Retail Alchemy, Spring 2018 – Store performance: are some stores just “bad”?

Rhys Jones , Retail Alchemy

Store performance: are some stores just “bad”?

In a previous article, we investigated call file selection and how a client can identify key considerations when selecting stores. By applying an analysis to a cross section of post campaign results from a range of our clients, we were able to conclude that as many as 47% of stores in a typical call file underperform versus the investment of time spent in store. These results implied that the time spent in these underperforming stores may be better spent visiting other stores more frequently or visiting previously unvisited stores.

But what makes a store “bad”? And, moreover, are some stores just ‘bad’ stores in general?

To find out, we took a cross section of the bottom 100 performing stores in our database and assessed them across 4 main areas:

  1. How many times the store appears in the bottom 100 across multiple categories
  2. The average base rate of sale in store vs the category average
  3. Of all the interventions that could be made, what proportion were actually made on visit
  4. When an intervention was made, how many of that specific intervention type were made versus the category average

The chart below demonstrates our results:


Inspection of the chart immediately reveals that in general, rate of sale is an issue in the bottom performing stores, with rates of sale in such stores around half that of the average of all the other stores in the call file. However, note that this isn’t universally true: of all the stores in the bottom 100 for each category, only 43% of them appear more than once in multiple categories indicating that low rate of sale is often a category, or indeed brand specific, issue.

So does this indicate then, that, although not all stores are bad, bad performance is a base rate of sale issue and therefore nothing can be done about poor performance?

The average levels of intervention opportunity and rate would seem to indicate so, with opportunity only at 70% of the overall call file and rate at 76%. However, as is always the way with averages, the truth is somewhat less clear when you dig a little deeper:


Average rates of intervention opportunity versus rate, bottom performing store, multiple categories

In the chart above, we have mapped our results in terms of average opportunity to intervene versus proportion of interventions made, with bubble size determined by rate of sale.

As you can see, the result is now somewhat less clear cut. Although average rate of sale is a particular issue for some categories (the grey and green bubbles respectively), others are impacted by insufficient opportunities to either make the full suite or number of interventions that are required (blue bubbles). Other still (yellow bubbles) suffer from something else entirely – possibly the wrong type of intervention being made in store.

So where does that leave us? Well the answer is simple, and goes back to one of the first articles I wrote on ROI: that better ROIs for field sales are achieved by having more well established filed teams than having shorter term, more tactical type arrangements. As the analysis above demonstrates, store performance, or lack of, isn’t down to one single universal characteristic: rather it is often down to a combination of factors and only a knowledgeable, well established team will have the store and category knowledge to turn around store performance.