Interviews, insight & analysis on digital media & marketing

How retailers can use data as a super-power

By Russ Groombridge, VP of Data and Analytics, CODE Worldwide (RAPP Group)

Around 9 in 10 (89%) of adults in the UK have reported that their cost of living has increased, that’s 46m individuals that are affected, and this has grown from 6 in 10 (62%) over the last 12 months. 

For various retailers that sell bigger ticket items such as furniture or appliances, concerns about price rises will see 68% of consumers spend less in the coming year and 65% will actively look for ways to save money on these purchases. (Mintel, 2022). It follows that retailers needs to react fast and provide alternative solutions to their customers and support them where they can, so when they are in market their brand is top of their shopping list.

One of the ways retailers can achieve this is to use data as their super-power.

Offer products that customers can afford 

Everyone knows that customers come in all shapes and sizes and the cost-of-living crisis is impacting consumers at all income levels. Historically a lot of brands have used income data to target customers on the assumption that the more you earn, the more you spend. But this can be grossly inaccurate as two families with the same household income can have very different spending power. One might have a large family, a hefty mortgage, whilst the others may be living mortgage and family free. 

There are varying third party data sources that can be used to understand a customer’s affordability which come highly recommended, but the real power comes from when this is combined with first party data looking at what ranges’ customers buy (premium or value), whether customers buy during sale periods, whether they take advantage of promotional discounts etc. 

By utilising this data, retailers can build highly effective affordability models and score their customer base so they can target customers with products at prices that fit their budget so they can still get the products they’re after, without breaking the bank. 

Putting the finishing touches to your home

Home makeovers and considered purchases such as consumer electronics occur infrequently and can be expensive, particularly when there’s a cost-of-living crisis

Retailers can use data in a clever way to make their customers’ lives easier and help them with those all-important finishing touches to get that pinterest style makeover.  

Sales data indicates that customers start with the bigger purchases first – so they buy a bed, wardrobe, TV, washing machine etc. and then ‘accessorise’. This will naturally indicate what the customers ‘mission’ is in terms of what room they are focusing on. From here brands can use ‘next best product’ propensity models to target customers with the most relevant products to finish their room in style, either at the time of purchase or through post purchase trigger comms. 

Traditional ‘next best product’ models are effective but can also be supercharged by incorporating even richer product meta-data, for example, colour, materials, pattern, texture etc. which will improve targeting and hence conversion. 

Tailor your offers to customers 

During this cost-of-living crisis, many brands will have to resort to (even more) promotional offers and discounts to encourage customers to shop. This year’s Black Friday turned into a Black November for many retailers. 

But how can brands use offers more strategically so as not to erode margin and get stuck in a vicious cycle of having to constantly discount to get customers through the doors or online. 

One way that I have seen this done successfully is by developing ‘offer elasticity’ models. A customer’s offer threshold can vary dramatically and whilst some will need a significant offer to get them to shop, others would be happy to pay full price. 

Using rich first party data on who has responded to previous offers, purchased at full price, who only ever shops in sales or engages with specific promotional offers etc. You can then score the customer base and put customers in segments or deciles and use this to push the right offer to the right customer. 

As well as using this logic for large-scale trade driving activity, this can be incredibly effective when it comes to personalised 1:1 triggered comms such as abandoned baskets and winback comms. It will also ensure that retailers continue to drive sales whilst at the same time keeping margins at an acceptable level and as the economy (hopefully) starts to recover, retailers can adapt this strategy. 

So while we may have to accept that consumers will be spending less, retailers should still be able to use data as their super-power to ensure that when they spend, they spend it with them. These tips will not only deliver a commercial benefit by putting your brand front of mind but importantly will also benefit and support consumers during the cost-of-living crisis.