Retailers Bridge the Boomers-Gen Alpha Divide with Data

23/09/2025

Blog, Retail

2 min

People shop differently: men, women, young, old, Western, Eastern. So many cultural and personal variables are at play every time a customer walks into a store or showroom. And while it’s impossible to know all those variables, retailers that don’t measure what they can measure—in a responsible and privacy-compliant way—are leaving money on the table.

The Problem with “Geriatric Millennials”

Boomers. Gen X. Millennials. Gen Z. Gen Alpha! Gen Beta!!! All of them have different strategies and expectations for the indoor shopping experience—well, maybe not Gen Beta, yet. But even if retailers could know the exact age of everyone entering the store, that might not be very useful. Why? Because generations are somewhat arbitrary demarcations, with starting and ending points that don’t always accurately reflect personal sentiment.

“Geriatric Millennials,” those born near but outside the 1981–96 cutoffs, are a great example of the challenge. Age-wise, these shoppers may fit squarely into Gen X territory, but their shopping style and interests are likely very different. For example, they may be less interested in buying Smiths t-shirts.

Why Apparent Age Matters

What really matters for traditional retailers is the visitor’s apparent age—how a person expresses their age. Apparent age offers clues about interests and shopping styles, and it’s a demographic data point that retailers are ready to capitalize on.

Accurately detecting customers’ apparent age without collecting biometric data, without infringing on privacy rights, and simply by using machine learning algorithms may sound futuristic, but it is already happening today.

What does that look like in practice? The use cases are many, but one of the most exciting is dynamic in-store marketing—real-time ads that respond to a visitor’s apparent age.

This matters because customer success in brick-and-mortar retail today is tied to personalization. A store that understands differences between customer groups can adjust promotions, signage, and layout to improve satisfaction while boosting commercial success. Dynamic, data-driven engagement means customers not only feel seen but also supported in their shopping journey.

Dynamic Promotions, Privacy-Compliant Insights

Promotions are most effective when they’re timely, relevant, and personalized. That’s where age estimation and other demographic insights come in. With real-time awareness of who is in the store, retailers can adjust digital signage and offers to better fit the preferences of different age groups. Whether it’s highlighting fast-moving products for Gen Z or promoting premium goods for Baby Boomers, the right message at the right moment makes decision-making easier for the customer.

Retailer demand for privacy-compliant, real-time demographic statistics has been a motivating factor for many of the features Xovis has developed for its AI-powered sensors. The new Apparent Age Estimation extension expands Xovis’ portfolio of People Attributes—which already covers Gender Expression Estimation,, Group Counting, and Adult/Child differentiation data—as well as Object Detection.

When used together, these tools help retailers improve and automate in-store processes in ways tailored to the profile of their visitors. This is understanding your customer—arguably the first rule of retail—in a new and powerful way. And it’s a win-win: businesses drive growth, and customers feel understood, valued, and respected.

VodafoneZiggo Case Study Retail Use Cases

Anne Wyder

Anne Wyder is an experienced retail professional with a background in Customer Relationship Management, Sales and Marketing, and Digitization. She is passionate about helping organizations harness the power of data-driven people flow solutions.

Retailers Bridge the Boomers-Gen Alpha Divide with Data

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