The essential guide to retail analytics

Types of data analysis in retail

Retail data analytics can be bucketed into one of four categories: descriptive, diagnostic, predictive and prescriptive. Each category can help you understand what happened, why it happened, what will likely happen next and how to maximize return on investment (ROI). Let’s dive into the four types of retail data analysis: 

• Descriptive analytics: Summarizes current and historical data, helping retailers develop a better understanding of their present state and past performance. This foundational form of analytics is often depicted with visuals such as charts, dashboards and graphs.  
• Diagnostic analytics: Explains performance by identifying correlations in the data. Retailers can use this technique to uncover influencing factors and understand the root cause of a particular outcome. 
• Predictive analytics: Forecasts future impacts by developing predictive models based on historical data. This advanced form of analytics often uses statistical models and artificial intelligence (AI). 
• Prescriptive analytics: Recommends actions that will drive the best results based on predicted outcomes. By using mathematical models and AI, this category of analytics runs simulations to evaluate different decision scenarios and enables retailers to take proactive steps to achieve their business goals. 

The benefits of retail analytics

Analytics empower you to make more data-driven decisions, increase profitability and cultivate stronger customer relationships. In this section, we discuss how analytics can help you understand your customers, improve acquisition, deepen customer loyalty, improve merchandising strategies and inform more effective real estate decisions. 

Greater understanding of customers

A key component of retail analytics is the ability to collect relevant first-party data — such as interactions on the web or mobile app, purchase history, customer feedback — and external, third-party data — such as broader consumer spending trends and competitive intelligence. 

Customer behavior analysis uses this data to better understand customer interests, preferences and pain points. Analytics can reveal how each of your customer segments interacts with your competitors, what segments should be prioritized and which groups of customers have the most headroom.

Customer spending data can also help you:

• predict peak sales days
• manage inventory 
• establish benchmarks
• understand how well you attract and retain customers
• price and reallocate inventory
• design promotions
• navigate partnership opportunities

Customer story

A bulk retailer wanted to understand its customers’ broader spending patterns. Enlisting the services of Mastercard Advisors, they conducted an Affinity Analysis, using Mastercard’s proprietary anonymized and aggregated transaction data to develop customer profiles. With these in place, they analyzed those profiles to pinpoint affiliated industries and filtered the results to identify over 24 potential merchant partners.

Improved acquisition efforts

Winning new customers is critical but doing so is becoming increasingly challenging. Retailers operate in a saturated market where consumers are easily overwhelmed with generic messages and offers. Analytics can help you break through the noise by identifying which messages and tactics are most effective at acquiring new customers. 

Some retail analytics, for example, can identify geographies that have high concentrations of target customers and assess if expanded operations can capture these customers. Others can reveal where to focus marketing efforts. By leveraging common industry affinities, you can deliver personalized offers to specific customer segments. 

Customer story

A major technology retailer wanted to reach top spenders looking to purchase electronic gifts for the holidays. They used Mastercard Audiences to build a custom campaign that leveraged anonymized and aggregated data on transactions, date, time and geography. The campaign enabled the retailer to reach segments of tech-savvy customers and drove a 4x increase in likelihood to purchase compared to industry norms, as well as a 7x increase in likelihood to purchase compared to category norms. 

Deeper customer loyalty

It isn’t enough to just acquire new customers. Retaining them is vital, too.  

When you understand customer preferences, you can create more effective loyalty offers. You can use analytics to understand how loyalty customers’ behaviors are changing, how different loyalty tiers perform, if tiers should be adjusted, which customers are likely to move to higher loyalty tiers and how to drive greater share of wallet. With these loyalty-based insights, you can develop stronger relationships with shoppers, turning anonymous customers into known, loyal and profitable brand ambassadors.

Customer story

A beauty retailer wanted to boost customer loyalty with their existing mass beauty offerings. Using proprietary methodology and anonymized aggregated data, Mastercard Customer Data Enhancement segmented customers across key dimensions, like loyalty to the retailer and spend in the beauty industry at large. With deeper insight into the retailer’s target customers, Mastercard recommended paths to reaching all customers by: (1) upselling luxury beauty products to loyal, high-spending customers, (2) driving engagement with low-spending, but also loyal customers through education campaigns and (3) targeting cohorts with high share of wallet for retention efforts.

Effective merchandising strategies

Merchandising strategies directly influence sales, customer satisfaction, brand perception and operational efficiency. By refining merchandising approaches, you can better meet customer demands, optimize product placement and pricing, and enhance overall business performance and market competitiveness.

You should use consumer behavior, sales trends and market dynamics to examine four key dimensions: overall basket composition, product loyalty, assortments and purchase behavior. 
 

• Basket composition can reveal how frequently key items are purchased and what other items are in a customer’s basket when they purchase a certain item or bundle.
• A solid understanding of product loyalty can showcase how loyal customers are to certain products and how product loyalty changes after promotions or events.
• A strong assortment strategy can be used to inform the optimal product mix, including which low performing items would make good candidates for removal. 
• Purchase behavior highlights customer shopping patterns before, during and after a purchase.

Customer story

A large home furniture retailer wanted to adjust its store layout and assortment mix to drive incremental sales. The retailer used Market Basket Analyzer, a software platform that analyzes transaction-level data sets to identify top selling item combinations and to gain a 360-degree view of product, basket and customer insights. With these insights, the retailer decided to move a product category to the front of the store. Using Test & Learn® to quantify the impact of the new store layout, the analysis indicated that larger inventory levels and shelf-space for the product category would drive a 20% lift in sales for the product category and a $6 million increase in annual margin across the store network.

Advanced real estate planning

Real estate planning is all about strategically choosing and managing the best physical locations for new stores. This requires analyzing demographics, foot traffic, local competition, zoning rules and current market trends.

It may seem like a lot to analyze, but the potential value is enormous: choosing a slightly more profitable store location can yield immense revenue when compounded over years.

Analytics can help identify the optimal markets to open new stores, expand existing ones or close underperforming locations.

You can take a four-step approach to prioritize new store locations more confidently:
 

• Step one: Conduct a whitespace analysis to identify a list of high opportunity candidate areas for new store openings.
• Step two: Create forecasts for new store performance based on historical openings, store characteristics and internal/external data sets.
• Step three: Quantify the potential cannibalization of sales from surrounding stores in the network to determine which new locations will have the most positive impact.
• Step four: Combine these insights to prioritize candidate sites.

Customer story

A large specialty retailer wanted to expand their store network to service new customers while limiting cannibalization of existing stores. The retailer used Test & Learn® to identify key drivers of performance for retail sales, quantify the cannibalization effect of new store openings on surrounding stores and define the most profitable markets for future network expansion. After simulating over 150K store openings across the US, the retailer was able to identify the best 50+ locations to prioritize.

Retail analytics software and tools

A major facet of any business decision is to improve the consumer experience. But you can’t rely solely on gut intuition. 

By engaging consumers directly and gathering consented first party insights, analytics software and tools can then help retailers harness the power of data to drive innovation and inform smarter decisions. Each tool offers you unique functionalities that cater to specific needs: 

• Business intelligence (BI) tools transform raw data into useful information, usually in the form of data visualizations and/or interactive dashboards. Action: Assess your current state and monitor key performance indicators (KPIs).
• Personalization engines help you understand the best experience for an individual customer or prospect based on past interactions, current context and predicted intent. Action: Deliver tailored messaging such as content, offers and other interactions across customer touchpoints.
• Real-time analytics solutions integrate with other data systems to deliver real-time insights for agile business operations. Action: Get instant insights into operational performance, customer interactions and market trends. 
• Statistical analysis and modeling software includes business experimentation platforms, which take a test and learn approach to pinpoint the true cause-and-effect of an initiative. This can help identify opportunities to tailor and target a program rollout and maximize ROI. Action: Perform complex data analysis, identify trends, forecast demand and optimize business decisions ranging from pricing and promotions to labor and operations. 
• Web analytics platforms analyze online customer behavior and assess website performance. They track website traffic, visitor demographics, conversion rates and marketing campaign effectiveness. Action: Optimize digital marketing strategies, improve online user experience and maximize e-commerce revenue.

Conclusion

Retail analytics insights are key to staying competitive.

With the software and techniques outlined above, you can better understand customers, improve targeted acquisition efforts, increase customer loyalty and more.

Embrace modern analytics to stay agile, customer-focused and poised for sustained growth in a dynamic retail environment.
 

Endnotes

¹ “How Retailers Can Drive Growth With Revenue Diversification” A commissioned study conducted by Forrester Consulting on behalf of Mastercard, May 2024