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)

    Consumer insights

      • 360-degree view of the customer
      • Customer segmentation based on demographics, geography, psychographics, and behavior
      • Customer interaction monitoring across all channels
      • Net promoter score calculation
      • Customer experience and engagement analytics
      • Customer satisfaction scoring and analysis
      • Customer conversion analysis
      • Customer acquisition analysis
      • Customer churn analysis
      • Customer profitability analysis and LTV calculation
      • Customer decision trees (CDTs)
        • Marketing campaign effectiveness tracking and analysis (traffic, sales volumes, repeat customers, website traffic, social media sentiment)
        • Analysis of average basket composition by customer segments
        • Marketing channel performance assessment and analysis
        • Analysis of customer sensitivity to product promotions
        • Product placement efficiency analysis and optimal placement modeling
        • Upselling and cross-selling
        • Markdowns planning
        • Loyalty programs analysis
        • Customer choice modeling
        • Next best action modeling

        Location intelligence

          • Market analysis (market penetration level, marketplace gap analysis, market opportunities and threats)
          • Product sales analysis by store and region
          • Monitoring the stock level of individual retail operators
          • Detection and analysis of underperforming stores (ongoing viability, ability to raise profits)
          • Site locations for new outlets and warehouses based on the location of most profitable customers, the proximity of competing stores, and transport routes
          • Forecasting store-specific budgets based on the size of the surrounding population and location-specific information
          • Developing sustainable customer loyalty programs based on location-based marketing metrics
          • Indoor analytics (traffic, conversion, dwell time) and mapping
          • Macro space planning

          Product pricing

            • Customer demand analysis
            • Determination of price sensitivity per customer segments/markets
            • Lost sales analytics
            • Determination of customer perceived value
            • Price benchmarking
            • Price elasticity and gap analysis
            • Price modeling (initial pricing, promotional pricing, markdown pricing) based on consumer demand, product seasonality, competitor prices, retail channels, market conditions, industry predictions, and competition
            • Custom pricing strategies for specific customer groups
            • Regional and international pricing strategies

            Supply chain intelligence

              • Multi-channel inventory monitoring
              • Tracking inventory across multiple stores (stock on hand, excess inventory, low-stock inventory, aged inventory, inventory location, etc.)
              • Inventory analytics (average inventory turnover rate, inventory profitability analysis, inventory shrinkage, inventory carrying costs, backorder rate, etc.)
              • Optimal inventory stock levels forecasting (product/SKU, sales channels, store locations)
              • Inventory replenishment/acquisition/liquidation/allocation planning
              • Balancing of interstore inventory assortment
              • Supplier performance analysis (orders delivered in full, on time, share of accurate orders and damaged goods)
              • Supplier benchmarking (assortment, price, contract terms and conditions, regulatory compliance, social responsibility)
              • Supply-demand forecasting and balancing
              • Supply chain optimization modeling

              Sales intelligence

                • Sales KPIs tracking and analysis (sales by channel, region, store, brand, product category, SKU, per rep, sales target, average purchase value)
                • Benchmarking (sales per region, product category, store location, representative, sales channel)
                • Forecasting sales volumes for a particular brand, product category, or SKU based on sales pipeline factors
                • Team analytics (sales rep performance, overall sales team effectiveness, best performers, etc.)
                • Sales pipeline visualization and analysis
                • Sales methods analytics
                • Lead analytics
                • Sales leaderboards
                • Shopping cart analysis

                Implement your retail BI solution with Itransition

                Contact us

                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

                Key features
                • Native integrations with 80 data sources, including Salesforce, Google Analytics, Google Sheets, Cloudera, Hadoop, Amazon Athena, SQL Server, Dropbox, Presto, SingleStore
                • Self-service data preparation
                • No-code analytical data querying
                • Support for time series and forecasting
                • Easy real-time collaboration and sharing
                • Advanced visualization capabilities
                • Intuitive dashboard creation
                • NLP capabilities
                • Custom dashboard creation
                • Row-level security
                • Mobile-ready
                • Embedded analytics
                Platform pricing
                • Tableau Creator
                • $70 per user/month
                • Tableau Explorer
                • $35/user/month (fully hosted by Tableau)
                • Tableau Explorer
                • $42/user/month (on-premises or public cloud)
                • Tableau Viewer
                • $12/user/month (fully hosted by Tableau)
                • Tableau Viewer
                • $15/user/month (on-premises or public cloud)
                • Free trial
                Product differentiators
                • An intuitive analytics experience based on its patented VizQL engine
                • A user-experience-focused design
                • High premium pricing
                • A steep learning curve

                Key features
                • Seamless connectivity to hundreds of data sources, including Salesforce, Amazon Redshift, Azure Synapse Analytics, DropBox, Google Analytics, Google BigQuery, Microsoft Excel, Microsoft SQL Server, and Oracle
                • Auto-generated analysis, chart recommendations, and data combination
                • Data storytelling
                • Group sharing and collaboration
                • Support for multiple user types
                • 1
                • 1
                • Drag-and-drop report and dashboard creation
                • NLP capabilities
                • Smart search
                • Row- and column-level security
                • Automated ML capabilities
                • Embedded analytics
                • Mobile-ready
                Platform pricing
                • Qlik Sense Business
                • $30/user/month
                • Qlik Sense Enterprise SaaS
                • custom pricing is available upon direct request
                • Free trial
                Product differentiators
                • Deployment flexibility, including enterprise SaaS and customer-hosted options
                • Multi-cloud and on-premises installation, without limiting customers to any particular cloud
                • Product pricing complexity

                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?

                Contact us

                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.

                Retail POS

                CRM software

                Pricing software

                Ecommerce platform

                Marketing campaign management software

                Supply chain management software

                IoT devices

                Core integrations

                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

                Customer relationship management (CRM) software

                Import customer personal data, customer interaction data, purchase history, customer service requests, and customer feedback to:

                • Get a golden customer record
                • Enable dynamic customer segmentation
                • Conduct comprehensive customer analytics
                • Model customer behavior

                Pricing software

                Import pricing data, price lists, sales, and transactional data to:

                • Formulate the best pricing strategy for various scenarios (initial pricing, markdown pricing, and discount pricing)
                • Analyze product pricing
                • Model price elasticity of demand
                • Develop data-driven pricing strategies (regional, international, seasonal)
                • Generate custom pricing strategies for specific customer segments

                Ecommerce platform

                Import customer data, purchasing history, cart abandonments, shipping details, customer requests, and feedback to:

                • Identify the demographics of a company’s customer base and buying preferences
                • Study customer buying behavior
                • Measure customer response to marketing campaigns
                • Evaluate the performance of marketing and sales channels
                • Estimate the cost of new customers acquisition
                • Measure conversion (sales conversion rate, average order value, and cart abandonment rate)

                Marketing campaign management software

                Import data generated by previous marketing campaigns, customer data (age, income, interests, and spending habits), and customer survey data to:

                • Perform dynamic customer segmentation
                • Conduct market basket analysis
                • Tailor marketing campaigns to individual customer segments across different marketing channels
                • Track and evaluate the performance of marketing campaigns
                • Simulate marketing campaigns to predict customer and prospect behavior
                • Run customer churn and customer retention analysis

                Supply chain management software

                Import stock inventory data, supplier capacity data, shipment data, and product data to:

                • Identify optimal inventory levels
                • Plan procurement/replenishment
                • Calculate inventory carrying costs
                • Assess supplier performance and conduct supplier benchmarking
                • Forecast supply chain risks

                IoT devices

                Import data from motion tracking systems, cashier-less payment systems, smart carts, and video cameras to:

                • Identify customer needs in real-time
                • Gain a deeper understanding of customer purchasing patterns
                • Manage smart retail inventory
                • Allocate and localize macrospace
                • Optimize in-store operations

                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

                Do you want to ensure the success of your BI project?

                Contact us

                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.

                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.

                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.

                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.

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