The personalisation wave: how search, commerce, and decision-making can become hyper-personalised.

The case for personalisation is my general thesis of breakthrough consumer technology: it takes us back to the way things once were – the ancestral comforts we long for – but with more scale and efficiency.

In the ancient world of small tribes and towns, we were known. We long for the days when our local storekeepers greeted us by name and our neighbourhood restaurants remembered our likes and dislikes. We want to be welcomed, served, and remembered for who we are - and valued for our loyalty. For example, I’d be fine with every restaurant in the world knowing that I am a vegetarian. I’d be fine with every e-commerce store knowing that I wear a size 9.5 men’s shoe. However, I want to know HOW they know this information. I don’t want my data to be sniffed or scraped, I want control.

Over the past few centuries, as commerce and hospitality scaled through cities and technology, personalisation was left behind. We became “logged out” visitors in all parts of our lives, greeted with generic menus and generalised calls-to-action. Today, we experience the lowest common denominator of digital experiences on a daily basis, and are oblivious victims of data collection and retargeting campaigns. All this, despite the fact that brands thrive or die based on conversion and retention.

Well, here’s a bold vision: the future should be personalised to your preferences. Every digital experience should be personalised for you based on the information you want brands to know about you – always in your control. E-commerce websites should welcome you by name, use your preferred language, know your gender, preferred sizes, colours, and the ages of your kids. Restaurants should know your allergies, favourite dishes, and dietary restrictions. Your preferences should be managed by you and shared purposefully, not deduced or purchased from others.

How might recent advances in AI make this world possible, and what are the implications?

• Step one, sync yourself. On the consumer side, you’ll have the opportunity to sync yourself (a personalised model trained off your data aggregated from a variety of sources) with any application or experience you engage with. Now, you won’t want ALL of your data to be used by EVERY application, so the killer technology here will be an intelligent arbiter – an “agent” of sorts – that acts on your behalf to personalise every experience as appropriate (and learns from what it gets wrong). Your model, and the enterprise models behind every experience in your life, will have a real-time agreement or negotiation of sorts to personalise your experience while optimising for your interests and privacy.

• “Recommendations” kill “Favourites”: No doubt, AI has reached the point in some verticals where it knows our taste better than we do. In such a world, the model’s recommendations may please us more than our own favourites. This is a forecast I have been brewing since December 2021 when I stopped compulsively saving playlists that I discovered and loved on Spotify and fully surrendered myself to the algorithms. I now know that any song I like leads to a playlist I’ll like, and any playlist I have will always dynamically evolve and get better. In my world of music, recommendations have started to take the place of favourites. Where else will this happen in our daily lives? Will your favourite travel experiences suggest recommendations that transcend a Google search or a travel agent? I think we’ll look back and realise that “favourites” were always quite limiting. “Favourites” has long been the way we, as humans, ensure that every circumstantial discovery and experience we love can be repeated. Whether it is a favourite song, a favourite restaurant, or a favourite hotel… all these places and creations we discovered by accident end up consuming our attention at the expense of new and (by the law of probability) even better experiences. So, I welcome the era of AI to tap the collective actions of everyone with similar interests to make sure I don’t miss something I’d love.

• The benefits of personalisation compound over time. Many of us have already experienced the benefits of ChatGPT remembering our previous questions. I am now thinking a lot about the compounding benefits of a relationship with a personalised machine-learning model after months if not years of working together. Does the model discover our quirks and biases? Does it develop an attitude or degree of sarcasm in order to engage us on our own terms? And what does this mean for marketing and the world’s largest brands giving their customers a more personalised experience? At Adobe, we’re thinking about how to help some of the world’s largest brands deliver personalised experiences to their customers. Today, most personalisation is done by “segmenting” customers, but perhaps every brand will have a fine-tuned model for their brand as phase 1, and then a fine-tuned version of that model for every single customer as phase 2? Prediction: average retention for most brands will be dramatically stronger in just a few years.