The Measurement Minute

The Measurement Minute by Gary Angel

 

The traditional calculation of Shopper to Associate Ratios (STARs) is simple: Door Count divided by the number of Associates staffed that day. But when you start doing full journey measurement, you’re measuring areas inside the store at much finer increments of time and suddenly it’s not so clear what makes the best metric. After all, shoppers can walk through one corner of an area and spend six seconds in it. Should they be counted? Ditto for Associates. What if a shopper enters an area, leaves and then comes back? Should they counted twice? Associates may enter and leave an area countless times in an hour – does that matter? The latest Measurement Minute dives into the many ways to think about measuring intra-day STARS.

 

The Measurement Minute by Gary Angel

 

Measuring Associates is a vital part of effective in-store measurement. It’s necessary to ensure proper shopper counting and it’s interesting in it’s own right. But there are two different strategies for measuring Associates – badging and passive detection. Both work, but there are some distinct advantages to passive detection.

 

The Measurement Minute by Gary Angel

 

Knowing the state of the store is essential for shopper journey analytics. You have to know what’s in the store and where it us. Otherwise, shopper journeys aren’t interesting. Unfortunately, that knowledge is often scarce in the retail enterprise – and there’s often no institutional memory. So even if people know what the store is like right now, everyone’s forgotten what it was like last season. Fortunately, one of our core measurement technologies provides a great solution for this problem.

 

 

 

 

We’ve recently been using a data visualization focused on measuring the intra-day impact of shopper to labor ratios on conversion in the store that’s worth a look.

 

Why is STARS data important? It’s one of the best and most direct uses of shopper journey data- an intra-day STARs analysis can significantly improve labor models. Everybody knows overall store traffic and PoS, but without detailed traffic intra-day, it’s impossible to know if you have the right number of Associates working the floor. Too few Associates and your potentially leaving money on the table OR creating bad customer experiences. Too many Associates and your reducing margins. By combining Shopper Traffic and Associate presence by time of day, you get the intra-day STARs ratio. Take that ratio and compare it to conversion rates by area and you have powerful statistical measurement of whether you’re appropriately staffed at any given time for any given day of the week.

 

The visualization is called a STARS (that’s Shopper to Associate Ratio) Clock.

 

 

 

The Measurement Minute by Gary Angel

 

The traditional measure used to optimize space in store layout is sales per square foot. Optimizing sales per square foot doesn’t mean that the merchandise with the highest sales should have the most square feet. Some merchandise doesn’t need a lot of space. Some does.   The important metric isn’t the actual sales per square foot of an area, it’s the incremental gain or loss in sales if you took a square foot away or added one. A store layout is optimal from a space perspective when changing the size of any area would reduce sales per square foot.

 

That’s fine from a conceptual perspective but it’s nearly impossible to get at from a testing or metric perspective. And that’s where measures of density come in. The goal of density metrics is to help provide a behavioral proxy for potential problems in space layout.

 

You can measure how many shoppers use an area AND you can measure whether the conversion rate or basket size is impacted by crowding.

 

The simplest measure of density is just foot-fall. In our DM1 platform, for example, we can measure exactly how many shoppers visited an area down to individual square feet of the store. But foot-fall isn’t a very good measure of density because it doesn’t capture where shoppers are spending time. Time spent is a better measure that can be turned into a density metric by dividing it by the underlying size of an area.  The higher the number, the higher the density.

 

Another way to skin this metric cat is to measure occupancy. We measure both average occupancy – the number of shoppers typically in the area during a period of time and peak occupancy – the highest number of shoppers recorded in the area at any point during the period.

 

By combining any of these metrics with conversion rates for the period, you can measure whether and when density impacts sales. And understanding how often that’s happening is a great way to drive layout testing that involves expanding or shrinking the size of an area.

 

 

 

 

 

 

 

The Measurement Minute by Gary Angel

 

Self-selection makes understanding optimal store layout challenging. Using measures of independence, you can get a strong sense of how dependent a merchandising section is on its placement in the store. This lets you target layout experimentation into places that are likely to significantly impact store performance. Not only does this Minute explain dependence metrics, but it covers some of the most common including Draws, First Purchase Rates and Time in Store Rates.

 

 

 

 

The Measurement Minute by Gary Angel

 

For shopper measurement, self-selection introduces massive biases in behavioral data. Different shopper populations behave differently not because of what we do or how the store works, but simply because they have different intent. When two stores perform differently, it may be because one store is better than the other. But just as or even more likely is that it’s because one store has a shopper population with higher buying intent. What all this means is that when using seemingly straightforward metrics like Conversion per Opportunity, we need a way to contextualize them with shopper intent.

 

 

 

 

 

 

The Measurement Minute by Gary Angel

 

Combining PoS data with shopper journey data yields one of the most powerful in-store metrics: conversion per opportunity. To see why it’s such a powerful metric, you have to understand why traffic per square foot and sales per square foot are each misleading in their own way.

 

The Measurement Minute by Gary Angel

 

Shortly before their $1.4 Billion acquisition by a SPAC, I sat down with Gerald Becker of Quanergy to discuss the role of LiDAR in people-measurement. This wide-ranging talk covers everything from his background in analytics, the basics of how LiDAR works, the way Quanergy differentiated themselves in a crowded marketplace, and the key role middle-ware plays in delivering LiDAR advantages to non-automotive markets. It’s a great listen!

 

The Measurement Minute by Gary Angel

 

If conversion is the all important metric at the bottom of the in-store funnel, it’s disappointingly ambiguous when it comes to in-store journey data. The problem is that while full path shopper journey data can track a shopper to the cash-wrap, it can’t tell you what they bought. And what they bought is essential to understanding which areas and displays drove conversion. Fortunately, with highly-accurate people-measurement technologies, there is a solution.