Improving Capture Rates with Window Shopping Analytics
Regardless of the season, capture rate—the percentage of passersby that enter a store—is a critical metric for retailers interested in improving engagement and sales. Accurately measuring capture rates by monitoring areas outside a shop is becoming common practice for retailers committed to fact-based decision-making.
Window Shopping Analytics
For retailers that count on window dressings to stir interest—which could be as diverse as fashion brands to power tools—knowing what is effective in bringing passersby one step closer to making a purchase can make a big impact on profitability. As with layout changes in the store, testing is an integral part of arriving at window displays that effectively capture shoppers’ attention.
All tests in the retail environment have an opportunity cost. Minimizing that cost is one of the main goals of retailers working with a behavior analytics solution as part of a data-driven strategy. It’s also why retailers chose an automated, robust visitor tracking system.
Accurate AI-Driven Demographics
Measuring capture rates is quite straightforward: Count every person who passes in front of a storefront and divide by those who enter. The difficulty in practice is deploying a solution that can accurately count passing crowds, especially in areas with dense foot traffic flows.
Several retailers use CCTV for security purposes inside and outside retail locations. Unfortunately, these cameras rarely have a level of accuracy that can be relied on for strategic decision-making. But low accuracy and potential privacy issues are just a few shortcomings that make the technology ill-equipped for window shopping analytics.
The lack of robust AI features is another drawback, one that can deprive retailers of rich insights into the makeup of passersby and how they engage with window displays. Knowing how many people passed and how many of those entered is a minimum; knowing the gender breakdown of passersby, at which point they focused, and how long they engaged with a display before entering the store are insights retailers can use to optimize operations in and outside the store.
Take it Outside
Measuring capture rates is already part of the behavior analytics strategy for many different types of brick-and-mortar retail types, including indoor and outdoor shopping centers.
When deployed throughout an indoor shopping center, an analytics solution can give a clear and precise view of where and when visitors are shopping. Mall operators can use the data to adjust security or cleaning staff or to attract other retailers to a particular region of the center.
Thanks to advances in weatherproofing, accurately counting visitors in open air spaces in all four seasons is much easier and more reliable. Xovis worked closely with retailers focused on capture rates when developing its all-weather sensor model, a light-distortion-resistant data capture device supporting a range of use cases.
Whether an indoor or outdoor shopping space, positioning a data capture solution on a shop’s exterior can also be a good way to check the effectiveness of digital signage. Measuring how long and how many passersby interact with digital signage and at what times or days is also a great way to maximize marketing budgets.
Many retailers are already moving their analytics solution from the door—mainly used for measuring conversion rates—to the floor, where they can measure more valuable and complex KPIs. And a growing number are also going from door-to-floor to outside-the-door in search of metrics that can optimize layout, limit testing costs, and generate more revenue.
Tags: | retail | AI-Driven| capture rates | KPI | shopping space | conversion rates |
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