January 27, 2020
When the term “personalization” was originally coined, it referred to the ability to tailor digital content and experiences, based on the expected behavior of groups. At best, marketers and merchandisers chose those shopper cohorts based mostly on demographic data, with some light behavioral analysis. This “old school” personalization treated you the same way as it treated a million other people that looked like you on paper and behaved in roughly similar ways in the real world. As it turns out, this wasn’t very “personalized.”
Hyper-personalization Is Now Possible
If you’re shopping for a personalization engine, remember that there are five things that it must have:
Ability to learn
Today, a personalization engine must include artificial intelligence (variants of which include machine learning or deep learning). Why? When a site sells millions of products to hundreds of millions of shoppers, there is no way for human experts to keep up with all the behavioral signals.
AI that augments human intelligence
Augmented intelligence solutions combine the best of humans and machines. They use machine learning to analyze data and detect patterns at a superhuman scale and leverage automation to act at superhuman speed. They leverage human expertise to go beyond the confines of the existing data, source additional data, reason, make judgment calls, and engage with other humans.
Ability to scale dynamically
As product catalogs grow and user behavior shifts, it becomes increasingly difficult for digital merchandisers to understand all the signals that flow from shoppers on the site and then use those to update the business rules.
Use machine learning, clustering and classification to eliminate many of the rote, repetitive tasks of maintaining rules in an engine from the likes of Endeca. This frees human beings to use their human intelligence for creativity and innovation.
An acute sense of intent
Another area where static approaches to personalization fall flat are the intent. Each and every search might be treated as a distinct event, floating in time and space. In the real world, consumers use very different search strategies. They use different words to mean the same things. Some people can’t spell so well.
That’s where signals come in. By capturing log files, transaction data, what people click on — and what they don’t, you can better analyze user intent.
Visible, intuitive analytics
modern personalization engines consist of dynamic, variable, interconnected parts. Each of those components can be individually tuned or optimized, but quite often the whole does not equal the sum of its parts. You want a system that can show system administrators and eCommerce
merchandisers how business users and customers interact with the system. The system should let you visualize data categorically, and even understand the individual customer or user journeys.