Emily owns a bakery that’s famous for its cupcakes and doughnuts, and she’s happy with how her business flourishes within a short period. However, her products sell quickly, and she can’t adequately meet customer demand before long. After researching some business intelligence examples, she decides to invest in a business intelligence solution.

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How does business intelligence help? It lets her predict consumer demand accurately and optimize production to keep her customers happy and full of delicious pastries. Other business intelligence examples in this article show how a BI solution helps improve performance and boost productivity.

This Article Covers:

• What Is Business Intelligence?
  • How Does It Work?
  • Why Is It Important?
• Benefits
• Examples
• Techniques
• Application Types
• Trends

What Is Business Intelligence?

Business intelligence encompasses processes that ingest data and convert it into visuals, reports and dashboards to identify trends, patterns and points of interest. These insights help you gauge business performance and make data-driven decisions.

BI tools enable you to access different data types, including structured and unstructured, historical and current, in-house and third-party.

How Does It Work?

BI solutions rely on data warehouses for information. A data warehouse accumulates information from multiple sources to support analysis and reporting. BI software queries the warehouse to obtain, transform and visualize data to present the results in dashboards and reports.

Why Is It important?

Business intelligence helps you gain valuable insights from historical and current data to improve decisions, identify problems, spot market trends and explore new opportunities to boost revenue.

It can help answer important questions such as:

• Why are sales dropping in a particular region?
• What factors contribute to high profits in a specific year?
• Which employee is performing better in this quarter, and more.

BI helps your business become data-driven, boost performance and gain a competitive edge. It can also:

• Boost ROI by allocating optimal resources to meet objectives.
• Analyze customer needs and preferences to tailor products and services accordingly.
• Monitor business operations and make amendments based on insights.
• Improve supply chain management.
• Increase customer satisfaction.

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Primary Benefits

Business intelligence solutions offer the following benefits:

• Create intuitive reports and dashboards.
• Identify market trends.
• Gain valuable business insights.
• Make data-driven decisions.
• Boost operational efficiency and revenue.
• Mitigate costs.
• Provide a seamless customer experience.
• Conduct robust competitive analysis.
• Facilitate collaboration.

Key Business Intelligence Examples

Let’s look at some business intelligence examples from global market champions that use these tools:

Starbucks

Have you ever wondered how your favorite coffee shops use business intelligence to deliver a seamless experience? It analyzes data based on various parameters, including consumer demographics, population density, traffic patterns, average income levels, public transport hubs and business types in the vicinity to help them stay on top.

Coffee shops reside at the intersection of the home and office to ensure visibility and offer the best deals to customers. BI lets them provide loyalty card programs and a mobile app to track individual purchase data. It also helps predict purchases and send personalized offers via app or email to entice customers and boost sales.

Netflix

As the largest subscription streaming service, Netflix leverages business intelligence and data science to gain new subscribers and enhance user experiences. BI solutions create personalized watch lists and compelling artwork while persuading users to watch their favorite genres. The recommendation system promotes content to the right audience at the right time.

Chipotle Mexican Grill

Chipotle, an American restaurant chain, opted for a self-service BI platform to obtain a centralized view of its operations across 2,400 locations worldwide. It gives them access to real-time data, tracks operational effectiveness at a national level and delivers performance reports faster.

Tesla

Tesla, an innovative automotive company, leverages business intelligence to connect its cars wirelessly to corporate headquarters for data collection. It analyzes data to correct and avoid issues like component damage, traffic or road hazards. BI helps this automotive disruptor boost customer satisfaction and inform decisions on future upgrades.

Uber

This California-based transportation company uses business intelligence to monitor traffic conditions, journey times and durations, driver availability and customer demand in real time. It leverages surge pricing and a rating system as a part of its business model to manage fares and maintain trust between drivers and customers.

Walmart

Walmart uses business intelligence to gauge customer behavior online and in-store based on customer purchases. It helps them make data-driven decisions around assortments, inventory and merchandising, resources, promotions and more to drive profits. Data-based apps shine a light on customer preferences and curate specific loyalty programs for different segments.

HelloFresh

HelloFresh, a meal kit company, automates reporting processes and empowers its teams to create personalized, regional marketing campaigns through business intelligence. They use three different buyer personas to reach target audiences with customized products. As a result, they continually improve conversion rates and retain customers.

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Techniques

Let’s look at some popular business intelligence techniques:

• Analytics: Extracts and identifies trends, patterns and correlations from historical data to drive decisions.
  

  Dashboard showing financial metrics and KPIs. Source

• Dashboards: Collects and displays intuitive visualizations, KPIs and metrics to monitor business performance.
• Data Mining: Employs statistical and machine learning techniques to unearth anomalies, patterns and correlations within large datasets to predict outcomes.
• ETL: Extracts data from multiple sources, cleans it to improve quality and loads it into systems for access and manipulation.
• OLAP: Performs complex analytical queries on transactional systems to solve problems from different perspectives.
• Predictive Modeling: Uses machine learning and data mining techniques to predict future outcomes by analyzing historical data.

Application Types

Some different business intelligence applications are as follows:

• Ad Hoc Analysis: Run queries to gauge specific business issues. Create ad hoc reports on the fly to make data-driven decisions.
• Mobile BI: View dashboards on smartphones and tablets. It provides real-time data access to boost efficiency, performance and collaboration.
• Real-Time BI: BI applications analyze real-time data to provide an up-to-date view of business operations, customer behavior and financial markets. Real-time analytics streams data and supports decision analytics such as credit scoring, stock trading and more.
• Collaborative BI: Lets team members work together and share information. Annotate data with comments to highlight trends, patterns and other important information.
• Location Intelligence: Analyze location and geospatial data with map-based visualization tools. Potential applications include site selection for corporates and retail stores, location-based marketing and logistics management.
• Embedded BI: Integrate data visualizations, reports and dashboards while analyzing data within the application. It boosts data usability and decision-making capabilities.

Notable Trends

Let’s take a quick look at some BI trends that businesses are adopting:

• Augmented Analytics: BI tools offer natural language querying capabilities and AI and machine learning algorithms that help you perform data cleaning and analysis while creating charts, reports and other visuals.
• Low Code or No Code: Offers a graphical user interface that allows you to develop BI applications with minimal or no coding.
• Cloud Adoption: BI tools and warehouses are rapidly shifting to the cloud to mitigate risks and foster scalability.
• Data Literacy: Boosting data literacy ensures new users understand and work with data without getting bogged down in complexity.

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Conclusion

Businesses generate large amounts of data daily, and leveraging this information is vital to derive actionable insights. The business intelligence examples we talked about earlier show how companies use data to improve business performance and gain a competitive advantage.

Can you think of more business intelligence examples? Let us know in the comments below!