There is a food market close to my office where if you go often enough, the market traders prepare your order before you even have to ask. With the sheer amount of resources today’s marketers pump into their campaigns, why is it that I get a more personalised experience from a market stall than from a large corporation?
We live in an age saturated with marketing messages. Marketers are spending an increasing amount of time and energy trying to figure out how to cut through the noise in new and innovative ways. The tried and tested method of targeting consumers at the right time, with the right message and in the right medium still holds true, however – and despite this being the foundation of any good marketing, it is still a mix that marketers are struggling to get right.
With the glut of data, we marketers currently have at our fingertips, achieving the right messaging at the right time and place is now easier than ever before. Simply put, there is now no excuse for poorly personalised and timed messaging.
One way marketers can provide consumers with the messages they really want, is through a personalisation engine. In layman’s terms, this is a nifty piece of data science that takes all of your marketing and sales data and uses it to create personalised recommendations for your customers.
Everything from a customer’s online browsing behaviour, response to previous marketing campaigns, social media, loyalty card information and previous purchase data can be used to fuel the personalisation engine. The recommendations offered can get even more detailed when you begin to feed in external data sources, trend data from news sites and blogs, for instance, or open data such as weather information.
The engine can then use all this different data to come up with interesting insights, and not just common sense recommendations. It’s easy to suggest that a female buying eveningwear would also consider a clutch bag and heels. The real value of the personalisation engine lies in its power to predict purchases you may not have considered – the same female may also be more likely to buy nail varnish and hairspray.
More importantly, a personalisation engine may recommend items that the customer themselves have not considered before. With the right data, a personalisation engine could suggest products that are suitable for a customer based on their needs, interests and desires. That female with the eveningwear may have a long running interest in Parisian style homeware, and she may have recently been browsing for dinner party recipes. Recommending your new line of French-themed home accessories suddenly seems like a no-brainer.
In this situation, you’ve not only managed to sell some extra products to a customer who otherwise may not have known you stocked them, but you’ve also strengthened your relationship with that consumer. They could be over the moon that you offered them a product so completely aligned with their interests and needs at that exact moment, and you may very well become their first port of call for any future dinner party planning.
Of course, it’s all well and good to have the engine, but you shouldn’t just keep it parked in one place. That is to say, the power of your personalisation engine shouldn’t be restricted to one part of the marketing mix. The recommendations that personalisation engines give are most effective when exposed to all aspects of your marketing strategy.
Having an overview of what products are popular amongst different customers now and in the future will help you plan what products you should spend time promoting, where the products should be promoted, and who should receive the promotions.
Personalisation engines don’t have to exist only in the online world either. As marketers struggle to unite their bricks with customers’ clicks, personalisation engines can help bridge the gap between online and offline. Recommendations based on a customer’s browsing behaviour can be served to that individual as they walk through the doors of a store.
Returning again to our female purchasing eveningwear, she may have spent a few months searching for the right dress and accessories only to decide to go to a shopping centre to try the items on in person. A retailer could use her browsing data to determine that she was interested in buying a number of different evening dresses from both itself and its competitors. It could then use this information to offer her an incentive as soon as she walked through the doors of its store. Beacons placed at the store entrance would be able to identify her through the retailer’s app and could then be used to offer a discount on the dress she’d considered, plus recommendations for related accessories and beauty products.
Personalisation engines are still a niche tool, known to only the savviest marketers. Given their ease of use and huge potential however, it is only a matter of time before every marketer hitches a ride. Fast-forward a few more years and personalisation engines will become as commonplace in marketers’ toolkits as email is now. It’s worth taking the time to explore where your engine can take you before the competition catches up.