The Measurement Minute

The Measurement Minute by Gary Angel

 

PoS is the motherlode of retail integrations with shopper journey data. It’s almost a must-have. But all integrations can be frightening – less for the technical challenges than for the analytic ones. When you start down the path of PoS integration with in-store customer behavioral data, you’ll want to think about why you’re doing it, what level you need to do it at, what’s involved in maintenance, how to handle modern twists on checkout and PoS, and what your roadmap is for using the data.

 

The Measurement Minute by Gary Angel

 

Dynamic associate allocation is one of the most powerful applications of people-measurement in retail – particularly for larger stores. But when it comes to implementing dynamic allocation, there isn’t one strategy that fits every need. Fortunately, advanced people-measurement provides enough detail to support a variety of different techniques.

 

Of those, the two most common strategies are variations on load-balancing (moving Associates to the busiest areas of the store) and behavioral targeting (using specific shopper behaviors to trigger interventions). Both strategies have lots of potential customizations and variations to explore.

 

 

 

The Measurement Minute by Gary Angel

 

Real-time store journey data creates opportunities to manage the store in ways that have never existed before. It lets managers or software dynamically allocate Associates to tasks and even specific customers. That’s what the military calls a force multiplier – letting a fixed number of Associates do more. But the advantages to dynamic allocation don’t end there. Dynamic allocation can support interaction strategies impossible to consistently achieve no matter how expert a team. And because dynamic allocation is rule-based and consistent, it helps turn the store into a learning machine.

 

 

The Measurement Minute by Gary Angel

 

Greeter interactions form a special sub-class within a more general scheme of Associate/Shopper interaction classifications. It’s easy enough to identify Greeter interactions (based on where they occur), but it’s also possible to create a more comprehensive and detailed look at Greeter-specific classifications if you’re trying to optimize greeting strategies, decide if greeting is worthwhile or even measure the performance of different store greeters.

 

This Minute lays out a simple to implement classification system with four basic types of Greet and a couple of additional sub-types. It’s a great foundation for analysis of Greeter strategies and performance.

 

 

The Measurement Minute by Gary Angel

Unlike the Call-Center, there’s no easy way to classify what kind of interaction an Associate had with a shopper on the floor. That means we have to rely on behavioral classification for analysis. Fortunately, it’s relatively easy to distinguish between the most basic types of shopper and Associate interaction. This Measurement Minute describes a four-level classification of interactions that will serve as a general model for most stores and that can provide significant analytic richness to interaction measurement.

 

 

The Measurement Minute by Gary Angel

 

The single most important Associate measurement is probably interactions. After all, shopper interaction is the whole point of having Associates on the floor. With basic interaction measurement, the key metrics are probably interaction rate (the % of shoppers in a section with an interaction) and interaction conversion rate (the % of shoppers with an interaction who converted). But while these are both critical metrics for understanding performance, neither will necessarily help you craft an interaction strategy. For that, you need to go deeper into Associate and Interaction measurement.

 

The Measurement Minute by Gary Angel

 

Groups of shoppers can have a surprisingly large impact on Key Performance Indicators (KPIs) like conversion rate and STARs. If you only focus on individual shopper counts, you may be getting a misleading view of performance. Metrics like conversion rate will shift significantly when groups are taken into account, but the real problem is that those metrics will shift differentially. In other words, it isn’t just that the conversion rate will change if you measure groups, the important part is that conversion rates will change differently by store, by section and by daytime part. That means that when you compare conversion rates without accounting for groups, your answer isn’t just shifted, it’s misleading. And since all measurement is ultimately about comparison, group impact on key metrics becomes very important.

 

So how can you measure groups of shoppers?

 

 

 

The Measurement Minute by Gary Angel

 

Measuring shopper groups is important – not least because it has a huge impact on conversion rates. Shoppers in groups reduce total opportunity – and because the percentage of shoppers in groups can vary wildly by day-time part and section even at the same store, conversion rates that don’t reflect groups may be off by a factor of 4 or 5.

 

Of course, Shopper Group measurement isn’t just about conversion rates. It has a huge impact on Associate allocation models and allocation strategies as well. The number of people in an area isn’t as important as the number of shopping groups – and if those two numbers are very different, your allocation models need to be adjusted.

 

 

 

 

The Measurement Minute by Gary Angel

 

The third component of a good high-level store dashboard based on people-measurement should focus on operational efficiency. Associates are a huge part of a store’s operational efficiency and cost. They are also, without doubt, the most variable component of performance.

 

For a dashboard, you’ll want to measure the relationship between Associates and Shopper Traffic (the STARs rate). You’ll also want to measure the relationship between STARs and Conversion.

 

The STARs Clock Viz described in an earlier post is a great tool for doing that. But you can go further by measuring Associate Time and Interaction Rates and tracking each against allocated Labor hours.

 

Put all that together and you have a dashboard that delivers great context around the level of staffing and store performance.

 

 

The Measurement Minute by Gary Angel

 

Any good dashboard of store performance based on shopper data should cover shopper quality, merchandising effectiveness and operational efficiency. The last minute covered shopper quality, this one covers  merchandising effectiveness. It’s probably the single most important part of a good store report. After all, merchandising is the heart of the store –getting shoppers in front of product is the point.

 

Fortunately, measuring merchandising performance is the kind of thing that shopper journey measurement does best. From time-based metrics of engagement to using the shopper focus segmentation, here’s what you need to know about high-level reporting of store effectiveness.