Business intelligence in the retail sector:
key features, benefits, and platforms

Business intelligence in the retail sector: key features, benefits, and platforms

March 3, 2023

The role of BI in the retail industry

of consumers expect companies to deliver personalized interactions


increase in top-line sales is due to analytics applications in retail


revenue is generated by companies that excel at personalized marketing


Retail business intelligence use cases

Product assortment optimization

    • Identifying underperforming and well-selling products
    • Assessing SKU uniqueness for the customer
    • Economic performance monitoring and analysis, including total product sales and gross margin
    • Evaluating SKU productivity by analyzing financial performance, uniqueness, value to the customer, the cost to serve, and strategic importance
    • Calculation of product penetration rate
    • Assessing a new product’s expected incremental financial contribution and novelty value for customers
    • Cost-to-serve analysis (SKU end-to-end logistics cost per store, wastage ratio, out-of-stock ratio)
    • Product assortment planning (organization, store chain, individual store, sales channel)

    Implement your retail BI solution with Itransition

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    Real-life example of BI in the retail sector

    Event tracking
    Backend services
    Event collecting
    Behavior event collector
    Transactional data collector
    Event processing
    Event channel
    Events filtering
    Events processing
    Data storage
    Raw data
    Master data
    Data processing
    Business intelligence
    Adhoc querying
    Content personalization
    Recommendation engine
    Integration layer

    The customer is an online fashion retail company with 20+ million registered customers. With 200,000+ website and mobile app users daily, the customer had to process large amounts of data to know their customers’ needs. This is why the retailer decided to get a centralized BI solution that would collect data, store, and analyze user behavior as well as build predictive models to forecast buyer conversion rates, product interest, and future sales.

    Itransition developed a solution that gathers and analyzes clickstream data, mobile data, server events, and email campaign engagement data in near real-time mode, enabling predictive analytics along with website and mobile app personalization. Implementing a retail-specific BI solution for data collection and analysis has helped the customer decrease monthly infrastructure costs by 50%, better understand online user behavior, and increase sales through AI-powered personalization, which resulted in the visitors-to-buyers conversion rate increasing by 8%.

    Top BI platforms for the retail sector

    Key features
    • 150+ data source connectors, including Salesforce, Google Analytics, Amazon Redshift, Oracle, and Google BigQuery
    • Seamless integration with Azure ecosystem (Azure Data Lake Storage Gen2, Azure Synapse Analytics, Azure SQL database, Azure Machine Learning Studio)
    • Self-service data preparation, analysis, reporting, and visualization
    • Visual-based data discovery
    • Interactive dashboards
    • Augmented analytics
    • Text, sentiment and image analytics
    • 1
    • NLP capabilities
    • Pre-built customizable visuals
    • Data storytelling capabilities
    • Team commenting and content subscriptions
    • Row-level security
    • Mobile-ready
    • Embedded BI
    • Available as a SaaS solution running in the Azure cloud or as an on-premises solution in Power BI Report Server
    Platform pricing
    • Power BI Desktop
    • free
    • Power BI Pro
    • $9.99 per user/month
    • Power BI Premium
    • $20 per user/month or $4,995 per capacity/month with an annual subscription and an unlimited number of users
    • Power BI Embedded
    • from $1.0081/hour
    • Two-month free trial
    • for every new user
    Product differentiators
    • Augmented analytics capabilities, including intelligent narratives and anomaly detection capabilities
    • Can be used as a stand-alone, free self-service BI tool
    • An on-premises version has functional gaps compared to the cloud service
    • Azure-only deployment

    Retail business intelligence software: selection checklist

    To draw up an optimal retail BI technology stack, companies need to carry out a careful analysis of their unique business needs, goals, and requirements for business intelligence. The wrong technology choices might not only prove more expensive than expected but also frustrate business users, turning them against the whole idea of business intelligence. To help companies stick to the budget, guarantee faster returns, and facilitate quicker user adoption, we outline the must-have functionalities to look for in BI platforms:

    Data source connectivity

    for connecting to, querying, and ingesting data from all the required cloud and on-premises data sources

    Data preparation capabilities

    including support of user-driven data aggregation from different data sources

    Platform security

    including user administration, platform access audit, and authentication

    Augmented analytics capabilities

    to automatically generate analytics insights for end users with ML techniques

    Data visualization

    including the support for common chart forms (bar/column, line/area, pie, and geographic maps) as well as highly interactive dashboards

    Data storytelling

    for combining interactive data visualization with narrative techniques and presenting analytics content compellingly and comprehensively

    Reporting capabilities

    to create and distribute reports to colleagues and customers on a scheduled or event-triggered basis

    Data governance

    for tracking a BI platform usage and managing business sharing

    Data catalogs

    for users to quickly access analytics content

    Natural language support

    to enable users to ask questions, query data, and get insights in natural language

    Need help with making the best choice for your BI project?

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    Essential integrations for retail business intelligence

    Retail business intelligence platforms need to be integrated with multiple types of retail-related software to import data, analyze it, and export analytics insights further across the enterprise.

    Core integrations

    Retail POS

    CRM software

    Pricing software

    Ecommerce platform

    Marketing campaign management software

    Supply chain management software

    IoT devices

    Retail POS

    Import sales data, inventory data, customer purchase history, and customer personal information to:

    • Identify customer spending behavior patterns (coupon usage, preferred payment methods, shopping frequency)
    • Assess store performance and merchandising practices
    • Monitor marketing campaigns' effectiveness
    • Measure staff performance
    • Get full control over inventory
    • Identify complementary products for upselling and cross-selling

    Key benefits of BI for the retail sector

    Improved customer experience

    Retail BI helps companies track and analyze customers behavior across all touchpoints. Drawing on this analysis, retailers can create and run hyper-personalized customer campaigns and loyalty programs, create unique shopping experiences by personalizing every step of the customer journey, design eye-catching store layouts, and deliver consistent user experience across multiple channels.

    Targeted marketing efforts

    Retail BI benefits companies that want to track customer spending behavior and patterns to identify their motivations as well as monitor and assess how customers respond to marketing incentives. Equipped with these insights, retailers can enhance their marketing initiatives to retain the most profitable customers and acquire new ones.

    Enhanced supply chain management

    Business intelligence solutions for retail help address most common supply chain challenges such as long supply cycles, fluctuating product demand, under-, and over-stocking, balancing inventory between several stores/across multiple channels, and high inventory costs by enabling near-real-time insight into supply and demand dynamics and accurate demand forecasting.

    Optimized shop floors and product placement

    Retailers can leverage business intelligence to adjust floor plans and product placements and encourage consumers to shop longer, simplify product searches, and trigger impulsive buying by displaying popular product bundles.

    Competitor benchmarking

    A retail BI solution allows you to get an insight into your competitors' offerings and pricing, benchmark your company's performance against competitors, determine missed opportunities, fine-tune product assortment, and optimize pricing strategies.

    Getting a competitive advantage

    Retail BI automates business data gathering, cleansing, and analytics activities and helps improve customer retention, devise store-specific or channel-specific marketing strategies, and optimize in-store operations. Such automation level leads to quicker and smarter business decisions and data democratization, which becomes an unmatched advantage over competitors who majorly rely on manual data processing or guesswork.

    Retail business intelligence implementation: cost factors

    Retail BI implementation cost factors

    Retail BI adoption costs depend on multiple factors, including:

    • The number of data sources for analysis
    • Data volume
    • Data structure and format
    • Initial data quality and data quality requirements
    • The complexity of data transformation requirements
    • The complexity of the data storage layer
    • Data analytics complexity
    • The complexity of data visualization and reporting
    • Data security and compliance requirements

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    Common retail business intelligence challenges

    Security concerns

    Retail BI platforms ingest a significant volume of sensitive data – personal data, financial data, intellectual property, and trade secrets – which should never be compromised.

    To prevent data breaches and unauthorized access to business information, as well as to ensure business continuity and regulatory compliance, comprehensive data governance and data security practices and rules should be applied when implementing and managing a BI platform. Business intelligence software should offer capabilities for:

    • Automatic discovery and masking of sensitive data
    • End-to-end data encryption
    • Restricting access to data according to user roles
    • Multi-factor user authentication
    • 24/7 user activity monitoring
    • Regular risk assessment

    Poor data visualization and reporting

    Lack of interactive data visualization, static reports, and inconsistent experience from various devices hinders users from deciphering valuable insights and stalls BI adoption.

    Companies should take into account good data visualization design practices and self-service BI capabilities from the start of the BI project. We recommend companies consider the following features:

    • Pre-built report templates with tailored KPI sets for different user groups 
    • Scheduled and event-triggered reporting
    • Interactive dashboards with configurable filtering capabilities 
    • Ready-to-use and custom visuals
    • NLP support 
    • Drag-and-drop capabilities 
    • Embedded BI 
    • Mobile support
    • Sharing and collaboration capabilities 

    Low data quality

    Retail data for BI often comes from siloed systems, which results in inconsistent, duplicated, inaccurate or outdated data used for analytics.

    To prevent data quality from compromising business insights, we recommend companies incorporate data quality assurance into all BI functions and processes. That implies appointing dedicated team members to manage data quality, establishing a solid data quality measurement and management framework, and adopting suitable data quality management software.

    Embrace retail BI for faster and smarter decisions

    These days, retail business intelligence is seen as an important solution to both address future opportunities and solve current business challenges, such as inflationary pricing, economic uncertainty, and geo-political factors. Still, successful BI adoption requires not only significant investments but also a solid BI implementation framework. With 15+ years of experience delivering full-scale custom and platform-based BI solutions, we are ready to design and implement an effective BI solution for retailers within the set time and budget frames for retailers.