The face of the retail industry continues to evolve. This revolution is being fueled by customer demand for seamless shopping experiences across channels and increased personalization at every touchpoint. A recent report found that Millennials and Gen Z, which are projected to comprise 67% of the U.S. population by 2030, are accelerating digital-first purchasing behaviors, presenting merchants with new opportunities to meet the shopping preferences of this growing cohort.
The rapid changes taking place throughout the retail landscape are made possible by customer data that can be harnessed to make better, more strategic business decisions. By leveraging insights on customer shopping patterns and preferences, as well as peer performance, retailers are empowered to improve marketing and operations efforts.
This trend is borne out in a survey conducted by the American Marketing Association, which found 87% of marketers viewed consumer insights as highly effective in measuring marketing return on investment, while 99% agreed that understanding consumer data is essential for resource allocation. And a 2025 KPMG report concluded that over the past year, 52% of consumer and retail businesses are harnessing customer data, which represents a sharp increase from the 38% percent previously using data management programs.
The clear conclusion is that a focus on data opens the door to actionable customer insights, thus enabling smarter decision-making, which can lead to strategic partnerships, the launch of new products and services, and access to retention metrics that can help build loyalty and deliver on shoppers’ demands.
The Undeniable Power of Customer Insights
Customer insights in retail refers to the understanding of a shopper’s behaviors, including preferences, needs, and buying patterns, all based on the data collected throughout the customer journey. Payment data can be seen as a goldmine, providing real-time evidence of purchase drivers. The true power of customer insights lies in their ability to reveal not just what customers are doing, but why they are doing it.
The strategic value of this data is wide-ranging. It can unlock a greater understanding of customer behavior through purchase patterns that might include time of day, seasonal shopping or online versus brick-and-mortar. Data can show transaction types, entry modes, processing volume, authorization rates, costs, and disputes to see customer preferences. It can reveal changes in loyalty or uncover early signs of customer churn. It can allow for analysis of how price, promotions, or channel affect buying behavior. Data can help tailor marketing offers, recommend relevant products or services based on prior purchases, and segment customers into high-value versus occasional buyers for smarter resource allocation.
Armed with data-driven customer insights, retailers can more closely examine the strategic business impact of the payment options available. This data can be used to optimize pricing, manage inventory, and refine customer acquisition strategies founded on who converts and what they buy. Going further, data can help to benchmark performance across stores, regions, or customer segments.
Following are a few scenarios of how purchase data can be used to make better-informed business decisions:
• Enhancing Customer Experience – A retailer uses purchase data to power predictive analytics that anticipate customer needs by identifying patterns that typically precede major life events, such as a pregnancy or moving. Leveraging this customer insight, the retailer can improve personalization of its offers timed to their likely needs, thereby driving higher conversion rates, improving loyalty and increasing sales.
- Improving Competitive Positioning – In the face of a highly-competitive marketplace, one retailer turned customer purchase data into actionable insights. This involved collecting granular purchase data from a loyalty program and mobile app to better understand customer behavior, including buying frequency and timing, and regional, as well as seasonal, purchase preferences. Armed with these insights, the retailer personalized offers, introducing new product innovations and localized specialties, all with an eye toward improving the business’s competitive edge.
- Optimizing Location Selection – A quick service restaurant (QSR) wants to open new store locations in a crowded market. They use purchase data to understand where current customers live and work, as well as the frequency of purchases at nearby fast-casual competitors. By analyzing purchase and mobility data, the QSR is able to determine where unmet demand exists, which helps to make more successful location expansion decisions.
- Understanding Shopping Drivers – A clothing retailer can tailor marketing for peak shopping days, such as Black Friday and Cyber Monday, boosting both eCommerce and in-store sales. Market data reveals that across the clothing industry, Black Friday delivers 1% higher sales than the rest of the year, and Cyber Monday boosts sales by 7% as compared to the rest of the year.
These are just a few examples of how purchase data can be used to empower payments team, technology teams, real estate teams, marketing teams, and other stakeholders to make better-informed decisions.
Where Retail Innovation is Heading
As the retail industry continues to evolve rapidly, several key future trends are emerging. These trends all rely on purchase data-driven decision-making. Amongst the innovations for future retail will be a shift from personalized to predictive marketing that utilizes artificial intelligence (AI) and machine learning to deliver exactly what customers want. The use of data will further enable frictionless customer experiences across mobile, online, and in-store journeys, helping to create seamless omnichannel integration. Real-time purchase data will fuel AI-powered merchandising that will allow retailers to dynamically adjust pricing, and forecast demand to more nimbly respond to market changes.
Customer insights will help to unlock innovation, such as smarter product development, create more agile store and format designs based on in-store transactions and foot traffic. Behavioral data will allow brands to identify customers at risk of churn and intervene with timely offers or loyalty incentives. Predictive models will be able to forecast lifetime value and guide retention strategies accordingly.
The Value of a Forward-thinking Approach
By integrating customer insights into business strategy, merchants are doing more than just shaping the future of retail; they’re defining it. Forward-thinking businesses that use the power of data-driven decision-making are better positioned to deliver meaningful customer experiences and drive long-term growth.
Source #1: The Changing Retail Landscape: A Shopper Driven Evolution, Nielsen IQ, 2025.
Source #2: The State of Consumer Insights 2024: Strategies for Marketing Success, American Marketing Association 2024.
Source #3: KPMG Global Tech Report: Consumer and Retail insights, KPMG, 2025.