Over the years, many consumer service providers in e-commerce, talent search, food delivery, streaming entertainment, social media, etc. began providing users with personalized search results.
But almost all personalization efforts have focused on using whatever derived understanding brands gain about the user and the content/services they consume.
In technical terms, this means that search personalization is seen as providing different, yet relevant, results for the same query depending on the user and the context.
This effectively puts the user’s “request” aside and takes it as given. In practice, the “query” is any information that the user has provided during the search journey.
By defining search personalization in this way, there is a real danger that when brands attempt to improve personalization, they will focus entirely on external/observed/indirect user data. They stopped looking at user interaction as a powerful source of “personalization”. This is exactly what we are seeing happening in the industry right now. Nearly 100% of the focus on personalization and recommendation is to predict user needs, without any real direct user involvement.
So why don’t we consider user interaction as a key mechanism to improve personalization? There are of them objections to the user’s consultation:
- The first objection to exploring user interaction is that it is unnecessary.
This assumes that with enough data (from third-party, second or proprietary sources) and enough machine learning power, brands can “predict” what the user wants – perhaps even before the user does. realize it.
For impulse purchases, it would seem plausible that brands could meet the needs of a decent proportion of users in this way (even if user responses to recommendations today show that platforms are far from having a crystal ball! ). But how big is a “decent proportion”? And can we afford to focus only on this group, shrugging off the experience of others?
This was Amazon’s original strategy for gaining market dominance – offering a predicted set of products that meets a significant proportion of users’ needs – but now that this method of dominance is nearly exhausted, they are looking at how to best meet the needs of the rest. Privacy issues are also gradually reducing the amount of user data available, reducing the accuracy of predictions.
When it comes to thoughtful purchases, the research journey is an iterative learning process, and one-time predictions are often frustrating. User engagement then becomes an absolute necessity.
- The second objection is that user interaction creates friction, i.e. users “might not like it”.
True. But the benefits derived from knowledge what the user wants, rather than guessingare huge.
Users appreciate being able to share their needs and feedback, and the unique personalized recommendation is a very random affair. A hit is an instant success; a failure is an instantaneous failure that too often leads to the abandonment of research. The key is that users should be “able to” convey their needs and wants, instead of being “forced”.
When users are able to engage, they can enable us to increase personalization 10x, as well as recover from incorrect or unnecessary assumptions. On top of that, brands can always add an extra layer of personalization based on observed user data and the context of their query. But when users are unable to engage, the ability to personalize is severely limited by very limited knowledge about them and their current intent.
Taking personalization to the next phase
Why do brands limit themselves to simply predicting what users want when they have the possibility of knowing it? It’s time for brands to rethink their approach to how users search for products on their websites and take personalization to its next logical phase – where we escape predictions and instead focus on truly understanding the consumer intent.
The good news is that the path to the next phase of personalization does not involve a radical overhaul of existing methods/approaches.
Brands need to remember that people are at the heart of all search queries. Yes, they have the technology to provide various algorithms to facilitate online shopping experiences, but without interaction they simply cannot keep guessing what the consumer wants. It is time to involve them, to understand and to know what they want.
Twan Vollebregt is co-founder and CEO of Cross. He is a serial entrepreneur and leader whose background is centered around innovation. He led and exited two previous tech startups whose products are still very much alive for acquiring companies today. In 2014, Vollebregt won the prestigious Risk Magazine Innovation of the Year award for its work on a web platform product. He holds a doctorate in operations research.