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What is Retail Business Intelligence (Retail BI)?
Retail business intelligence (BI) is the process of collecting and analyzing data from a wide variety of sources to assess retailer performance and make data-driven business decisions. Store owners, chain managers, consumer-goods suppliers, and commercial real estate (CRE) professionals use retail BI solutions to derive actionable insights that help them optimize operations and market their products. Investors and stakeholders also rely on retail BI to assess the health of any enterprise that sells products directly to consumers.
When done right, retail BI offers a comprehensive picture of broader market trends and specific business health.
Key Metrics in Retail BI
Effective retail BI relies on tracking the right Key Performance Indicators (KPIs). While sales figures are the most obvious metric, a robust analysis includes a mix of financial, operational, and AI-based location analytics.
Some of the most important metrics used in retail business intelligence include:
- Financials: Comparable store sales (comps), sales per square foot, cost of goods sold (COGS), and gross margin return on investment.
- Transaction Data: Average basket size, transaction value, and inventory turnover.
- Customer Loyalty: Conversion and retention rates
- Location Intelligence: Brick-and-mortar visitation trends, trade area demographics, and cross-shopping patterns.
- Digital Engagement: Online website traffic and social media metrics.
Harnessing Location Intelligence for Retail BI
Robust business intelligence must account for a broad range of factors, and since the vast majority of retail sales still take place offline, a comprehensive solution must include data on offline consumer behavior and preferences.
Until recently, executives lacked the tools to reliably quantify offline consumer behavior. And while internal stakeholders could track sales and inventory, they were often blind to what happened outside their four walls – such as how many customers visited a competitor or how far shoppers traveled to reach a store.
Today, powerful AI-based location intelligence solutions handle massive amounts of data from the physical world and provide accurate and actionable insights for decision-makers.
6 Use Cases for Location Analytics in Retail BI
Many of the insights needed to successfully run and evaluate an offline retail business rely on location analytics. Here are six key ways this data drives decision-making.
1. Analyzing Venue Performance
Location intelligence reveals the true performance of a store beyond transaction counts.
Example: A superstore analyzes year-over-year (YoY) visit data to benchmark two of its locations against its regional portfolio.
Example: A manager uses location intelligence to measure the impact of new opening hours and adjust staff schedules accordingly.
2. Understanding Consumer Behavior Patterns
A comprehensive BI solution provides visibility into the customer journey.
Example: A CRE broker used Placer’s cross-shopping data to show how a clothing store’s presence would complement a shopping center’s existing tenants.
3. Identifying Broader Market Trends
Strategists use location intelligence to understand wider industry trends.
Example: An asset manager compares the performance of retail categories within a DMA to decide on the right "anchor tenant" for a new development.
4. Keeping Tabs on the Competition
Location-based business intelligence allows analysts to peak behind the curtain of their competitors’ operations and audience.
Example: A grocery store compares its trade area median HHI with that of its competitors to identify underserved demographics and develop a competitive targeting strategy.
5. Quantifying Cannibalization Risks
AI-powered location intelligence helps retailers mitigate cannibalization risks by quantifying the reach of each location in their store fleet.
Example: A retail chain uses location intelligence to optimize its site selection process and fuel growth. AI tools revealed locations with the right audience fit and minimal system overlap.
6. Creating Targeted Advertising Campaigns
AI- based foot traffic analytics enables highly targeted offline advertising.
Example: A downtown eatery uses location intelligence to identify the origin zip codes of their diners. They then focus their advertising spend on the relevant residential areas, lowering their customer acquisition costs.
Placer.ai: Your Brick-and-Mortar Retail BI Solution
Placer.ai leverages a panel of tens of millions of devices and utilizes machine learning to make accurate estimations for foot traffic across the country – from specific points-of-interest (POIs) to large markets. Visitation data is enhanced with Placer Marketplace 3rd party datasets that further describe businesses, audiences, and markets.
Using an AI-based location intelligence platform like Placer.ai enables professionals to gather Business Intelligence with unprecedented visibility and greater precision.
Whether you need AI-tools to optimize your business, understand market conditions, or benchmark properties, Placer.ai provides the data and insights to power your strategy.
Key Takeaways
- Retail business intelligence (Retail BI) brings together a broad set of measurable performance metrics. By combining financials, transaction data, customer behavior, digital engagement, and location intelligence, retail BI provides a holistic view of business performance and market dynamics.
- Location intelligence is essential for modern retail BI. While revenue and comps matter, leading retailers track visit trends, trade area demographics, and consumer behavior to understand both performance and potential.
- AI-powered foot traffic analytics fills critical visibility gaps – revealing who visits, where they come from, how often they return, and how stores perform relative to competitors.
Frequently Asked Questions (FAQ)
Q: What is the difference between traditional Retail BI and Location Intelligence? A: Traditional Retail BI focuses on internal metrics like sales, inventory, and staffing costs. Location Intelligence adds an external layer, analyzing foot traffic, trade areas, and competitor visits to explain why those sales numbers are happening.
Q: How can AI-powered location intelligence help with site selection? A: By analyzing the reach, demographics, and psychographics of a potential site's trade area, retailers can predict if a new store will succeed. It also helps calculate the "cannibalization risk" to ensure the new site does not hurt existing nearby stores.
Q: Is Retail BI only for large enterprise chains? A: No. Small businesses and single-store owners use Retail BI to optimize staff scheduling, refine marketing spend, and understand their local customer base better.
Q: What is "cross-shopping" in the context of Retail BI? A: Cross-shopping analysis identifies the other businesses that visitors to a specific retailer visit. For example, if 40% of a grocery store’s customers also visit a specific coffee chain, that grocer may decide to open a store near that coffee chain to capture shared traffic.
