How do you use store analytics?
This leads us to the chicken and the egg, nestled conveniently in a store near you.
There’s a general feeling that store analytics, using video technology to monitor the movement of customers, their dwell times, engagement rates and conversion rates within stores, has a great deal to offer in terms of store productivities, however how to use the analytics effectively to achieve both short -term wins and long –term goals still seems tantalisingly out of reach for many.
Why the chicken? Why the egg?
Well, there are fundamentally 2 ways to use analytics, and what it boils down to, if you’re the egg I guess, is how to identify what to do in stores to improve performance. Because one thing is for sure – analytics is a measurement tool; it will not improve things simply by having it. The only thing that leads to improvements are changes to conventional store practices, processes and disciplines. Analytics allow actions in an informed way. Analytics allows those actions to be measured in an accurate way.
So to the eggs.
The first use of analytics is the collection of “big data.” Analytics installed and data collected across a number of stores leads to the possibilities of identifying sales patterns and trends, which lead to actions across all stores to maximise these patterns – possibly in terms of store layouts, staffing or assortment planning. Big data will also allow you to identify anomalies in your store chain – stores which fail to perform not just in terms of overall sales, but traffic, dwell times, engagement and conversion leading to decisive actions to improve these “stores of shame.”
The utopian dream for the “egg-heads” is the development of market-wide databases of retail patterns and benchmarking standards, so that retailers can identify where their performance falls short. Such “consortiums” already exist, with retailers anonymously putting “all their eggs literally in one basket” for the benefit of all. However they don’t exist to the level of store detail that analytics promises.
This is all impressive, however it should be recognised that “big data” here is a longer term goal, with large but longer term benefits, and requires a significant initial outlay in terms of technology and analytics.
And so to the chickens,
and let’s face it, any of us who have worked in stores are often to be found running around them like chickens, and often headless. But this is where the short-term, quick-win opportunity for analytics “lay,” if you’re a chicken. When used in conjunction with traditional store appraisal and benchmarking, and a modicum of retail “nouse,” analytics can be used to monitor the impact of decisive quick actions, where the lessons can be rolled out to all stores, driving productivity and safeguarding ROI in things such as store design, fixtures, stock allocation, visual merchandising techniques, display principles and staff levels. In effect analytics when used in this way stop the “chickens being headless.” For once, we now know not only what we are doing, but why we are doing it, with the guarantee that the running around will be very much worthwhile.
Also in its favour, the chicken route, rather than the egg, is quicker and easier to implement, less initial outlay and more immediate return on investment.
how best to use analytics in a retail business will vary company by company, and an important part of the whole process, is a thorough and initial appraisal of how best to use them – how many stores, how many parameters to measure and for how long. These will be determined by internal commercial initiatives and objectives, and by identified opportunities in the current delivery from assortment planning, store layout, display, promotions, events and customer service.
The bottom line.
Store analytics can improve every retailer’s stores, so don’t be a chicken when it comes to adoption. Which chickens and eggs to adopt? Well generally, don’t put all your eggs in one basket but apply analytics to give you the quick wins, as well as planning for the potential benefits of analysing “big data.”
VM-unleashed works with analytics software to identify and analyse shopping patterns within stores, and recommends on actions to maximise sales opportunities to measure…
…here are a few people that we’ve helped that you may have heard of too…
Ferrari, Luxottica, Marks & Spencer, Primark, AllSaints, Carrefour, Camper, Cortefiel, Boots, Sainsbury, Sonae, Otto Versand, BonPrix, National Geographic, Flex, Gruppo Vestebene, Alessi, Eroski, Coin, Oviesse, Bally, Adidas, Sony, Clarks, Benetton, Orange, KappAhl, Imaginarium, Porcelanosa, Trucco and Ben Sherman.
some of our clients…
if you would like to know more about our expertise, and how it could work for you, then please drop us a line.
+44 (0)7967 609849