Are tech retailers’ analytics missing a trick?

Tech retailers are missing a trick by not making the most of data analytics for uses other than marketing, says FICO analytic consulting director Mark Thompson.

I’m going to let you in on a little secret. Some of your competitors are quietly stealing ahead of you in their use of analytics. They’re figuring out how to use customer data better than you do.

The race to use analytics better has heated up in many areas, and customer management is definitely one of them. This is important, because it’s much easier to get more business from your current customers than to woo new ones.

The new approach to analytics mixes traditional “what is this customer like?” analytics with “how can I change this customer’s behaviour?” analytics – so called action-effect modelling.

One type of analytics tech retailers are using to distinguish themselves builds on propensity models – models that predict how likely each one of your customers are to buy a specific product. Say a propensity model predicts a customer is highly likely to buy a given product – should you then go to the expense of sending a promotional offer?

To make that determination, tech retailers are starting to use uplift models to determine whether an investment is likely to be worth the result. Uplift models can save retailers millions by enabling them to avoid offering discounts to customers who will purchase without them.

For example, such a model might predict whether or not sending an offer for 20 per cent off is likely to increase a particular customer’s propensity to buy a laptop within the next two weeks. The retailer can then send the coupon only to customers whose behaviour it’s likely to change.

On the other hand, when uplift modelling indicates a customer’s behaviour is likely to be affected by a promotion, it can also help retailers determine which promotion will have the most impact.

Will 20 per cent off be much more effective than 10 per cent off? Than free shipping? Is offering 12 months of interest-free credit necessary, or will six months be nearly as enticing? Uplift models provide the analytical insights retailers need to make precise decisions about where to put marketing spend for higher ROI.

One of our retail clients, which helps to ‘make markets’ for new products by spending heavily on promotion, is using uplift models to increase its return on this investment.

The analytics provide insights that are enabling the retailer to accelerate the purchasing behaviour of so-called “laggers” – customers who historically haven’t been among the first to purchase.

By targeting these customers with offers that are likely to change their historical behaviour, the retailer is increasing the concentration of sales in the first two months of the product lifecycle – its critical period before competitors can draft off of their momentum.

Given shrinking product lifecycles, pushing sales forward in this way is becoming ever more critical to this retailer’s success.

Uplift modelling is just one of the ways retailers in your market are using analytics to get ahead. If you’re stuck with more traditional models, it’s time to get a move on.

About the author

Mark Thompson is analytic consulting director at software company FICO.