With Valentines day fast approaching, as Ian Webster, Chief Customer Officer at Big Data for Humans gives his top tips for using big data to understand your customers and keep them in a long and prosperous relationship.
Despite the rose-tinted pictures painted by romantic comedies, relationships can be a minefield. With plenty of faux-pas pitfalls standing between you and the love of your life, it is no wonder that so many relationships end in disaster. The same plight is felt by countless retailers, who after wooing and acquiring their perfect customer, find themselves dumped in favour of a competitor.
Here are seven tips for using big data to understand your customers and keep them in a long and prosperous relationship.
1. Boys & Girls
It is easily done but don’t take anything for granted when it comes to understanding your customers. People don’t always act as you would expect, particularly when you try to pigeonhole them based on attributes like gender. Consider fashion retail for instance: in case after case we see that up to half of the most loyal menswear customers are women, and plenty of the keenest handbag-buyers are men!,. Not only is gender stereotyping likely to upset your wider customer base, but gender is far less important when deciding how to communicate with customers than the things that they buy.
2. Age is just a number
Likewise, don’t be tempted to group your customers based on age groups. A large bookseller found that a large proportion of its customers buying Mind, Body and Spirit books were actually in their 70s and 80s. This not only proves that you are never too old for yoga and Pilates, but also that defining your customer by their age demographic is not always the best approach.
3. Try a little tinder-ness
As with matching on Tinder, data and algorithms are your friend. Just as dating apps may match you with people based on a dater’s social network, your customers are likely to be receptive to products liked by fellow consumers that have similar tastes. As with dating, your customers may have an idea of what they want, but that doesn’t mean they aren’t open to exploring other options. Retailers have all the data at their fingertips to make these suggestions and doing so is the key to a blossoming relationship with customers.
4. Plenty more fish in the sea
Retargeting is one of the most common tactics in marketing, however understanding when it is appropriate is vital. If you have swiped left to reject a Tinder prospect, you probably don’t want to see their face on every web page you open. Using Big Data to predict what a customer might buy, not sell them what they’ve already bought, can add value and make you stand out from other retailers attempting to woo with tired retargeting.
5. Get your chat up lines right
A generic ‘Hello, how are you?’ is one of the least successful chat up lines of all time. Customers are far less likely to respond to a generic email that doesn’t understand who they are. Personalisation makes them feel valued and big data allows a retailer to understand their customer on a granular level and automate targeted campaigns based on their preferences, giving their chat up lines the personal touch.
6. He loves me, he loves me not
Regardless of how big you are as a retailer, some customers can fall out of love with you. Understanding quickly if your customer is ‘not that into you’ anymore can help you focus your upsell efforts on customers that are interested, and your retention efforts on those who aren’t feeling the love any more.
7. I’m just not sure they’re right for me
There are a variety of ways that you can get to know your customers, however many approaches can feel like a slog. Traditional bespoke approaches are akin to taking your customer base on 20 dates to a variety of Michelin starred restaurants – slow and very expensive. Automating customer insights and focusing on the key predictors of customer lifetime value can give you the key to a whirlwind romance, allowing you to keep your customers happy and see a rapid return on your investment.
By Ian Webster
Chief Customer Officer
Big Data for Humans