unchanged when using Fisher’s z or correlations.

The Q statistic is computed by summing the squared deviations of each study’s effect estimate from the overall estimate, weighting each study by the inverse of its variance, and has a chi-square distribution with k – 1 degrees of freedom ( k = number of effect sizes). A statistically significant Q statistic indicates the effect size varies across studies. The Q statistic has low power to detect heterogeneity when the number of studies is small or sample size within studies is low. Thus, it should be interpreted cautiously.

Following Cohen ( 1992 ), we deem a correlation of 0.10 as small, 0.30 as medium, and 0.50 as large. Notably, the magnitude and the statistical significance of the correlations of CS with retention and WOM are similar to those reported in Szymanski and Henard ( 2001 ) and Curtis et al. ( 2011 ).

The very small sample size for goods and mixed exchanges precludes meaningful statistical comparisons.

We use Cohen’s ( 1992 ) standards for effect sizes in our interpretation. It may be the case that higher/more conservative standards are required because lower-level variability influences higher-level effects (e.g., individual-level variability is ignored in the estimate of firm-level effects).

Notably, the magnitude and the statistical significance of the correlations of CS with market share, sales, profit, Tobin’s q , and stock returns are similar to those reported in Otto, Szymanski, and Varadarajan ( 2020 ).

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5 customer satisfaction goals to improve loyalty and reduce churn

Creating a website that helps customers meet their immediate needs will bring traffic to your site and increase revenue.

But to drive long-term business growth, you need to go beyond meeting users’ basic demands. Setting and measuring customer satisfaction goals is the best way to delight your customers and keep them coming back long term.

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In this article, we show you how to set and measure effective customer satisfaction goals to draw on user insights and improve their product experience (PX).

Set, measure, and achieve your customer satisfaction goals with Hotjar

Learn how to use product experience insights to build customer loyalty.

5 effective customer satisfaction objectives you should set

Customer satisfaction is the backbone of a successful business. Setting clear objectives allows you to check and track your customers' experiences with your brand and product and improve their overall journey.

We’ve chosen the top five customer satisfaction goals any online business should be tracking. To prioritize the right goals for you as you go through the list, consider which of these strategies will help you build stronger relationships with your customers and meet your unique organizational KPIs. 

Goal 1: improve customer loyalty 

Building loyalty with your existing customers drives long-term business growth. Tracking customer loyalty also shows you how well your company is providing its core services.

Customer loyalty is important because:

65% of a company’s revenue comes from repeat business , and even a small increase in customer retention results in a significant profit increase

Retaining existing customers is less expensive than pursuing new leads 

Marketing to existing customers is easier because they’re already interested in your brand and the products or services you offer

How to set and measure customer loyalty goals

To set customer loyalty goals, be clear on what you want to achieve and set clear targets to measure your success. Here’s how:

1. Use OKRs

Objectives and key results (OKRs) is a collaborative goal-setting methodology that breaks goals into two steps: the objective (what we want to achieve) and the key results (how we know if we’re getting there). OKRs give your team a clear direction and actionable steps to achieve customer loyalty.

For example, a customer loyalty OKR could be: 

Objective : retain a bigger proportion of existing customers.

Key result 1: increase customer retention by 10% in the next quarter.

Key result 2: write and send out an email to lapsed customers. 

Key result 3: conduct 100 surveys with lapsed customers each month.

2. Focus on improving retention rates 

Set specific, time-bound goals to get key retention statistics. You can measure retention rates by calculating:

Customer retention rate (CRR) to measure the number of customers who stay with your business during a specific time frame

Annual churn rate (ACC) to find out the percentage loss of customers—or revenue—per year

Cohort churn rate using A/B testing to find out which groups of customers are more likely to churn compared to other groups

3. Decrease churn 

Customer churn happens when your customers no longer purchase or interact with your business. Setting goals for reducing churn is essential as keeping your customers for longer will increase revenue.

To better understand your churn rates, compare Hotjar (👋) Session Recordings of loyal customers and customers who’ve churned.  This helps you identify specific issues or blockers that a customer may have experienced with your product—like a non-intuitive user interface (UI), a product tour that fails to engage users, or bugs and features that don’t work.

You can also place Hotjar churn surveys on pages where users unsubscribe to get voice-of-the-customer (VoC) feedback about the product experience (PX).

How to improve customer loyalty goals 

Existing customers often feel like they’re a low priority and they don’t get access to the best offers—like when a TV streaming service tempts new users with a discount for the first year, this can leave the older, loyal client base wondering why they stay. To improve customer loyalty , your existing customers should know you care about their experience and see that you’re constantly finding ways to improve it.

The best way to create a loyal customer base is to listen to your customers and make sure your existing users are delighted with their product and post-purchase experience. 

Start with these tips:

Follow-up after purchases: use customer retention emails to create a great post-purchase experience. Send a pre-delivery email to highlight brand values and get users ready for your product or service. Then, send a post-delivery email asking ‘How did we do?’ to collect Customer Satisfaction Scores (CSAT), which measure short-term customer satisfaction with a product or service.

Customer loyalty programs: send discount codes to customers after they make a purchase on your e-commerce site—or as part of a periodic email campaign—to show you value them and to encourage repeat business 

Let existing customers have their voices heard: use feedback surveys to get your customers' opinions on your product to make them feel valued and show you're interested in their views

Goal 2: increase customer service satisfaction rates 

When your customers contact your help desk or customer support team it’s usually because something has gone wrong with your product or service. 

But a positive customer service experience that resolves user issues can turn a dissatisfied customer into a loyal one. 

How to set and measure customer service goals 

Set customer service goals that relate to your user's experience with your help center. You can also set objectives for evaluating how effectively your customers can self-serve.

Here are some ideas for goals you can set to increase customer service satisfaction: 

1. Reduce customer support tickets 

Comparing your customer support ticket rates month on month helps you evaluate whether your customers are getting the help they need and/or are able to resolve issues themselves. 

To understand what’s causing your customers to request help in the first place, give users a range of query types to choose from when they contact your help service. Then, review and analyze the types of issues that generate tickets. 

2. Increase help center satisfaction rates

Find out what your customers think of their customer service experience by asking them to complete an on-site survey after using your help center. You can also read through a selection of conversations between users and help center staff to identify if there are issues with the tone of voice or product solutions offered. 

3. Make your FAQs more effective

Watch Hotjar Session Recordings or view Heatmaps of your users interacting with FAQ pages to see how much time they spend there and which questions and answers they read. Apply filters to see Recordings and Heatmaps for customers who’ve submitted a support ticket, and compare their journey with that of other users. Then, use the insights you gather to make sure your FAQs help customers find the information they need.

How to increase customer service satisfaction rates

Showing your customers you want to answer their questions, resolve their issues, and give them the support they need will go a long way to achieving better customer service satisfaction.

Use these techniques to meet your goals and delight your customers in their customer service interactions: 

Let customers choose their preferred means of communication: offer users a range of communication options, from live chat to email, so they can use the channel they’re most comfortable with

Maintain up-to-date help resources: keep up-to-date and searchable FAQs and help center resources so customers can find the answers they need and fix issues themselves 

Create a smooth customer service journey: let customers know exactly where they are in the customer service process through a clear navigation hierarchy, including numbered pages

Give users real-time customer support: using AI chatbots that transition to a real-life help desk team member will give your customers immediate support. Hotjar also lets you include a Feedback button on your website that sits at the edge of a page to get in-the-moment feedback where users let you know what they like and dislike about their customer experience (CX).

Goal 3: increase product advocacy 

Up to 55% of people rely on word-of-mouth recommendations and customer references for purchase decisions, so increasing product advocacy is a great way to win new customers without increasing your advertising spend.

How to set and measure product advocacy goals

While many product advocacy goals tie in with overall customer satisfaction objectives, you can also set specific goals to increase customer referral rates or manage reviews about your product and user experience on public sites.

1. Increase customer referrals

Find out how likely your customers are to refer your product or service to other customers with Net Promoter Score® (NPS) surveys. Also, track events like call-to-action (CTA) button clicks for users who come through referral programs from social media links or email campaigns.

2. Improve customer feedback and public reviews

Track the percentage of positive reviews you’re getting, and seek to increase it by using customer feedback to make product or website improvements. Monitor reviews platforms and social media groups and reply to negative reviews to turn a negative situation into a positive one. You can set key results in terms of the number of reviews you respond to and compare negative feedback rates monthly.

How to increase product advocacy

First and foremost, increased product advocacy comes from delighting customers in general. You can also design product advocacy campaigns to encourage customer recommendations and raise awareness of your brand on social media. Here's how:

Create referral programs: incentivize referrals by offering customers a free gift or discount on their next purchase

Engage with customers on social media: create and maintain a social media presence, follow users that follow you, like and share their content, and shout out social media users who contribute positively to your posts

Goal 4: improve product usability 

A clunky website that makes it difficult for customers to find what they need wastes users’ time and energy and is bad for business.

Improving your product’s usability allows you to create a streamlined customer experience that helps users get the most out of your site and turns them into loyal, returning customers.

How to set and measure product usability goals 

Base your overall product usability goals on the big picture of how users experience your site as a whole. You should also set objectives for smaller, more concrete aspects of the user journey —like the percentage of users who ‘rage click’ or ‘u turn’—to uncover obstacles that are getting in the way of a positive user experience (UX). 

Below are some ideas for product usability goals you can set:

1. Improve user feedback on usability

Use an on-page Feedback widget to collect instant feedback when a user runs into an issue—or spots a feature that really works for them. On your Hotjar Dashboard , you can filter users’ positive and negative feedback to monitor how well you’re meeting your usability goals. 

2. Improve user satisfaction with new features before launching

Measure the success of new features by running A/B testing to check user responses before releasing them to all customers. As well as measuring which options early users prefer, get a more granular understanding of how product updates affect usability by viewing Session Recordings and Heatmaps. For example, 7-8% of the population are colorblind and will struggle to use a site with certain color combinations. Testing your new color scheme before launch will save you from rolling out an update that could be unusable for part of your customer base.

How to achieve better product usability

So how can you nail your product usability goals?

Combine usability testing with direct user feedback: use Feedback tools in your usability testing to hear how customers experience your site, product, or features, and let users point out any blockers or bugs. Customer feedback adds another dimension to usability research, helping you uncover issues that won’t necessarily emerge from testing statistics.

Watch your users interact with your site: use Session Recordings to see how users engage with your site across a full session. You’ll be able to notice areas of confusion and reveal opportunities to improve the overall user experience. 

Continually test and develop new products: constantly update your site based on user needs to take advantage of the latest research in usability and deliver an increasingly streamlined product experience

Get product feedback: use feedback widgets to find out about issues or blockers that users encounter in your product. Add the Hotjar Feedback widget to different pages along the user journey to collect feedback and hear how your product makes customers feel, in their own words.

Goal 5: drive successful cross-team collaboration

When teams and individuals are connected through cross-functional collaboration and communication , it becomes easier to set and achieve customer satisfaction goals as everyone is aligned on what needs to be done to delight users.

How to set and measure goals for cross-team collaboration 

Though internal collaboration goals are a little more difficult to track, it’s important to know what your objectives are to drive cross-team collaboration. Here are some ideas: 

1. Increase information-sharing across the business

Sharing PX insights across teams will help you prioritize the product experience and create user-centric products that put your customers first. Set goals for a certain number of cross-team meetings or updates per month.

2. Increase employee satisfaction with your collaboration culture

Ask your team members how they feel about your efforts to create a collaborative culture with surveys and interviews, and aim to improve their satisfaction ratings.

How to achieve better cross-team collaboration

Overcome organizational silos with these actions: 

Set up dedicated cross-team collaboration channels: use communication tools—like Hotjar’s Slack integration —to share customer and product insights across teams. For example, you could set up a channel to share Hotjar Highlights , which allows you to capture snippets of your Recordings and Heatmaps, to help all teams spot new customer opportunities and get buy-in from stakeholders.

Align goals: creating company-wide key performance indicators (KPIs) helps different departments focus on business-oriented goals. For example, you might want to achieve a 10% reduction in customer churn next quarter. By making your goal visible to all teams, you can all work together to achieve it.

Set up cross-team projects: have your product, development, and marketing teams collaborate on a new feature for your site. Use Hotjar’s Google Optimize integration to design and test hypotheses about a new feature and work together to get buy-in from stakeholders.

Use customer satisfaction goals to power up your business

Developing a loyal base of satisfied customers is crucial for the future growth of your business, and setting clear customer satisfaction goals will help you focus on what you want to achieve and how to get there. By defining clear objectives for customer service, product experience, and user loyalty, you can measure your success and make effective, data-informed decisions. 

To meet your customer satisfaction objectives, you’ll need to go beyond quantitative data and seek to deeply understand your users through surveys, interviews, and product experience insights.

Set, achieve, and measure customer satisfaction goals with Hotjar

Learn how to create loyal customers using insights from Hotjar

What factors affect customer satisfaction?

The factors that affect customer satisfaction are product quality, value, and customer service. When you satisfy users in these three areas, you get loyal customers.

What customer satisfaction goal should you aim for?

The customer satisfaction goals you should aim for are: improving customer loyalty, increasing customer service satisfaction rates, increasing product advocacy, improving product usability, and driving successful cross-team collaboration.

How can Hotjar help you improve customer satisfaction?

Hotjar helps you improve customer satisfaction by collecting feedback from users, and with tools like Session Recordings and Heatmaps that give you a closer look at how your visitors interact with your site.

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A guide to customer satisfaction research in B2B, including asking the right questions

September 29, 2023

Lots of businesses claim to be customer-centric, yet not enough companies ensure their customers are actually satisfied.

Tracking customer satisfaction, identifying any issues, and then taking action to improve these results in happy product buyers and users. They are more likely to buy again, increase their spending, remain loyal customers, and recommend you to their contacts.

These contacts could soon become customers themselves, without any acquisition costs or business development efforts on your part. However, on the flip side, most dissatisfied customers will leave – you can replace them, but that needs more time and resources. 

Keeping customers costs up to 25 times less than capturing new ones, Hubspot reports . Not only that, but Profitwell has estimated a 60% increase in customer acquisition costs over five years – and these costs are higher in B2B than B2C .

Therefore, every B2B company can benefit from a regular B2B customer satisfaction study to help retain satisfied customers. 

The research reveals customers’ needs and wants, so you know what they expect from you. Satisfaction tracking sets a benchmark for your business – standards to maintain, or aim for if your current performance is below par.

It also outlines what customers think of your brand and gives less vocal ones a platform to share their views on perceived service quality constructively. An insightful B2B customer satisfaction research study can help boost both the top line and bottom line, by showing you:

• How to keep customers, growing revenues 
• How to improve customer loyalty, reducing acquisition costs
• What customers’ unmet needs and wants are
• Where your business needs to improve customer satisfaction
• How your business is performing against competitor benchmarks

How to do B2B customer satisfaction research projects

Areas of customer satisfaction to explore

Best practices for customer satisfaction research in B2B

Here is our advice for conducting customer satisfaction research projects end-to-end with a B2B audience including:

• Research setup
• Methodology
• Design and fieldwork
• Analysis and reporting

#1 Research setup

First things first, engage key internal stakeholders at the outset and put a plan in place to keep them informed throughout the project. This is crucial if your customers interact with different departments across the business, e.g. service, product, sales, and account teams. 

Gain the support of stakeholders who oversee customer relationships as soon as possible. Don’t bypass them otherwise, they and/or their customers may react badly and question why you kept them out of the loop.

Stakeholders may:

• Need to grant access to customer contact details or check data-sharing permissions
• Require convincing that customer satisfaction research will bring benefits
• Have their own questions to ask, so they should have input in the design stage
• Need to follow up on the research results

This last point is vital. Any key stakeholders excluded from the research process could challenge the final insights and recommendations – querying if you asked the right questions or included the right mix of customers.

This is where it helps to have a market segmentation , splitting your customer base into different groups with comparable wants, attributes, and behavior. Recruiting for a customer satisfaction research project based on your segments means you can compare results later on and develop separate follow-up action plans.

If specific customer accounts, segments, international markets , etc. spend significantly more than others, factor these differences into your research setup. Make sure you not only include key groups in the research, but you factor in their relative importance.

Aim to explore strategically important relationships in-depth. Try to focus on major accounts as a priority, but sometimes stakeholders are keen to include everyone.

Work with internal stakeholders to decide upfront which customers’ feedback matters most, before recruiting respondents. Set target quotas to ensure a representative spread of your customer base – in terms of your main buyer segments, customer spending, etc.

#2 Methodology

When it comes to deciding between quantitative and qualitative research to measure customer satisfaction:

• Quant is traditionally the more common approach for customer satisfaction research, with businesses looking for robust satisfaction metrics to track over time.
• But, in B2B research, note that a customer satisfaction survey will usually have a smaller base size in comparison with a B2C one. The target market is relatively smaller and senior decision-makers willing to take part in research are much harder to find in B2B .
• Tracking customer satisfaction metrics is only useful coupled with an understanding of why you received that rating. Ideally, resources permitting, aim to get qual insights from across your customer base, on top of a quant survey. 
• Qual depth interviews with decision-makers at your major accounts help reveal the reasons behind customer satisfaction scores. It also ensures their opinions have sufficient sway on the program’s insights.
• Moreover, qual interviews are a good way to make key clients feel appreciated, with a less indirect or impersonal approach compared to online surveys.

Beyond quant survey data, other metrics you already have access to, such as website engagement statistics, may also add context to your research results. Social media research can also provide rich insights into the customer journey and experience.

It’s best to take an open-minded approach to research methods and instead prioritize getting the most useful results possible.

Whether you’re going to use a quant questionnaire or qual topic guide, there’s an important balancing act to get right. It’s very valuable to include questions on competitor perceptions but without making the research interviews too long, to avoid the risk of respondent fatigue.

Context is crucial with satisfaction scores – if 90% of your customers are satisfied with your performance, that sounds great, but not if 95% are satisfied with a competitor’s service quality. If competitors in your space receive higher satisfaction scores for similar services, you need to know the reasons why.

Your competitor intelligence will shed light on where your business requires improvement – to get more customers, make them satisfied, and take a greater market share. You’ll also get a competitor-based customer satisfaction benchmark to compare your company against.

#3 Design and fieldwork

The research design will determine how useful your results are. Depending on your objectives, the type of customer satisfaction survey questions you need will differ. 

Don’t use customer satisfaction survey templates found online – these will be too generic to deliver actionable insights that are relevant for B2B industries.

Below are some of the main customer satisfaction survey examples to consider. Something simple and tactical, such as asking customers to use five-star rating systems, is quick and easy – but very transactional. 

Arguably scores such as these only add value and provide powerful insights in combination with more strategic and insightful research. More strategic studies are very specifically tailored to your business and research objectives – e.g. by using bespoke metrics combined with exploratory questioning around the scores.

• Overall customer satisfaction score (CSAT): e.g. On a scale of 1-10, where 10 is extremely satisfied and 1 is extremely dissatisfied, how would you rate your satisfaction with [company/service]?
• Net promoter score (NPS): e.g. How likely are you to recommend [company] to a friend or colleague?
• Customer effort score (CES): e.g. How easy was it for you to register for our free trial today?
• Customer churn analysis: e.g. What was the main reason why you decided to stop using [company]?
• Five-star rating: e.g. Please rate the service you received today: ☆☆☆☆☆
• Bespoke metrics based on your industry: e.g. How many times do you use our software each week, on average?

Traditionally, the first three are popular in customer satisfaction surveys, but basing your strategy around these isn’t always the best overall approach in B2B

It’s possible to track metrics for each of these with just one or two questions – often straight after a customer touchpoint – but collecting data this way is piecemeal and difficult to draw conclusions from.

But in a 10-minute online survey, there could be scope to include most of the questions you need for each of #1-6, depending on how many other research objectives you have. A typical CSAT-based survey often includes an NPS question and might also explore reasons why customers have lapsed or churned.

However, there isn’t always enough time – or need – to cover everything. Besides, because there are different types of loyalty in B2B industries, in our experience, NPS questions are not as useful as they are in B2C.

Also, there’s little value in getting lots of scores for the sake of it. Usually, it’s more useful to base a study around one of the above approaches, e.g. a bespoke metric, then do a deep-dive into customers’ reasons for their scores.

#4 Analysis and reporting

Customer satisfaction research projects that ask respondents for too many scores often lead to a ‘data dump’ report that’s difficult to read and act on.

Whether the customer interviews were long or short, set aside data that isn’t interesting or unique. There’s no need to include the answers to every question in a report:

• For any qual results, analyze the results thoroughly – look for common themes and run brainstorming sessions to find the most relevant and interesting stories.
• For quant results, look for statistically significant stories. Always add significance testing to the tables, so that some of the most noteworthy findings stand out.

If you have enough data to filter your results, e.g. by customer spend, it leads to more powerful conclusions and more commercially-prioritized next steps after the debrief.

There is more to B2B customer satisfaction research than getting an overall score and the underlying reasons.

Customers may be happy with some aspects of your business but less so with others. Exploring satisfaction for different aspects of your client-facing operations helps you narrow down where you’re performing well versus the areas for improvement.

Then you’ll know more specifically where remedial action is needed. Common areas of satisfaction, or dissatisfaction, to explore in a project include:

• Overall: As above
• Product/Service: Ask customer satisfaction questions about your product range, quality, reliability, durability, appearance, speed, and so on, where relevant
• Buying process : E.g. How easy is it to find what you’re looking for on our website? How satisfied are you with the order customization options?
• Customer service: Ask questions about staff’s availability, knowledge, helpfulness, responsiveness, proactivity, and complaint handling.
• Account management: E.g. How satisfied are you with your account manager? Why/why not?
• Delivery: Ask questions about delivery speed, meeting deadlines, costs, recyclable packaging, and so on.
• Pricing : Explore satisfaction around your product/service pricing, discounts, invoice clarity, and value-for-money perceptions

The above list is an example – some of these may not be relevant to your business and there will likely be other areas you’d like to explore in a customer satisfaction research study.

Want to know if your research objectives need B2B customer satisfaction research?

#1 Explore areas of both satisfaction and dissatisfaction

When creating customer satisfaction surveys, it’s tempting to prioritize questions that aim to identify and explore weaknesses or areas for improvement.

But it’s important to include enough questions that let customers confirm what you’re doing well and give reasons why. That way, you capture the positive experiences that more of your customers need to have.

Similarly, when analyzing the results, don’t only focus on negative outcomes and plans to address these. Also, highlight the positive stories to learn from and keep aiming for.

#2 Use statistical techniques to explore subconscious satisfaction

Statistical trade-off techniques such as regression analysis explore beyond respondents’ given answers, identifying hidden factors that influence their perceptions.

Your customers may not realize the true reasons behind their views. Regression analysis shows the relationship between a dependent variable – e.g. overall satisfaction – and independent variables like factors, product features, and so on.

Statistical models may also identify a ‘shadow effect’ – one major factor with a significant effect on satisfaction scores regardless of your performance in any other area.

#3 React in real-time to any issues that customers raise

Some customers may use the survey as a forum to raise their queries, concerns, or issues, so be prepared to respond quickly.

Include a clear option to flag comments for customer service staff or their account manager, so you can close the loop promptly.

It helps stop any problems from escalating and also avoids the risk of data analysts treating the comments as standard open-ended responses. 

#4 Capitalize quickly on the momentum from your research

After the research, take time to thank customers for theirs. Explain how their feedback will drive change – set customer expectations by telling them what you’re doing next and ideally, when they’ll start seeing the impact.

Then the real work starts. You could collaborate with a select few key customer accounts to develop action plans, based on the results, to make sure your next steps will land well.

Start by running workshops with your customer-facing staff, so they understand the areas driving positive and negative outcomes. Don’t wait long, otherwise, priorities may change.

Looking to run some B2B customer satisfaction research?

Tracking customer satisfaction, identifying any issues, and then taking action to improve these, leads to happy customers. They’re more likely to buy again, increase their spending, remain loyal to your company, and pass on recommendations to their contacts.

B2B customer satisfaction research can show you: how to keep customers and grow revenues; how to improve customer loyalty, reducing acquisition costs; what customers’ unmet needs and wants are; where your business needs to improve customer satisfaction; and how your business is performing against competitor benchmarks.

Engage key internal stakeholders at the outset and put a plan in place to keep them informed. If specific customer accounts, segments, international markets, etc. spend significantly more than others, factor these differences into your research setup.

Ideally, resources permitting, aim to get qual insights from across your customer base, on top of a quant survey. It’s also valuable to include questions on competitor perceptions but make sure the research interviews are still concise to avoid respondent fatigue.

Don’t use a customer satisfaction survey template found online – they’re too generic for B2B – but consider including overall customer satisfaction score (CSAT); net promoter score (NPS); customer effort score (CES); customer churn analysis; five-star rating; and/or bespoke metrics.

Common areas of satisfaction, or dissatisfaction, to explore in a customer satisfaction research project include: overall; product/service; buying process; customer service; account management; delivery; and pricing.

This list is an example – some of these may not be relevant to your business and there will likely be other areas you’d like to explore in a customer satisfaction research study.

In customer satisfaction research, we also recommend that you: explore areas of both customer satisfaction and dissatisfaction; use statistical techniques to explore subconscious satisfaction; react in real-time to any issues that customers raise; and capitalize quickly on the momentum from your research.

Chris Wells

Chris Wells is a B2B marketing researcher and strategist. He was previously on the management team at B2B research specialist Circle Research, winners of the Best Research Agency at the 2016 MRS Awards. Chris has helped to deliver hundreds of research and strategy projects for B2B organizations.

Got a B2B market research project you’d like to discuss?

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10 Ways to Boost Customer Satisfaction

• G. Tomas M. Hult
• Forrest V. Morgeson

Takeaways from an analysis of millions of consumer data points.

Customer satisfaction is at its lowest point in the past two decades. Companies must focus on 10 areas of the customer experience to improve satisfaction without sacrificing revenue. The authors base their findings on research at the ACSI — analyzing millions of customer data points — and research that we conducted for The Reign of the Customer : Customer-Centric Approaches to Improving Customer Satisfaction. For three decades, the ACSI has been a leading satisfaction index (cause-and-effect metric) connected to the quality of brands sold by companies with significant market share in the United States.

Despite all the effort and money poured into CX tools by companies, customer satisfaction continues to decline . In the United States, it is now at its lowest level in nearly two decades, per data from the American Customer Satisfaction Index (ACSI). Consumer sentiment is also at its lowest in more than two decades. This negative dynamic in the customer-centric ecosystem in which we now live creates the challenge of figuring out what is going wrong and what companies can do to fix it.

• GH G. Tomas M. Hult is part of the leadership team at the American Customer Satisfaction Index (ACSI); coauthor of The Reign of the Customer: Customer-Centric Approaches to Improving Customer Satisfaction ; and professor in the Broad College of Business at Michigan State University. He is also a member of the Expert Networks of the World Economic Forum and the United Nations’ World Investment Forum.
• FM Forrest V. Morgeson is an assistant professor in the Broad College of Business at Michigan State University; (Former) Director of Research at the American Customer Satisfaction Index (ACSI); and coauthor of The Reign of the Customer: Customer-Centric Approaches to Improving Customer Satisfaction .

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Measuring and Managing Customer Satisfaction

It takes continuous effort to maintain high customer satisfaction levels. As markets shrink, companies scramble to boost customer satisfaction and keep their current customers rather than devoting additional resources to chase potential new customers. The claim that it costs five to eight times as much to get new customers than to hold on to old ones is key to understanding the drive toward benchmarking and tracking customer satisfaction.

Measuring customer satisfaction may be a new concept to companies that have been focused almost exclusively on income statements and balance sheets. Companies now recognize that the new global economy has changed things forever. Increased competition, crowded markets with little product differentiation, and years of sales growth followed by two plus decades of flattened sales curves and a global pandemic indicate to today’s sharp competitors that their focus must change.

Related Survey Types

Competitors that are prospering in the new global economy recognize that measuring customer satisfaction is key. Only by doing so can they hold on to the customers they have and understand how to better attract new customers. Successful competitors recognize that customer satisfaction is a critical strategic weapon that can bring increased market share and increased profits.

The problem companies face, however, is exactly how to measure customer satisfaction and do it well. They need to understand how to quantify, measure, and track customer satisfaction. Without a clear and accurate sense of what needs to be measured and how to collect, analyze, and use the data as a strategic weapon to drive the business, no firm can be effective in this new business climate. Plans constructed using customer satisfaction research results can be designed to target customers and processes that are most able to extend profits.

Too many companies rely on outdated and unreliable measures of customer satisfaction. They watch sales volume. They listen to sales reps describing their customers’ states of mind. They track and count the frequency of complaints. And they watch aging accounts receivable reports, recognizing that unhappy customers pay as late as possible — if at all. While these approaches are not completely without value, they are no substitute for a valid, well-designed customer satisfaction survey program.

It’s no surprise to find that market leaders differ from the rest of their industry in that they have programs in place to hear the voice of the customer and achieve customer satisfaction. In these companies:

• Marketing and sales employees are primarily responsible for designing (with customer input) customer satisfaction surveying programs, questionnaires, and focus groups.
• Top management and marketing divisions champion the programs.
• Corporate evaluations include not only their own customer satisfaction ratings but also those of their competitors.
• Satisfaction results are made available to all employees.
• Customers are informed about changes brought about as the direct result of listening to their needs.
• Internal and external quality measures are often tied together.
• Customer satisfaction is incorporated into the strategic focus of the company via the mission statement.
• Stakeholder compensation is tied directly to the customer satisfaction surveying program.
• A concentrated effort is made to relate the customer satisfaction measurement results to internal process metrics.

To be successful, companies need a customer satisfaction surveying system that meets the following criteria:

• The system must be easy to understand.
• It must be credible so that employee performance and compensation can be attached to the final results.
• It must generate actionable reports for management.

Defining Customer Satisfaction

The concept of customer satisfaction is widely used by many organizations, but it’s important to be clear on exactly what’s meant by the term.

Customer satisfaction is the state of mind that customers have about a company when their expectations have been met or exceeded over the lifetime of the product or service. The achievement of customer satisfaction leads to company loyalty and product repurchase. There are some important implications of this definition:

• Because customer satisfaction is a subjective, nonquantitative state, measurement won’t be exact and will require sampling and statistical analysis.
• Customer satisfaction measurement must be undertaken with an understanding of the gap between customer expectations and performance perceptions.
• There is a connection between customer satisfaction measurement and bottom-line results.

“Satisfaction” itself can refer to a number of different aspects of the relationship with a customer. For example, it can refer to any or all of the following:

• Satisfaction with the quality of a particular product or service.
• Satisfaction with an ongoing business relationship.
• Satisfaction with the price-performance ratio of a product or service.
• Satisfaction because a product/service met or exceeded the customer’s expectations.

Each industry could add to this list according to the nature of the business and the specific relationship with the customer. Customer satisfaction measurement variables will differ depending on what type of satisfaction is being researched. For example, manufacturers typically desire on-time delivery and adherence to specifications, so measures of satisfaction taken by suppliers should include these critical variables.

Clearly defining and understanding customer satisfaction can help any company identify opportunities for product and service innovation and serve as the basis for performance appraisal and reward systems. It can also serve as the basis for a customer satisfaction survey program that can ensure that quality improvement efforts are properly focused on issues that are most important to the customer.

Objectives of a Customer Satisfaction Survey Program

In addition to a clear statement defining customer satisfaction, any successful customer survey program must have a clear set of objectives that, once met, will lead to improved performance. The most basic objectives that should be met by any customer surveying program include the following:

• Understanding the expectations and requirements of your customers.
• Determining how well your company and its competitors are satisfying these expectations and requirements.
• Developing service and/or product standards based on your findings.
• Examining trends over time in order to take action on a timely basis.
• Establishing priorities and standards to judge how well you’ve met these goals.

Before an appropriate customer satisfaction surveying program can be designed, the following basic questions must be clearly answered:

• How will the information we gather be used?
• How will this information allow us to take action inside the organization?
• How should we use this information to keep our customers and find new ones?

Careful consideration must be given to what the organization hopes to accomplish, how the results will be disseminated to various parts of the organization, and how the information will be used. There is no point asking customers about a particular service or product if it won’t or can’t be changed regardless of the feedback.

Conducting a customer satisfaction survey program is a burden on the organization and its customers in terms of time and resources. There is no point in engaging in this work unless it has been thoughtfully designed so that only relevant and important information is gathered. This information must allow the organization to take direct action. Nothing is more frustrating than having information that indicates a problem exists but fails to isolate the specific cause. Having the purchasing department of a manufacturing firm rate the sales and service it received on its last order on a survey scale of 1 (terrible) to 6 (magnificent) would yield little about how to improve sales and service to the manufacturer.

The lesson is twofold. First, general questions are often not that helpful in customer satisfaction measurement, at least not without other more specific questions attached. Second, the design of an excellent customer satisfaction surveying program is more difficult than it might first appear. It requires more than just writing a few questions, designing a questionnaire, calling or emailing some customers, and then tallying the results.

Understanding Differing Customer Attitudes

The most basic objective of customer satisfaction surveys is to generate valid and consistent customer feedback (i.e., to receive the voice of the customer), which can then be used to initiate strategies that will retain customers and thus protect one of the most valuable corporate assets — loyal customers.

As it’s determined what needs to be measured and how the data relate to loyalty and repurchase, it becomes important to examine the mind-set of customers the instant they are required to make a pre-purchase (or repurchase) decision or a recommendation decision. Surveying these decisions leads to measures of customer loyalty. In general, the customer’s pre-purchase mind-set will fall into one of three categories — rejection (will avoid purchasing if at all possible), acceptance (satisfied, but will shop for a better deal), and/or preference (delighted and may even purchase at a higher price).

This highly subjective system that customers themselves apply to their decisions is based primarily on input from two sources:

• The customers’ own experiences — each time they experience a product or service, deciding whether that experience is great, neutral, or terrible. These are known as “moments of truth.”
• The experiences of other customers — each time they hear something about a company, whether it’s great, neutral, or terrible. This is known as “word-of-mouth.”

There is obviously a strong connection between these two inputs. An exceptional experience leads to strong word-of-mouth recommendations. Strong recommendations influence the experience of the customer, and many successful companies have capitalized on that link.

How does a customer satisfaction surveying program allow you to make the connection between the survey response and the customer’s attitude or mind-set regarding loyalty? Research conducted by both corporate and academic researchers shows a relationship between customer survey measurements and the degree of preference or rejection that a customer might have accumulated. When the customer is asked a customer satisfaction question, the customer’s degree of loyalty mind-set (or attitude) will be an accumulation of all past experiences and exposures that can be indicated as a score from 1 (very dissatisfied) to 6 (very satisfied).

Obviously, the goal of every company should be to develop customers with a preference attitude (i.e., we all want the coveted preferred vendor status such that the customer, when given a choice, will choose our company), but it takes continuous customer experience management, which means customer satisfaction measurement, to get there — and even more effort to stay there.

If you would like to know more about how NBRI can help you, please contact us at 800-756-6168.

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How to measure customer satisfaction: 4 key metrics.

22 min read Customer satisfaction is about more than just minimizing complaints. Here’s an introduction to the subject, along with 4 key customer satisfaction measurements that are critical to your business success.

Customer satisfaction is a common method used to determine how well you meet – or exceed – customer expectations . It is used as a key performance indicator of customer service and product quality.

Customer satisfaction may be best understood in terms of customer experience. Customer experience (or CX) is the total sum of a customer’s perceptions , interactions, and thoughts about your business.

Customer satisfaction is a composite of many different aspects, and it is likely to change over time. Here’s a model of the various facets that contribute to customer satisfaction (or not):

Get started with our free CSAT survey template

Why should you measure customer satisfaction?

Customers who develop attitudinal brand loyalty – that is, they have a positive emotional connection to a brand – have been shown to be less price sensitive than their less-loyal counterparts. They’re also more likely to convert when they buy from you. Highly satisfied customers are also likely to tell friends and family about their experiences and to promote your brand.

According to Mckinsey, you can see the impact when you improve customer satisfaction below:

The cost of serving customers decreases, while revenue increases when customer satisfaction improves.

Customer centricity pays off, as meeting – or better yet exceeding – customer’ expectations makes you more competitive. You’ll be more likely to keep your customers, and prevent them from going to a competitor. Merkle found that 66% of consumers care more about their experience than the costs when making a brand decision . But in times of economic uncertainty, if the experience isn’t worth the cost, they’ll go elsewhere. The Word of Mouth Marketing Association (WOMMA) estimates that good experience reviews spread by word of mouth recommendations account for 13% of consumer sales and represent $6 trillion in yearly consumer spending . It’s clear there are tangible benefits to improving customer satisfaction.

These are good reasons to aim for a level of customer experience and customer satisfaction that exceeds rather than simply meets customers’ expectations. But accurately knowing that you provide great customer service can be difficult without measuring customer satisfaction.

So how do we start effectively measuring customer satisfaction?

4 key customer satisfaction metrics to track

Here are 4 key customer satisfaction measurements that are critical to your business success. They take into account the different dimensions of customer satisfaction, such as affective (emotional) and cognitive (rationally judged) reactions to a product or service and behavioral intentions (such as likelihood to recommend or repurchase) as well as taking overall scores of satisfaction as judged by the respondents.

1. Overall Satisfaction Measure (Attitudinal)

This question reflects the overall opinion of a consumer’s satisfaction experience with a product he or she has used.

The single greatest predictors of customer satisfaction are the customer experiences that result in attributions of quality.

Perceived quality is often measured in one of three contexts:

• Overall quality
• Perceived reliability
• Extent of customer’s needs fulfilled

It is commonly believed that dissatisfaction is synonymous with purchase regret while satisfaction is linked to positive ideas such as “it was a good choice” or “I am glad that I bought it.”

By using the perception of quality and product satisfaction as a guide, we can better measure customer satisfaction as a whole.

2. Customer Loyalty Measurement (Affective, Behavioural)

This single-question measure is the core NPS (Net Promoter Score) measure.

Customer loyalty reflects the likelihood of repurchasing products and services. Customer satisfaction is a major predictor of repurchase but is strongly influenced by explicit performance evaluations of product performance, quality, and value.

Loyalty is often measured as a combination of measures including overall satisfaction, the likelihood of repurchase , and the likelihood of recommending the brand to a friend (as measured by Net Promoter Score).

A common measure of loyalty might be the sum of scores for the following three questions:

• Overall, how satisfied are you with [brand]?
• How likely are you to continue to choose/repurchase [brand]?
• How likely are you to recommend [brand] to a friend or family member?

Understanding customer loyalty in this form of metric helps you to measure customer satisfaction from the angle of future behavior. It can be helpful not only for understanding customer satisfaction now but also for developing future purchase predictions.

3. A series of Attribute Satisfaction Measurements (Affective and Cognitive)

Example question: How satisfied are you with the “taste” of your entre at La Jolla Grove?

Example question: How important is “taste” in your decision to select La Jolla Grove restaurant?

Affect (liking/disliking) is best measured in the context of product attributes or benefits. Customer satisfaction is influenced by the perceived quality of product and service attributes and is moderated by expectations of the product or service. The researcher must define and develop measures for each attribute that is important for customer satisfaction.

Consumer attitudes toward a product developed as a result of product information or any experience with the product, whether perceived or real.

Again, it may be meaningful to measure attitudes towards a product or service that a consumer has never used, but it is not meaningful to measure customer satisfaction when a product or service has not been used.

Cognition refers to judgment: the product was useful (or not useful); fit the situation (or did not fit); exceeded the requirements of the problem/situation (or did not exceed), or was an important part of the product experience (or was unimportant).

Judgments are often specific to the intended use application and use occasion for which the product is purchased, regardless of whether that use is correct or incorrect.

Affect and satisfaction are closely related concepts. The distinction is that satisfaction is “post-experience” and represents the emotional effect produced by the product’s quality or value.

Using this metric to measure customer satisfaction helps you to narrow down the causes of customer satisfaction levels. Unhappy customers may have a particular emotive response to products and services, rather than quality being the issue, for example.

4. Intentions to Repurchase Measurements (Behavioural Measures)

When wording questions about future or hypothetical behavior, consumers often indicate that “purchasing this product would be a good choice” or “I would be glad to purchase this product.” Behavioral measures also reflect the consumer’s past experience with customer service representatives.

Customer satisfaction can influence other post-purchase/post-experience actions like communicating to others through word of mouth and social networks.

Additional post-experience actions might reflect heightened levels of product involvement that in turn result in an increased search for the product or information, reduced trial of alternative products, and even changes in preferences for shopping locations and choice behavior.

How to use these metrics to develop customers satisfaction KPIs

Measuring customer satisfaction to gather your customer feedback , illuminate the risk of customer churn , and discern loyal customers is useful, particularly over time.

However, it is better to measure customer satisfaction with particular goals in mind. By having scores you’re aiming to meet, whether that is an internal or industry benchmark, you’re able to track your progress over time and react to how you’re doing. If your actions aren’t improving your CSAT score, you might need to re-evaluate where you’re going wrong.

So how do you set a realistic goal for your customer satisfaction score that can act as your KPI?

Improve on your past customer satisfaction score

The most obvious answer is to consistently be improving customer satisfaction feedback. Taking an initial score as a benchmark and taking stock at regular intervals will help to not only measure customer satisfaction over time but to constantly improve your service. Your score might refer to one part of the customer journey – for example, ordering a new car, or picking it up. Try to figure out what is causing the scores you’re receiving – speak to customers, product teams, frontline staff – all of them have useful insights to help you improve. Of course, customer satisfaction will continue to change and evolve and you should treat it as such.

Just because your score is high doesn’t mean it will stay that way – constantly look to improve customer satisfaction! Customer expectations will flux and evolve, and your efforts to create happy customers will need to follow suit.

Take a look at the competition

Your competition will almost certainly be measuring customer satisfaction. Understanding – to whatever extent you can – where you stand in comparison to your competitors will help you to set yourself customer satisfaction goals for the future. They are likely seeing the importance of customer satisfaction – so don’t get left behind.

Judge by industry benchmarks

Your industry will almost certainly have customer satisfaction benchmarks that will provide you with a solid guideline for measuring customer satisfaction. If you’re not meeting your industry’s baseline, then it’s likely that your customer experience is falling short of the expectations of your consumer base.

How to measure customer satisfaction for increased performance

You understand each customer satisfaction metric you need to score – but how do you actively gather your data on the customer experience? What are the best practices for gathering customer satisfaction information, and once you have it, what do you do with it?

Here are ways of measuring customer satisfaction for more happy customers and business growth, as well as recommendations for best practice:

Use agile customer satisfaction surveys to gauge success and take action

Gathering customer satisfaction data and developing KPIs is an important process, but measuring customer satisfaction is often seen as a rote exercise to complete.

A customer satisfaction survey is a useful tool in a brand’s arsenal for gauging success, but it is often seen as a “must-do” action rather than a useful tool. Instead, to prioritize customer success, brands need to develop an agile, adaptable approach to customer surveys.

Developing a system of delivering customer surveys that is agile and well-targeted will help you to not only take the pulse of customer sentiment , but it will also help to create targeted actionable insights on an ongoing basis.

A quarterly or an annual measurement will only provide you with a snapshot of customer success. It won’t help you to measure the reaction to a new launch, or the integration of a new system. It also won’t help you to narrow down whether overall customer sentiment has changed, or whether specific actions you’re taking have had an effect.

Collecting customer feedback in an ongoing approach will help you to see the micro-trends of customer satisfaction. You can quickly adjust your customer journey to help new customers experience the best of your brand, rather than take delayed action.

Always be listening to your customers, no matter where they are

Your customer satisfaction scores aren’t everything. Though they’re very useful, improving customer satisfaction is about understanding the underlying reasons why loyal customers and satisfied customers feel the way they do – as well as finding out what would make dissatisfied customers stay.

For example, using natural language understanding (NLU) and conversational analytics to gauge how customers are feeling in real time as they speak to you or about you allows you to see the reasons behind the scores. Is it that your customer support efforts are lacking, causing feelings of frustration? Have you provided a particularly exceptional customer experience that left customers feeling elated?

Understanding this type of customer feedback gives you more detail and background information than metrics or customer surveys can. It gives you insight into how customers feel, and that is vital when looking to increase customer satisfaction. Positive customer emotions can lead to a high customer satisfaction score and repeat customers, while failing to make customers happy can drive down customer satisfaction scores.

Taking action to improve customer satisfaction

As outlined previously in this article, there are four key metrics that you should use to help you improve customer satisfaction.

However, simply gathering this customer satisfaction data isn’t enough to help your business thrive. Narrowing down the key triggers for unhappy customers and taking action to improve customer satisfaction is the most vital part of the process.

Whether it’s poor customer service or customer frustration at a particular ordering process, finding the core causes of customer dissatisfaction – and conversely, what makes customers happy – is the right approach. Ideally, you’re completing these actions in real-time, using conversation analytics and other tools to resolve issues in the moment.

The customer satisfaction process will constantly need improvement to meet new demands and to avoid stagnation in a highly competitive market.

For example, this diagram shows a potential customer satisfaction process improvement cycle:

Here, customer follow-ups and customer satisfaction surveys are a fundamental part of the development of customer experience. At each stage of the customer interaction, gathering customer data and formulating a response is a given part of the process – meaning your customers’ satisfaction is never left to chance.

Your internal process should include a number of stages that will form an understanding of  customer sentiment and take appropriate action :

1. Customer satisfaction data gathering

Listen to what your customers are saying on a rolling basis. This data can be gathered effectively through customer satisfaction surveys , but it can be bolstered by social listening and unsolicited customer feedback (customer lifetime value, etc). Conversational analytics can be used to analyze customer emotion, sentiment and intent in real time, no matter where the conversations are being had or with whom.

Often, a customer satisfaction survey will return insights at the extremes, such as highly negative feedback and a very positive review. Customer interactions at particular points in the customer service journey (such as customer service conversations) may also generate more extreme results. Gathering further data, particularly in real time, and collating it all within one platform can help you to tease out the truth of customer satisfaction.

2. Understanding customer journey touchpoints and their effect

Knowing the particular journey your customer has experienced is important for determining touchpoint value. This is again why ongoing customer satisfaction surveys or conversational analytics can be more effective than taking a static, scheduled approach. When you track customer satisfaction across the customer journey, you’re able to take the best action, rather than applying the same approach to the pre, during and post-checkout experiences.

Once you understand how customer satisfaction is tied to particular touchpoints , you can prioritize action more effectively. Fixing issues in the moment – such as increasing customer support efforts when emotions are volatile – can go a long way to get more positive reviews and achieve customer satisfaction.

 3. Narrowing down the drivers of customers satisfaction

It’s not enough to know how your customer base feelsl – discovering the drivers of their satisfaction is key for progress. There are many deciding factors behind customer satisfaction, and they’re likely to differ between customers. Determining which drivers affect each audience segment helps you to better meet their needs and expectations.

For example, a key driver could be communication. How long has it taken for a customer to get a response? How quickly was their query resolved? Did you provide status updates throughout, and were they given on the channel they’d prefer? Customers might expect that you’ll acknowledge and resolve issues quickly – but if you’re only getting back to them a week after they reach out and they’re constantly asking you for updates, you’ll get negative customer feedback from dissatisfied customers.

4. Empowering your employees to take action

Brands need to evolve their internal processes to help drive customer satisfaction, but they also need to empower their employees to take action. Employee coaching can also help to create customer experiences that are not only satisfactory, but memorable.

Creating a culture of action – where issues are identified and closing the loop is consistently achieved – will help your employees to be proactive in their approach to making customer satisfaction important. Enable your entire company, from frontline employees to sales team to marketing and more, to see relevant insights that will improve your overall customer satisfaction.

For example, it’s no good if your customer service team is the only one seeing a disconnect between what you promise your company’s products can do and how they actually perform. Your marketing team, sales team and product teams should know if repeat issues are being flagged in customer feedback, word of mouth reviews or social media posts. Use the right tools to not only track customer satisfaction, but share key insights as well.

5. Automating your actions

Another way to ensure your employees are able to take quick, effective action is to automate the process. Rather than relying on human effort to ensure that tickets, alerts, and follow-up actions are scheduled, use technology to improve customer satisfaction at scale.

You can deliver actionable insights to the right teams at the right time automatically – meaning you’re never missing a step when it comes to addressing customer dissatisfaction. By uncovering and taking actions for problems on a micro level, your team has the time to tackle wider strategic and macro issues more effectively.

Why you should use customer satisfaction measurement tools

Learning how to measure customer satisfaction is only part of the wider customer experience picture. Customer satisfaction is complex and ever-changing, and as a result, it’s important to take frequent measurements across a range of metrics in order to get the most accurate picture possible.

The wider measurement picture

Your customer satisfaction score should always be considered among a broader picture of data, including customer effort score, Net Promoter Score (NPS), conversational analytics and more. This will help you to understand customer sentiment and customer loyalty in relation to the service you’re providing.

As mentioned, there are more ways of measuring customer satisfaction than a customer satisfaction survey. Social media monitoring, focus groups, customer retention data, and more can help you to establish why existing customers stay and why new customers might not develop their customer relationship with you.

But how do you keep track of all those customer satisfaction metrics, and how do you analyze them relative to one another to one-another and gather actionable insights?

Measure customer satisfaction with Qualtrics

As mentioned, we recommend taking an ongoing approach to customer satisfaction along with other metrics as part of a broader customer experience program .

Increase satisfaction, boost loyalty and lower customer churn by listening to what customers are saying to or about you, all the time. Using Qualtrics XM™ allows you to listen 24/7, schedule surveys, automate tickets, send actionable insights and more to ensure you’re tracking satisfaction at every part of the journey, and improving broken experiences in real-time. Use our customer service support products like XM Discover to understand how customers feel in real-time to enhance your customer satisfaction efforts.

By measuring and analyzing your customer satisfaction metrics within a single platform, you’ll not only benefit from powerful analytic tools and easy-to-interpret results, but you’ll also be able to integrate your findings with other elements of your customer experience data. But most importantly, you’ll be able to take action on your insights across the organization far more easily, resulting in more satisfied customers.

Start measuring customer satisfaction today with our free CSAT survey template

Related resources

What is csat 8 min read, customer delight 18 min read, improving customer satisfaction 11 min read, customer satisfaction 16 min read, customer satisfaction (csat) surveys 21 min read.

Customer Journey

Customer Interactions 11 min read

Customer Service

Customer Service Experience 13 min read

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• Home > Publications > Customer Satisfaction Research Surveys: How to Measure CSAT

Customer Satisfaction Research Surveys: How to Measure CSAT

It seems self evident that companies should try to satisfy their customers. Satisfied customers usually return and buy more, they tell other people about their experiences, and they may well pay a premium for the privilege of doing business with a supplier they trust. Statistics are bandied around that suggest that the cost of keeping a customer is only one tenth of winning a new one. Therefore, when we win a customer, we should hang on to them. Conducting a customer satisfaction research survey is a good way to start measuring where you stand in terms of customer loyalty.

Why Customer Satisfaction Is So Important

Why is it that we can think of more examples of companies failing to satisfy us rather than when we have been satisfied? There could be a number of reasons for this. When we buy a product or service, we expect it to be right. We don’t jump up and down with glee saying “isn’t it wonderful, it actually worked”. That is what we paid our money for. Add to this our world of ever exacting standards. We now have products available to us that would astound our great grandparents and yet we quickly become used to them. The bar is getting higher and higher. At the same time our lives are ever more complicated with higher stress levels. Delighting customers and achieving high customer satisfaction scores in this environment is ever more difficult. And even if your customers are completely satisfied with your product or service, significant chunks of them could leave you and start doing business with your competition.

A market trader has a continuous finger on the pulse of customer satisfaction. Direct contact with customers indicates what he is doing right or where he is going wrong. Such informal feedback is valuable in any company but hard to formalize and control in anything much larger than a corner shop. For this reason customer surveys are necessary to measure and track customer satisfaction.

Developing a Customer Satisfaction Research Program

Developing a customer satisfaction program is not just about carrying out a customer service survey. Surveys provide the reading that shows where attention is required but in many respects, this is the easy part. Very often, major long lasting improvements need a fundamental transformation in the company, probably involving training of the staff, possibly involving cultural change. Customer satisfaction research is critical in identifying these areas for improvement. The result should be financially beneficial with less customer churn, higher market shares, premium prices, stronger brands and reputation, and happier staff. However, there is a price to pay for these improvements. Costs will be incurred in the customer satisfaction research survey. Time will be spent working out an action plan. Training may well be required to improve the customer service. The implications of customer satisfaction studies go far beyond the survey itself and will only be successful if fully supported by the echelons of senior management.

A Six-Stage Process For Customer Satisfaction Research Surveys

There are six parts to any customer satisfaction program:

• Who should be interviewed?
• What should be measured?
• How should the interview be carried out?
• How should satisfaction be measured?
• What do the measurements mean?
• How to use a customer satisfaction study to greatest effect?

Who Should Be Interviewed?

Some products and services are chosen and consumed by individuals with little influence from others. The choice of a brand of cigarettes is very personal and it is clear who should be interviewed to find out satisfaction with those cigarettes. But who should we interview to determine the satisfaction with breakfast cereal? Is it the person that buys the cereal (usually a parent) or the person that consumes it (often a child)? And what of a complicated buying decision in a business to business situation. Who should be interviewed in a customer satisfaction survey for a truck manufacturer – the driver, the transport manager, the general management of the company? In other b2b markets there may well be influences on the buying decision from engineering, production, purchasing, quality assurance, plus research and development. Because each department evaluates suppliers differently, the customer satisfaction survey will need to cover the multiple views.

The adage in market research that we turn to again and again is the need to ask the right question of the right person. Finding that person in customer loyalty research may require a compromise with a focus on one person – the key decision maker; perhaps the transport manager in the example of the trucks. If money and time permit, different people could be interviewed and this may involve different interviewing methods and different questions.

The traditional first in line customer is an obvious candidate for measuring customer satisfaction. But what about other people in the channel to market? If the products are sold through intermediaries, we are even further from our customers. A good customer satisfaction program will include at least the most important of these types of channel customers, perhaps the wholesalers as well as the final consumers.

One of the greatest headaches in the planning of a b2b customer satisfaction survey is the compilation of the sample frame – the list from which the sample of respondents is selected. Building an accurate, up-to-date list of customers, with telephone numbers and contact details is nearly always a challenge. The list held by the accounts department may not have the contact details of the people making the purchasing decision. Large businesses may have regionally autonomous units and there may be some fiefdom that says it doesn’t want its customers pestered by market researchers. The sales teams’ Christmas card lists may well be the best lists of all but they are kept close to the chest of each sales person and not held on a central server. Building a good sample frame nearly always takes longer than was planned but it is the foundation of a good customer satisfaction project.

Customer satisfaction surveys are often just that – surveys of customers without consideration of the views of lost or potential customers. Lapsed customers may have stories to tell about service issues while potential customers are a good source of benchmark data on the competition. If a customer survey is to embrace non-customers, the compilation of the sample frame is even more difficult. The quality of these sample frames influences the results more than any other factor since they are usually outside the researchers’ control. The questionnaire design ( further reading: The 7 Steps of Questionnaire Design ) and interpretation are within the control of the researchers and these are subjects where they will have considerable experience.

What Should Be Measured?

In customer satisfaction research we seek the views of respondents on a variety of issues that will show how the company is performing and how it can improve. This understanding is obtained at a high level (“how satisfied are you with ABC Ltd overall?”) and at a very specific level (“how satisfied are you with the clarity of invoices?”).

High level issues are included in most customer satisfaction surveys and they could be captured by questions such as:

• What is your overall satisfaction with ABC Ltd?
• How likely or unlikely are you to buy from ABC Ltd again?
• How likely or unlikely would you be to recommend ABC Ltd to a friend or colleague?

It is at the more specific level of questioning that things become more difficult. Some issues are of obvious importance and every supplier is expected to perform to a minimum acceptable level on them. These are the hygiene factors. If a company fails on any of these issues they would quickly lose market share or go out of business. An airline must offer safety but the level of in-flight service is a variable. These variables such as in-flight service are often the issues that differentiate companies and create the satisfaction or dissatisfaction.

Working out what questions to ask at a detailed level means seeing the world from the customers’ points of view. What do they consider important? These factors or attributes will differ from company to company and there could be a long list. They could include the following:

The list is not exhaustive by any means. There is no mention above of environmental issues, sales literature, frequency of representatives’ calls or packaging. Even though the attributes are deemed specific, it is not entirely clear what is meant by “product quality” or “ease of doing business”. Cryptic labels that summarize specific issues have to be carefully chosen for otherwise it will be impossible to interpret the results.

Customer facing staff in the research-sponsoring business will be able to help at the early stage of working out which attributes to measure. They understand the issues, they know the terminology and they will welcome being consulted. Internal focus groups with the sales staff will prove highly instructive. This internally generated information may be biased, but it will raise most of the general customer issues and is readily available at little cost.

It is wise to cross check the internal views with a small number of depth interviews with customers. Half a dozen may be all that is required.

When all this work has been completed a list of attributes can be selected for rating.

How Should The Interview Be Carried Out?

There are some obvious indicators of customer satisfaction beyond survey data. Sales volumes are a great acid test but they can rise and fall for reasons other than customer satisfaction. Customer complaints say something but they may reflect the views of a vociferous few. Unsolicited letters of thanks; anecdotal feedback via the salesforce are other indicators. These are all worthwhile indicators of customer satisfaction but on their own they are not enough. They are too haphazard and provide cameos of understanding rather than the big picture. Depth interviews and focus groups could prove very useful insights into customer satisfaction and be yet another barometer of performance. However, they do not provide benchmark data. They do not allow the comparison of one issue with another or the tracking of changes over time. For this, a quantitative survey is required.

The tool kit for measuring customer satisfaction boils down to three options, each with their advantages and disadvantages. The tools are not mutually exclusive and a self-completion element could be used in a face to face interview. So too a postal questionnaire could be preceded by a telephone interview that is used to collect data and seek co-operation for the self-completion element.

When planning the fieldwork, there is likely to be a debate as to whether the interview should be carried out without disclosing the identify of the sponsor. If the questions in the survey are about a particular company or product, it is obvious that the identity has to be disclosed. When the survey is carried out by phone or face to face, co-operation is helped if an advance letter is sent out explaining the purpose of the research. Logistically this may not be possible in which case the explanation for the survey would be built into the introductory script of the interviewer.

If the survey covers a number of competing brands, disclosure of the research sponsor will bias the response. If the interview is carried out anonymously, without disclosing the sponsor, bias will result through a considerably reduced strike rate or guarded responses. The interviewer, explaining at the outset of the interview that the sponsor will be disclosed at the end of the interview, usually overcomes this.

How Should Satisfaction Be Measured?

Customers express their satisfaction in many ways. When they are satisfied, they mostly say nothing but return again and again to buy or use more. When asked how they feel about a company or its products in open-ended questioning they respond with anecdotes and may use terminology such as delighted, extremely satisfied, very dissatisfied etc. Collecting the motleys variety of adjectives together from open ended responses would be problematical in a large survey. To overcome this problem market researchers ask people to describe a company using verbal or numeric scales with words that measure attitudes.

People are used to the concept of rating things with numerical scores and these can work well in surveys. Once the respondent has been given the anchors of the scale, they can readily give a number to express their level of satisfaction. Typically, scales of 5, 7 or 10 are used where the lowest figure indicates extreme dissatisfaction and the highest shows extreme satisfaction. The stem of the scale is usually quite short since a scale of up to 100 would prove too demanding for rating the dozens of specific issues that are often on the questionnaire.

Measuring satisfaction is only half the story. It is also necessary to determine customers’ expectations or the importance they attach to the different attributes, otherwise resources could be spent raising satisfaction levels of things that do not matter. The measurement of expectations or importance is more difficult than the measurement of satisfaction. Many people do not know or cannot admit, even to themselves, what is important. Can I believe someone who says they bought a Porsche for its “engineering excellence”? Consumers do not spend their time rationalizing why they do things, their views change and they may not be able to easily communicate or admit to the complex issues in the buying argument.

The same interval scales of words or numbers are often used to measure importance – 5, 7 or 10 being very important and 1 being not at all important. However, most of the issues being researched are of some importance for otherwise they would not be considered in the study. As a result, the mean scores on importance may show little differentiation between the vital issues such as product quality, price and delivery and the nice to have factors such as knowledgeable representatives and long opening hours. Ranking can indicate the importance of a small list of up to six or seven factors but respondents struggle to place things in rank order once the first four or five are out of the way. It would not work for determining the importance of 30 attributes.

As a check against factors that are given a “stated importance” score, researchers can statistically calculate (or “derive”) the importance of the same issues. Derived importance is calculated by correlating the satisfaction levels of each attribute with the overall level of satisfaction. Where there is a high link or correlation with an attribute, it can be inferred that the attribute is driving customer satisfaction. Deriving the importance of attributes can show the greater influence of softer issues such as the friendliness of the staff or the power of the brand – things that people somehow cannot rationalize or admit to in a “stated” answer.

How Many Organizations Measure Customer Satisfaction In 2021?

In a 2021 survey of marketing, insight, CX and business strategy decision-makers at B2B brands , capturing customer satisfaction ratings is the most common method organizations are using to measure customer loyalty.

Overall, 68% of organizations surveyed captured customer satisfaction ratings, while other key metrics such as customer retention, NPS and customer lifetime value were captured less frequently (60%, 51% and 31% respectively).

What’s important to note here is that CX Leaders (companies who are strong on at least 5 of B2B International’s 6 CX excellence indicators) are far more likely to capture customer satisfaction ratings compared to their average (87% vs 70%) or low performing counterparts (87% vs 54%).

This highlights the importance of measuring customer satisfaction if a brand wants to deliver a leading customer experience.

What Do The Measurements Mean?

The scores that are achieved in customer satisfaction studies are used to create a customer satisfaction index or CSI. There is no single definition of what comprises a customer satisfaction index. Some use only the rating given to overall performance. Some use an average of the two key measurements – overall performance and the intention to re-buy (an indication of loyalty). Yet others may bring together a wider basket of issues to form a CSI.

The average or mean score of satisfaction given to each attribute provides a league table of strengths and weaknesses. As a guide, the following interpretation can be made of scores from many different satisfaction surveys:

Someone once told me that the half way point in a marathon is 22 miles. Given the fact that a marathon is 26.2 miles it seemed that their maths was awry. Their point was that it requires as much energy to run the last 4.2 miles as it does the first 22. The same principle holds in the marathon race of customer satisfaction. The half way point is not a mean score of 5 out of 10 but 8 out of 10. Improving the mean score beyond 8 takes as much energy as it does to get to 8 and incremental points of improvement are hard to achieve.

Other researchers prefer to concentrate on the “top box” responses – those scores of 4 or 5 out of 5 – the excellent or very good ratings. It is argued that these are the scores that are required to create genuine satisfaction and loyalty. In their book ‘The Service Profit Chain’, Heskett, Sasser and Schlesinger argue that a rating of 9 or 10 out of 10 is required on most of the key issues that drive the buying decision. If suppliers fail to achieve such high ratings, customers show indifference and will shop elsewhere. Capricious consumers are at risk of being wooed by competitors, readily switching suppliers in the search for higher standards. The concept of the zone of loyalty, zone of indifference and zone of defection as suggested by the three Harvard professors (JL Heskett, The Service Profit Chain; The Free Press; New York 1997) is illustrated below in diagram 1:

Diagram 1 : Customer satisfaction and the effect on customer loyalty

This raises the interesting question – what is achievable and how far can we go in the pursuit of customer satisfaction. Abraham Lincoln’s quote about fooling people could be usefully modified for customer loyalty research to read “You can satisfy all the people some of the time, and some of the people all the time, but you cannot satisfy all the people all the time”. As marketers we know that we must segment our customer base. It is no good trying to satisfy everyone, as we do not aim our products at everyone. What matters is that we achieve high scores of satisfaction in those segments in which we play. Obtaining scores of 9 or 10 from around a half to two thirds of targeted customers on issues that are important to them should be the aim. Plotting the customer satisfaction scores against the importance score will show where the strengths and weaknesses lie, (see diagram 2) with the main objective to move all issues to the top right box.

Diagram 2 : XY graph to show where customer satisfaction needs to improve

How To Use A Customer Satisfaction Research Survey To Greatest Effect

No company can truly satisfy its customers unless top management is fully behind the program. This does not just mean that they endorse the idea of customer satisfaction research studies but that they are genuinely customer orientated.

Yodel’s Customer Satisfaction Journey

A recent example of how to use customer satisfaction scores to greatest effect comes from our own experience in working with Yodel, one of the largest delivery companies in the UK.

Company leadership began a program to “own” what customers really thought by asking real customers for feedback.

To gather feedback from as many customers as possible, they added a simple link onto delivery notifications for the millions of online shoppers using their services every week. The results were immediate with tens of thousands of responses coming through every week. The volume of responses meant data could be generated and tracked through to the region, local service centre and ultimately to the delivery driver.

With this data in hand, thoughts turned to how they could use it to drive the voice of customers into their daily operational performance and company values. After the first million reviews, Yodel asked themselves a critical question – “what does it take to get 100% CSAT?”.

With the data available, they were able to boil it down to 4 key aspects that customers were looking for. These were that parcels were delivered on time in a good condition, with a good attitude whilst being kept informed throughout.

Yodel now had a simple and clear way of explaining their mission to get to 100% CSAT, and also knew what happened to CSAT when one or more of these aspects were below customer expectations.

Why Customer Satisfaction Scores Are Only Part Of The Story

A customer satisfaction index is a snapshot at a point in time. People’s views change continuously and the performance of companies in delivering customer satisfaction is also changing. Measuring satisfaction must be a continuous process. Tracking surveys provide benchmarks of one’s own company’s performance and, if competitor suppliers are also being measured, there will be measurements of relative performance. This places considerable onus on the researcher to design a customer service survey that will accurately show real differences, one survey to another. The questionnaire needs to be consistent so there is no dispute about answers differing because of changes to questions. The sample of each survey must be large enough to provide a reliable base and the selection of the sample must mirror earlier surveys so like is being compared with like.

Benchmarking in customer satisfaction can go beyond comparisons with direct competitors. Some firms have taken this type of benchmarking a step further. Instead of just developing a benchmark on competitors, they identify the best firm in any industry at a particular activity. L.L. Bean may be benchmarked for telephone order processing or customer service. American Express may be benchmarked for billing and payment transactions.

There has been considerable research into the links between customer satisfaction and employee satisfaction – Kaplan & Norton (1996), McCarthy (1997), Heskett, Sasser & Schlesinger (1997). The argument is a very obvious one. Happy employees work harder and try harder and so create satisfied customers. A co-ordinated customer satisfaction program should consider linking with an employee attitude survey. The employee attitude survey could also be used to check out how well staff believe they are satisfying customers as there could be a dangerous gap between internal perceptions of performance and those of customers.

Developing An Action Plan That Rectifies The Weaknesses And Builds On The Strengths

The purpose of customer satisfaction research is to improve customer loyalty and yet so often surveys sit collecting dust. Worse than that, customers have generously given their time to assist in the customer satisfaction survey believing that some positive action will take place. Their expectations will have been raised. The process of collecting the data seems easier than taking action to improve satisfaction levels.

In any customer satisfaction survey there will be quick fixes – actions that can be taken today or tomorrow that will have immediate effect. These could be quite specific such as a newsletter, changes to the invoicing, or a hot-line for technical information. In the longer term, cultural changes may well be required to improve customer satisfaction, and that is more difficult.

A five-step process can be used to make these longer-term improvements.

Video: A 5-Step Process to Making Longer-Term CX Improvements

Step 1: Spot the gap

• Look at the survey data to see where there are low absolute scores and low scores relative to the competition
• Pay particular attention to those issues that are important to customers
• Assume the scores are correct unless there is irrefutable evidence to the contrary – and remember, perceptions are reality

Step 2: Challenge and redefine the segmentation

• How do satisfaction scores vary across different types of customer?
• Are segments correctly defined in the light of the customer satisfaction survey findings?
• How could a change in segmentation direct the offer more effectively and so achieve higher levels of satisfaction?

Step 3: Challenge and redefine customer value propositions, customer journeys and understanding of customer needs

• Are satisfaction scores low because the customer value proposition (CVP) is not being communicated effectively to the market?
• Are scores low because the CVP is not being effectively implemented?
• Is the CVP right for the segment? How could a change in CVP achieve a higher customer satisfaction index (CSI)?
• Is a broader focus on customer experience management required? This goes beyond just conducting feedback surveys – Instead “customer centricity” lies at the heart of the organization’s decision-making.
• This wider discipline of CX management often focuses the business on customer requirements through techniques such as customer journey research , buyer persona research and customer needs research

Step 4: Create an action plan

• Describe the problem
• Think through the issues that need to be addressed and list them out
• Identify the root cause of the problems
• Identify any barriers that could stop the improvement taking place
• Set measurable targets
• Allocated resources (usually money and people)
• Assign people and time scales to the tasks
• Measure and review progress

Step 5: Measure and review

• How has the customer satisfaction index (CSI) moved?
• Is the movement significant/real?
• Has the action recommended in the plan, taken place? Has it been enough? Has it had enough time to work?
• Revisit the steps – spot the gap, challenge the segmentation and CVP, more action

Many of the issues that affect customer satisfaction span functional boundaries and so businesses must establish cross-functional teams to develop and implement action plans. One of the best ways of achieving this involvement by different groups of employees is to involve them in the whole process.

When the survey results are available, they should be shared with the same groups that were involved right at the beginning. Workshops are an excellent environment for analyzing the survey findings and driving through action planning. These are occasions when the survey data can be made user friendly and explained so that it is moved from something that has been collected and owned by the researcher to something that is believed in and found useful by the people that will have to implement the changes.

As with all good action planning, the workshops should deliver mutually agreed and achievable goals, assigned to people who can make things happen, with dates for achievements and rewards for success. Training may well be required to ensure that employees know how to handle customer service issues and understand which tools to use in various situations. Finally, there should be a constant review of the process as improving customer satisfaction is a race that never ends.

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The scourge of customer satisfaction surveys

On a scale of 1 to 5, how likely are you to share this story with a friend?

All I can really say about the appointment at my kid's allergist is that it occurred. We waited weeks to get in, got some tests, received a diagnosis and a treatment plan, had a weird insurance thing that wasted our time. American healthcare took place.

Then I got a survey. 

The email contained the usual set of questions . How would I rate the service I received? How likely was I to recommend them to a friend? But I've gotta say, getting asked how satisfied I was with the care provided by a pediatric allergist was baffling to me. My child received necessary medical treatment at a speed commensurate with its urgency. It was fine. What aspect of it could I possibly evaluate? I don't need to express an opinion about the chairs in the waiting room.

The whole thing vexed me enough that I started to really notice customer satisfaction surveys — and, as I'm sure you've seen, they are everywhere . It seems like every interaction I have with a money-involving organization also comes with a polite request for my feedback. A restaurant. A hotel. A shop. The insurance company that wasted my time. Every time I buy something or interact with someone: another survey. While I was pitching this story to my editor, his email dinged. A survey! How'd we do? How long was your wait time? How satisfied were you with the knowledge and professionalism of the salesperson who served you?

Most of the time I'm not asked to evaluate the quality of a product or service. I'm asked to evaluate the experience , the meta-consumption that drives our hyperactive service economy. A tsunami of surveys has turned us all into optimization analysts for multibillion-dollar companies. Bad enough I'm providing free labor to help a transnational corporation improve its share price or "evaluate" a low-paid, overworked, nonunion employee. It's more than annoying. I'm starting to suspect it's unethical.

This isn't just my imagination. We're all getting more requests for feedback. Global spending on market research has doubled since 2016, to more than $80 billion a year. More than half of that money is doled out in the United States, and a fifth of it — $16 billion! — is devoted to customer surveys.

Consider the experience of Qualtrics, one of the largest survey-data companies. In the past year, the firm has analyzed 1.6 billion survey responses. That's a 4% increase over the prior year — and responses for the first quarter of 2024 were 10% above what Qualtrics projected. Its analysis of "non-structured data," which is to say customer-service phone calls and online chatter, hit 2 billion conversations last year. This year the company projects an increase of 62%.

Why are there suddenly so many surveys? Because people have so many options today that they're not bothering to complain when something sucks. They just move on to a different, equally accessible website. A company pisses them off or disappoints them, and poof! They're gone.

"When a customer has a poor experience, 10% fewer of them are telling the company about it than they did in 2021," says Brad Anderson, the president of product and engineering at Qualtrics. "What's happening is they're just switching." So companies are using surveys in a bid to hang on to those unloyal customers. After all, it's way more expensive to acquire a new customer than keep an old one.

The tricky part is marketing research has shown that the objective quality of a product, its nominal goodness, matters less than whether it meets customer expectations. "Quality," as one research paper put it, "is what the customer says it is." Customer satisfaction correlates with profitability, with share price , with success .

Now, to get all philosophical for a moment, what even is satisfaction , anyway? People tried for decades to figure that out. Then, in 2004, a Bain consultant named Fred Reichheld came up with an answer. He called it the Net Promoter Score. 

Before I tell you what that is, let me ask you a question: On a scale of 1 to 10, how likely would you be to recommend this article to someone else?

That's it. That's what the Net Promoter Score does. If you'd recommend something to someone else, it has by definition satisfied you. Mystery solved.

The NPS came along at the same time as the widening use of the internet and social media, which made it very easy to ask about. Phone calls, snail mail — that stuff is time-consuming and expensive. But surveys sent via email and text are fast and cheap. 

"People don't choose based on objective quality anymore," says one marketing expert.

In American marketing, NPS became an unstoppable craze. Other metrics followed: the Customer Satisfaction Score, the Customer Effort Score, measurements of the entire Customer Experience. A survey, or monitoring calls to customer service, could reveal loyalty, intent to buy again, the specific parts of the "customer journey" that were most pleasant. "People don't choose based on objective quality anymore," says Nick Lee, a marketing professor at the Warwick Business School. "Value is added by way more than what we would call objective product features."

At the peak of the so-called sharing economy, customer surveys were all-powerful. They went both ways: Suddenly, Uber drivers and Uber riders both had star ratings to care about. Customer surveys were going to fix asymmetrical marketplace information. But of course, the whole thing was frothier than a five-star milkshake. By the late 2010s it was becoming clear that all those reviews and ratings were getting less useful over time. They were subject, it turned out, to " reputation inflation ." Eventually everything gets four stars out of five.

The glut of customer surveys has created an additional problem for marketers. Email surveys are like the robocalls of old: You hit delete without even looking at them. "People receive so many survey requests that they're more likely to refuse to participate in any survey," says James Wagner, a researcher at the University of Michigan's Institute for Social Research. It's called oversurveying, and it makes people less likely to respond . Which means that, for statistical validity, companies have to send out more surveys. Which lowers the response rate even further, which means that companies have to send out yet more surveys, in a never-ending doom loop. On a scale of 1 to 5, customer satisfaction with customer-satisfaction surveys is headed to zero.

In reality, nobody's even sure these surveys are measuring the right thing . "Companies regularly collect customer-satisfaction measures, Net Promoter Scores, things like that," says Christine Moorman, a business-administration professor at Duke University who heads up a semiannual survey of hundreds of chief marketing officers. "But then the question is what do they do with it, and to what strategic ends? Most of them are doing it out of habit, not because they're thinking about the larger strategic questions they have."

Big survey companies don't just dump a giant Excel spreadsheet on their clients and send them an invoice. They offer sophisticated analyses of the data they collect. But unless those numbers are tied to possible changes the client might make, what's the point? "It's a huge arms race," says Lee, the Warwick marketing prof. "If you can give me more data rather than less data, I want more data. But the business model as to whether that data is valuable, it's sometimes questionable. Because people don't know what to do with the data, and they let the agency tell them what it says." Just because a company gets a bunch of survey results doesn't mean it knows what to do with them.

Customer surveys aren't just bad for companies. After reading the copious research on how surveys are actually used, I've come to the conclusion that they're even worse for us, the oversurveyed customers.

Any time a scientist wants to do research involving humans, it's a whole thing. That always comes with risks, from exposing people to an untested drug to simply wasting their time. To get approved by an Institutional Review Board, the potential results have to be worth the risks, to provide some benefit to humanity. That's called "equipoise." And if a proposed experiment on living things doesn't have it, you ain't supposed to do the experiment. 

Perhaps customer surveys should be evaluated for "equipoise." What if they're only being used to discipline or fire employees?

Perhaps customer surveys should be evaluated for equipoise. If the companies actually use the data to improve a product or experience, that's good for us subjects. But what if it's used only to improve the company's share value or profitability? Or to discipline or fire employees? That only helps the company. And that doesn't even take into account whether I, the surveyed one, gave my consent for data I provided to be used in that way — a key to ethical research. 

"Maybe we should have to pre-tell people what we're going to do with the data before we get it," Lee says. "That would be a way to stop companies from doing it indiscriminately." But he knows that's a nonstarter. "We'd be adding bureaucracy into the system. Never a popular thing to do with companies."

Worse, for vast swaths of services, you and I are the last people anyone should be soliciting opinions from. Things like doctor visits, legal services, or school classes are "pretty hard for the user to evaluate," Lee says. "We ask for customer feedback on these things all the time, but it's hard for a customer to give you immediate feedback, because a customer doesn't know what quality is yet." The college class you hated because it was hard, and at 8 a.m., might turn out to be your favorite academic memory and the foundation for your professional skill set 15 years later. Whether a visit to the mechanic was pleasant doesn't tell you how well they fixed your car. You have to drive around with your new drive shaft awhile to know whether you got shafted. 

Lee has unpublished data, which hasn't been peer-reviewed, comparing hospital performance in Britain's National Health Service with surveys of both patients and employees. "It's not surprising that the best hospitals have the best patient feedback and best worker feedback," he says. But what is surprising is that worker feedback, not customer responses, correlates most closely with quality. Users, it turns out, aren't very good at telling what's what.

You know what is good at sorting through tons of data? Artificial intelligence. As email surveys get lower and lower response rates, consumer marketing companies have begun to tout their acumen at applying AI to the unstructured verbiage of online reviews, social-media posts, and call-center transcripts. Maybe these new tools, based on large language models, will be able to coax better responses from oversurveyed consumers. "It's the ability to be able to detect when there's a low-quality answer and come back and ask the customer for more data," Anderson says. "When we ask the second question, 40% of the time the customer engages and provides more data. The number of syllables in the second response increases by 9x." 

Now, if I get a callback from a customer-survey robot, there's a good chance most of those additional syllables will be profane. How will I rate my experience getting interviewed by an AI? It might get more actionable data out of me than that email from my kid's allergist did. But I'm pretty sure I won't recommend it to a friend.

Adam Rogers is a senior correspondent at Business Insider.

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