If you’ve browsed the website of any big retailer recently, from Argos to Zalando, there’s a good chance you’ll have noticed something that once would have seemed peculiar. Whether you viewed a wheelbarrow or a pair of glow-in-the-dark trainers, you might have been served an advert for the very same product on the next website you visited. As I write, I see a Just Eat Facebook ad probably inspired by my searches for good noodle places.
There was a time when that kind of ‘coincidence’ might have seemed creepy – but few of us are surprised anymore. It’s online personalisation – the curation of our respective Internet decors – and marketers are generally familiar with the rudiments of this practice. It’s being used by airlines, sports stores, restaurant chains, banks, jewellers, supermarkets – most retail verticals in most online spaces, not just on Amazon.
Personalisation isn’t mysterious, but rather the being focused on individuals, personalisation needs user patterns, and many brands are focused on micro-level personalisation that helps them to remarket, which can lead them to miss some parts of the bigger picture.
In most online experiences, user footprints are tracked, allowing remarketing opportunities – that wheelbarrow you viewed, those noodles I viewed. But we can automate further improvements to the online marketing experience by curating what users who have tread similar paths to us have opted for.
Of course, the more data available – job titles, marital statuses and so on – the closer some marketers feel we come to a much-awaited era of hyper-personalisation. But there lies the rub – or rubs. This level of individual detail is something businesses are struggling with for various reasons.
Some brands lack the technology to gather the data. Others struggle to make sense of the data once observed. Still others are rightly concerned about appearing ‘creepy’ when their personalisation efforts are tied to the past searches of individuals. And that’s all made more complex by the fact that individuals are largely unpredictable, when taken in isolation at least.
Enter personalisation based on commonality – or personalisation for tribes as much as specific individuals. At Quidco we see data every day that shows us that there are patterns at play in consumer behaviour – patterns that can best be seen from further away and by viewing data sets in context of each other.
This can be some of the most fascinating and beneficial market intelligence retailers can use, showing that the intersections of user trends – from the new parents, to the students, to the 40-somethings – reveal crossover retail behaviours that can complement the specificities of individual personalisation.
Take, for example, a group of users who, over the course of several months, book a couple of city breaks on Monday mornings, book their train travel on Tuesday lunchtimes and buy high street cosmetics on Sunday afternoons more often than any other time. Each individual can be delivered their personal preferences, sure, but by recognising these individuals as members of a tribe of commonalities, we can predict what marketing will be effective by referring to the wider patterns of similar users.
In our example, the person who has booked the city breaks and train travel at these pre-identified times, by Sunday, may be looking for cosmetics on Sunday, per pattern data.
This is just one of many examples of user groups with crossover and of course distinctions. These help develop efficiency in retail demand generation, as well as efficiency in consumer base building and retention. We can then curate content to connect consumers and products in a natural way that makes us an asset.
There are many ways to segment user bases or individuals, but how far do you go? Personalisation based on commonalities between different people is a hugely effective way of driving traffic and engagement – just as important as personalisation based on the individual. They are two sides of the same coin.
Ido Padani is chief commercial officer at QuidcoImage credits: