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5 Business Intelligence & Analytics Case Studies Across Industry

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

When businesses make investments in new technologies, they usually do so with the intention of  creating value for customers and stakeholders and making smart long-term investments. This is not always an easy thing to do when implementing cutting-edge technologies like artificial intelligence (AI) and machine learning. Business intelligence case studies that show how these technologies have been leveraged with results are still scarce, and many companies wonder where to apply machine learning first  (a question at the core of one of Emerj’s most recent expert consensuses.)

Artificial intelligence and machine learning have certainly increased in capability over the past few years. Predictive analytics can help glean meaningful business insights using both sensor-based and structured data, as well as unstructured data, like unlabeled text and video, for mining customer sentiment. In the last few years, a shift toward “cognitive cloud” analytics has also increased data access, allowing for advances in real-time learning and reduced company costs. This recent shift has made an array of advanced analytics and AI-powered business intelligence services more accessible to mid-sized and small companies.

In this article, we provide five case studies that illustrate how AI and machine learning technologies are being used across industries to help drive more intelligent business decisions. While not meant to be exhaustive, the examples offer a taste for how real companies are reaping real benefits from technologies like advanced analytics and intelligent image recognition.

1 – Global Tech LED :Google Analytics Instant Activation of Remarketing

Company description:  Headquartered in Bonita Springs, Florida, Global Tech LED is a LED lighting design and supplier to U.S. and international markets, specializing in LED retrofit kits and fixtures for commercial spaces.

How Google Analytics is being used: 

• Google Analytics’ Smart Lists were used to automatically identify Global Tech LED prospects who were “most likely to engage”, and to then remarket to those users with more targeted product pages.
• Google’s Conversion Optimizer was used to automatically adjust potential customer bids for increased conversions.

Value proposition:

• Remarketing campaigns triggered by Smart Lists drove 5 times more clicks than all other display campaigns.
• The click-through rate of Global Tech LED’s remarketing campaigns was more than two times the remarketing average of other campaigns.
• Traffic to the company’s website grew by more than 100%, and was able to re-engage users in markets in which it was trying to make a dent, including South Asia, Latin America, and Western Europe.
• Use of the Conversion Optimizer allowed Global Tech LED to better allocate marketing costs based on bid potential.

2 – Under Armour : IBM Watson Cognitive Computing

Company description:  Under Armour, Inc. is an American manufacturer of sports footwear and apparel, with global headquarters in Baltimore, Maryland.

How IBM Watson is being used:

• Under Armour’s UA Record™ app was built using the IBM Watson Cognitive Computing platform. The “Cognitive Coaching System” was designed to serve as a personal health assistant by providing users with real-time, data-based coaching based on sensor and manually input data for sleep, fitness, activity and nutrition.   The app also draws on other data sources, such as geospatial data, to determine how weather and environment may affect training.   Users are also able to view shared health insights based on other registered people in the UA Record database who share similar age, fitness, health, and other attributes.
• The UA Record app has a rating of 4.5 stars by users; based on sensor functionality, users are encouraged (via the company’s website and the mobile app) to purchase UA HealthBox devices (like the UA Band and Headphones) that synchronize with the app.
• According to Under Armour’s 2016 year-end results , revenue for Connected Fitness accessories grew 51 percent to $80 million.

3 – Plexure (VMob) : IoT and Azure Stream Analytics

Company description:  Formerly known as VMob, Plexure is a New Zealand-based media company that uses real-time data analytics to help companies tailor marketing messages to individual customers and optimize the transaction process.

How Azure Stream Analytics is being used:

• Plexure used Azure Stream to help McDonald’s increase customer engagement in the Netherlands, Sweden and Japan, regions that make up 60 percent of the food service retailer’s locations worldwide.
• Azure Stream Analytics was used to analyze the company’s stored big data (40 million+ endpoints) in the cloud, honing in on customer behavior patterns and responses to offers to ensure that targeted ads were reaching the right groups and individuals.
• Plexure combined Azure Analytics technology with McDonald’s mobile app, analyzing with contextual information and social engagement further customize the user experience. App users receive individualized content based on weather, location, time of day, as well as purchasing a and ad response habits. For example, a customer located near a McDonald’s location on a hot afternoon might receive a pushed ad for a free ice cream sundae.
• McDonald’s in the Netherlands yielded a 700% increase in customer redemptions of targeted offers.
• Customers using the app returned to stores twice as often and on average spent 47% more than non-app users.

4 – Coca-Cola Amatil : Trax Retail Execution

Company description:  Coca-Cola Amatil is the largest bottler and distributor of non-alcoholic, bottled beverages in the Asia Pacific, and one of the largest bottlers of Coca-Cola products in the region.

How Trax Image Recognition for Retail is being used:

• Prior to using Trax’s imaging technology, Coca-Cola Amatil was relying on limited and manual measurements of products in store, as well as delayed data sourced from phone conversations.
• Coca-Cola Amatil sales reps used Trax Retail Execution image-based technology to take pictures of stores shelves with their mobile devices; these images were sent to the Trax Cloud and analyzed, returning actionable reports within minutes to sales reps and providing more detailed online assessments to management.
• Real-time images of stock allowed sales reps to quickly identify performance gaps and apply corrective actions in store. Reports on shelf share and competitive insights also allowed reps to strategize on opportunities in store and over the phone with store managers.
• Coca-Cola Amatil gained 1.3% market share in the Asia Pacific region within five months.

5 – Peter Glenn : AgilOne Advanced Analytics

Company description:  Peter Glenn has provided outdoor apparel and gear to individual and wholesale customers for over 50 years, with brick-and-mortar locations along the east coast, Alaska, and South Beach.

How AgilOne Analytics is being used:

• AgilOne Analytics’ Dashboard provides a consolidated view across online and offline channels, which allowed Peter Glenn to view trends between buyer groups and make better segmentation decisions.
• Advanced segmentation abilities included data on customer household, their value segment, and proximity to any brick-and-mortar locations.
• Peter Glenn used this information to launch integrated promotional, triggered, and lifecycle campaigns across channels, with the goal of increasing sales  during non-peak months and increasing in-store traffic.
• Once AgilOne’s data quality engine had combed through Peter Glenn’s customer database, the company learned that more than 80% of its customer base had lapsed; they were able to use that information to re-target and re-engage stagnant customers.
• Peter Glenn saw a 30% increase in Average Order Value (AOV) as a result of its automated marketing campaigns.
• Access to data points, such as customer proximity to a store, allowed Peter Glenn to target customers for store events using advanced segmentation and more aligned channel marketing strategies.

Image credit: DSCallards

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Business Intelligence in Finance – Current Applications

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4 Examples of Business Analytics in Action

• 15 Jan 2019

Data is a valuable resource in today’s ever-changing marketplace. For business professionals, knowing how to interpret and communicate data is an indispensable skill that can inform sound decision-making.

“The ability to bring data-driven insights into decision-making is extremely powerful—all the more so given all the companies that can’t hire enough people who have these capabilities,” says Harvard Business School Professor Jan Hammond , who teaches the online course Business Analytics . “It’s the way the world is going.”

Before taking a look at how some companies are harnessing the power of data, it’s important to have a baseline understanding of what the term “business analytics” means.

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What Is Business Analytics?

Business analytics is the use of math and statistics to collect, analyze, and interpret data to make better business decisions.

There are four key types of business analytics: descriptive, predictive, diagnostic, and prescriptive. Descriptive analytics is the interpretation of historical data to identify trends and patterns, while predictive analytics centers on taking that information and using it to forecast future outcomes. Diagnostic analytics can be used to identify the root cause of a problem. In the case of prescriptive analytics , testing and other techniques are employed to determine which outcome will yield the best result in a given scenario.

Related : 4 Types of Data Analytics to Improve Decision-Making

Across industries, these data-driven approaches have been employed by professionals to make informed business decisions and attain organizational success.

Check out the video below to learn more about business analytics, and subscribe to our YouTube channel for more explainer content!

Business Analytics vs. Data Science

It’s important to highlight the difference between business analytics and data science . While both processes use big data to solve business problems they’re separate fields.

The main goal of business analytics is to extract meaningful insights from data to guide organizational decisions, while data science is focused on turning raw data into meaningful conclusions through using algorithms and statistical models. Business analysts participate in tasks such as budgeting, forecasting, and product development, while data scientists focus on data wrangling , programming, and statistical modeling.

While they consist of different functions and processes, business analytics and data science are both vital to today’s organizations. Here are four examples of how organizations are using business analytics to their benefit.

Business Analytics Examples

According to a recent survey by McKinsey , an increasing share of organizations report using analytics to generate growth. Here’s a look at how four companies are aligning with that trend and applying data insights to their decision-making processes.

1. Improving Productivity and Collaboration at Microsoft

At technology giant Microsoft , collaboration is key to a productive, innovative work environment. Following a 2015 move of its engineering group's offices, the company sought to understand how fostering face-to-face interactions among staff could boost employee performance and save money.

Microsoft’s Workplace Analytics team hypothesized that moving the 1,200-person group from five buildings to four could improve collaboration by increasing the number of employees per building and reducing the distance that staff needed to travel for meetings. This assumption was partially based on an earlier study by Microsoft , which found that people are more likely to collaborate when they’re more closely located to one another.

In an article for the Harvard Business Review , the company’s analytics team shared the outcomes they observed as a result of the relocation. Through looking at metadata attached to employee calendars, the team found that the move resulted in a 46 percent decrease in meeting travel time. This translated into a combined 100 hours saved per week across all relocated staff members and an estimated savings of $520,000 per year in employee time.

The results also showed that teams were meeting more often due to being in closer proximity, with the average number of weekly meetings per person increasing from 14 to 18. In addition, the average duration of meetings slightly declined, from 0.85 hours to 0.77 hours. These findings signaled that the relocation both improved collaboration among employees and increased operational efficiency.

For Microsoft, the insights gleaned from this analysis underscored the importance of in-person interactions and helped the company understand how thoughtful planning of employee workspaces could lead to significant time and cost savings.

2. Enhancing Customer Support at Uber

Ensuring a quality user experience is a top priority for ride-hailing company Uber. To streamline its customer service capabilities, the company developed a Customer Obsession Ticket Assistant (COTA) in early 2018—a tool that uses machine learning and natural language processing to help agents improve their speed and accuracy when responding to support tickets.

COTA’s implementation delivered positive results. The tool reduced ticket resolution time by 10 percent, and its success prompted the Uber Engineering team to explore how it could be improved.

For the second iteration of the product, COTA v2, the team focused on integrating a deep learning architecture that could scale as the company grew. Before rolling out the update, Uber turned to A/B testing —a method of comparing the outcomes of two different choices (in this case, COTA v1 and COTA v2)—to validate the upgraded tool’s performance.

Preceding the A/B test was an A/A test, during which both a control group and a treatment group used the first version of COTA for one week. The treatment group was then given access to COTA v2 to kick off the A/B testing phase, which lasted for one month.

At the conclusion of testing, it was found that there was a nearly seven percent relative reduction in average handle time per ticket for the treatment group during the A/B phase, indicating that the use of COTA v2 led to faster service and more accurate resolution recommendations. The results also showed that customer satisfaction scores slightly improved as a result of using COTA v2.

With the use of A/B testing, Uber determined that implementing COTA v2 would not only improve customer service, but save millions of dollars by streamlining its ticket resolution process.

Related : How to Analyze a Dataset: 6 Steps

3. Forecasting Orders and Recipes at Blue Apron

For meal kit delivery service Blue Apron, understanding customer behavior and preferences is vitally important to its success. Each week, the company presents subscribers with a fixed menu of meals available for purchase and employs predictive analytics to forecast demand , with the aim of using data to avoid product spoilage and fulfill orders.

To arrive at these predictions, Blue Apron uses algorithms that take several variables into account, which typically fall into three categories: customer-related features, recipe-related features, and seasonality features. Customer-related features describe historical data that depicts a given user’s order frequency, while recipe-related features focus on a subscriber’s past recipe preferences, allowing the company to infer which upcoming meals they’re likely to order. In the case of seasonality features, purchasing patterns are examined to determine when order rates may be higher or lower, depending on the time of year.

Through regression analysis—a statistical method used to examine the relationship between variables—Blue Apron’s engineering team has successfully measured the precision of its forecasting models. The team reports that, overall, the root-mean-square error—the difference between predicted and observed values—of their projection of future orders is consistently less than six percent, indicating a high level of forecasting accuracy.

By employing predictive analytics to better understand customers, Blue Apron has improved its user experience, identified how subscriber tastes change over time, and recognized how shifting preferences are impacted by recipe offerings.

Related : 5 Business Analytics Skills for Professionals

4. Targeting Consumers at PepsiCo

Consumers are crucial to the success of multinational food and beverage company PepsiCo. The company supplies retailers in more than 200 countries worldwide , serving a billion customers every day. To ensure the right quantities and types of products are available to consumers in certain locations, PepsiCo uses big data and predictive analytics.

PepsiCo created a cloud-based data and analytics platform called Pep Worx to make more informed decisions regarding product merchandising. With Pep Worx, the company identifies shoppers in the United States who are likely to be highly interested in a specific PepsiCo brand or product.

For example, Pep Worx enabled PepsiCo to distinguish 24 million households from its dataset of 110 million US households that would be most likely to be interested in Quaker Overnight Oats. The company then identified specific retailers that these households might shop at and targeted their unique audiences. Ultimately, these customers drove 80 percent of the product’s sales growth in its first 12 months after launch.

PepsiCo’s analysis of consumer data is a prime example of how data-driven decision-making can help today’s organizations maximize profits.

Developing a Data Mindset

As these companies illustrate, analytics can be a powerful tool for organizations seeking to grow and improve their services and operations. At the individual level, a deep understanding of data can not only lead to better decision-making, but career advancement and recognition in the workplace.

“Using data analytics is a very effective way to have influence in an organization,” Hammond says . “If you’re able to go into a meeting, and other people have opinions, but you have data to support your arguments and your recommendations, you’re going to be influential.”

Do you want to leverage the power of data within your organization? Explore Business Analytics —one of our online business essentials courses —to learn how to use data analysis to solve business problems.

This post was updated on March 24, 2023. It was originally published on January 15, 2019.

About the Author

23 Case Studies and Real-World Examples of How Business Intelligence Keeps Top Companies Competitive

Business intelligence (BI) provides data that helps companies make timely and informed decisions. We explain how implementing BI software can give companies of any size a competitive edge. Plus, we share examples of how some of the most tech savvy companies are using BI.

What Is Business Intelligence (BI)?

Business intelligence refers to the technology that enables businesses to organize, analyze and contextualize business data from around the company. BI includes multiple tools and techniques to transform raw data into meaningful and actionable information.

BI systems have four main parts:

• A data warehouse stores company information from a variety of sources in a centralized and accessible location.
• Business analytics or data management tools mine and analyze data in the data warehouse.
• Business performance management (BPM) tools monitor and analyze progress towards business goals.
• A user interface (usually an interactive dashboard with data visualization reporting tools) provides quick access the information.

German market research firm Statista estimates the volume of data created worldwide by 2024 will be 149 zettabytes. This vast amount of data, or "big data," has made business intelligence systems relevant for companies that want to harness its power for a competitive advantage. Many BI systems use artificial intelligence (AI) and other capabilities as a part of business analytics.

Key Takeaways:

• Business intelligence offers a wide variety of tools and techniques to support reliable and accurate decision-making.
• The most successful companies use BI to make sense of ever-increasing amounts of data in a fast and economical way.
• BI-based, data-driven decision-making helps companies stay relevant and competitive.

Where Is BI Used?

Sales, marketing, finance and operations departments use business intelligence. Tasks include quantitative analysis, measuring performance against business goals, gleaning customer insights and sharing data to identify new opportunities.

Here are examples of how various teams and departments use business intelligence.

Data scientists and analysts:

Analysts are BI power users, and they use centralized company data paired with powerful analytics tools to understand where opportunities for improvement exist and what strategic recommendations to propose to company leadership.

By blending financial data with operations, marketing and sales data, users can pull insights from which decisions can be acted upon and understand factors that impact profit and loss.

Business intelligence tools help marketers track campaign metrics from a central digital space. BI systems can provide real-time campaign tracking, measure each effort’s performance and plan for future campaigns. This data gives marketing teams more visibility into overall performance and provides contextual visuals for sharing with the company.

Sales data analysts and operation managers often use BI dashboards and key performance indicators (KPIs) for quick access to complex information like discount analysis, customer profitability and customer lifetime value. Sales managers monitor revenue targets, sales rep performance along with the status of the sales pipeline using dashboards with reports and data visualizations.

Operations:

To save time and resources, managers can access and analyze data like supply chain metrics to find ways to optimize processes. Business intelligence can also ensure that service level agreements are met and help improve distribution routes.

In a genuinely data-driven company, every department and employee can take advantage of BI-generated insights.

What Is the Value of Business Intelligence?

Business intelligence's highest value is its ability to support data-driven decisions. BI transforms pools of raw data into useful information that informs decisions and leads to actions that yield positive bottom-line impact.

BI systems drive decisions based on historical, current and potential future data.

Descriptive analytics:

These analytics reveal what has happened or is happening and are part of dashboards, business reporting, data warehousing and scorecards. When managed well, you’ll have a better understanding of problem areas in your business and can find opportunities to improve.

Predictive analytics:

These advanced analytics use data mining, predictive modeling, and machine learning to help make projections of future events and assess the likelihood that something will happen.

Prescriptive analytics:

These analytics reveal why you should take a particular action. Prescriptive analytics enable optimization, simulation, decision modeling and provide the best possible analysis for business decisions and actions.

BI software gathers sales, production, financial and many other business data sources. Many companies use industry data to benchmark performance against competitors.

The Benefits of Business Intelligence

Business intelligence has many benefits and can be a useful tool to achieve positive outcomes for your business.

Case Studies: Real-World Examples of Business Intelligence at Work

Fast, data-informed decision-making can drive success. High customer expectations, global competition and narrow profit margins mean many organizations, regardless of size or sector, look to BI for a competitive advantage.

What is an example of business intelligence? Using data to serve up personalized ads based on browsing history, providing contextual KPI data access for all employees and centralizing data from across the business into one digital ecosystem so processes can be more thoroughly reviewed are all examples of business intelligence. Here are some case studies that show some ways BI is making a difference for companies around the world:

Lotte.com: BI Increases Company Revenue

Lotte.com is the leading internet shopping mall in Korea with 13 million customers.

• Challenge: With more than 1 million site visitors daily, company executives wanted to understand why customers abandon shopping carts.
• Solution: The assistant general manager of the marketing planning team implemented customer experience analytics, the first online behavioral analysis system applied in Korea. The manager used the information to understand customer behavior and implement targeted marketing and transform the website.
• Results: With the insights from the new BI analytics program, there was an increase in customer loyalty after one year and an increase of $10 million in sales. The changes came from identifying the causes of shopping cart abandonment, such as a long checkout process and unexpected delivery times and remedying the situation.

Cementos Argos: BI Improves Financial Efficiency

Cementos Argos is a cement company with operations in the U.S., Central and South America and the Caribbean.

• Challenge: The company looked for an overall competitive advantage and a way to support better decision-making.
• Solution: Cementos Argos created a dedicated business analytics center. The company invested in experienced business analysts and data science teams and used BI to leverage data.
• Results: The company standardized the finance process and applied big data to gain more in-depth insight into customer behavior which yielded a higher profitability level.

Baylis & Harding: BI Provides Decision Making Process Support

Baylis & Harding is a wholesale distributor specializing in world-class toiletries and gift sets found in major and independent resellers.

• Challenge: The company needed to give managers and executives greater visibility into financial, customer and sales data to make better decisions and expand the business.
• Solution: Managers and executives used business intelligence tools to create standard and ad hoc reports.
• Results: Company executives and managers now have instant access to the business data they need to act proactively. They can create custom dashboards with KPIs relevant to their areas of focus and share the goals and performance details with their teams without having to request a custom report from IT.

Sabre Airline Solutions: BI Accelerates Business Insights

Sabre Airline Solutions provides booking tools, revenue management, web and mobile itinerary tools, as well as other technology, for airlines, hotels and other companies in the travel industry.

• Challenge: The travel industry is remarkably fast paced. And Sabre's clients needed advanced tools that could provide real-time data on customer behavior and actions.
• Solution: Sabre developed an enterprise travel data warehouse (ETDW) to hold its enormous amounts of data. Sabre executive dashboards provide near real-time insights in user-friendly environments with a 360-degree overview of business health, reservations, operational performance and ticketing.
• Results: The scalable infrastructure, graphic user interface, data aggregation and ability to work collaboratively have led to more revenue and increased client satisfaction.

Spear Education: BI Streamlines Internal Processes and Workflow

Spear Education is a leader in continuing education for dentists.

• Challenges: Spear's phone system was lacking functionality that could make its customer service reps work more efficiently and provide better customer service. For example, their phone system didn’t record calls and wasn’t connected to a customer relationship management (CRM) tool.
• Solution: After some research, Spear connected its call center software with its BI solution to maintain more thorough customer interaction records and provide a complete view of customer interactions.
• Results: After implementing a new solution for their contact center, Spear increased agent efficiency and saved the company 35 hours of rep time per week. Spear's agents now reinvest that time by placing 4,000 more outbound calls every week.

Univision: BI Increases Market Spend Efficiency

Univision is an American Spanish-language, free-to-air television network. It’s the largest provider of Spanish-language content in the country.

• Challenge: Univision wanted more visibility into its data to unify and focus on targeted ad campaigns.
• Solution: Programmatic TV is an automated and data-driven approach to buying and delivering ads against video content on television, including ads served across the web, mobile devices and connected TVs, as well as linear TV ads served across set-top boxes. With BI powered with information from applications like Facebook, Google Analytics and Adobe Analytics, the company can obtain more value from its programmatic advertising.
• Results: Univision achieved an 80% growth in yield during the first quarter after implementing business intelligence.

New York Shipping Exchange: BI Reduces IT Dependency

New York Shipping Exchange (NYSHEX) is a shipping-technology company working to improve the process of shipping overseas.

• Challenge: To make sense of overall company performance, NYSHEX would manually extract data from its proprietary application and various cloud apps and then import it into Excel. This was a laborious process and few people had access to the data, and most of the requests for reports fell on the engineering team to execute.
• Solution: NYSHEX invested in BI, centralized its data into one system and gave the entire company access empowering those with no coding knowledge to dive deep into analysis.
• Results: Thanks to business intelligence and other efforts, in 2019, the company more than tripled its volume shipping between Asia and U.S.

Stitch Fix: BI Connects Departments, Data and Processes

Stitch Fix provides online personal clothing and accessory styling services. The company uses recommendation algorithms and data science to personalize clothing items based on size, budget and style.

• Challenge: The company wants to reduce returns, keep repeat customers and generate word-of-mouth business with recommendations from customers to their friends and family.
• Solution: Stitch Fix collects data within BI throughout the buying process, meaning the more a customer shops with Stitch Fix, the better the styling team comprehends their taste in clothing. The company hired astrophysicists to decode the different personal style components—intricate work that would be impossible without the powerful analytics of BI.
• Results: Using business intelligence to profile buyers and their preferences, the company, which started in 2011, reported a customer base of 3.4 million in 2020 and revenues of $1.7 billion in fiscal year 2020.

SKF: BI Streamlines Manufacturing Processes

SKF is a Sweden-based global manufacturer and supplier of bearings, seals, mechatronics and lubrication systems with 17,000 distributor locations.

• Challenge: SKF's broad geographic coverage and product diversity required consistent market size and product demand forecasting to adjust its manufacturing. The company needed to simplify the complex Excel files used to produce a demand forecast.
• Solution: Management realized it needed to implement a business intelligence to serve as a single source of reliable information. Maintaining the system is easier than trying to manage everything with Excel, and now employees don’t have to rely on outdated spreadsheets and can access simple-to-understand reports and dashboards.
• Results: By centralizing data assets into a single system, SFK was quickly able to share data and analyses between several departments — including sales, manufacturing planning, application engineering, business development and management. SKF now combines demand forecasts between departments and has improved the planning process.

Expedia: BI Builds Customer Satisfaction

Expedia is the parent company of some top-tier travel companies, including Expedia, Hotwire and TripAdvisor.

• Challenge: Customer satisfaction is essential to the company's mission, strategy and success. The online experience should mirror a good trip experience, but the company had no visibility into the voice of the customer.
• Solution: The company had mountains of data they were manually aggregating, leaving little time for analysis. Using business intelligence, the customer satisfaction group was able to analyze customer data from across the company and link results with 10 objectives related directly to corporate initiatives. Owners of those KPIs build, manage and analyze data to discover trends or patterns.
• Results: The customer service team can see how well it is doing against KPIs in real-time and take corrective steps if necessary. Plus, other departments can use the data. For example, a travel manager can use BI to discover high volumes of unused tickets or offline booking and create strategies to adjust behavior and increase overall savings.

Use Cases: Examples of Business Intelligence Strategies Prominent Companies Use

The most successful companies use BI to drive revenue, customer loyalty, operational effectiveness, ad delivery, drive shareholder value, predict customer behavior and develop new business opportunities.

Examples of How Leading Companies Use BI to Propel Their Success

What companies use business intelligence? From financial institutions like American Express to social media giant Facebook and outdoor retailer REI, the most advanced and successful companies in the world leverage BI. Here’s how some are using BI to power their prosperity.

American Express:

Business intelligence is instrumental in the finance industry. American Express has been using the technology to develop new payment service products and market offers to customers. The company's experiments in the Australian market have rendered it capable of identifying up to 24% of all Australian users who will close their accounts within four months. Using that information, American Express takes steps to retain customers. BI also helps the company accurately detect fraud and protect customers whose card data may be compromised.

Chipotle Mexican Grill:

The restaurant chain has more than 2,400 restaurants worldwide. It implemented BI to track operational effectiveness. Chipotle can now monitor every restaurant's operational efficiency and serve up detailed information in dashboards. By standardizing the reporting and working from the same data ecosystem, Chipotle was able to make uniform KPIs for benchmarking and sharing improvement and success stories. That solution saves thousands of hours for the company.

With 35 million Twitter followers and a whopping 105 million Facebook fans, Coca-Cola benefits from its social media data. Using AI-powered image-recognition technology, the company can tell when photographs of its drinks post online. This data, paired with the power of BI, gives the company important insights into who is drinking their beverages, where they are and why they mention the brand online. The information helps serve consumers more targeted advertising, which is four times more likely than a general ad to result in a click.

Delta Airlines:

Big data and BI support customer service and differentiate the Delta experience. Flight attendants now have the tools to personally thank and recognize valued corporate travelers. Positive customer experience coupled with thoughtful programs help position Delta as a leader in the business travel space. While any Delta customer can receive personal recognition, the airline goes the extra mile to serve corporate travelers and its medallion members. This enhancement provides more opportunities to thank flyers and build customer loyalty.

The company processes 35% of U.S. mortgage applications. Record low-interest rates created a high demand for loan processing. To make data more accessible for lenders, Ellie Mae developed a hosted data warehouse model that allows lenders to analyze data by connecting a BI application directly to their systems without replicating the data to a local data warehouse. Capital market teammates can use that data to navigate volatile markets, allowing them to provide excellent service and process loans for their customers.

The home improvement company uses business intelligence to merge what the customer tells them with actual behavior occurring online and in the store. They use this data to discover deeper insights that lead to better product assortment and staffing at specific store locations. The process of data analysis drives sales and also serves the customer. For instance, Lowe's uses predictive analytics to load trucks specific to individual zip codes, so the right store gets the right type and amount of product.

The online entertainment company's 148 million subscribers give it a massive BI advantage. How does Netflix use business intelligence? Netflix uses data in multiple ways. One example is how the company formulates and validates original programming ideas based on previously viewed programs. Netflix also uses business intelligence to get people to engage with its content. The service is so good at targeted content promotion that its recommendation system drives over 80% of streamed content.

REI uses its business intelligence platform for customer segmentation analysis, which helps inform decisions like member lifecycle management, shipping methods and product category assortments. BI-based decisions also inform member acquisition initiatives with detailed demographics on factors such as gender to personalize ads. The insights from BI help determine everything from how to display content on the website and how to segment email campaigns.

Through its popular loyalty card program and mobile application, Starbucks owns individual purchase data from millions of customers. Using this information and BI tools, the company predicts purchases and sends individual offers of what customers will likely prefer via their app and email. This system draws existing customers into its stores more frequently and increases sales volumes.

The innovative automotive company uses BI to connect their cars wirelessly to their corporate offices to collect data for analysis. This approach links the carmaker to the customer and anticipates and corrects problems such as component damage, traffic or road hazard data. The result is a high customer satisfaction score and better-informed decisions on future upgrades and products.

The social media company deploys BI with AI to fight inappropriate and potentially dangerous content on its platform. Algorithms rather than human users identify 95% of suspended terrorism-related accounts.

BI and AI also support fine-tuning to improve the overall user experience. Twitter personnel and its business intelligence tools monitor live video feeds and categorize them based on subject matter. They use this data to enhance search capabilities, and help algorithms identify videos users might be interested in viewing.

The company uses business intelligence to determine multiple core aspects of its business. An example is surge pricing. Algorithms monitor traffic conditions, journey times, driver availability and customer demand in real-time, meaning prices adjust as demand rises and traffic conditions change. Dynamic pricing in real-time action is akin to what airlines and hotel chains use to adjust cost based on need.

The retail behemoth uses BI to understand how online behavior influences online and in-store activity. By analyzing simulations, Walmart can understand customer purchasing patterns, for example, how many eyeglass exams and glasses are sold in a single day, and pinpoint the busiest times during each day or month.

How to Improve Your Business Intelligence to Make Your Company a Leader

BI and tools like AI may seem complicated. However, current user interfaces are straightforward and easy to use. So even smaller companies can take advantage of data to make profitable and positive decisions.

Examples of Business Intelligence Tools and Techniques

What are examples of business intelligence tools? Predictive modeling, data mining and contextual dashboards or KPIs are just some of the most common BI tools. Here are more tools and how they’re used.

A BI technique that probes data to extract trends and insights from historical and current findings to drive valuable data-driven decisions.

Dashboards:

Interactive collections of role-relevant data are typically stocked with intuitive data visualizations, KPIs, analytics metrics and other data points that play a role in decision-making.

Data mining:

This practice uses statistics, database systems and machine learning to uncover patterns in large datasets. Data mining also requires pre-processing of data. End-users use data mining to create models that reveal patterns.

Extract Transfer Load (ETL):

This tool extracts data from data-sources, transforms it, cleans it in preparation for reports and analysis and loads it into a data warehouse.

Model visualization:

The model visualization technique transforms facts into charts, histograms and other visuals to support correct insight interpretation.

Online Analytical Processing (OLAP):

OLAP is a technique for solving analytical problems with multiple dimensions from various perspectives. OLAP is useful for completing tasks such as performing CRM data analysis, financial forecasting and budgets.

Predictive modeling:

A BI technique that utilizes statistical methods to generate probabilities and trend models. With this technique, predicting a value for specific data sets and attributes using many statistical models is possible.

Reporting involves gathering data using various tools and software to mine insights. This tool provides observations and suggestions about trends to simplify decision-making.

Scorecards:

Visual tools, such as BI dashboards and scorecards, provide a quick and concise way to measure KPIs and indicate how a company is progressing to meet its goals.

Examples of Business Intelligence Trends

BI is continually evolving and improving, but four trends – artificial intelligence, cloud analytics, collaborative BI and embedded BI – are changing how companies are using expansive data sets and making decisions far easier.

Artificial intelligence:

AI and machine learning emulate complex tasks executed by human brains. This capability drives real-time data analysis and dashboard reporting.

Cloud analytics:

BI applications in the cloud are replacing on-site installations. More businesses are shifting to this technology to analyze data on demand and enrich decision-making.

Embedded BI:

When BI software is integrated into another business application, it’s called embedded BI or embedded analytics . Some of the benefits of embedded BI include enhanced reporting functionalities, and it’s been shown to improve sales and increase customer retention.

Many companies look to cloud-based or software-as-a-service (SaaS) instead of on-premise software to keep up with growing warehousing requirements and faster implementations. A growing trend is the use of mobile BI to take advantage of the proliferation of mobile devices.

Examples of Business Intelligence Software and Systems

BI software and systems provide options suited to specific business needs. They include comprehensive platforms, data visualization, embedded software applications, location intelligence software and self-service software built for non-tech users.

Here are some examples of the latest BI software and systems:

Business intelligence platforms:

These are comprehensive analytics tools that data analysts use to connect to data warehouses or databases. The platforms require a certain level of coding or data preparation knowledge. These solutions offer analysts the ability to manipulate data to discover insights. Some options provide predictive analytics, big data analytics and the ability to ingest unstructured data.

Data visualization software:

Suited to track KPIs and other vital metrics, data visualization software allow users to build dashboards to track company goals and metrics in real-time to see where to make changes to achieve goals. Data visualization software accommodates multiple KPI dashboards so that each team can set up their own.

Embedded business intelligence software:

This software allows BI solutions to integrate within business process portals or applications or portals. Embedded BI provides capabilities such as reporting, interactive dashboards, data analysis, predictive analytics and more.

Location intelligence software:

This BI software allows for insights based on spatial data and maps. Similarly, a user can find patterns in sales or financial data with a BI platform; analysts can use this software to determine the ideal location to open their next retail store, warehouse or restaurant.

Self-service business intelligence software:

Self-service business intelligence tools require no coding knowledge to take advantage of business end-users. These solutions often provide prebuilt templates for data queries and drag-and-drop functionality to build dashboards. Users like HR managers, sales representatives and marketers use this product to make data-driven decisions.

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How NetSuite Improves and Increases the Value of BI for Your Organization

BI tools can have an enormous impact on your business. They can help you improve your inventory control, better manage your supply chain, identify and remove bottlenecks in your operations and automate routine tasks. But for BI tools to be most effective, you first have to centralize data that’s stored in multiple disparate systems.

NetSuite business intelligence tools take the data stored in your enterprise resource planning (ERP) software and provides built-in, real-time dashboards with powerful reporting and analysis features. By centralizing data from your supply chain, warehouse, CRM and other areas with an ERP, NetSuite business intelligence tools can help you identify issues, trends and opportunities, along with the ability to then drill down to the underlying data for even further insight.

It’s likely your business has large amounts of data that could be used to boost your profitability. The challenge is organizing and structuring your data in such a way that you can then glean insights. From there, you need to create clear, concise and actionable reports and data visualizations and distributing them to key stakeholders on your team. None of this can be done without advanced software, such as ERP products that collect and manage all your data.

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10 Case Studies On The Benefits of Business Intelligence And Analytics

Table of Contents

1) Why Is Business Intelligence So Important?

2) What Are The Benefits of Business Intelligence?

3) 10 Real-World BI & Analytics Use Cases

4) BI & Analytics Practical Examples

Using business intelligence and analytics effectively is the crucial difference between companies that succeed and companies that fail in the modern environment. Why? Because things are changing and becoming more competitive in every business sector, the benefits of using business intelligence and proper use of data analytics are key to outperforming the competition.

For example, in marketing, traditional advertising methods of spending large amounts of money on TV, radio, and print ads without measuring ROI aren’t working like they used to. Consumers have grown increasingly immune to ads that aren’t targeted directly at them.

The companies that are most successful at marketing in both B2C and B2B are using data and online BI tools to craft hyper-specific campaigns that reach out to targeted prospects with a curated message. Everything is being tested, and the successful campaigns get more money put into them, while the others aren’t repeated.

But what is the true value of BI? In this post, we will explore ten advantages of business intelligence supported by real-world BI case studies. By the end, you’ll need to double down on creating a data-driven culture at your company, and you’ll have some hard evidence you can use to persuade skeptical teammates. 

Let’s get started!

Why Is Business Intelligence So Important?

The main use of business intelligence is to help business units, managers, top executives, and other operational workers make better-informed decisions backed up with accurate data. It will help them spot new business opportunities, cut costs, or identify inefficient processes needing reengineering.

BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions. BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way. Business intelligence can also be called “descriptive analytics,” as it only shows past and current state: it doesn’t say what to do, but what is or was. The responsibility to take action still lies in the hands of the executives.

This “test, look at the data, adjust” methodology is at the heart and soul of business intelligence. It’s all about using data to understand reality better so that your company can make more strategically sound decisions (instead of relying on gut instinct or corporate inertia).

Ultimately, business intelligence and analytics are about much more than the technology used to gather and analyze data. They’re about having the mindset of an experimenter and being willing to let data guide a company’s decision-making process.

What Are The Benefits of Business Intelligence?

The benefits of business intelligence and analytics are plentiful and varied, but they all have one thing in common: they bring power. The power of knowledge. Whichever unit they impact, they can deeply transform your organization and how you do business. Here is an overview of 10 main business intelligence benefits:

• Make informed strategic decisions: Business intelligence and analytics provide valuable data insights, empowering decision-makers to make more informed choices.
• Identify trends and patterns: By analyzing data from multiple sources, companies can delve into customer behaviors, business performance, and the industry landscape. This deep dive allows them to hone their competitive advantages and identify areas for improvement.
• Drive performance and revenue: Using targeted strategies, businesses can identify new revenue opportunities, optimize pricing, and maximize profitability.
• Improve operational efficiency: BI tools offer a bird' s-eye view of all operations, allowing companies to identify bottlenecks, streamline processes, and optimize performance. 
• Find improvement opportunities through predictions: Businesses are better positioned to anticipate market changes and project needs and proactively adjust their marketing and operational strategies using predictive analytics.
• Smarter and faster reporting: With data and analytics in a single place, users can quickly and easily report on performance and progress, share with stakeholders, and choose their next best actions.
• Mitigate risk: Analyzing trends and historical data can better understand risks and potential threats.
• Enhance data quality: BI initiatives often require data cleansing and standardization exercises, both of which can ensure you’re collecting accurate and usable data.
• Increase accountability: BI provides transparency into key performance metrics and business processes, which can foster accountability within organizations.
• Gain essential customer insights for ongoing growth: Learning more about customer preferences, behaviors, and needs can lead to more targeted marketing and positioning and greater customer satisfaction.

Benefits Of Business Intelligence: 10 Real-World Valuable Use Cases

Here are ten use cases that illustrate the different benefits of business intelligence.

1) Informed Strategic Decisions 

As the first and most impactful of all benefits of analytics, we can make informed strategic decisions backed by factual information. Experts say that BI and data analytics make the decision-making process 5x times faster for businesses. Let's look at our first use case.

Renowned author Bernard Marr wrote an insightful article about Shell’s journey to become a fully data-driven company. Although the oil company has been producing massive amounts of data for a long time, with the rise of new cloud-based technologies and data becoming increasingly relevant in business contexts, they needed a way to manage their information at an enterprise level and keep up with the new skills in the data industry. 

In order to do this, they first defined what data was the most relevant for the company. As Dan Jeavons, Data Science Manager at Shell, stated: “What we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business”. With this information in hand, the company started thinking about how to invest in data quality standards, and the required technology to support it. 

Skills were a big challenge for Shell. However, the company developed tailored training programs for its employees so that they could learn to use data to solve problems. Additionally, it invested in professionalizing the core work of data scientists for more complex operations. 

Shell’s initiatives were successful because they implemented a data-driven culture in their entire organization. Empowering all levels of employees to use data for their decision-making process means extracting relevant insights at every level of the company. Undoubtedly, one of the big benefits of data analytics and professional self-service BI tools is the democratization of data. 

2) Identify Trends and Patterns 

As mentioned above, one of the great benefits of business intelligence and analytics is the ability to make informed, data-based decisions. This benefit goes hand in hand with the fact that analytics provide businesses with technologies to spot trends and patterns that will optimize resources and processes. Business intelligence and analytics allow users to know their businesses more deeply. Let’s see it with a real-world example. 

The famous Boston Celtics basketball club hopped on the analytics bandwagon , too, so as to understand how their market evolves and also so as to evaluate their players.

Thanks to the data they collected on their customers, they have been able to analyze who they are, where they sit, and how much they pay. That is precious insight for the sales team, who can look into the data in real time and understand its leverages. It helped them quickly create promotions to sell more tickets and conduct revenue analyses based on these trends.

Moreover, visualizing their data helped them see how much revenue a given seat is producing during a season and compare the different areas of the stadium. Given that the Celtics have a very complex ticket pricing structure (over a hundred different prices depending on the package, section, individuals, students, competitive games, etc.), it is all the more important to understand at a glance which seat brings what, to make decisions on the fly for promotions.

A simple example is: If many low-cost seats are still available for an upcoming game, the sales team can send a customized email offer to local students.

The results?

Regular “five-figure” returns from promotions are based on analytics, according to Morey, senior VP of operations at the Boston Celtics. But it is just the beginning. After analyzing the fans’ seating plan, the sales team can redraw the lines for price breaks for the next season.

Of course, the purpose is to make more money, but it is not just for money’s sake. The money they get from these analytics will be reinvested in the players and their training, which means that players will get better, and so will the games.

3) Drive Performance And Revenue

Driving performance and revenue is one of the relevant benefits of business analytics for companies. McKinsey realized a business intelligence case study on a fast-food chain restaurant company with thousands of outlets worldwide. That company wanted to focus on its personnel and analyze deeper any data concerning its staff to understand what drives them and what they could do to improve business performance.

After exhausting most of its traditional methods, the company was looking for other ways to improve customer experience while tackling its high annual employee turnover, which was above the average of its competitors. The top management believed that tackling this turnover would be key to improving the customer experience and that this would lead to higher revenues.

To do so, the company started by defining the goals and finding a way to translate employees’ behaviors and experiences into data to model against actual outcomes. The goals were multiple: revenue growth, customer satisfaction, and speed of service. They then analyzed three areas: employee selection and onboarding, daily staff management, and employees’ behaviors and interactions in the restaurants.

The restaurant used the data collected to build regression and unsupervised learning models to determine the potential relationship between drivers and outcomes. They then started to test over a hundred hypotheses, many of which had been championed by senior managers who strongly believed in these methods after their experience. That was a powerful experience as it confronted senior managers with evidence against what they believed was true and practiced for years.

All the insights they gleaned challenged their beliefs and experience, but the results after implementing new measures, according to their findings, were indisputable: customer satisfaction scores had increased by more than 100% in four months, the speed of service improved by 30 seconds, attrition of new hires had decreased considerably, and sales went up by 5%.

4) Improve Operational Efficiency

Technology giant Microsoft was looking for a way to improve productivity and collaboration in the workplace. For this purpose, a senior researcher from the company conducted a study to understand the common problems faced by remote work on Microsoft. The findings showed that the main challenges included “communication in planned meetings, ad-hoc conversations, awareness of teammates and their work, and building trust relationships between teammates.”

These findings validated the theory that team members' awareness degrades with physical distance. The study even showed that employees who are situated in the same building but on different floors are less likely to collaborate. With this issue in mind, Microsoft came up with the idea of moving 1,200 people from 5 buildings to 4 to improve collaboration.

As a result of the relocation, the analytics team analyzed metadata attached to employee calendars and found a 46% decrease in meeting travel time, translating into estimated savings of $520,000 per year in employee time. As seen in the chart below, the team found out that “minutes saved for each employee equates to hundreds of thousands of dollars in cost-savings for an organization over time.”

** Source : hbr.org**

The analysis also showed that the number of weekly meetings per person increased from 14 to 18. Overall, data analysis in this use case showed a significant increase in employee collaboration and operational efficiency for the company. Chantrelle Nielsen, director of research and strategy for Workplace Analytics, said, “Companies must take these metrics and direct them thoughtfully towards the design of office spaces that maximize face time over screen time.” This is a great way to illustrate the operational benefits of business intelligence.

5) Find Improvement Opportunities Through Predictions  

The fifth benefit of implementing business intelligence and data analytics into your company is using predictive analytics. A great use case of this benefit is Uber. This company was originally founded in 2009 as a black car-hailing service in San Francisco. Although the service costed more money than a regular taxi ride, customers were attracted to the experience of ordering a car from their smartphones. 

Now, you might be wondering how this small San Francisco start-up turned into the successful global company it is today. The answer is data analytics and business intelligence.

Uber has an algorithm that takes valuable data from every driver and passenger and uses it to predict supply and demand. The gathered data includes everything from customers’ waiting times, peak demand hours, traffic for each city, a driver’s speed during a trip, and much more. All this data is then used to set pricing fees, meet demand, and ensure an excellent service for both their drivers and clients. For example, by using prediction models, they can generate a heatmap to tell drivers where they should place themselves to take advantage of the best demand areas. 

According to this case study on business analytics, one of the most interesting uses of data from Uber is its surge pricing method. The algorithm makes Uber more expensive during peak traffic hours, holidays, rainy days, etc. Uber has made this system using real-time predictions based on traffic patterns, supply, and demand. While this is a successful pricing system that other enterprises praise, the higher fares have brought the company a lot of backlash for trips that are twice as expensive. To avoid this issue, Uber has recently announced that they will use machine learning technologies to predict future demand and ensure that more drivers are redirected to the high-demand areas to avoid surge pricing and offer their clients a fair fee. 

This is a clear example of the advantages of business analytics and how predictive analytics can help businesses spot improvement opportunities, optimize their processes, and ensure higher customer satisfaction levels.

6) Smart and Faster Reporting 

Next, in our rundown of the top benefits of BI and analytics, we discuss data management and visualization. One of the powers of BI tools is that they open the door to a more efficient reporting process, which also makes data analytics accessible for everyone without the need for prior technical knowledge. Let’s put this into perspective with a success story from datapine. 

Lieferando is a European online food-ordering service acquired by Just Eat Take Away in 2014. The brand, which operates mainly in Germany, the UK, and Sweden, has a clear mission of providing a fast and easy way for its 98 million customers to get food from their favorite restaurants. With millions of consumers and more than 580,000 partner restaurants within 25 countries, the company faced issues related to data management and access to massive amounts of enterprise-level information. 

Their main challenges were combining different data sources in real-time in one central location, optimizing their marketing campaigns with data-based insights, and getting a comprehensive view of their entire customer lifecycle. Additionally, they needed a tool that allowed all employees in the company to deal with data without involving the IT department. 

With the implementation of datapine's BI reporting tool into their system, the company was able to manage large amounts of data in real-time while significantly cutting the time they spent on report generation. This allowed for a faster decision-making process, streamlining of their marketing and sales activities, and the overall optimization of several processes at an internal and external level.

Team members at Lieferando said that “our new real-time dashboards allow us to monitor all major business operations through customized Key Performance Indicators. We can instantly act on changes and can now better adapt to new business challenges right when they occur, not weeks or even months later.”

7) Risk Mitigation

Businesses like to see risks coming a mile away, or at least in enough time to devise a defense strategy. That’s why companies are increasingly interested in applying BI to their risk mitigation efforts. Data can tell a number of compelling stories, including those that are on the brink of coming to life. 

To better understand how it works, we can review a case study on business intelligence based on using BI in agricultural insurance. BI tools are essential for effective risk management, leveraging organizational data to minimize uncertainties and improve decision-making. They play a role in developing early warning systems, which are common tools in industries that regularly deal with crises.

Gaining a heads-up about potential risks was essential to the Iran Agricultural Insurance Fund, the only active insurance company in the country's agriculture sector. The fund aims to protect farmers and ranchers whose crops become damaged by pests, weather, drought, disease, and other disasters. It’s essential to understand the capacity of this fund, paid compensations each year, and the need to increase and improve existing agriculture insurance products based on risk, all of which can be solved with BI.

Model development involved creating a multidimensional online analytical processing (MOLAP) architecture using SQL server analysis services (SSAS) and BI tools. MOLAP for risk management identified financial risks and predicted trends in premium earnings and paid compensation, aiding decision-making for agricultural insurance branches. Predictive analyses based on historical data offered insights into future trends, enabling insurers to manage their risk management strategies proactively.

8) Enhanced Data Quality

Companies are increasingly dependent on their data and need to be able to trust that data in a moment of need rather than spending time fact-checking it. When you have good data, you can make good decisions that ultimately lead to good outcomes. The opposite is also true: when you have bad data, it can lead to a catastrophic series of events. 

This was the unfortunate case for Unity Technologies, which experienced a data quality issue that cost them $110 million and a 37% drop in share price. The 3D content platform attempted to use its Audience Pinpoint tool to help game developers target and acquire players. However, after ingesting bad data from a large customer, the effectiveness of its tool eroded significantly. They targeted the wrong audiences because the machine learning algorithms were trained on inaccurate data, which yielded poor results.

CEO John Riccitello said the blunder cost them a combined $110 million in revenue losses, model rebuilding and retraining, and delayed launches of new revenue drivers. Investors also lost faith in the company, causing share prices to tumble. 

It was an eye-opening experience for Unity, prompting them to focus more on data quality as the costs of not doing so became glaringly evident. A well-implemented BI system and software can ensure a level of data quality that will drive accurate and successful decision-making. 

9) Increased Accountability

Data transparency can create a sense of belonging and ownership. Employees no longer work in the dark, pushing buttons and pulling levers to make things happen. With data, they can understand how their actions create subsequent events and the overall impact they make.

To see this in action, we look to Customer Alliance , a long-time datapine customer. The company provides a rating system for hotels to review and analyze customer data. By learning more about customers’ experiences, they can better attract website visitors and drive bookings. CEO Moritz Klussman said this visibility has made all the difference in their sales teams’ efforts.

They now have an easy way to provide management teams with access to all data and dashboards. They can filter to their level of interest and drill into reports for more details. Reviewing relevant customer data is faster, and data can be combined from multiple sources to generate new insights for the sales team. In Klussman’s words, it “has increased the number of closed deals significantly.”

10) Gain Essential Customer Insights

The more you know about your customers, the better you can adjust your messaging, products, and acquisition techniques to attract and retain them. Ideally, you can look at your existing customers to find common denominators: What industry are they in? How did they find you? How much are they spending with you? What do they buy from you? What makes them choose you vs. a competitor? What makes them stop doing business with you?

These are all essential questions with answers that directly impact your growth potential. To answer them, we can use BI to find patterns, trends, and other connections between data that would be too time-consuming or complex for humans to identify alone.

Deloitte shares this case study about how they helped a major media company develop a customer insights platform to turn data into business value. The organization knew it had valuable data but lacked an efficient way to utilize it. The company built a platform using Amazon Web Services to improve customer segmentation, predict churn, and quantify total customer value for millions of global subscribers. The platform aggregates data from disparate legacy systems and uses AI to analyze the data and develop insights.

The new models and dashboards accelerated growth by identifying customer trends. Improved segmenting led to hyper-targeted campaigns and better content delivery, increasing customer satisfaction. Clean and accessible customer data, coupled with AI utilization, enables the client to extract actionable insights and drive real business value—in this case, that value turned out to be worth millions of dollars.

Now that you understand the value that BI and analytics can bring to your organization, let’s examine some practical examples of how it would look in practice.

Business Intelligence and Analytics Benefits: Practical Examples

Business intelligence is key to monitoring business trends, detecting significant events, and, thanks to data, getting the full picture of what is happening inside your organization. It is important to optimize processes, increase operational efficiency, drive new revenue, and improve the company's decision-making.

We’re living in the most competitive business market in history. Technological advances and a global economy have created a pressure cooker of competition, with weaker companies being swallowed or broken down. Luckily, business intelligence tools have developed the necessary technology for companies to manage their data efficiently. BI dashboards like the ones presented below provide a centralized view of the most important metrics businesses need to stay ahead of their competitors. Not only that but getting a visual overview of the performance of several areas also empowers employees to use data for their decision-making process.

Let’s review a few examples of this:

1. Sales KPI Dashboard

With the essential sales dashboard at your fingertips, you can understand the current state of incoming funds and see whether you’re on target to meet company goals. 

**click to enlarge**

Comparison data helps you see trends and patterns, allowing you to understand where to adjust to stay on track. For example, if customer churn suddenly spikes, you can start looking at other areas to figure out why and find creative ways to plug the holes. Having these insights at a glance is useful in mitigating risk, making informed decisions, and gaining essential customer insights that directly impact your top line.

2. Financial KPI Dashboard

Our financial dashboard answers your most pressing questions about invoicing, budgeting, and overall financial stability. Get a glimpse of your working capital, assets, and liabilities in a single snapshot to gauge your financial health at any given moment.

Having these insights is a key piece of risk mitigation. For instance, if your cash conversion cycle is upward trending, your risk of running into a cash flow issue is higher. We can also see use cases for improving operational efficiency and accountability. Take the vendor payment error rate, for example. Things like duplicate payments, wrong addresses, and incorrect amounts can all affect vendor payment error rates, and these spikes may show where the accounts payable department can improve its processes.

3. Marketing KPI Dashboard

Lastly, we have a marketing dashboard , which ultimately ties back to your bottom line. Here we see some important figures showing us how marketing efforts are paying off, which can provide jumping-off points to dive deeper into other data.

Helpful use cases include strategic decision-making, increased accountability, and gaining essential customer information. Marketers can see how small tweaks and whole campaigns are acquiring customers and generating revenue and the most effective channels to reach customers.

BI Benefits: Final Thoughts

Given the current state of affairs, your company can’t afford not to use BI tools, especially after we examined ten business intelligence case studies that showed the incredible ROI possible from using them and the many benefits of business analytics. Such business intelligence ROI can come in many forms. You need to know what’s going on in the minds of your customers, who your next best customers will be, and how to serve them most effectively. These areas can be answered with data – which you need BI and analytics tools to process. However, be aware of any faux pas, and remember that there are some business intelligence best practices to know and some worst practices to avoid!

When your company has to rely on internal or external IT staff to generate data reports, it creates a huge barrier to what is most needed: a data-driven corporate culture where decisions are validated by clearly seeing reality.

If you’d like to take your first step towards using an intuitive self-service business analytics tool, try our 14-day free trial and test what datapine can do for you.

Top 20 Analytics Case Studies in 2024

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Although the potential of Big Data and business intelligence are recognized by organizations, Gartner analyst Nick Heudecker says that the failure rate of analytics projects is close to 85%. Uncovering the power of analytics improves business operations, reduces costs, enhances decision-making , and enables the launching of more personalized products.

In this article, our research covers:

How to measure analytics success?

What are some analytics case studies.

According to  Gartner CDO Survey,  the top 3 critical success factors of analytics projects are:

• Creation of a data-driven culture within the organization,
• Data integration and data skills training across the organization,
• And implementation of a data management and analytics strategy.

The success of the process of analytics depends on asking the right question. It requires an understanding of the appropriate data required for each goal to be achieved. We’ve listed 20 successful analytics applications/case studies from different industries.

During our research, we examined that partnering with an analytics consultant helps organizations boost their success if organizations’ tech team lacks certain data skills.

*Vendors have not shared the client name

For more on analytics

If your organization is willing to implement an analytics solution but doesn’t know where to start, here are some of the articles we’ve written before that can help you learn more:

• AI in analytics: How AI is shaping analytics
• Edge Analytics in 2022: What it is, Why it matters & Use Cases
• Application Analytics: Tracking KPIs that lead to success

Finally, if you believe that your business would benefit from adopting an analytics solution, we have data-driven lists of vendors on our analytics hub and analytics platforms

We will help you choose the best solution tailored to your needs:

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

AIMultiple.com Traffic Analytics, Ranking & Audience , Similarweb. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics , Business Insider. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are , Washington Post. Data management barriers to AI success , Deloitte. Empowering AI Leadership: AI C-Suite Toolkit , World Economic Forum. Science, Research and Innovation Performance of the EU , European Commission. Public-sector digitization: The trillion-dollar challenge , McKinsey & Company. Hypatos gets $11.8M for a deep learning approach to document processing , TechCrunch. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million , Business Insider.

To stay up-to-date on B2B tech & accelerate your enterprise:

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Business intelligence and analytics case studies

• MT School of Management

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T1 - Business intelligence and analytics case studies

AU - Fjermestad, Jerry

AU - Kudyba, Stephan

AU - Lawrence, Kenneth

PY - 2018/4/3

Y1 - 2018/4/3

UR - http://www.scopus.com/inward/record.url?scp=85044582095&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85044582095&partnerID=8YFLogxK

U2 - 10.1080/10919392.2018.1444360

DO - 10.1080/10919392.2018.1444360

M3 - Editorial

AN - SCOPUS:85044582095

SN - 1091-9392

JO - Journal of Organizational Computing and Electronic Commerce

JF - Journal of Organizational Computing and Electronic Commerce

Top 10 Business Intelligence Case Studies and Solutions

Business intelligence case studies are generally scenario-based questions that ask you to work through a solution to a proposed business problem.

For example, in a business intelligence case interview, you might be asked: How would you de-duplicate product listings that don’t have the same title, SKU, or description?

Your job is to ask the interviewer for more information, make assumptions about the case, propose a solution, and finally, consider the trade-offs of your solution. For business intelligence engineering roles, business case studies tend to fall into two broad categories:

Analytics - Analytics questions test your understanding of metrics and how they relate to business goals. Your job is to ask the interviewer for more information, make assumptions about the case, propose a solution, and finally, consider the trade-offs of your solution.

Database Design - Database/ technology questions ask you to design or discuss a tech solution to a given business problem.

Generally, business intelligence case studies are the most difficult part of business intelligence interviews, but using frameworks and understanding how they are graded can help you to prepare for your next BI case study interview.

What Does a Business Intelligence Engineer Do?

Business intelligence (BI) engineers are technology specialists who ensure that analysts and data scientists have access to the right data and technologies. A key responsibility is ensuring that the company’s data is organized and accessible. BI engineer case interviews mirror the type of work that candidates will perform on the job.

Specific tasks business intelligence engineers do include:

Creating reports, developing dashboards, and implementing analytics applications such as DataMiner or Tableau Desktop

Designing, developing, and maintaining data warehouses to store large volumes of structured, semi-structured, and unstructured data

Selecting hardware, software, and database management systems for data warehousing projects in line with organizational goals

Training and onboarding users to use business intelligence software

What Is a Business Intelligence Case Study?

Case studies are a common business intelligence interview question that present the interviewee with a specific business problem. The interviewee must then talk the interviewer through a potential solution for that problem.

Most business intelligence case studies cover designing dashboards or creating databases to function for business needs. Therefore, most problems are general business case studies or technical SQL case studies, and the interviewee must solve a problem relating to how data is being presented or stored.

A typical framework for solving business intelligence case questions includes four steps:

1. Clarify - Your first step should be to gather more information from the interviewer. Case studies tend to be vague and lack information. You’re responsible for digging in and finding out exactly what the question is asking.

2. Make Assumptions - Start forming a hypothesis and talk through your reasoning. Your goal should be to land on one hypothesis/solution for the problem, which you will analyze further.

3. Propose a Solution - State your solution, and talk the interviewer through your processes for building the solution.

4. Conduct Further Analysis - For analytics case studies, you’ll want to narrow your investigation to one key metric and support your hypothesis with data. For database design case studies, you’ll walk the interviewer through the schema for a database.

How Are Business Intelligence Case Interviews Graded?

There is not a set grading rubric for business intelligence case studies, as it’s often at the discretion of the interviewer. However, there are some areas you should focus on that will make your response stronger:

Curiosity- Clarifying questions helps you narrow your response. An Amazon business intelligence engineer told us: “If you don’t ask questions, the interviewer could fail you, because they wanted to give you some information to steer the discussion down a particular path.”

Ability to take direction - Our source said: “The interviewer decides where the candidate needs to end up in their solution.” Therefore, it’s important to take hints and coaching during the interview.

Thoroughness - Case questions assess the depth of your problem-solving approach. You can show this by asking clarifying questions, providing multiple data points for analysis, and making assumptions (and checking that those assumptions are correct).

Ability to adapt - Inevitably, something unexpected will come up in a case study question, like your preferred method isn’t feasible, and you will have to adapt. Take the cues the interviewer provides, and always be willing to change courses if needed.

Communication - BI cases assess your ability to summarize your solution and clearly explain your thought processes and assumptions. One tip: Ask the interviewer if they have any questions throughout your response. This can help you clarify your answer at the moment.

There’s no right or wrong answer to case study questions. Rather, candidates are graded on the quality of their responses. Using a framework will help you structure your response more clearly.

Business Intelligence Case Study: Mock Interview

Let’s take a look at an in-depth mock interview solution to a business intelligence case question asked at Amazon:

1. You want to de-duplicate products from multiple sellers in a large eCommerce database. How would you approach this?

More context. Products are listed under different seller names. So for the same product, we might see many variations, e.g., iPhone X and Apple iPhone 10. However, let’s say this example shows up for a lot of different products in various categories.

See a full mock interview solution to this question on YouTube .

Example Solution:

Here’s an edited solution from the mock interview:

Interviewee: “If it’s an established e-commerce company, I would assume that they would have some kind of an ID for every product in their inventory. So something like an SKU or an ID. And if it’s Amazon, then that’s pretty unique, and you know that even if the description is different under different sellers, I would assume that they would have the same SKU.”

“So if you just look at the list of all the SKUs and different sellers and then do a distinct GROUP BY on SKU across all sellers, you’ll find out which SKUs are replicated. And then, once you have that, you can go to the business team saying what you want to do with them.”

Follow-Up Question 1: Let’s say you don’t have an SKU. People create their product titles, along with an image and descriptions.

How would we then do the mapping to the SKU, or would you think of a different approach towards solving the problem?

Identifying Similar Images

“If we have images for these products that we think may be duplicated, we could try to use an algorithm to identify similar images. Then once you have that list of similar images, you look at the descriptions and build a string similarity algorithm that outputs which descriptions sound similar or are close to each other. Now you would have at least two data points that you know these two products are similar. Then it’s probably going to be a little bit of manual intervention to identify if they really are similar or not.”

Similar Product Reviews

“The other thing that I can think of is maybe reviews on different products. So imagine that there are two different products just named differently, but both of them are the Apple iPhone 10. You would assume that the reviews are pretty much talking about a phone and that it’s manufactured by Apple. They probably have the same kinds of experiences and reviews, so you could see if the reviews are very similar to each other, and that would give a good indication that the product is probably the same. ”

Follow-Up Question 2: We’re looking at similarities across images, descriptions, and reviews, and we’re getting this score for each one of them. Now how do we go about deciding if we can de-duplicate them or not?

Would we have a human review every single one? Do we do some sort of scaling process? Because let’s say we have to do this for thousands and thousands of products, right? What’s the next step?

“Well, from the beginning, we don’t really know which products are the same or not, so we can’t do a supervised learning method. It needs to be an unsupervised technique that first tries to identify what products are similar to each other. I probably would do a clustering technique based on just descriptions and reviews.”

“We’ll definitely need to do some cleaning and tokenization for the text data to bring it to a structured format. Then we can run a TF IDF on different descriptions and reviews to find out which documents are similar. We’ll get some scores depending on how many documents end up in a particular cluster, and we will definitely have to do a manual step to see if they’re actually the same or not.”

“ I’m unaware of a clustering technique that works on images, but we would probably have to build out features from the image, bring it to a structured format, and then do clustering on top. So we might identify ten different clusters if there are ten items that are duplicated and then look at the clusters’ descriptive statistics to see if the customer in reviews is really talking about a phone, a tablet, or a computer. And then try to go about in a manual investigation from that point.”

Follow-Up Question 3: Let’s say we do that. We’re going through these clusters, and we find that the algorithm clustered just phones together instead of doing a specific enough cluster for the same product. Or maybe we’re getting thousands of different clusters that may or may not be duplicated.

Is there any way that we can optimize our manual intervention or scale this problem out so that we use the least amount of manual oversight while also figuring out a way to deduplicate efficiently?

“I guess it would depend on the features that we actually extract, as the more granular the features in our dataset, the better the clusters could be. If we are creating clusters just on the type of device, then you’re right. I think all phones and all computers will just end up together.”

“But if we are given that these are also duplicate listings, we would definitely want to look at more information in the listing itself; like the price of the product, the different types of colors that are available, and then the features in iPhones and Androids that are similar to each other. The features need to be as close to the product itself so that our clusters are more identifiable amongst each other and not as generic as phones and computers.”

“Finally, we could look at customers to see purchasing behavior. iPhones typically tend to sell out as soon as they are launched, so we can try to use the information around when a particular product was launched and then look at the purchase pattern during that time and then try to integrate these features into the dataset.”

Mock Interview Feedback

The example response provided some solid jumping-off points to solve the problem. However, there were missing factors that could have made the response stronger:

Consider the Scope

The response focused primarily on the example provided in the problem statement, e.g., iPhone X vs. Apple iPhone 10. However, in business case studies, it’s important to consider the broader context and incorporate that into your answer. In this case, considering a wider variety of products would have made the response stronger.

Having Multiple Data Points

In the example, there was just one type of product proposed. Having more data points to explain these concepts would have made the response more thorough and would have provided more examples to illustrate the proposed solution.

Considering Limitations

This particular response would have benefited from considering trade-offs to the proposed solution. In particular, the response didn’t address limitations like threshold error rates and automation. How accurate can we get with an automated word-matching solution, and would we be satisfied with the threshold?

Ultimately, the response could have benefited from a dialogue about implementation and business impact, as well as the technical details.

Additional BI Case Study Questions

Practice for the business intelligence interview with these sample database design and analytics case study questions:

2. Your company is launching a software product. Would you hire a customer success manager or use a free trial to grow the product?

The hypothesis you want to test is: Does a free trial result in cheaper engagement and acquisition costs, compared to using a CS manager?

Since this is a business intelligence role, you’ll want to frame the question in terms of metrics. Some of the metrics to consider include:

• Costs of hiring the CSM (continuous) vs cost of free trial implementation (fixed)
• Conversion rate from free to paid
• Total revenue gained
• Future product value

Each one of these metrics can be segmented additionally into new vs existing users. And if we apply weighting to each of these metrics we can ultimately come up with an equation that can maximize our goals.

3. What do you think are the most important metrics for WhatsApp?

An easy BI case study question like this assesses your data sense and the depth of your analytics knowledge. You might start with a clarifying question: Are we interested in revenue statistics (e.g. WhatsApp for Business) or more general user metrics?

For more general user engagement metrics, you could propose something like:

• Daily active users (how would you define this?)
• Average time spent on the platform by DAU
• Average number of messages sent per user
• Year-over-year or month-over-month increase in the average number of messages by users in different percentiles
• Churn rate and retention curve

As you propose metrics, be sure to tie them back to the business. Answer this question: Why does this metric matter to WhatsApp?

4. What metrics would you look at to determine the demand for rides on a ride-sharing app? What metrics would tell you there is high demand and low supply?

First, define some of the metrics. Demand would be the number of ride requests, while supply would be the number of available drivers. How would you further analyze ride requests to measure demand?

See a full mock interview solution for this question on YouTube:

5. In an insurance database, the marriage attribute column is marked TRUE for all customers. How would you debug what happened?

Follow-up question: What data would you look into and how would you find out who is actually married and who is not?

With this question, you’d want to start with some clarifying questions like:

• What’s the table structure like?
• How long has the bug existed?
• Where is the insurance company located?

One first step would be to look at what went wrong. You could look at UPDATE and INSERT queries to identify what might have caused the problem initially.

Next, you might look for an easy solution. Are there dimensions or columns related to marriage? If there was a column spouse.name, for example, this would provide insights into whether a client is married or not. You could also look to see if reverting the data would show the correct marriage status before the bug existed.

A more complex approach might be to GROUP clients by the last name and then see if entries with the same last name share an address, insurance plan ID, etc.

6. You work for a SAAS business. To catch up to end-of-quarter revenue goals, would you send an email blast to your entire customer list?

Broadly speaking, sending a mass email blast to a list of customers is generally not a good idea, especially when the objective of the email is to increase sales.

A better solution is to segment the audience and personalize the messaging by the audience. For example, if a customer was about to reach their licensing limit, we could send a personalized offer to add more licenses, while a win-back campaign could be used for recently churned users.

7. You work for a 14-month-old SAAS company. You’re asked to calculate the average customer lifetime value. How would you do it?

More context: We know that the product costs $100, an average 10% monthly churn, and the average customer sticks around for 3.5 months.

Average lifetime value is defined by the prediction of the net revenue attributed to the entire future relationship with all customers averaged. Given that we don’t know the future net revenue, we can estimate it by taking the total amount of revenue generated divided by the total number of customers acquired over the same period of time.

However, there’s a catch: it’s only a 14-month-old company. As a result, the average customer length is biased , because the company hasn’t existed long enough to correctly measure a sample average that is indicative of the mean.

How would you calculate this? Try to find the expected value of the customer at each month as a multiplier of retention times the product cost.

More Business Intelligence Resources

Prepare with these business intelligence interview questions , which are 29 commonly asked BI questions in areas like SQL, generic scenario-based cases, Python, and database design. Also, see our guide to Amazon business intelligence interviews and Google business intelligence interviews for more BI interview prep help.

Business Intelligence, Analytics and Cognitive: Case Studies and Applications (COGS) Minitrack

Permanent uri for this collection.

The purpose of this minitrack is to invite case studies of both successful and unsuccessful, but informative, applications of business intelligence, data analytics, cognitive systems, & data, analytics & cognitive-enabled smart services across industries and societies. Business intelligence and data analytics have continued to make substantial inroads in the operational, managerial and strategic corporate decision-making processes. Recently, the emergence of cognitive computing systems that augment the creativity and productivity of people, and which are trained using artificial intelligence and machine learning algorithms to predict, infer and, up to some extent augment cognitive capabilities, has also extended the range of business intelligence and data analytics solutions on the market.

Multiple methods are encouraged. For example, instead of using an extensive statistical survey, case study, action research or design science may use qualitative methods to describe individual or organizational change. We are looking for reports that improve our understanding of how BI, Analytics, Cognitive technologies and Smart Services are currently used across industries and societies.

We encourage papers that report on lessons learned, on topics which include, but are not limited to, the following:

• BI, Data Analytics & Cognitive: How to improve corporate data literacy
• Data to Insight and Wisdom: Do universities make the grade?
• The emergence of the Chief Analytics Officer and marketplace for specialists
• The emergence of the Chief Digital Officer and marketplace for specialists
• The diffusion stages of these information systems and competitive advantages
• ROI of these information systems (BI, Analytics, Cognitive, Smart Services)
• David vs. Goliath: SME and MNC using BI, Analytics, and Cognitive
• “Big science” and “citizen science” applications of BI, Analytics, Cognitive and Smart Services
• Opportunities and challenges of self-service, collaborative and mobile BI
• Next generation of cognitive computing applications in business & education
• Addressing grand challenges with intelligence, analytics, smart services, and cognitive assistants and mediators
• Digital transformation with smart services and cognitive assistants

Minitrack Co-Chairs:

Sergey Belov (Primary Contact) IBM East Europe/Asia Email: [email protected]

James C. Spohrer IBM Almaden Research Center Email: [email protected]

Haluk Demirkan University of Washington – Tacoma Email: [email protected]

5 Business Intelligence & Analytics Case Studies Across Industry

TechEmergence provides five case studies that illustrate how AI and machine learning technologies are being used across industries to help drive more intelligent business decisions. While not meant to be exhaustive, the examples offer a taste for how real companies are reaping real benefits from technologies like advanced analytics and intelligent image recognition.

• Field Optimizer Optimize your field force
• Contract Compliance Ensure trade agreements are executed in store
• Perfect Store Achieve your perfect store
• Dynamic Merchandising Activate a flexible, data-driven workforce
• Category Excellence Become a category captain
• Shopper Engagement Drive engagement & sales without eroding margins
• Category Analysis and Planning Strategic category analytics and tools
• Signal-Based Merchandising Deploy merchandising resources in Real Time
• In-store Execution & Merchandising Control of your Retail SKUs
• Dynamic Workforce Management Integrated AI-driven staffing solution
• eCommerce Create repeat customers
• Full-Funnel Engagement Increase traffic, sales, and purchase frequency
• Inventory Optimization Use shelf-level data
• Omnichannel Customer Engagement Increased Reach, Increased Revenue
• Online Fulfilment Accurate shelf information for eCommerce
• Price & Promotion Compliance Simplify and Perfect Promotion Compliance
• Retail Supply Chain Predict and manage inventory
• Retail Snapshot Our crowdsourced solution — the Trax Crowd
• Store Optimization Optimize with real-time shelf monitoring
• Retail Workforce Management Optimized and On-demand Labor
• Sales Management
• Trade Marketing
• Category Management
• Shopper Marketing
• Store Operations
• Emerging & Growth Brands
• Get In Touch

Business Intelligence for Teaching Analytics: A Case Study

• Conference paper
• First Online: 12 February 2021
• Cite this conference paper

• Alessio Maria Braccini 4 ,
• Carla Limongelli 5 ,
• Filippo Sciarrone 6 &
• Marco Temperini 7  

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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• The International Research & Innovation Forum

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In recent years, there has been a radical change in the world of teaching and training. This is causing numerous schools, universities and companies to adopt the most modern Information and Communication Technologies, mainly based on the Web, for distance education. Moreover, the widespread use of web-based environments is producing a considerable volume of data which could be used to monitor the learning processes to improve them. While Educational Data Mining analyzes this data from a technical point of view, Learning Analytics focuses on educational aspects to optimize online learning opportunities, involving all the stakeholders. In this paper, we present a case study concerning the analysis of data generated by a learning process, in a Learning Management System (LMS). The main goal is to test a particular Business Intelligence platform, the Knime platform, to extract hidden significant educational features from data. The case study strengthens our approach, with interesting pedagogical results.

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Department of Economics and Entrepreneurship, Tuscia University, Via del Paradiso, 47, Viterbo, Italy

Alessio Maria Braccini

Department of Engineering, Roma Tre University, Via della Vasca navale, 79, Rome, Italy

Carla Limongelli

Engineering Department, Roma Tre University, Via della Vasca navale, 79, Rome, Italy

Filippo Sciarrone

Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Viale Ariosto 25, Rome, Italy

Marco Temperini

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Anna Visvizi

Effat University, Jeddah, Saudi Arabia

Miltiadis D. Lytras

Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

Naif R. Aljohani

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Braccini, A.M., Limongelli, C., Sciarrone, F., Temperini, M. (2021). Business Intelligence for Teaching Analytics: A Case Study. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_26

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Laser Clinics was a global success story, but like so many before, a franchising scandal could bring it undone

Analysis Laser Clinics was a global success story, but like so many before, a franchising scandal could bring it undone

"Con artists", "amateurs", "Avoid! Avoid! Avoid!" are some of the more polite reviews that lit up social media when a global retail franchise network started closing stores with little thought for its customers who had pre-paid for services.

Laser Clinics, which offers laser hair removal, injectables and other non-surgical cosmetic treatments, first opened in Australia in 2008 and now has more than 200 clinics globally. It's battling a PR crisis as some of its UK stores close, attracting the wrath of customers on social media, in newspapers and on TV.

It comes as some franchisees threaten legal action, claiming they are being driven out of business by the owner of the franchise, private equity giant KKR, famously referred to as "barbarians at the gate".

The Laser Clinics website includes a map with more than 100 stores in Australia, 20 in New Zealand, 50 in the UK and seven in Canada. Its Asian clinics have disappeared from the website after the franchise expanded there just a few years ago.

The bad publicity is spreading like wildfire, with one customer posting on trustpilot.com: "One after the other the clinics are closing down … This is the downfall of [the] major Australian company Laser Clinics … The customers are left with no notice of the closures, the management should refund all customers' hard-earned money. Media has unveiled the reality about their methods of operations, so public is not duped."

On May 8, the BBC's popular consumer program , Watchdog on The One Show, shone a national spotlight on the situation when it interviewed a series of customers who had bought treatments in advance only to find the clinic had shut down. They said they were either told they couldn't get a refund or advised to go to the nearest clinic that was still operating, which was an hour away by transport.

It isn't the first time Laser Clinics has found itself at the centre of controversy.

In 2021, the company  made headlines in Australia when 52 of its franchised clinics sent the firm   a legal letter alleging they were being gouged on costs for equipment and supplies and by an aggressive discounting of treatments.

That stoush ended when KKR did a deal with most of the 52 aggrieved clinics to buy them out.

Since then, things have gone downhill for many franchisees, both here and overseas. COVID hit, more competitors entered the industry and, in Australia, a series of regulatory reforms including advertising restrictions made it harder to attract and retain customers.

A case study in what happens when franchising goes wrong

Laser Clinics first opened in Australia in 2008 as a franchise network set up by former ­actuary Babak Moini and legal IT expert Alistair Champion. It was sold in 2017 to KKR, which expanded its footprint globally, including opening in the UK in 2019.

Unlike the original founders, who franchisees described as passionate about the business and having treated   the franchise as a partnership, franchisees say the very nature of private equity is to buy assets with a view to squeezing as much out of them then flipping them in three to seven years. They say decisions are made based on numbers and with a short-term horizon.

Franchising represents almost 10 per cent of Australia's GDP, employs more than half a million workers and, according to the Franchise Council of Australia, includes 1,200 franchisors and 94,000 franchise outlets, many of them hard-working Australians who have used their retirement savings to buy a ready-made business with a brand and systems in place.

When franchising works, it works well — but when it doesn't, it can be devastating for franchisees and workers.

In the past few years, the industry has been dogged with scandal after scandal, including convenience store giant 7-Eleven , which was recently sold, Retail Food Group, whose brands include Donut King, Brumby's, Gloria Jean's, Pizza Capers, Crust Gourmet Pizzas and Michel's Patisserie , and came under scrutiny when it was found to have squeezed its franchisees mercilessly with a string of fees, royalties, rebates and refurbishment costs. Others include listed pizza giant Domino's, which was exposed in 2017 over some unscrupulous business practices .

To put it into perspective, in the past 30 years there have been 18 inquiries into franchising. The last one, in 2019, found that the regulatory system had "manifestly failed to deter systemic poor conduct and exploitative behaviour and has entrenched the power imbalance".

It likened what was happening in the franchising sector to the bad behaviour uncovered in the banking royal commission.

Franchisees 'cannot wait to leave'

This behaviour has spread to the UK, with a number of disgruntled Laser Clinics franchisees speaking to me on the condition of anonymity.

One, who still runs a clinic in the UK, said Singapore had gone and Canada was also struggling. She said it was difficult to find a buyer for franchisees trying to exit the Laser Clinics network.

"No-one will invest in this company … It's a complete shit show," the franchisee said.

"Articles in the national newspapers, franchisees going legal. It's just crazy. I simply cannot wait to leave."

Another said a marketing strategy of constantly discounting the treatments was a tipping point for many. Pricing is controlled by the head office and franchisees in the UK and London claim that customers have been conditioned to wait for the sales.

In the UK, some stores are being bought back from KKR for one pound. This offer has been made to walk away from the clinic while still possibly incurring further costs of tens of thousands of pounds but being relieved of the main debt and released from any personal guarantees other than for laser machines bought on finance if the clinic is to close permanently. So far four franchisees are said to have accepted the offer.

Some of the stories are harrowing, including couples investing their life savings only to discover their dream of running a small business with global partners was more like being an employee trapped by their money.

"Despite working 15 to 17 hours a day, seven days a week, I received no support from Laser Clinics system and head office staff," said one of the many current and former franchisees I spoke to.

The franchisee, who asked for anonymity due to fears of retribution, said Laser Clinics had destroyed them in ways they never thought possible.

"This lack of guidance and assistance took a toll on my mental and physical health, resulting in severe depression and heart palpitations and even drove me to contemplate suicide," they said.

Another said the experience of becoming a Laser Clinics franchisee had been devastating.

"In such a short time my marriage, family and friendships have collapsed and I'm not living with my children anymore. I am now unable to even pay [for] the most basic [things] like food, and all my bills and debts are uncontrollably continuing to soar," they said.

Some franchisees sent a legal letter in February this year outlining their concerns. The letter claimed that head office misrepresented the financial outlook of the clinic they bought into and that operating costs were understated, such as inaccurate assurances as to the number of staff required to run each clinic adequately. It said fixing prices at which the clinics could sell treatments had an adverse impact on business.

"As such, they have been saddled with franchise operations that are far less profitable than anticipated and, in some cases, loss-making and likely to remain so," the legal letter said.

Laser Clinics was sent a list of questions. It ignored some of them and instead provided a statement that said it continued to invest strongly in its leadership, offering and people "as we deliver on our ambition of being the global leader in skin treatments".

It said its goal was to support the long-term success of all its franchisees. "They are front and centre of our strategic plan and their success is pivotal to our growth and to our joint venture model."

In the UK, it said it was working to re-open and invest in several clinics. For those that had been permanently closed, it said, where possible, any pre-paid treatments had been transferred to the closest available clinic. "If a suitable alternative is not available, Laser Clinics is working with the client to consider their options in line with our legal obligations.

"For our clients and team members in our larger clinic network in the UK, this change has no impact and it is business as usual."

But the poor treatment of some UK franchisees and the brand damage caused by mishandling some customers has sent a chill wind through the global network which it will need to address. If it continues to brush it off as "business as usual" it may find itself holding an asset with limited appeal.

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