customer satisfaction and loyalty research

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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 Customer Satisfaction and Customer Loyalty

• Reference work entry
• First Online: 03 December 2021
• Cite this reference work entry

• Sebastian Hohenberg 4 &
• Wayne Taylor 5  

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Measuring customer satisfaction and customer loyalty represents a key challenge for firms. In response, researchers and practitioners have developed a plethora of options on how to assess these phenomena. However, existing measurement approaches differ substantially with regard to their complexity, sophistication, and information quality. Furthermore, guidance is scarce on how firms can leverage and combine these approaches to implement a state-of-the-art satisfaction and loyalty measurement system. This chapter attempts to address this vacancy. The authors first define and conceptualize customer satisfaction and customer loyalty. Next, the authors provide an overview of the different operationalization and measurement approaches that companies face when designing a customer satisfaction and loyalty measurement system. The authors also discuss some of the common modeling challenges associated with measuring loyalty, namely, dealing with self-selection bias. Finally, the authors project what the future holds in this area.

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Customer Satisfaction and Loyalty Measurement: A Two-Sided Approach

Customer Loyalty: An Empirical Investigation of Operationalized Measures of Loyalty

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Hohenberg, S., Taylor, W. (2022). Measuring Customer Satisfaction and Customer Loyalty. In: Homburg, C., Klarmann, M., Vomberg, A. (eds) Handbook of Market Research. Springer, Cham. https://doi.org/10.1007/978-3-319-57413-4_30

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Relationship Between Customer Satisfaction and Loyalty

Sharma, A., Gupta, J., Gera, L., Sati, M., & Sharma, S. (2020) Relationship between Customer Satisfaction and Loyalty. Social Science Research Network.

12 Pages Posted: 30 Aug 2021 Last revised: 16 Feb 2022

Delhi School of Business

Date Written: December 25, 2020

One of the most important aspects to ensure the attention of the customers is to provide the best and the most favourable products at this competing market. With customer satisfaction comes customer loyalty. The topic for this review article is to determine the relationship between customer satisfaction and loyalty and the factors influencing these concepts. Further we will get to know that how these concepts affect the relationship that customers have with the organization that helps the organization to be at a better place in the market or beat the competitors. A highly satisfied customer will spread positive WOM and a loyal customer leads to an increase in both sales and profitability. Customer satisfaction affects the trust and customer trust is an antecedent of loyalty. When customers connect with emotions through the product/ services of the brand then it creates a bond between the customer and the brand. The relationship between satisfaction and loyalty influences the profits. The more customer is satisfied, the more loyal towards the brand. A loyal customer leads to an increase in both sales and profitability. Customer satisfaction mediates the relationship between customer loyalty and service quality.

Keywords: Customer Satisfaction, Customer Loyalty, Trust, Service quality

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Customer Satisfaction vs. Customer Loyalty (& How to Improve Them)

Published: December 16, 2019

When you make a sale, it's easy to assume you've just served another satisfied customer. Unfortunately, this isn't always the case. The customer experience comes in all shapes and sizes, and the more you know about your business's experience, the easier it is to make it better.

For brick-and-mortar stores, face-to-face interaction can give you some idea of how a customer experience is going. It's safe to assume a customer who is yelling at a sales rep, for example, isn't having a great experience. It's safe to assume someone who comes in frequently with friends and family, on the other hand, is a loyal customer.

But on the web or phone, it can be trickier to tell what your customer thinks. You can follow the path the customer took to buy an item, but your website isn't expressive enough to give you deep insights into how the customer is feeling. They could be forwarding their email confirmation to a friend to let them know about your great offers, or they could be slamming their head into the keyboard in frustration, exasperated that it was that difficult just to make a simple purchase or change a subscription.

For every touchpoint between your business and your customer, getting a read on how customers feel can help you better understand where you might need to improve, both on a transactional basis and in cultivating consistent long-term loyalty that pays off through referrals and reviews.

Customer loyalty and customer satisfaction aren't the same thing, which means your business should be measuring both. But one should take precedence over the other -- and we'll teach you how to figure out which one.

Customer Satisfaction vs. Customer Loyalty

Customer satisfaction is a measurement of a customer's attitude toward a product, a service, or a brand. It's usually measured by a customer satisfaction survey on a numerical scale. Customer loyalty is a set of behaviors and attitudes that a customer exhibits that demonstrate loyalty to a product, service, or brand, such as repeat purchases or choosing the brand over a competitor.

Imagine you just went to the store and bought a brand new 4K TV you'd been eyeing. Someone hands you a survey after you've made the purchase, asking you whether or not you're satisfied with your experience. Of course you are -- you got the TV you wanted at the price you were willing to pay, and you're chomping at the bit to set it up at home.

Now imagine you were instead handed a different survey at the end of the transaction -- this one asking you how willing you are to recommend the store to friends. Well, you got your TV. But the sales manager wasted time trying to sell you on a bigger one. You probably could've bought the thing just as easily on Amazon, but you wanted to get it that day so you could set it up for your Olympics watching party tonight. Unless they're in a big hurry, you probably wouldn't recommend a friend comes here for their TV.

One of these surveys measures the past ( customer satisfaction ), and the other measures the future ( customer loyalty ). As you see in this example, they can produce very different feedback, each of which has its own implications for your business's success.

How to Measure Customer Satisfaction

Customer satisfaction is the sentiment of the customer after completing a transaction with your business. You can use it to understand whether or not the customer experience met expectations. It's also a great way to let upset customers vent, giving them a private channel to express their feedback, rather than expressing an angry opinion publicly on social media or in a review.

The most straightforward customer experience metric, customer satisfaction score (CSAT) can help you understand if your store is performing its most fundamental tasks. A high CSAT tells you that your shopper's journey to buy something in your store is smooth, predictable, and doable. A low CSAT may indicate you're losing customers before they're ready to check out.

You've probably seen CSAT surveys in the past. They simply ask you to rate on a point scale (1-5 or 1-10, for example) how satisfied you were with your experience.

For web stores and touchless sales processes, when you survey for customer satisfaction is important. Common times to show a survey are on the "checkout successful" page after a purchase, in a follow-up email later on, or a few months before the end of a subscription. CSAT surveys are also used widely throughout the customer support experience, from online help troubleshooting to customer support rep calls, to measure how helpful the interaction was for the customer.

How to Measure Customer Loyalty

Customer loyalty is difficult to measure because it's subjective, and it can vary by business or industry. However, you can use a mix of qualitative and quantitative data to determine how committed your customers are to your business. Start with analyzing customer feedback, then compare these trends to other reports like product usage, repeat sales, churn rate, etc.

Although CSAT is an important metric, customer loyalty may be even more crucial for your business. Loyal customers write positive reviews, spread the word to friends and family, and come back to your store to buy and spend more -- all of which generate new and repeat business for your company.

And, unlike customer satisfaction, customer loyalty is forward-thinking. It's a measure of how much value you may get out of your customers over the long term. Knowing this information can help you make productive changes to the customer journey and provide consistent value to your active user base. 

Let's review some of the data you should be analyzing when measuring customer loyalty. 

Net Promoter Score

To measure customer loyalty, you can use Net Promoter Score® (NPS). You measure NPS in a similar way to CSAT, using a survey on a ten-point scale that asks the question, "How likely are you to recommend [store/product] to a friend?" For each survey response, you can put your customer into one of the following buckets.

• Promoter (Score: 9-10): Your best customers are advocates for your store, returning often to buy again and referring their friends and family, in turn generating more sales for your store.
• Passive (Score: 7-8): These customers may score well on CSAT surveys, but they don't have much loyalty to your store beyond that. If they find a better deal somewhere else, they'll likely take it.
• Detractor (Score: 0-6): The widest survey score range, unfortunately, is reserved for customers who may actively look to damage your brand through negative reviews.

To find your NPS, subtract the Detractor percentage from the Promoter percentage . The NPS ranges from -100 to 100. Where your store lands on the scale is a good indicator of whether or not it's doing the hard work of creating loyal customers.

Customer Engagement

Just because customers aren't leaving reviews after each interaction, doesn't mean they aren't loyal. After all, if we look at the graph below, it's more likely for them to leave a review after a negative experience than after a positive one. If you're limiting yourself to surveys and NPS, you may only be getting half of the story from your customers. 

One metric that's hard to measure but speaks volumes about your business is customer engagement. This is the likelihood and frequency that customers will interact with your company. This can be in your stores, over the phone, on social media, or through third-party review sites. If people are talking about your business, your company should be recording it and analyzing it for patterns. 

When customers engage frequently with your company, they're more likely to become loyal to your business. That's because with each interaction your company has a chance to strengthen the relationship with that customer. The more you capitalize on these opportunities, the more loyal customers you'll generate for your business. 

Customer Lifetime Value

Customer lifetime value , or CLTV, indicates the total revenue that a business can expect a single customer to generate. To calculate it, you need to determine how much revenue the average customer contributes to your business, then multiply that by the average customer lifespan. 

We can see how this works using the formula below.

CLTV is relevant to customer loyalty for two reasons. First, it summarizes how much tangible, monetary value that your customers bring to your business. Therefore, improving customer loyalty should directly increase in CLTV. 

The second reason is your customer lifespan. Identifying your customers' shopping frequency is key to determining whether or not they're loyal to your business. If their lifespan is significantly longer than the expiration date of your products or subscriptions, that may signal that your business needs to step up its loyalty program. 

Share of Wallet

If you're noticing that your CLTV seems low, you may want to take a look at your share of wallet (SOW) as well. Share of wallet refers to the ratio of customer spending in your industry. It analyzes how much your customers are spending on your products as compared to similar products in the marketplace. This is a great metric to look at when trying to see how well your company is doing in comparison to its competitors. 

Share of wallet can be calculated by dividing the average amount of money spent on your products by the total amount that customers spend on products in your industry. This percentage represents how often customers return to your business when given a choice between you and your competitors. 

A great example where SOW is extremely relevant is a gas station. When it comes to purchasing gas, there are so many different potential buyer personas that it can be hard to keep track of loyalty across your customer base . Calculating average SOW is a great way to gain an overall view of how well your business is appealing to your target audiences. 

Customer Retention Rate

Another important metric that you'll want to keep track of is retention rate. This is the percentage of customers that remain with your business after a period of time. It's calculated by dividing the customers you have at the end of a period (minus any new customers you gained) by the number of customers you had at the start. 

While you should regularly monitor customer retention across your entire user base, you should also calculate a separate retention rate for your most loyal customers. After all, these people spend the most at your business, so you want to make sure you're retaining them first and foremost. If you're finding that this rate is lower than the majority of your customers, then it may be time to ramp up your loyalty program. 

Customer Loyalty Index

An alternative to NPS, customer loyalty index (CLI) is another survey that you can use to evaluate loyalty. While it uses the same point-scale as NPS, this survey asks participants hypothetical questions about repurchasing and upselling.

First, your customers rate their responses to three different questions. Each question is about the company's products and whether or not they would buy them again. Then, these values are averaged together into one score, which represents the degree of loyalty that the customer feels toward your business. 

We can see how this survey plays out in the example below. 

Image Source

The benefit of this approach is that it's more direct than NPS, so your feedback will be much more detailed and relevant to the goal you're trying to accomplish. The downside is that because you're so direct, you're showing your true intentions for delivering this form. Some customers may think you're looking for a certain response and will adapt their feedback to give you what you're hoping for, rather than telling you the truth. 

Now that we've discussed a few ways to measure customer loyalty, it's time we talk about how you can improve it at your business.

How to Improve Customer Loyalty

No store can score 100, which means there are always opportunities to improve your customer loyalty. Here are four areas to focus on as you work to improve your customer loyalty.

1. Exceed expectations.

You can -- and should -- ensure everything about your customer's shopping experience is optimized, easy, and fast. But living in today's age of efficient online and touchless purchases, you can do all of that and still only meet your customer's expectations. Just meeting expectations ensures customer satisfaction, but it doesn't go very far in building customer loyalty.

Exceeding expectations takes a little work, but it's worth it. You can exceed expectations by delighting your customer . A few ideas:

• Train customer service reps to go above and beyond for customers
• Add surprise discounts before your customer checks out or by email after the original purchase
• Send a free gift with a customer's order
• Check in after the customer receives their order to find out if they have any questions or complaints
• Provide new customer or user onboarding
• Offer free shipping

2. Communicate well.

Good communication is human . If you communicate well, it can add a human element to your brand that inspires a deeper, more emotional connection between the customer and your business Times to focus on strong communication include:

• Updates during the shipping process
• Check-in after the order is received
• Strong follow-ups to any customer support emails
• Timely explanations for any order delays or issues
• Touching base around the time of renewal

3. Reward loyal customers.

Conditioning good behavior should be a core part of your customer loyalty program. If your customer buys from you frequently or generates new customers for your business, shouldn't they get something out of it?

Of course they should, which is why it's important to create strong rewards programs for return customers and customer loyalty programs for customers who spread the word with friends and family.

4. Use metrics to improve your business.

CSAT and NPS aren't the only metrics you should use to improve your business. Keeping a close eye on the health of your business means understanding the customer journey and measuring your after-sale impact.

Measuring if your email marketing campaigns are generating more sales, for example, is a good way to see where you can improve customer loyalty.

Remember, a satisfied customer isn't always a loyal customer. Benchmarking your customer experience is a critical part of keeping a business scaling and moving forward. If you aren't measuring them already, consider creating surveys to measure CSAT and NPS for your business.

Want more? Learn about the difference between Net Promoter Score vs. customer satisfaction next.

Net Promoter, Net Promoter System, Net Promoter Score, NPS and the NPS-related emoticons are registered trademarks of Bain & Company, Inc., Fred Reichheld and Satmetrix Systems, Inc.

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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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The main influencing factors of customer satisfaction and loyalty in city express delivery

1 School of Management, Xihua University, Chengdu, Sichuan, China

Huawei Duan

Liping zhang.

2 School of Electronic and Information, Southwest Minzu University, Chengdu, China

Fangyao Liu

Associated data.

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

At present, customers’ low satisfaction and loyalty to city express service have restricted the development of city express. It is particularly important to analyze the factors causing customers’ low satisfaction and loyalty, which will promote the development of city express industry effectively. Based on SERVQUAL model and CCSI model, this paper constructs a new evaluation index system from the perspective of service quality. Through this new system, this paper first explores the factors that affect customers’ satisfaction and loyalty, respectively, by fuzzy analytic hierarchy process (AHP) and hierarchical regression analysis, taking the expected and perceived service quality as conversion variables. And then it analyzes the common factors that affect customers’ satisfaction and loyalty comprehensively. These two analyses will provide reference for solving the problem of low customer satisfaction and loyalty of city express enterprises. The results show that popularity and credibility, delivery time commitment, and mailing security are the common main factors affecting customer satisfaction and loyalty. Easy-to-understand receipts, the three-level index corresponding to the empathy dimension, is the most significant factor affecting customers’ loyalty in city express industry; Delivery time commitment, the three-level index corresponding to the reliability dimension, is the most significant factor affecting customers’ loyalty in city express industry.

Introduction

City express service, also known as “the last mile logistics,” is one of the three major services in the express industry, with a large market demand and plenty of room for growth. According to State Post Bureau data, the volume of express delivery business in the same city has increased from 2.29 billion pieces in 2013 to 12.17 billion pieces in 2020. Despite the epidemic, it is still growing steadily, with a year-on-year growth of 10.2%, accounting for 14.6% of total express delivery business, making it the fastest growing sub-industry in China’s logistics industry. The mature development of city express has become a strong guarantee for the improvement of the operational efficiency of the urban economy.

However, despite the business’s growth, service offerings, and product diversification, extensive operation mode has been unable to satisfy demand from customers. The city express industry in China has entered a phase of transition from the initial “labor-intensive” distribution of commodities reliant on manual labor and antiquated mechanical facilities to the “intelligent logistics” of high-tech transformation, artificial intelligence, big data, and high-tech. As a result, issues such a lack of specialization, excessive distribution costs, and subpar after-sales support grow more and more prevalent, which lowers customer happiness and loyalty. The “throwing express” event involving Sto has received a lot of media attention. Only in the first half of 2021, the net loss reached 144 million yuan, and the earnings declined 285.69% year on year, J&T ranked last in the China Post’s service satisfaction survey for the third quarter of 2021 at the end of 2021. Many phenomena show that enterprises should no longer simply consider the cost and price, but should pay more attention to customer satisfaction, and change the pursuit of single profit maximization into overall profit maximization. Therefore, how to improve customer satisfaction and loyalty to occupy more market share has become a difficult problem plaguing the development of enterprises.

In the service industry, the overall service quality of enterprises determines the level of customer satisfaction and loyalty. Meanwhile, it is the key to increasing the market share of enterprises. Existing studies on the influencing factors of satisfaction and loyalty mainly focus on hotels and restaurants, e-commerce, tourism, supermarkets, banks, and other aspects. However, there are few studies on customer satisfaction of city express delivery. Therefore, this paper draws on the experience of other industries in customer satisfaction to study city express. The selected research methods also draw on the research contents of other industries. As a result, the motivation for this paper is to determine which aspect of service improvement can more effectively and quickly improve customer satisfaction of city express delivery and thus increase customer loyalty. The main factors affecting customer satisfaction and loyalty are explored by comparing the factor differences between customers’ expected revenue before purchasing service and actual revenue after receiving service.

Literature review

Compared with other industries, the products provided by service industries are services. Different from tangible products, service products are more difficult to be measured and perceived due to their unique intangibility, inseparability, heterogeneity, and perishable nature.

The research content of this paper mainly involves three aspects: service quality, customer satisfaction, and customer loyalty. The currently recognized service quality evaluation model was that proposed by Zeithaml et al. (1985) . This model measures service quality by measuring the difference between customer service expectation and service perception from five aspects: reliability, responsiveness, security, empathy, and tangible. In addition, Oliver’s “expectation inconsistency theory” laid a solid foundation for the research of satisfaction. The research on customer satisfaction in China began in the early 21st century. The research on the satisfaction of express delivery industry focuses on the analysis of influencing factors and innovative evaluation methods. The “attitude loyalty theory” proposed by Hallowell in 1996 provides a theoretical basis for indirect evaluation of service quality by using customer loyalty. The following is an overview of the correlation among the three:

In the field of express logistics, researches on the relationship between service quality and customer satisfaction show that service quality is the primary factor affecting customer satisfaction. In 1980, Oliver (1980) pointed out that service quality is an important driver of customer satisfaction, whether it is transaction-oriented satisfaction or cumulative satisfaction. Based on the theoretical basis of customer satisfaction and express service, Qiyuan (2022) found that service quality has a significant impact on customer satisfaction, and perceived quality is the main factor. However, through systematic theoretical analysis and practical investigation, he found that the corporate image, service quality, and service price had a relatively significant impact on customer satisfaction. Among them, corporate image and service price are the basic factors affecting customer service reliability, responsiveness, convenience, and guarantee were low, it would lead to low customer satisfaction and affect the overall level and quality of express service.

On the whole, there is a significant positive correlation between customer satisfaction and loyalty. As early as 1993, Oliver (1993) proposed that there was a non-linear relationship between customer satisfaction and loyalty. Ming and Luming (2015) empirically analyzed the factors affecting customer loyalty and finally determined that customer expected quality, customer perceived quality, customer perceived value, corporate image, and customer satisfaction were the most important factors among many factors. Zhiyan et al. (2021) interpreted the perceived value with the perceived service quality and the perceived service cost, and established a structural equation model based on the CCSI model. Finally, he proved that perceived service quality and perceived service cost had the most significant impact on customer satisfaction of logistics distribution service, and the impact of brand image could also not be ignored. Meanwhile, customer satisfaction significantly determines customer loyalty.

At present, there are few studies on the relationship between express logistics service quality and customer loyalty. By integrating existing domestic and foreign literature, scholars generally believe that there is a positive correlation, but no direct influence. Rao et al. (2011) found that logistics distribution cost and service quality are key factors affecting customer loyalty in their research. In the study of Taylor and Baker (1994) , there was no strong positive correlation between service quality and repurchase tendency. Taylor and Xiao (2010) pointed out that the main factor affecting purchase intention is customer satisfaction rather than service quality, and the customer satisfaction has a significant influence on purchase intention, which is greater than that of service quality. With customer satisfaction and customer trust as the intermediary variable, Ying et al. (2016) research the relationship between logistics service quality and customer loyalty. They found that emergency handling quality, information interaction, and logistics distribution quality had a direct and positive impact on customer loyalty. And the quality of personnel service indirectly affects customer loyalty by affecting the intermediary variable of customer trust.

Research framework and hypothesis development

In order to explore the main factors affecting customer satisfaction and loyalty of same-city express delivery, and at the same time make the results more consistent with the status quo of China’s same-city express delivery industry, the CCSI model is combined to correct the model based on the SERVQUAL model in this paper.

In this paper, expected service quality and perceived service quality are added as conversion variables between service quality and customer satisfaction. And the value of expected service quality minus perceived service quality is used as a method to evaluate the influencing factors of customer satisfaction and loyalty. The structural model is shown in Figure 1 .

Evaluation model.

The components of the structural model in this paper present a causal relationship. This paper analyzes the expected service quality of customers before enjoying the service and the actual perceived service quality after experiencing the service from the five dimensions of tangibility, reliability, responsiveness, assurance, and empathy of the express service in the same city. Customers’ subjective requirements for perceived service quality will be higher as expected service quality increases. When the expected service quality is high and the perceived service quality is low, the difference between perceived service quality and expected service quality will be smaller, and the customer satisfaction will be low. The decrease of customer satisfaction will lead to the decrease of customers’ trust and loyalty to the product, and they will increase conversely. In a word, the model takes into account the comprehensive satisfaction of customers when they choose services and after they experience services, so as to evaluate customers’ future purchasing attitudes and achieve synchronous improvement of customer satisfaction and loyalty.

Based on the existing research of city express and combined with the structural model, the author proposed the following hypotheses:

Shuai (2015) put forward that the delivery quality of goods has the greatest impact on customer satisfaction. Customers are most concerned about the security of deliverables, followed by the timeliness and timeliness of delivery.

H1 : Timeliness and security factors under the reliability dimension are the most influential factors that affect the change degree of satisfaction of city express delivery.

Zhiyan et al. (2021) solved the problem and concluded that in the logistics distribution industry, expected service quality has a negative direct impact on customer loyalty, while perceived service quality has a positive direct impact on customer loyalty.

H2 : The expected service quality of city express is negatively correlated with customer loyalty.

H3 : The perceived service quality of city express is positively correlated with customer loyalty.

Xuefang et al. (2015) pointed out that in the banking industry, customer satisfaction is not equal to customer loyalty, but customer satisfaction has a significant positive correlation with customer loyalty, and the most significant factor affecting customer satisfaction is also the most prominent factor affecting customer loyalty.

H4 : The most significant factor for improving the satisfaction of city express delivery is also the most effective factor for improving customer loyalty of city express delivery.

Research design

Evaluation system construction.

In this paper, the implicit variables and explicit variables in CCSI model and SERVQUAL model are integrated, and the main reasons for the slowdown of the development of China’s urban express delivery industry and the factors causing this phenomenon are combined with the special national conditions of China in the report on the Market Operation management and Investment Prospects of China’s Urban Express Delivery Industry in 2021–2027. Establish an index system suitable for the express industry in the same city. This index system is divided into three levels, as shown in Table 1 .

Satisfaction indication system.

Questionnaire and data collection

This chapter designs the questionnaire according to the index system. The collected data will be used as the basic data of hierarchical analysis and fuzzy comprehensive analysis.

The questionnaire is divided into three parts, namely, “basic information,” “survey on the perceived service quality of city express,” and “survey on the expected service quality of city express.” Basic information mainly includes “have you ever used the city express?,” “frequency of use,” “age,” “income,” “the company which frequently choose,” the reason for choosing this company’s services for a long time.” According to the satisfaction index system, the survey of perceived service quality (expected service quality) of city express delivery is divided into 5 parts, with a total of 25 questions and measured by Likert-type scale.

This questionnaire was collected from July 2021 to August 2021. It was distributed on multiple network platforms and offline, as well as on WeChat, Weibo, and other communication apps. A total of 327 questionnaires were collected, excluding 20 invalid ones, 307 valid ones were obtained with an effective recovery rate of 93.88%. Among the 307 valid questionnaires, 45 did not use city express, accounting for 14.66%; 32.57% used once a week; Use it once or twice a week 33.55%; Three to five times a week 17.26%; More than 6 times a week 1.95%.

Combined with the questionnaire data, the basic characteristics of the sample are as follows:

• In terms of age, the main customers of city express are 18–40 years old, accounting for 82.44%;
• In terms of income, customers with an income of 2,000–8,000 yuan use city express the most, accounting for 75.19%;
• Yunda Express (23.66%), SF Express (22.9%), and ZTO Express (15.27%) are the top three companies that often choose to provide local express services.

Reliability and validity analysis

In order to ensure that the questionnaire can accurately reflect the question, the reliability and validity of the questionnaire are analyzed first. In the reliability analysis, Cronbach’s coefficient (Cronbach α) was used to measure the reliability of questionnaire data. In the validity analysis, KMO sampling suitability quantity and Bartlett Test of Sphericity were used to analyze whether there was a strong correlation between each measurement index. The specific results are shown in Tables 2 , ​ ,3. 3 . The reliability coefficients of the questionnaire are all greater than 0.9, and the KMO value is greater than 0.8. Moreover, the Bartlett sphericity test value of p is 0.000, and the variables are not independent.

Reliability.

Main influencing factors of customer satisfaction

In order to get the main factors that affect the satisfaction of local express delivery, this paper chooses fuzzy analytic hierarchy process to explore.

First, according to the expert questionnaire, using analytic hierarchy process to get the weight, and normalized processing. The calculation results show that the weight vector of second-level index relative to first-level index is Customer satisfaction = (0.12, 0.47, 0.10, 0.23, 0.08); The weight vector of the third-level index relative to the second-level index is: Tangibility = (0.10, 0.05, 0.31, 0.17, 0.37), Reliability = (0.12, 0.42, 0.23, 0.23), Responsiveness = (0.35, 0.35, 0.16, 0.14), Assurance = (0.07, 0.25, 0.51, 0.17), and Empathy = (0.18, 0.35, 0.39, 0.08).

Secondly, score set V = (very dissatisfied, dissatisfied, general, satisfied, and very satisfied) is defined to set the corresponding scoring range, and S is the corresponding score value of comments in V, as shown in Table 4 .

Questionnaire score table.

Then, fuzzy evaluation matrices of expected service quality and perceived service quality were established respectively, and the comprehensive scores of secondary indicators were shown in Table 5 .

Second-level index score.

As can be seen from Table 5 , the difference between reliability and empathy is−0.6 and-0.5, indicating that the perceived value of customers’ actual experience does not meet customers’ expectations. Improving reliability and empathy can effectively improve overall customer satisfaction. Then, the fuzzy comprehensive evaluation method was used to score the reliability of the second-level indicators and the third-level indicators corresponding to empathy, respectively. The results are shown in Tables 6 , ​ ,7 7 .

Reliability corresponds to the third-level index scores.

Empathy corresponds to the three-level index score.

It can be seen from Table 6 that the third-level index corresponding to the second-level index “Reliability” has the largest difference value of “Mailing security” with a different value of-2.4, followed by “Delivery time commitment” and “Popularity and credibility” with a different value of-2.2 and-1.8, respectively. In terms of “Matching degree between price and service,” customer perception value higher than expected. As can be seen from Table 7 , the third-level indicator corresponding to the second-level indicator “Empathy” has the largest different value of “Easy-to-understand receipts” with a difference value of-2.6, followed by “Exclusive APP for business handling” and “Humanized way of receiving” with a different value of-1.9 and-0.7, respectively. In terms of “Gradient price standard,” customer perception value is slightly higher than expected. Therefore, the main factors affecting customer satisfaction of express delivery in the same city are “Popularity and credibility,” “Delivery time commitment,” “Mailing security,” “Exclusive APP for business handling,” “Humanized way of receiving” and “Easy-to-understand receipts.” Among them, the difference value of “Easy-to-understand receipts” provided is −2.6, which is the most significant factor to improve customer satisfaction.

Main influencing factors of customer loyalty

According to the existing literature, it cannot be directly proved that there is an inevitable connection between the five dimensions of service quality and customer loyalty in the city express delivery industry. In order to solve this problem, hierarchical regression method is adopted in this paper. It is known that there is a positive correlation between customer satisfaction and loyalty. The research in 5.1 proves that perceived service quality and expected service quality have a significant impact on customer satisfaction. Similarly, the five dimensions of service quality have a significant impact on perceived service quality and expected service quality. Therefore, the analysis method in this paper is divided into three layers. The specific results are shown in Table 8 .

Results of stratified regression analysis.

The basic data used to analyze the main influencing factors of customer loyalty comes from the loyalty questionnaire. According to the evaluation model and literature reference, the index system for evaluating customer loyalty is basically the same as customer satisfaction. It also makes it easier to compare satisfaction with loyalty.

As can be seen from Table 8 , tier 1 takes customer satisfaction as the independent variable and customer loyalty as the dependent variable for linear regression analysis. As can be seen from the above table, the model R square value is 0.109, meaning that customer satisfaction can explain 10.9% of the reasons for the change of customer loyalty. The model passed the F -test ( F  = 31.916, p  < 0.05), that is, customer satisfaction must have an impact on customer loyalty, and the model formula is: customer loyalty = 0.030 + 0.447* customer satisfaction. The final specific analysis shows that: the regression coefficient value of customer satisfaction is 0.447, and presents a significant value ( t  = 5.649, p  = 0.000 < 0.01), which means that customer satisfaction has a significant positive impact on customer loyalty.

When expected service quality is added on the basis of tier 1, F value changes significantly ( p < 0.05), which means expected service quality, and perceived service quality has explanatory significance to the model. In addition, R square value increased from 0.109 to 0.237, indicating that expected service quality, perceived service quality can have 12.8% explanatory power to customer loyalty. Specifically, the regression coefficient value of expected service quality was −0.841 and presented a significant value ( t  = −6.333, p  = 0.000 < 0.01), which means that expected service quality has a significant negative impact on customer loyalty. The regression coefficient of perceived service quality was 0.852 and presented a significant value ( t  = 6.522, p  = 0.000 < 0.01), indicating that perceived service quality has a significant positive impact on customer loyalty.

After adding five dimensions of service quality on the basis of tier 2, the change of F value presents significant ( p < 0.05), which means that the addition of five dimensions has explanatory significance to the model. In addition, the value of R square increases from 0.237 to 0.714, which means that the five dimensions of perceived service quality and expected service quality can explain 47.7% of customer loyalty. Specifically, it can be seen that the five dimensions of expected service quality have a negative impact on customer satisfaction, while the five dimensions of perceived service quality have a positive impact on customer loyalty except responsiveness.

According to the regression coefficient, factors with a regression coefficient higher than 0.3 can be taken as the main factors affecting customer loyalty, and it can be concluded that the main factors affecting customer loyalty are reliability of expected service quality and reliability of perceived service quality. Again, the corresponding three-level indicators are taken as independent variables, and the reliability of expected service quality and perceived service quality is taken as dependent variables for regression analysis. The results are shown in Tables 9 , ​ ,10 10 .

Regression results of three-level indicators and expected service quality reliability.

Regression results of three-level indicators and perceived service quality reliability.

Results and conclusions

Through the above analysis, it can be concluded that the main factors affecting customer satisfaction of city express delivery are in sequence: “Easy-to-understand receipts,” “Mailing security,” “Delivery time commitment,” “Exclusive APP for business handling,” “Popularity and credibility,” and “Humanized way of receiving.” The main factors affecting customer loyalty are in proper order: “Delivery time commitment,” “Matching degree between price and service,” “Popularity and credibility,” and “Mailing security.” Thus, the results of the previous research hypothesis can be obtained, as shown in Table 11 .

Experimental results of the hypothesis.

There are various reasons why the test results do not conform to the hypothesis. According to the results of influencing factors, we can know that, compared with the research results of the main influencing factors of customer satisfaction in other industries, “Easy-to-understand receipts” is the most important factor to improve customer satisfaction of city express delivery. The main reason is that the main delivery items of city express delivery are documents and other small parcels. The main service objects are enterprises, companies, schools, government agencies, etc. Simple and clear documents can be used for account reimbursement and information storage more quickly, saving users’ time.

By comparing the main influencing factors of customer satisfaction and loyalty, it can be found that “Popularity and credibility,” “Delivery time commitment,”

“Mailing security” are common factors affecting customer satisfaction and loyalty, but the importance of these three factors for satisfaction and loyalty is different. The reason for this result is that satisfaction is the evaluation of the service purchased in a single time or a short time, while loyalty is the long-term perception of the enterprise and its services.

The research methods adopted in this paper mainly refer to the satisfaction research methods of other industries. The purpose of this paper is to provide a reference for the research of urban express satisfaction. City express enterprises should combine the actual situation when choosing how to improve customer satisfaction quickly.

In the future, the author’s research content will be divided into two aspects. The first is to research the methods to analyze the customer satisfaction of express delivery in the same city. The second is the research on the evaluation system and index of customer satisfaction of city express. In order to expect for the city express industry to put forward more development proposals.

Data availability statement

Author contributions.

ZL: methodology, data analysis, and writing-original draft preparation. HD: conceptualization, writing-review and editing, and funding acquisition. LZ: index system and questionnaire design. DE: software and validation. FL: data collection and software. All authors contributed to the article and approved the submitted version.

This work was supported by Social Science Planning project of Sichuan Province (grant number SC21B116); Natural Science Foundation Project of Sichuan Province (grant number: 2022NSFSC1865); and Key scientific research fund of Xihua University (grant number: Z17131).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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• DOI: 10.18860/ed.v12i1.25431
• Corpus ID: 271564843

BUILDING A CUSTOMER BENEFIT MODEL DRIVEN BY CUSTOMER SATISFACTION AND CUSTOMER LOYALTY BASED ON DIGITAL EXPERIENCE

• Luthfi Abdillah , Nur Asnawi , Indah Yuliana
• Published in EL DINAR: Jurnal Keuangan dan… 30 April 2024
• Business, Computer Science, Economics
• EL DINAR: Jurnal Keuangan dan Perbankan Syariah

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