About conversion and measuring retail store performance

Juha Mattsson

Is Aldi really ahead of Asda in retail performance

Last week we saw Retail Week publish details on the top 30 bricks-and-mortar retailers in the UK, ranked by sales density. By taking a quick look at this chart, one might conclude that Aldi has taken over its UK counterparts like Asda, Tesco, Sainsbury’s and Morrisons in conquering Britain’s grocery space.
The competitive shake-up within the top five is definitely a topical theme right now as the price wars intensify between Morrisons, Aldi and the others. Intriguingly, as sales density is a direct indicator of brick-and-mortar retail performance, chain wide price adjustments naturally have a direct impact on retailers’ standings on such rankings.

However, skimming through the analysis published by Retail Week, a few additional thoughts immediately popped into my mind regards comparing retailers against sales density.

Measuring retail performance in physical stores 

To begin with, when we talk about sales density, we refer to sales per square foot – which is a key metric when it comes to retail performance. It standardises performance over store size, format and brand, thus being a relatively universal metric for comparing stores and retail chains over each other.

Essentially, it shows how much you can sell goods from a given amount of store space on the average, and ignoring everything else. We know it can help compare business results against other stores and often provides the basis of what needs to be changed when things go wrong.
However, like all generalisations, sales density quickly breaks down as a metric for capturing the actual business viability of a retailer. Consider the following:

  • Costs are not considered – some retailers might be able to manage their assets much more effectively, or otherwise have cost advantages. Lower sales density can sometimes be a more profitable business!
  • Location - A peripheral grocery store with a substantially lower sales per square foot might still be a more profitable business, just because sheer differences in space rents
  • Conversion between online and offline – today most retailers operate both physical and online stores. Obvious as it is, sales per square metre doesn’t attribute to the online side of the business – and especially how much of ‘offline’ activity is likely to convert to online sales, and vice versa
  • Loyalty and repeat visits – A lower sales density might still be much more stable and predictable business, given your stores are able to attract a steady flow of loyal customers
  • Which part of your store is underperforming? – Sales density may be useful when benchmarking against stores within a chain. Yet even then it’s only an indicator of something being wrong in a given store, at best. To start improving, you definitely need more detailed analytics data

Given the above, being able to really compare those 30 retailers against each other quickly becomes much more difficult. From the investor’s perspective, you’d need to understand differences at least in store concept, geographical coverage, business model and cost structure. From the store manager’s perspective, to take concrete actions to maximise your store’s sales density, you need to look into detailed customer behaviour and conversions inside your store, see which works and which doesn’t, and start optimising.
The devil’s in the details, as they say.

How Wi-Fi analytics helps retailers to improve earnings from existing stores

Well, battling the above devil is actually rather simple and straightforward. We at Walkbase have worked years with large retailers to make use of Wi-Fi positioning (with existing store Wi-Fi) and other in-store data sources to gain an in-depth and real-time view on customer behaviour in their stores – just like Google Analytics for online stores.

By taking advantage of modern indoor location based analytics and marketing solutions like Walkbase, retailers can better understand the story behind the aggregate numbers like sales density. By anonymously monitoring customer behaviour throughout the store, including paths taken, dwell time and items looked at, retailers can build a clear picture of actual customer activity. Using this anonymous data, retailers can then optimise individual categories and zones in their stores. This is not only to understand and improve retail performance over time but also manage physical assets like shopping trolleys and store fittings, and to create the best experience for the customer via digital advertising and smartphone engagement.
To get a better idea, here’s a few examples of detailed analytics to improve retail performance and benchmark stores against each other:

  • Conversion from category to sales – Measure and compare how many customers dwell at each store section, and how many of those actually convert to tills to buy something
  • Improve layouts, store format and merchandising – with real-time data, test how changes in store format and layouts affect people’s shopping behaviour
  • Window marketing – By counting the number of bypassing people and how many of them enter the store, retailers are able to monitor ‘capture rate’ which is a great metric for improving e.g. window marketing
  • Opening hours – By closely following customer activity in and outside a store, retailers can draw informed decisions regarding opening hours
  • Loyalty and engagement – Monitor customers’ repeat visit patterns as well as how stores are able to engage customers as they step in from the entrance
  • Asset management – Retailers can use the same technology to locate staff and assets, and optimise each for best customer service
  • Personalised in-store offers – we’ve witnessed great results in delivering offers and coupons directly to customers’ handsets while they enter a store, compared to sending them to email or snail mail

As part of this, retailers can also use this data to sell advertising on digital displays to brands. This allows personal adverts to appear on digital signs throughout a store in real time based on actual data on current customer patterns, to help influence purchase decisions. And of course to measure ad exposure and post-behaviour.

Sales density -- keep it or ditch it?

To sum things up, I would definitely not advise the retail industry to forget sales density as a metric. Given its simplicity and standard-alike comparability, it is a good metric especially for benchmarking store performance (i) across similar stores and (ii) over time in individual stores. This way, it can quickly point out anomalies and trends, and thus is a good tool for streamlining retail performance.
For assessing which retailer is the best.. I wouldn’t rely on it.


Walkbase provides a retail analytics solution for improving the impact of marketing on physical stores and personalising in-store shopping experience.