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12 Tableau Case Studies to Inspire You in 2024

As we venture into 2024, the need for robust data visualization tools has never been more critical. This is where Tableau becomes a game-changer in data analytics and visualization. Migrating to Tableau is not just a step toward better data handling; it’s a leap toward transformative business insights and decision-making.

With its intuitive interface and powerful analytics capabilities, Tableau has been revolutionizing how companies interact with their data. From small startups to global conglomerates, the stories of transformation are as varied as they are inspiring.

In this blog post, we bring you Tableau case studies to inspire you in 2024. Each case study is a testament to the versatility and impact of Tableau, showcasing how different organizations have harnessed this tool to drive growth, efficiency, and innovation. Whether you are considering migrating to Tableau or looking to maximize its potential, these stories will provide valuable insights and actionable lessons.

Tableau Case Study 1: Bentley Motors – Revolutionizing Luxury Automotive with Tableau

Bentley Motors, a paragon of luxury automotive excellence, recognized the imperative for a digital transformation to stay ahead in a rapidly evolving industry. Embracing Tableau self-service analytics marked a pivotal shift in their approach, leading to transformational changes aligned with their future-focused Bentley100 strategy. This strategy, targeting an all-electric vehicle portfolio by 2030, required a data-driven backbone to meet the challenges of an industry in flux.

Tableau’s impact is evident in various facets of Bentley’s operations. In manufacturing, Tableau’s drillable dashboards have revolutionized process management, enabling quick identification and resolution of issues, leading to more efficient production lines. Similarly, in the realm of customer experience, Bentley leverages Tableau to amalgamate and visualize customer data, enhancing personalized customer journeys and interactions. This unified data approach ensures each Bentley customer feels uniquely understood and valued.

Read the full case study on how Bentley Motors uses Tableau self-service analytics to drive transformational change as it looks to a sustainable future.

Tableau Case Study 2: Trajektory – Revolutionizing Sports Data Analytics with Tableau

Trajektory, a Chicago-based sports data startup, embarked on a journey to redefine how sports organizations manage and leverage their data. Specializing in aggregating diverse data sources into a singular portal, Trajektory faced the challenge of effectively visualizing this data to value sponsorship assets accurately and in real-time. Their goal was to provide sports teams with the tools to create comprehensive, insightful reports for sponsors, vendors, and partners, but they lacked the necessary infrastructure to integrate their data with Tableau’s powerful visualization software.

A significant milestone in this partnership was the development of a custom-built portal, integrating Tableau for front-end data visualizations. This integration was pivotal in enabling Trajektory to build and refine their own dashboards, enhancing the final presentations for their clients. The portal provided a secure, single-point access for clients to view all their data, a crucial feature for data-driven decision-making in sports organizations.

Read the full case study on how Trajektory uses Tableau to effectively visualize its data to value sponsorship assets accurately and in real time.

Tableau Case Study 3: Verizon – Enhancing Customer Experience with Tableau

Verizon, a leader in providing broadband Internet, TV, and landline services, embarked on a significant endeavor to enhance customer experience using Tableau. Faced with the challenge of managing vast amounts of data generated daily, Verizon’s Analytics Center of Excellence (ACE) team aimed to optimize operations across various functions like call centers, digital platforms, and dispatch services.

By integrating data from diverse sources like Hadoop, Teradata, and Oracle, Verizon’s ACE team developed over 1,500 Tableau dashboards. These dashboards were essential in ingesting billions of rows of data, thereby reducing customer service analysis time by 50% across multiple teams. This effort led to a remarkable 43% reduction in call volume and a 62% decrease in technical dispatches for certain customer cohorts, significantly enhancing the efficiency of their operations.

Read the full case study on how Verizon uses Tableau to reduce support calls by 43%, enhancing customer experience.

Tableau Case Study 4: Empower Physical Therapy – Streamlining Operations with Tableau

Empower Physical Therapy, a rapidly expanding therapist-led company with 40 locations across the United States, faced a significant challenge in managing and presenting their financial data. Their growth demanded a sophisticated solution for sourcing, integrating, and reporting financial data, particularly given the need to handle sensitive patient information in compliance with HIPAA regulations.

Key takeaways from this case study include the development of HIPAA-compliant Tableau dashboards, the delivery of actionable reports that streamlined office processes, and the successful integration of multi-platform technologies to create reports that underscored the company’s value to its investors.

Read the full case study on how Empower Physical Therapy streamlined its operations with Tableau.

Tableau Case Study 5: Mercado Libre – Fostering a Data-Driven Culture with Tableau

Mercado Libre, Latin America’s leading online retailer, embarked on a transformative journey to establish a data-driven culture across its rapidly growing organization. With over 30,000 employees and operations in multiple countries, the company faced the challenge of upskilling its workforce to harness the power of data effectively.

The solution involved a comprehensive approach to data culture, integrating technology, data collection methods, and cultural changes. A key component was the implementation of Tableau, which saw a 5x increase in adoption with 12,000 active users, 9.4K data sources, and 9.5K workbooks. This significant shift in data analytics capability was instrumental in redefining Mercado Libre’s business processes and enhancing the quality of employees’ working lives.

Read the full case study on how a robust upskilling program empowers transformation to a data culture at e-commerce leader Mercado Libre.

Tableau Case Study 6: Splunk – Enhancing Efficiency and Performance with Tableau Cloud

Splunk, a San Francisco-based leader in data-to-everything platforms, embarked on a strategic migration to Tableau Cloud, driving significant efficiency and dashboard performance improvements. This shift was part of Splunk’s commitment to reducing server administrative burdens and embracing a cloud-first approach, in line with their SaaS-based business model.

The migration to Tableau Cloud was a pivotal decision for Splunk, aiming to alleviate the increasing strain on server capacity and performance due to rapid growth in Tableau adoption. The transition streamlined their data visualization workflows and aligned with their software-as-a-service ethos, reducing the need for dedicated IT operations and data engineering management for the platform.

Read the full case study on how Splunk migrated to Tableau Cloud to eliminate server admin overhead and drive better dashboard performance.

Tableau Case Study 7: NYU Langone Health – Advancing Healthcare with a Data-Driven Culture Featuring Tableau

NYU Langone Health, a top-ranked U.S. hospital, has successfully built a data-driven culture, leveraging Tableau visual analytics to enhance healthcare quality and efficiency. This transformation has been instrumental in improving their national medical school ranking and boosting their NIH research funding portfolio.

By leveraging Tableau visual analytics, the institution has improved its operational efficiency and financial management and elevated the quality of patient care.

Read the full case study on how NYU Langone Health builds a data-driven culture featuring Tableau visual analytics.

Tableau Case Study 8: Cervey – Enhancing Healthcare Technology Solutions with Tableau

Cervey, a provider of technology solutions for pharmacies, Pharmacy Benefit Managers (PBMs), and Long Term Care facilities, faced a significant challenge in deploying Tableau dashboards to their clients. The primary concerns were ensuring HIPAA compliance for sensitive patient data security and developing visually appealing dashboards for enterprise-level customers.

The Tableau dashboards effectively integrated and streamlined daily operational processes, facilitating quick and secure access to essential data for pharmacy transactions and clinical workflows. This enhancement in their software solutions significantly elevated Cervey’s value proposition to their existing customer base and bolstered their attractiveness to potential new clients.

Read the full case study on how Cervey enhanced its healthcare technology solutions with Tableau.

Tableau Case Study 9: Carter’s Inc. – Fostering Data Literacy for Retail Excellence

Carter’s Inc., a leading children’s apparel company based in Atlanta, Georgia, embarked on a significant journey toward digital transformation and data literacy. Facing the immense challenge of managing 50 terabytes of enterprise data and shipping approximately 700 million units annually, Carter’s initiated a strategic shift to modernize its data infrastructure and analytics processes.

To cultivate data literacy across the organization, Carter’s established a Tableau CoE. This initiative focused on improving departmental data literacy, fostering comfort and enthusiasm in using data analytics technology. The CoE provided training and engagement programs, department-focused projects, and one-on-one sessions with data experts. This approach was particularly effective during the COVID-19 pandemic, enabling rapid adaptation to new supply chain and inventory management challenges.

Read the full case study on how Carter’s built a center of enablement with Tableau.

Tableau Case Study 10: UN World Food Programme – Data Literacy Driving Global Food Security

The United Nations World Food Programme (WFP), the world’s largest humanitarian organization, has embarked on a transformative journey to enhance global food security through a robust data-driven approach. Operating in over 120 countries and territories, WFP’s mission to deliver food assistance and build resilience among food-insecure communities is a colossal task, involving the support of over 100 million people annually.

WFP established a Tableau CoE focused on promoting data literacy and analytics usage across the organization. The CoE’s strategy included collaboration, peer leadership, and attracting top talent to drive data literacy. The data literacy program enabled WFP teams to use Tableau dashboards effectively for various projects, including strategic interventions in food-insecure regions.

Read the full case study on how the UN World Food Programme promotes organization-wide data literacy to advance food security worldwide.

Tableau Case Study 11: Mobile Tech RX – Revolutionizing Auto Repair with Data Analytics

Mobile Tech RX, a pioneer in the automotive repair software industry, embarked on a transformative journey to revolutionize the traditional, paper-based estimating and invoicing systems in auto repair and body shops. Founded in 2014, the company recognized the inefficiencies in the auto reconditioning industry and aimed to streamline the process with a comprehensive software solution.

The goal was to replace outdated pen-and-paper systems with a modern, digital solution. Many auto shops lacked organized internal reporting sources and struggled to utilize historical data effectively. Mobile Tech RX successfully launched a professional, well-styled software product, significantly enhancing the operational efficiency of auto repair shops. Since its launch, the Mobile Tech RX app has seen substantial growth in its active user base, becoming a leading solution for internal reporting and analytics in the auto repair industry.

Read the full case study on how Mobile Tech RX revolutionized auto repair with Tableau data analytics.

Tableau Case Study 12: Texas Rangers – Data-Driven Strategies Enhancing Fan Experience

The Texas Rangers, a renowned American baseball team based in Arlington, Texas, has made significant strides in utilizing data to enhance fan experiences and optimize operations. With a history dating back to 1972 and a large fan base, the organization faced the challenge of modernizing its data strategy to maintain competitive advantage and provide exceptional fan experiences.

By leveraging real-time data, the team optimized gameday operations. For instance, they tracked vehicle entries to manage parking efficiently and monitored gate entries during giveaway nights to enhance fan satisfaction.

Tableau was integrated into the Salesforce Service Cloud to address fan queries more effectively. This allowed for better visualization of frequently asked questions and improved communication with fans.

Read the full case study on how the Texas Rangers’ data strategy hit a home run with fans.

Modernize Your Data & Analytics Insights With Tableau and XeoMatrix

Modernizing your data and analytics insights with Tableau is a strategic move that can significantly transform your data management and analysis capabilities. By centralizing data, enabling real-time insights, scaling seamlessly, promoting collaboration, and prioritizing security and governance, your organization can gain a competitive edge and drive innovation in the dynamic digital landscape.

XeoMatrix offers expert assistance in modernizing your Data & Analytics infrastructure. Our team of skilled professionals can guide you through the implementation of a modern data stack, leveraging powerful tools like Tableau to unlock the true potential of your data-driven insights.  Contact us today  to embark on your journey toward data-driven success.

About XeoMatrix

XeoMatrix provides consulting services for data and analytics to assist companies in comprehending their data for informed business decisions. Our established methodology streamlines the development process, allowing our clients to concentrate on analyzing their business, enhancing data visualization, and ultimately boosting ROI. To learn more about our business intelligence and data warehousing solutions, don’t hesitate to  contact us today .

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Data Visualization with Tableau: 4 Best Case Studies to Know

Discovering the commercial benefits with data visualization..

Visualizing the information is more convenient than delving into the complex data table collections because the human brain easily digests the graphics, unlike the Excel spreadsheets due to their lack of information overload. That is why more and more people realize how to apply Tableau data visualization in the business context and discover the commercial benefits. In this article, you will find the examples of Tableau business cases with an ample outcome because of data visualization.

Impacting the businesses

Data visualization increases the revenue for enterprises. How, though? First of all, it allows to obtain the profound insights when answering the data-related questions: instead of trying to structure the messy information, one can easily observe trends in revenue, costs, customer count, conversion rates, and the other e-commerce metrics (MAU, DAU, CPC, CPA, LTV).

By visualizing the information, it’s easy to find inefficiencies, to determine seasonality and optimize the company’s strategy through development of profitable directions. Also, visualizing the data is the way of guaranteeing the objective source of truth for all levels of leadership and providing all of the departments with the up-to-date and truthful information. An example of efficient visual representation can be found down below:

Source: Tableau Online

Tableau, a suitable solution

Tableau is a good choice for the users in need of cross-platform reports (on tablets, smartphones, or desktops). Tableau is easy to use and is suitable for sharing the data with all the members of the company. At the same time, it is convenient for processing the large sets of information, regardless of the amount of sources.

In fact, Tableau leverages an extensive set of data connectors, such as MySQL, Google Analytics, Google SpreadSheets, Excel, CSV files, and others. Thus, it provides the users with advanced analytical dashboard capabilities along with assisted formula editing, forecasting, clustering, and flexible deployment options (in-cloud, on-premises, and online).

Coca-Cola: shaping the essence of analytics

Coca-Cola , the largest beverage company in the world, looked how to replace its daily 45-minute manual data reporting process. Previously, the team spent a considerable amount of time trying to connect over 200 million lines of data from over 100 different systems into single storage to then build one usable dashboard. To boost the efficiency and carry out a real-time data, Coca-Cola adopted Tableau. Because Tableau consolidates the data from multiple sources, various teams at Coca-Cola can now actively comprehend the metrics, including the budget, delivery operations, and profitability in a matter of few clicks. Simultaneously, the sales department can now access the data from the remote locations by using the iPads, which increased overall timeliness. Finally, the executive reports automatically refresh each day at 5:45 am, unlike the previous times.

Coca-Cola Tableau case study

Lenovo: a 95% increase in efficiency across the company

Lenovo, a global technology company, aimed to optimize its analytics experience across all the departments and worldwide offices. Previously, Lenovo operated with one single sales report that was delivered to 28 different countries. When different regions or company’s divisions wanted to adopt the report to extract the most valuable data, it required a commitment of eight to ten individuals and led to a massive number of on-hold tasks for the analytics team. In turn, Lenovo decided to use Tableau to orderly structure the data all across the company. As a result, Lenovo got a flexible dashboard with all the sales that can be adapted for the ad-hoc analyses, which also led to 95% efficiency improvement across 28 countries. With the help of Tableau dashboard ideas, Lenovo gathered the engagement metric, thus crafting a better experience and collecting more revenue.

Lenovo Tableau case study

LinkedIn: empowering 90% of the Sales Team

LinkedIn, a largest professional networking website, wanted to synchronize all the data across its internal databases ( Google Analytics , Salesforce.com , third-party tools). Previously, one analyst at LinkedIn would handle daily sales request from over 500 salespersons, which created a reporting queue of up to 6 months. To fix the issue, LinkedIn decided to use Tableau to centralize the spread out data and develop a series of customer access dashboards. As a result, thousands of individuals nowadays can access the Tableau Server on a weekly basis, which constitutes 90% of the LinkedIn sales force. With the interactive real time dashboards in Tableau, one can easily predict churn and track the current performance, which eventually created more revenue through the proactive cycle of sales.

LinkedIn Tableau case study

Bookimed: Building real-time analytical dashboards

Bookimed, a Ukrainian service for searching the best medical solutions worldwide, wanted to make an x2 increase in revenue by year. To do so, the companies that use Tableau would have to make reasonable decisions based on data. We understood that previously managers had issues with evaluating hypothesis and tasks prioritization because of the manual filtering through the information. Usual data analysis required next steps:

• Making a task for IT-department to load raw data in CSV file.
• Filtering this data by hand and exploring it to Excel.
• Manually linking data from Excel, CSV files to data from Google Analytics and Google AdWords.

In order to get quick and error-free insights from data and tracking the real-time state of the business, Bookimed decided to use Tableau and Tableau Online services. The main goal was to create the customizable for every department real-time dashboard system. To fulfill these tasks, we did undergo three steps:

• Connected Google Analytics, Google Adwords and MySQL to Tableau.
• Set-up Dashboards for every department at Bookimed.
• Additionally, data root labs incorporated Amazon Redshift + Amazon Kinesis for advanced data gathering and more sophisticated data analysis.

Here are some of the first graphics of the company (numbers are random and for visualization purpose only):

Graphics and Visualizations (Tableau). Source: Bookimed.com

Now, Bookimed.com has the system that delivers all the relevant data and information to every member of the company in real-time mode. Simultaneously, Bookimed got all of the following:

• Reduced the business analysis time from 1 week to 2 hours and ensured the data-driven decision making among employees.
• Build based on data reasonable growth plan for increasing the revenue by 10% monthly .
• Acquired the data visualization infrastructure that can be easily modified, scaled, and changed according to the business needs.

Other Industries where Tableau is used

Healthcare analytics.

• BJC , a company that provides healthcare to residents of Missouri and Illinois, reduced supply chain expenses from 23.5% to 19% .
• Seattle Children’s, a pediatric hospital, saved more than 40000 clinical hours each year and $100000 with demand flow.

Education Analytics

• Des Moines Public School District received an ability to quickly detect high-school students with the likelihood to drop-out .
• The University of Notre Dame got able to perform data analysis using Tableau 10x times faster .

Government Analytics

• The city of Tallahassee measured workload sewers’ efficiency and made utility decisions that improved productivity by 30% .
• Surrey County Council reduced analysis time from days to hours to track intervention at youth clubs.

Marketing Analytics

• Allrecipes, a largest digital food brand, increased the mobile site visits from 8 percent to more than three-fourth of total .
• PepsiCo cut analysts time by 90% .

Tableau Insurance Analytics

• EY, a professional risk management organization, saved clients millions of dollars and prevented fraud .
• MA Assist, the UK-based property services firm, cut insurance claim duration by 15% and improved business efficiency by 20% .

High Technology Tableau Analytics Examples

• GoDaddy, an international web hosting firm, scaled 13TB of data governance and and optimized product experience for over 17 million customers.
• Ancestry.com , a largest online resource for searching family history, visualized billions of rows of data for strategic decision-making .

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Cyclistic Case Study Using Spreadsheets, SQL and Tableau

In this case study, I analyze historical data from a Chicago based bike-share company in order to identify trends in how their customers use bikes differently. The main tools I use are spreadsheets, SQL and Tableau. Here are the highlights:

Tableau Dashboard: Cyclistic Bikeshare in Chicago

Slides: Where Rubber Meets Road in Converting Casual Riders to Cyclistic Members

GitHub: Cyclistic Case Study Repository

A more in-depth breakdown of the case study scenario is included below, followed by my full report.

Cyclistic is a bike-share company based in Chicago with two types of customers. Customers who purchase single-ride or full-day passes are known as casual riders , while those who purchase annual memberships are known as members . Cyclistic’s financial analysts have concluded that annual members are much more profitable than casual riders. The director of marketing believes the company’s future success depends on maximizing the number of annual memberships.

The marketing analytics team wants to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, the team will design a new marketing strategy to convert casual riders into annual members. The primary stakeholders for this project include Cyclistic’s director of marketing and the Cyclistic executive team. The Cyclistic marketing analytics team are secondary stakeholders.

Defining the problem

The main problem for the director of marketing and marketing analytics team is this: Design marketing strategies aimed at converting Cyclistic’s casual riders into annual members. There are three questions that will guide this future marketing program. For my scope on this project, I will anlyze the first question:

1) How do annual members and casual riders use Cyclistic bikes differently? 2) Why would casual riders buy Cyclistic annual memberships? 3) How can Cyclistic use digital media to influence casual riders to become members?

By looking at the data, we will be able to first get a broad sense of certain patterns that are occurring in the two different groups. Understanding the differences will provide more accurate customer profiles for each group. These insights will help the marketing analytics team design high quality targeted marketing for converting casual riders into members. For the Cyclistic executive team, these insights will help Cyclistic maximize the number of annual members and will fuel future growth for the company.

Business task

Analyze historical bike trip data to identify trends in how annual members and casual riders use Cyclistic bikes differently. #

Data sources

We’ll be using Cyclistic’s historical bike trip data from the last 12 months, which is publicly available here . The data is made available by Motivate International Inc. under this license . The data is stored in spreadsheets. There are 12 .CSV files total:

It is structured data, organized in rows (records) and columns (fields). Each record represents one trip, and each trip has a unique field that identifies it: ride_id . Each trip is anonymized and includes the following fields:

Bike station data that is made publicly available by the city of Chicago will also be used. It can be downloaded here . In terms of bias and credibility, both data sources we are using ROCCC:

Reliable and original: this is public data that contains accurate, complete and unbiased info on Cyclistic’s historical bike trips. It can be used to explore how different customer types are using Cyclistic bikes.

Comprehensive and current: these sources contain all the data needed to understand the different ways members and casual riders use Cyclistic bikes. The data is from the past 12 months. It is current and relevant to the task at hand. This is important because the usefulness of data decreases as time passes.

Cited: these sources are publicly available data provided by Cyclistic and the City of Chicago. Governmental agency data and vetted public data are typically good sources of data.

Data cleaning and manipulation

Microsoft excel: initial data cleaning and manipulation.

Our next step is making sure the data is stored appropriately and prepared for analysis. After downloading all 12 zip files and unzipping them, I housed the files in a temporary folder on my desktop. I also created subfolders for the .CSV files and the .XLS files so that I have a copy of the original data. Then, I launched Excel, opened each file, and chose to Save As an Excel Workbook file. For each .XLS file, I did the following:

• Formatted as custom DATETIME
• Format > Cells > Custom > yyyy-mm-dd h:mm:ss
• Calculated the length of each ride by subtracting the column started_at from the column ended_at (example: =D2-C2 )
• Formatted as TIME
• Format > Cells > Time > HH:MM:SS (37:30:55)
• Calculated the date of each ride started using the DATE command (example: =DATE(YEAR(C2),MONTH(C2),DAY(C2)) )
• Format > Cells > Date > YYYY-MM-DD
• Entered the month of each ride and formatted as number (example: January: =1 )
• Format > Cells > Number
• Entered the year of each ride and formatted as general
• Format > Cells > General > YYYY
• Calculated the start time of each ride using the started_at column
• Calculated the end time of each ride using the ended_at column
• Calculated the day of the week that each ride started using the WEEKDAY command (example: =WEEKDAY(C2,1) )
• Formatted as a NUMBER with no decimals
• Format > Cells > Number (no decimals) > 1,2,3,4,5,6,7
• Note: 1 = Sunday and 7 = Saturday

After making these updates, I saved each .XLS file as a new .CSV file.

BigQuery: further data cleaning and manipulation via SQL

Since these datasets are so large, it makes sense to move our analysis to a tool that is better suited for handling large datasets. I chose to use SQL via BigQuery .

In order to continue processing the data in BigQuery, I created a bucket in Google Cloud Storage to upload all 12 files. I then created a project in BigQuery and uploaded these files as datasets. I’ve provided my initial cleaning and transformation SQL queries here for reference: initial_setup_query.sql

The results from the COUNT DISTINCT query for each table are very interesting. We can see that the three summer months have the highest trip counts, followed by alternating spring and fall months before ending with winter months:

Create quarterly tables

In order to perform analysis by season, let’s combine these tables. We’ll create Q1, Q2, Q3 and Q4 tables for analysis. We’ll have two Q1 tables– one for 20221 and one for 2022 – since we have FEB/MAR data from 2021 and JAN data from 2022:

• Table 1) 2021_Q1 -> FEB(02), MAR(03)
• Table 2) 2021_Q2 -> APR(04), MAY(05), JUN(06)
• Table 3) 2021_Q3 -> JUL(07), AUG(08), SEP(09)
• Table 4) 2021_Q4 -> OCT(10), NOV(11), DEC(12)
• Table 5) 2022_Q1 -> JAN(01)

We’ll first create 2021_Q2 and then repeat for the remaining four tables:

Clean and transform day of week

Some additional data cleaning is needed on the new table. First, we’ll update the format for day_of_week from FLOAT to STRING . Then, we’ll change the values from numbers to their corresponding day names (i.e. 1 = Sunday, 7 = Saturday. We’ll start with 2021_Q1 and repeat for the remaining four tables:

Delete old tables

Now that we have our tables organized into quarters, we can delete the original monthly tables from BigQuery. We no longer need the monthly tables since the data is available in the quarter tables. Also, it costs money to store these datasets in BigQuery.

Analysis #1: Exploratory

2021_q1 - quarterly data exploration.

We’ll select a few columns from 2021_Q1 to preview in a temporary table. This will help give us an idea of potential trends and relationships to explore further:

The above query returned 278,119 rows. That is the number of recorded trips we have data for in this quarter. Let’s dive deeper into those trip totals.

Total trips

We’ll create total columns for overall, annual members and casual riders. We’ll also calculate percentages of overall total for both types:

Of the 278,118 total trips in 2021_Q1, 66% were from annual members while 34% were from casual riders.

Average ride lengths

How does average ride_length differ for these groups?

We can see that casual riders average about 23 more minutes per ride. That seems like a pretty big difference. What influence are outliers having on these averages? Let’s investigate.

Max ride lengths

We’ll look at the maximum values for ride_length to see if anything extreme is influencing the casual rider average:

As we suspected, the casual riders average ride_length was significantly impacted by at least one outlier. The longest trip duration for casual riders was 528 hours, or 22 days. Meanwhile, the longest for annual was about 26 hours.

Let’s take a look at the top 100 highest ride_length values for casual riders to confirm there is more than one outlier affecting the average:

Median ride lengths

Since there are more than a few outliers impacting the average, we’re going to use median instead of average. Median will be more accurate for our analysis:

Now we see a much closer number, with 18 minutes for casual riders and 10 minutes for annual members.

Busiest day for rides

Let’s see which day has the most rides for annual members and casual riders:

Unsurprisingly, Saturday is the most popular day for both annual members and casual riders.

Median ride length per day

Let’s look at the median ride lengths per day for both annual members and casual riders. Since Saturday is the most popular overall, do we think it will also have the highest median ride length?

Very interesting! The median ride length for casual riders on the top five days (SUN, SAT, MON, TUE, WED) is nearly double the amount for annual members on their top five days (SAT, SUN, MON, TUE, WED).

Total rides per day

Let’s look at total rides per day. We’ll create columns for overall total, annual members and casual riders:

Start stations

Next, we’ll look at the most popular start stations for trips. We’ll again include columns for overall, annual member and casual rider totals per start station:

We can begin to see some interesting patterns in the start station data. It looks like casual riders and annual members tend to favor different regions for beginning their trips. By updating the ORDER BY function to sort by casual DESC and member DESC in two separate queries, we can compare the top ten start stations for both:

Wow! There is only one start station that cracks the top ten for both lists. The Clark St & Elm St start station is ranked #1 for annual members and #10 for casual riders. The casual riders seem to favor stations near the water like Lake Shore Dr & Monroe St and Streeter Dr & Grand Ave , while annual members frequent start stations in the River North neighborhood like Dearborn St & Erie St and Kingsbury St & Kinzie St .

An initial hypothesis for casual riders could be that they tend to favor start stations near the water and close to tourist attractions because they use bikes for weekend entertainment. An initial hypothesis for annual members could be that they tend to favor start stations in downtown, retail areas because they are using bikes for their work commutes and shopping trips.

Quarterly data exploration (cont.)

Instead of walking through each quarter like we’ve done for 2021_Q1, I will instead provide links to the full SQL files. The queries used are similar to the ones above:

• analysis_2021_Q1.sql
• analysis_2021_Q2.sql
• analysis_2021_Q3.sql
• analysis_2021_Q4.sql
• analysis_2022_Q1.sql

I’ll included some high-level quarterly analysis notes in the next section.

Analysis #2: Summary

Full_year - trends, relationships and insights.

In order to analyze all twelve months together, we’ll combine the five quarterly tables into one table. The queries used to accomplish this are included here for reference. I’ve also provided the SQL file used for full year analysis: analysis_full_year.sql .

For a summary and overall visualization of my full year analysis, please visit the Tableau Public dashboard I created here: Tableau Dashboard: Cyclistic Bikeshare in Chicago . I will also highlight some of the interesting trends and relationships I discovered below.

Annual Members vs Casual Riders

Seasonal trends

Summer vs winter.

The busiest time of year for overall bike trips is Q3– July, August and September. This makes sense because these months are mainly summer time. Bike riding is better suited for warmer weather, which is also why we see a major drop-off in total rides during the winter months of Q1– January, February and March.

Annual members outnumbered casual riders in every quarter except Q3. Interestingly, the annual members nearly doubled the casual ridership in Q1 and Q4 while only slightly edging them out in Q2.

Median ride length

We can see that casual riders consistently have longer rides than annual members.

Day of week

Which days of the week have the highest number of rides for casual riders vs annual members? Let’s look at the mode for each quarter and for the full year:

Casual riders were extremely consistent, with Saturday revealing itself as their preferred day of week for each quarter and across the full year. Meanwhile, the annual members looked to favor the middle of the week for their bike use. The most popular day for them acrosss the full year was Wednesday . Let’s see how the total rides for each day stack up for both groups:

How about median ride length per day of week for both groups?

A few fascinating insights from the above chart:

U-shape pattern Sunday and Saturday are favored by both groups for longer rides, while ride duration decreases towards the middle of the week before increasing again on Friday. This results in a u-shape trend for both groups in the above chart, although it is much more dramatic for casual riders.

Range differences For annual members, difference between their longest day and their shortest day is 1 minute and 44 seconds. For casual riders, difference is 4 minute and 57 seconds. That is a 185.58% increase in difference for casual riders.

Annual members: day-to-day consistency The annual members may have shorter ride lengths when compared to casual riders, but they are extremely consistent with their bike use day-over-day.

Casual riders: weekend warriors The daily median ride length for casual riders is consistently higher than that of annual members. The range of their ride length duration varies at a greater amount than that of annual members. Sundays and Saturdays stand out as their longest ride days.

Do members and casual riders have different preferences for bike type? Are classic bikes more popular than electric bikes?

We can see that classic bikes are favored by both groups. Let’s look at the percentages of bike type use within each group:

Looking at the above, we might ask what exactly is a docked bike and why are only casual riders using them?

We can now see from the above charts that docked bikes are the culprit for the outliers affecting our ride length averages from earlier in our analysis. This is something we should discuss with our team further and address.

Start and end station use

In the Tableau Dashboard I created, which is again available here , there is a worksheet that allows the exploration of start and end station use by members, casual riders and combined overall rides. The snapshot below is from the overall view. While interacting with the dashboard, we can see that casual riders have a higher max than annual members. Annual members have a lower max, but we can see more colors represented across the member map versus the consistent coloring across the casual map. This tells us that rides by members are more distributed across stations while rides by casual riders are more top heavy in that a huge chunk are happening at the same few stations.

Stakeholder presentation and dashboard

I’ve provided links below for my dashboard and shareholder presentation, which includes the following:

• A summary of my analysis
• Supporting visualizations and key findings
• Three recommendations based on my analysis

Presentation: Where Rubber Meets Road in Converting Casual Riders to Cyclistic Members

May 10, 2024 Accountancy Business Administration Finance Student

Gies students shine in 2024 Business Sustainability Case Competition

Three winning teams were selected and awarded with cash prizes in the inaugural Business Sustainability Case Competition hosted by the Center for Professional Responsibility in Business and Society (CPRBS). The competition focused on mapping the sustainability activities at the University of Illinois Urbana-Champaign, and competing teams from all over campus created data visualizations showcasing the university’s achievements in the sustainability area.

The team at Sustainable FR –  Isabella Chew  (ACCY+DS),  Abbas Mirza  (Computer Science/Economics),  Julie Wang  (International/Global Studies),  Ekaterina Ftikas  (International/Global Studies), and  Olivia O’Leary  (Policy) – took home top honors for their ability to create a course search system. Sustainable FR collected data on courses offered at the University of Illinois Urbana-Champaign for the current school year.  Using Microsoft Excel in conjunction with Tableau, the team created visualizations showing the growth of courses that focus on one or more of the UN’s 17 Sustainability Goals.  Beyond just visualizations, Sustainable FR created an interactive program that allows students to filter and focus courses that pertain to specific goals.

“Going into college, one of my goals was to combine my environmental interest with my professional development, and this case competition provided the perfect opportunity,” said Chew, a first-year Gies student pursuing an accountancy + data science major. “I was delighted to see how Gies College of Business encourages its students to partake in sustainability efforts, emphasizing the importance of professional responsibility. My team can proudly say that we learned a great deal about data visualization and the progress of the UN Sustainable Development Goals at UIUC.”

Jay Lee  (MSTM), the competition’s only solo participant, secured second place with Illini Database. Lee, who is earning his Master of Science in Business Analytics degree at Gies, focused on research conducted by University of Illinois Urbana-Champaign faculty and  visualized how much of that research was related to the UN’s 17 Sustainability Goals . Data was collected from Illinois Experts' API, which included 2,856 researchers and 219,019 research articles. Using large language models and various machine learning techniques such as vector embedding and random forest, all articles are labeled with none, one, or multiple UN goals related to it. Lee also used dynamic dashboard, which visualizes changes and compares in how often each goal was researched over the last 66 years.

“Participating in the sustainability case competition was enlightening and rewarding,” Lee said. “I expected to showcase my data science skills, but I also learned about our school's sustainability efforts. I enjoyed the challenge of using complex technology to address real-world problems. This experience was beneficial as it pushed my technical skills and allowed me to contribute to sustainability goals.”

The third-place winner was Illini Impact, a team consisting of five Gies students in the Master of Science in Technology Management program.  Renuka Annachhatre ,  Indranil Mukte ,  Yash Oltikar ,  Aditya Patil , and  Prisha Sharma  visualized the breakdown of sustainability-related courses offered by the university into one of the UN’s 17 Sustainability Goals. The group gathered data from course titles and descriptions found on the university’s websites and, using Tableau, compared the number of courses across colleges offered in each goal.

"Right from the initial days of figuring out what problems we plan to address to presenting our findings to our esteemed judges on the final day, we took it upon ourselves to overcome obstacles, understand each group members’ strengths and leverage our collective skills which lead us in securing a proud 3rd place," said Mukte. "We would like to thank Gies and CPRBS for their unwavering support and organizing this case competition for us!”

“While all the finalists presented engaging and interesting visualizations of the University’s commitment to sustainability, these three teams stood out for their focus not only on what has been done, but also their ability to generate a creative solution that is useful for faculty, students, and university leadership,” said Fei Du, associate professor of accountancy and associate director for CPRBS. “These bright students will help enhance the awareness of sustainability efforts across campus.”

“By participating in this competition, students have embodied one of the Center’s primary goals – offering hands-on opportunities where students can grow as we aim to develop responsible decision-makers of tomorrow,” added  Du. "It has been an honor to witness the incredible talent and dedication displayed by all six finalist teams. They have left me more educated and energized about our future leaders. The energy, insight, and effort were inspiring.”

Judges for the competition included Abrita Chakravarty, adjunct instructor at the Grainger College of Engineering; Gretchen Winter, adjunct professor at Gies Business and the College of Law; and Warren Lavey, adjunct professor in the College of Agricultural, Consumer, and Environmental Sciences.

CPRBS, in collaboration with Executive Associate Dean and Professor Mark Peecher, is preparing the inaugural Gies sustainability report. The report is inspired by a data visualization created by Peecher and PhD student Rachel Lyman, which maps Gies faculty research to the 17 UN sustainability goals (updated in April 2024). The College's efforts will focus on integrating this sustainability activity mapping to survey the overall landscape, thereby fully illustrating Gies' contributions to the 17 UN sustainability goals and showcasing the College's excellent work in the ESG/CSR space.

The Center of Professional Responsibility in Business and Society  leads the discussion about society's expectations for a professional's responsibility at both the individual and organizational level. CPRBS supports the creation and sharing of academic research and educational materials that demonstrate the value of conducting business professionally so as to serve and protect the broader public interest.

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