Business intelligence in the retail sector: main use cases, benefits & platforms
October 10, 2024
Retail business intelligence is the process of analyzing business data and deriving actionable insights to help retailers gain a deeper understanding of their target audience, personalize customer experience, optimize assortment and pricing, and create winning marketing and sales campaigns.
Itransition provides comprehensive BI implementation services that enable companies to mitigate the impact of fluctuating customer demand and other field-specific challenges, gain a competitive advantage, and grow profits.Â
The role of BI in the retail industry
of consumers expect companies to deliver personalized interactions
McKinsey
of organizations expect to compete mainly based on customer experience
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revenue is generated by companies that excel at personalized marketing
McKinsey
Retail business intelligence use cases
Business intelligence tools enable companies to optimize both back- and front-office retail operations based on customer preferences and current market trends, leading to increased profits.
Product assortment optimization
Determining the right mix of products to offer in a specific customer segment, location, and channel to drive sales and reduce stockouts.
- 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 brick-and-mortar store, sales channel)
Consumer insights
Discovering your customers’ needs and behavioral patterns to improve their experience, increase sales, and build customer loyalty.
- 360-degree view of the customer
- Customer segmentation based on demographics, geography, psychographics, and behavior
- Omnichannel customer interaction monitoring
- Net promoter score calculation
- Customer experience and customer 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)
Creating cost-effective marketing strategies to acquire new customers, optimize marketing spend, and reduce churn.
- Marketing campaign effectiveness tracking and analysis (traffic, sales volumes, repeat customers, website traffic, social media sentiment)
- Data 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
Crafting effective location-specific promotions and offers and optimizing logistics and inventory operations based on geographic data to maximize the return on investment across multiple store locations.
- 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
Setting competitive prices and adjusting them in response to market conditions, competitor pricing, and customer demand to enhance profitability while avoiding customer dissatisfaction.
- 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
Increasing visibility into supply chain operations to balance inventory with demand fluctuations, optimize transportation costs, and minimize supply chain disruptions.
- 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
Analyzing sales performance and forecasting future sales to optimize sales operations, discover new sales opportunities, and reduce operational costs.
- 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
BI in the retail sector: a case study
Event tracking
Event collecting
Event processing
Data storage
Data processing
Integration layer
visitors-to-buyers conversion rate increase
The client is an online fashion retail company with 20+ million registered customers. With 200,000+ users accessing the website and mobile app daily, the company had to process large amounts of real-time data to meet its customers’ needs. Therefore, the retailer decided to get a centralized BI solution that would collect and store data sets from a variety of sources to analyze user behavior, as well as build predictive models to forecast buyer conversion rates, product interest, and future sales.
Itransition developed a solution that helps gather and analyze clickstream data, mobile data, server events, and email campaign engagement data in near real-time mode, enabling website and mobile app personalization. Implementing a retail-specific BI solution helped the customer cut monthly infrastructure costs, understand online user behavior better, and increase sales through AI-powered personalization.
Essential integrations for retail business intelligence solutions
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
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 customer 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
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
- $10 per user/month
- Power BI Premium
- $20 per user/month
- 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
Limitations
- 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
- $75/user/month
- Tableau Enterprise Creator
- $115/user/month
- Tableau Explorer
- $42/user/month
- Tableau Enterprise Explorer
- $70/user/month
- Tableau Viewer
- $15/user/month
- Tableau Enterprise Viewer
- $35/user/month
- Free trial
Product differentiators
- An intuitive analytics experience based on its patented VizQL engine
- A user-experience-focused design
Limitations
- 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 and chart recommendations
- Data storytelling
- Group sharing and collaboration
- [none]
- [none]
- Support for multiple user types
- 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
- Standard plan
- from $825 per month
- Premium plan
- from $2,500 per month
- Enterprise price
- custom pricing is available upon direct request to the vendor
- Free trial
Product differentiators
- Deployment flexibility, including enterprise SaaS and hybrid options
- Multi-cloud installation, without limiting customers to any particular cloud
Limitations
- Qlik Cloud has restrictions on the volume of data that can be processed and reports that can be generated simultaneously
Key features
- Flawless integration with multiple SQL databases and data warehouses
- Fast data analytics model creation thanks to customizable code blocks
- Predictive analytics and big data services
- Embedded analytics
- User-friendly self-service features
- Broad collection of visualization options
- Scheduled reports and personalized alerts
- Tools for team collaboration
- Natural Language Query (NLQ) interface
- Support for mobile devices
- Multicloud compatibility (Google Cloud, Microsoft Azure, AWS)
- Security measures including authentication, activity tracking, database access controls, and role-based permissions
Pricing
- Custom pricing
- available upon direct request
- Free trial
Product differentiators
- LookML, an original Looker modeling language, allows users to quickly and accurately structure raw data for analysis and define business logic so it can be used in multiple models
Limitations
- The cost can be too high for the small teams
- User training is required for its effective usage
- Only proficient users can perform data modeling
Key features
- Scheduled distribution of reports
- Tools for collaboration and sharing
- Mobile-friendly
- Multiple deployment options (cloud, on-premises, and hybrid)
- Available as a SaaS solution on Microsoft Azure and AWS
- Extensive data integration options (built-in gateways, drivers, custom connectors)
- Self-service data preparation and analytics features
- Capabilities for data discovery, wrangling, and visualization
- Easy-to-use dashboards and scorecards thanks to an extensive library of pre-designed grids, graphs, charts, and map templates
Pricing
- Essential plan
- $1,250 per month
- Pro and Enterprise plans
- are custom-priced
- Free trial
Product differentiators
- Allows non-technical users to perform search for valuable insights using natural language
- Leverages AI and ML through its SpotIQ engine to automatically discover trends, outliers, and patterns in data
Limitations
- Has only basic data preparation features
- NLQ-only interface limits the scope of operations that can be performed with this tool
Need help with making the best choice for your BI project?
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. Depending on your company’s specifics, determine which 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
Key benefits of BI for the retail sector
Improved customer experience
Retail BI helps companies track and analyze customer behavior across all touchpoints. By utilizing these data-driven insights, retailers can create and run hyper-personalized customer campaigns and loyalty programs, personalize 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 management costs by enabling near-real-time insight into supply and demand dynamics and accurate demand forecasting.
Optimized shop floors & 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 competitors' offerings and pricing, benchmark your company's performance indicators 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 leads to quicker and smarter decision-making 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 adoption costs depend on multiple factors, including:
- The number of data sources for analysis
- The complexity of the data storage layer
- Data volume
- Data analytics complexity
- Data structure and format
- The complexity of data visualization and reporting
- Initial data quality and data quality requirements
- Data security and compliance requirements
- The complexity of data transformation requirements
Do you want to ensure the success of your BI project?
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. To enact a solid security strategy, business intelligence software should have up-to-date security capabilities, including:
- 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 & reporting
Lack of interactive data visualization, static reports, and inconsistent experience from using various devices stalls BI adoption.
Lack of interactive data visualization, static reports, and inconsistent experience from using various devices stalls BI adoption.
Our BI developers recommend companies look for business intelligence software that features:
- Pre-built report templates with tailored KPI sets for different user groupsÂ
- Scheduled and event-triggered reporting
- Interactive dashboards with configurable filtering capabilitiesÂ
- Ready-to-use and custom visuals
- NLP supportÂ
- Drag-and-drop capabilitiesÂ
- Embedded BIÂ
- Mobile support
- Sharing and collaboration capabilitiesÂ
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 low-quality data 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.
BI services we offer
We provide comprehensive BI services that empower retailers to make data-driven decisions while crafting business strategies from price optimization to customer churn prevention.
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