Why heat maps is the least cool thing about retail analytics

Gareth Morgans-Mills

Having spent two months now at Walkbase, it amazes me every day how we are changing the way we utilise technology, such as Wi-Fi and Beacons, in bricks & mortar stores to use to their advantage. In fact, the main reason for my move to Walkbase was its focus on the Analytics and their application in order to enable retailers or any managers of physical environments to make real business decisions.

One of the most common questions I have been asked when discussing Walkbase's capabilities and retail analytics in my previous roles is, "can you provide heat maps?".

Taking a very simplistic point of view, my initial thoughts are this: either this is the only understanding of how location technology can be used, or the customer is at the very early stages of looking at analytics in their bricks & mortar stores, or any environment for that matter.

Either way, my answer is the same. Heat maps do have some value and look great as a visual on a presentation if used in the right way, however, the key thing is around providing actual usable tools and insights that can be used to make business decisions. And heat maps are quite a way down the line of importance.

The main uses for location analytics is in the numbers themselves to start with!

So what do I mean?

Well, the answer is simple, I would not overlay transaction data on a heat map - it just won't make sense. 

What I would overlay is numbers of people in that particular store or department/zone (footfall). This is because the best use of location analytics in the first instance is to understand how this relates to till conversion. The main use of any analytics service is how information can be used to understand store conversion to revenue and how can it be used to increase it.

Couple this with whether the customer simply walks past a location (potential customer) or visits the store and overlay this with time factors to analyse if a customer is engaged, and then relate this back to the number of transactions (the Sales Funnel).



The data being presented in the Sales Funnel then starts to make sense. By understanding what my store’s potential number of customers could be outside, all the way through, to how many transactions and amount of revenue are generated, you can start asking questions around performance. Funnels have been a popular tool in web analytics for many years, and it’s only natural to consider how they work in the real world too.   

Why didn't a customer come in today? Were they on their way to work? Was it a bad use of window display that didn't attract them in? Why did that customer come in and walk out? No staff to speak to or were they just browsing or just did not see anything they wanted. What engaged the customer? Were they met by a staff member, they had what the customer wanted to buy in stock, interested in a new product being advertised in store on digital screens perhaps. Finally, how many people actually transacted? What was the total amount of revenue generated and by how many people (transactions).

Time is of the essence

As I mentioned earlier, time/dwell becomes an important factor in all of this. How long a customer spends in store or is engaged with any part of that retail space, be it an advert, a window display, a member of staff or an interesting new product line, primarily dictates that customer’s propensity to purchase; the longer the engagement the more likely to transact. By understanding this and measuring change, you can make decisions and improve the performance of a store over time.

Would locking all the doors and keeping customers in for a while until they're more likely to purchase something help?

Not quite, every retailer is different. For one of our retail customer cases, we identified that if a customer is engaged for more than 8 minutes in store, their propensity to buy increased by 60%, regardless of how many people came in. This became a KPI measured in all of their stores, which previously they based purely on number of people through the door. However, there are so many aspects of retail that can be measured using footfall, location and time. Applying this to the growing Click & Collect services presents a turnaround, in that speed of collection is key to the performance of that particular transaction.

Is your bricks & mortar store coming to life and making you ask What, Why and How?

If it's not, then the only question I would have is - why not?

The customer journey is affected by many aspects, which can all be measured in this way; footfall, location and time and many more applications. By applying Walkbase's core services you can start to analyse not only customer behaviour and insights, but also operational performance, such as staffing levels and opening hours.

Did I mention the weather?

This is as British as it comes, but as much as we like talking about it, the weather factor is a key piece of data that affects high street sales. Now add this into the reporting elements I have already discussed, and you begin to see that the only heat information that makes sense to what I have described is that of the Sun. Heat maps have their place, but that's the subject for another time.

The IoT and the ever changing demands on retail make data key to any physical environment, this is just one application that can be achieved really easily and should be the first building block in the challenge to replicate online analysis for e-commerce in bricks & mortar stores and start driving revenue through insight.

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