Today retailers have a plethora of information, stored in vast data warehouses, on how their customers have behaved in the past. Utilising this data in a meaningful way to derive insights that can be turned into more profitable decision-making is, however, no mean feat.

People often tend to think about big data analytics, in the context of retail, as a set of tools used by the marketing department. Promotion targeting and personalisation are the buzzwords of the day. However, POS transaction data has a lot more to offer than this and even if you don’t have a loyalty card, the field of pricing analytics can deliver significant benefits.

Pricing (and promotion) analytics allows you to examine the effects of price, promotion, marketing, advertising, promotional display and other factors such as seasonality, weather and other regular events that may affect the sales of a product or group of products.

Isolating these effects can give you a great deal of insight into what is really driving sales. It allows questions like…

  • If I lower the price of a can of beans, how does that affect my sales and profit. Do I increase profits even though I may lose margin?
  • What will happen to my sales and profit if I promote the can of beans?
  • If I factor in the vendor discount into my cost, does that convert into increased profit?
  • When are my promotions most effective.

The key to decision making is to have the best available information to hand at the point you need to make that decision. Embedding an analytic approach into the process of pricing takes much of the guesswork away and propels your business forward.

I’ll be talking more about how this all works and what’s possible in future posts.