Understanding Shopper Data: A Key to Retail Growth

December 15, 2025 By Julia Borgini

Consumers provide so much information to retailers every time they shop. Loyalty program participation helps them understand what they buy, how they buy, and how often they buy since consumers scan their cards or information with every transaction.

All this information helps power retail growth for businesses that know what to do with it. Smart retailers combine shopper data across purchasing channels, their internal consumer databases, and industry trends to make better decisions and optimize operations. They can combine it with external data from EASI Demographics to enhance customer experience (CX) and improve profitability.

Why shopper data matters

There are three major reasons why retailers look to collect and analyze shopper data.

Improved consumer understanding: Analyzing shopper data helps retailers understand consumer preferences, habits, and decision-making processes. They can plan for upcoming changes they see before they happen and be ready ahead of competitors. For example, they can quickly identify popular products among specific demographic groups, allowing them to shift marketing and sales strategies sooner.
Better operational efficiency: Analyzing shopper data and using predictive models helps them optimize inventory management to avoid stockouts and overstocking. They can also use it to manage staffing levels and overall costs for better operations.
Higher ROI on marketing: Personalization based on shopper insights is more effective than generic ones, leading to higher conversion rates and reduced marketing expenses. Increase ROI on tactics that work and save on ones that don’t, for example, reducing spending on social media ads and increasing newsletter sponsorships.

What shopping data is useful today?

Smart retailers look beyond basic digital stats like website visits, social media clicks, or shopping cart abandonment rates. They’ll look at stats like transaction data and demographics to learn more about shoppers and segment their efforts accordingly.

Transaction data: Average transaction value, purchase frequency, product combinations, and day of week/time of day information can identify cross-selling opportunities and inform pricing strategies like dynamic pricing. (Dynamic pricing is when retailers dynamically change the price of products based on specific criteria to automatically take advantage of swings in consumer behavior.)
Demographic data: Segmentation based on age, location, shopping habits, and more helps retailers customize product and service offerings to these groups, manage pricing strategies, and optimize store layouts for physical locations. They may discover new buying patterns and groups they were unaware of and can use the demographic data to focus efforts on them, whether it’s for marketing or product development.

For example, there are some interesting insights in the transaction data for food ordered at restaurants, carry outs, and other takeaways in the EASI CEX Demographic Expenditure Report for Chicago and Joliet, IL. Chicago has roughly 20 times more households than Joliet (over 1,000,000 compared to just over 49,000), yet the average Joliet household spends more on restaurants and takeout food ($2,746.41) than Chicagoans ($2,515.86). Further study would be needed to see why that is, but smart retailers could use it to develop new marketing strategies, products, and launch schedules.

Challenges to using shopper data

While understanding shopper data is vital today, there are a few pitfalls businesses must be aware of when using it. Data privacy compliance is one challenge as consumers are more savvy today and want to know what data is being collected and how it’s being used. But it’s not just a consumer preference for privacy; many countries have data privacy laws that retailers must comply with, such as the California Consumer Privacy Act (CCPA) and GDPR compliance laws in Europe, which includes the “right to be forgotten.”

From a business perspective, there are also challenges to integrating data from multiple sources. Combining data from disparate databases, software applications, and cloud data stores can be hard without expert help. That can be expensive, as can the data analytics tools and human resources to manage it all. Business intelligence (BI) experts, tech staff, training, maintenance, and usage costs can significantly increase the cost to any retailer looking to use shopper data in complex ways.

Real-life examples of advanced shopper data use for retail growth

Many retailers today use shopper data in complex ways to boost retail growth and sales across the country. There are the chain coffee shops and fast food restaurants using loyalty programs to increase personalized offers and boost retention and sales based on historical transaction data and demographic segments.

Some retailers for mothers and toddlers leverage predictive analytics and demographic data like EASI Life Stages to identify expectant mothers based on historical purchase data. They then customize marketing campaigns for these consumers to draw them to retailers they might not have frequented before or even known about.

For example, running an EASI Life Stage report for the ZIP codes with Middle Age (35-44) Families with Children with Moderate Income for Oklahoma shows that the majority of those families live in and around the Oklahoma City area.

EASI Life Stage Report – ZIP code display – Oklahoma

Retailers with physical locations can use transaction data, demographic segmentation, and weather data to increase revenues by reorganizing store layouts. For example, a regional sporting goods chain discovered that customers who purchased accessories with main sporting equipment spend more per visit. So, they reorganized their layouts to place complementary accessories near primary equipment displays for seasonal sports based on weather reports.

Use shopper data effectively for positive results

Shopper data goes beyond historical purchase preferences and contact information. Retailers who combine their internal data with other sources will see higher growth as they’re able to develop products earlier, optimize sales opportunities, and understand their customers better than their competitors.

Adding EASI’s demographic data to the mix can add a new layer of understanding that improves growth across the organization, from marketing to sales to product development. To learn more about how EASI can help your retail business do that and more, contact us today. We’re here to help.