Nice from far, but far from nice: big data can help retail bring everyone ‘up close’


Big Data

It’s all about perspective. There are things, that looked at from afar seem perfect, but walk a little closer and the imperfections become glaringly obvious.

Most large-scale businesses operate under two distinct perspectives or paradigms. An “up-close” perspective, which considers how the business looks at the individual store front level and its day-to-day functioning with the people who frequent it. And a helicopter (the head office) perspective which interprets how the business looks and operates across all outlets, as a unified whole, taking into consideration the brand, the product mix, specials and the like. The pertinent question is: how differently would you do things if you had a realistic interpretation of the actual demographics in the region of your stores?

How would the products, logistics, shop layout and various other aspects of merchandising look for each particular store or business if you had this knowledge?

The “up-close” understands its footfall customers extremely well, there is direct interaction and on the ground knowledge is gained from this. The business owner has a specific that he extrapolates as “the people around my store are like the people who have visited my store”.

On the other hand the global view risks overgeneralising the customer base and therefore failing to properly accommodate localised needs and wants. Moreover, business owners are very reliant on national, average surveys like AMPS. This tends to blur their view across the national average and may lack the sharp, hyper-local focus.

This dichotomy is largely a legacy of the relatively high cost of data in the past, coupled with data scarcity, leaving us with a very dim light from which to garner insights. Historically, market insights came by way of on the ground research, 3rd party or academic research or nationalised research (such as AMPS).

Today, this has shifted to the other end of the scale and we are faced with a different problem: data surplus. On the surface, this may seem spectacular, but dig deeper and a host of new challenges emerge. These include:

  • Identifying the relevant sources for the data.
  • Centralising it all to one usable and secure location.
  • Sense checking it and eliminating noise and errors.
  • Aggregating and analysing it in a useful way.
  • Representing or visualising it in a useful way.
  • Ensuring that it remains up to date.

The optimistic aspect of data overabundance is that we could access substantial amounts of relevant data about people living around a place of business, ensuring that the business owner knows exactly who is walking through his shop and what their needs are. We could further generate data about these people through the ongoing interaction between the neighbourhood and the place of business. In this way, a business operation can not only be localized, it can go that critical and redefining step further and be hyper localised.

Imagine interpreting hyper-localised data in order to help you better understand what products your customers actually wanted and how they wanted these products presented to them? It could quite literally change the way you do business.

The hyper-localisation tools allow you to:

Operate with the market surrounding a store and not just those who frequent it. This information could help you better manage logistics, stocks and market and communicate more effectively.

Prospect new store locations with a better understanding of which areas are more or less suited to the overall brand, which areas are easier to service and access.

Develop greater efficiencies across a business network as data feeds back from all points matched to local data concerning neighbourhoods.

In present times, where business is more competitive than ever, micro or localised insights will give an organisation a superior advantage. Having a hyper-localised level of data at your fingertips allows for a perspective that is “up close and personal” with the immediate customer base.



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