As always, the turning of the new year inspires reflection. We look back on our accomplishments of last year and take out our crystal ball to make predictions for the coming year.
A traditional way of doing this is by making a list of the ‘Top 5 (or any other number of course) Tech Trends for 2018’. Almost all companies that want to show they have some thought leadership on the matter will publish lists like this. I have no doubt that Artificial Intelligence will have a place in all of those lists. And rightly so.
The use of artificial intelligence is maturing. No longer are companies implementing artificial intelligence only to take part in the hype, they are now slowly becoming more ‘literate’ in artificial intelligence solutions. They start to be able to differentiate their innovation partners based on companies that not only ‘talk the talk’ but also know how to ‘walk the walk’. In other words, corporates are starting to want artificial intelligence solutions to make a tangible business impact.
Rather than giving a very generic list of AI trends for 2018, I want to give a specific one. I believe that two trends will coincide in 2018. One is the customer driven approach to innovation called design thinking, the other is the data driven approach to innovation called machine learning. They will meet right in the middle to create the absolute best experience for your customers, while at the same time increasing revenue (life time value that is) and decreasing churn. That sounds absolutely wonderful, doesn’t it?
How? Through psychometric profiling. I am sure you might not want to read on after reading words like this, but bear with me. However, part of the tradition of the making-lists-at-the-end-of-the-year exercise is that the list must include new words to be printed on next year’s B/S bingo cards. No fun in playing that game if the words don’t change once in a while, right?
Then what does this actually mean? It means that we finally give true meaning to the word ‘personalisation’. Rather than the weak attempts at personalisation by creating customer segments based on socio-demographic data (gender, age etc), we start to see personalisation based on, well, personalities.
Sounds reasonable right? In life, groups are formed around interests based on an individual’s personality. Not around age or gender lines. Not all men like soccer and not all women like shopping. Not everyone above the age of 50 is absolutely digitally illiterate and trust me I know enough ‘digital natives’ that don’t understand social media or new digital technologies.
I think many customers dislike the very obvious stereotyping in ‘personalised’ campaigns. I know I don’t like e-mails in which a marketer clearly targets me as a 30 something (I mean 25-year-old ;) ) woman. Machine learning now brings us the tools to (relatively easily) distil customer segments based on a large number of variables. Using unsupervised learning algorithms, marketers can create much accurate targeting based on the groups the machine learner identifies.
And then, it is up to the human again. Machine learning can give us the profiles and important variables for the different segments, we still must make sense of these segments. Using a design thinking approach, we can identify the profiles and personality traits that unify the people in the segments and create a perfect campaign or communication strategy. The customer will finally feel truly understood by the brand and create a connection with the brand that is reflected in loyalty. Sounds like a good plan to start a project on for next year? Enjoy!
