KHWS Blog: Personalisation is overrated. How should retailers use data?

Personalisation is overrated – Why retailers must use big data sparingly


The retail sector has been irreversibly transformed by technology over the past two decades. Technology has given retailers an almost omniscient presence – enabling them to second guess what customers want to purchase before they have even decided to buy anything. Big data and analytics have become an integral part of retailers’ marketing and sales strategies, helping them understand the psychology of the modern shopper.

However, as the adage goes, with great power, comes great responsibility. It is tempting for retailers to get carried away with technology, believing it will solve all their challenges. There is certainly widespread thinking among retailers that by drilling deeper into data-led shopper insights, they can tackle issues such as how to build a better community and how to better measure the path to purchase.

Where the danger lies is that, in the rush to ensure they’re keeping up with the latest technology available, retailers are overestimating how important personalisation is, especially when it is powered by big data. While a retail strategy built from big data insight can be effective, big data in itself is still not sufficient, complex or nuanced enough to portray an accurate representation of shopper behaviour. Although big data metrics are advanced and granular in their detail, there are still inconsistences in the way it anatomises and targets shoppers and retailers.

Removing the element of serendipity 

There is no doubt that online personalisation, when done right, is beneficial for retailers and shoppers alike. For retailers, it allows them to maximise the lifetime value of existing customers and gain new ones.  On the other hand, consumers greatly appreciate it when an e-commerce site recommends a product that is exactly right. However, consumers also become frustrated when a website completely misses the mark, suggesting something irrelevant based purely on a previous purchase.

Personalisation is not only limited to online channels as it extends in-store as well, with 53% of UK consumers spending less than two hours browsing per physical shopping trip. Bricks-and-mortar stores will follow the lead of online counterparts by utilising big data to deliver more personalised and convenient shopping experience.

Big data analytics will continue to be a crucial part of retailer’s toolbox now and well into the far future. But they need to be careful not to overly exploit data analytics. No algorithm will be able to empathise with or pre-empt the unpredictable nature of shoppers. Contrary to what many retailers believe, hyper personalisation might not be giving consumers what they want. The problem is that data and prediction removes the element of serendipity which is one of the rewarding pleasures of shopping. People who browse bricks-and-mortar stores discover products they didn’t even know they wanted or needed.

Highly focused targeting online can be counterproductive as it potentially provides consumers with too little choice, pushing them away from a sale. Retailers should avoid continuously refining which products are pushed to individual consumers, to point that they remove the element of impulse shopping. They must find a middle ground.

Behavioural science – Understanding purchasing decisions

Behavioural science is not new to retailers, helping them identify how and why shoppers make purchasing decisions. However, with the ubiquity of digital technology, the way behavioural science is applied within a retail context needs to be re-evaluated.

To understand consumer behaviour, brands and retailers must turn to heuristics – the hardwired shortcuts people use to make purchase decisions, regardless of brand, category or channel. There are in fact 128 heuristics, however in partnership with Durham University’s Business School, KHWS has identified and reframed the nine most relevant purchase decisions. We call them Sales Triggers. These nine heuristics provide a framework that makes behavioural science usable for retailers, enabling to them to shape strategies and marketing activity which will drive retail sales.

With this behavioural science framework, retailers can begin to interpret the reasoning behind online shopping, moving beyond simply understanding the customer journey. Are they thinking fast or slow? Is it a habitual decision? Are they acting on recommendation? Or are they looking for something new? To achieve the optimum levels of personalisation, retailers need to be asking these questions all the time. By doing so, retailers can implement the right marketing and sales strategies which do not limit potential sales through overly narrowing recommendations, or overwhelm shoppers with choice when they are not in the right frame of mind to receive it.

Personalisation is only as effective as shoppers allow it to be. Consumers will only be receptive to product recommendations within the right context and time – this can vary depending on the time of day, the category, and the platform. In this age of big data, retailers should avoid the assumption that consumers want a personalised experience at every point of the customer journey. By applying the learnings from behavioural science to marketing and sales activity, retailers can effectively drive brand choice and overcome the temptation to give customers suggestions on what they believe they want. Sometimes, the best option for retailers is to let shoppers find out what they want by themselves.