Big Data and Machine Learning: how to achieve real hyper-personalization in e-commerce?

“Profiling” matches the user of a site to a typical profile in terms of behaviors such as “the user who comes to buy a single item”, or “the one who is exclusively interested in the world of sneakers “.

Where “classic” profiling relies on user profiles or segments defined by marketing or business teams, hyper-personalization leads us to go further. He is now possible thanks to Artificial Intelligence, for an e-commerce site, to create segments directly linked to user profiles and behaviors, covering many more uses.

Further personalization also offers the user a “unique” experience fully adapted to their needs. One of the best examples of hyper-personalization is YouTube or Instagram suggestions. They are based on the behavior of the user as well as that of others identified with similar interests, to offer more relevant content.

In what contexts is hyper personalization used?

With the advent of machine learning and the explosion of e-commerce, several opportunities have arisen for businesses:

  • Machine learning, which now makes it possible to offer real-time data processing as well as relatively simple application implementation by data scientists and technical teams.
  • The evolution of user behavior monitoring solutions that provide access to new data relating to consumer habits (purchasing behavior, segmentation, etc.) from retailers.

Hyper-personalization in e-commerce results in the collection of user data as well as the collection of interactions between the customer and the articles, while respecting privacy (RGPD, etc.). To make the best use of this data and make it speak, questions arise: through which channel did the user arrive? How did it interact with the product? What items have been viewed, added to cart, purchased? Which categories were consulted? How long was it on the page, etc. ?

The answers to these questions make it possible to define what pleases the user the most. It is this knowledge that makes it possible to create unique experiences with much more effective engagement funnels (succession of stages).

Hyper-personalization in e-commerce is mainly used on two axes: engagement and conversion rate.

By offering relevant articles to users, it is therefore possible to create funnels of engagement much more easily. The user experience is also considerably improved thanks to a deeper knowledge of the “profile”. With a well-categorized dataset, it is possible to know which categories are the most meaningful for a customer, and therefore to build his journey on the website according to these results. This personalization has a direct positive impact on the conversion rate.

What approach to take to do hyper personalization?

The approach currently most used by e-commerce sites is relatively simple. It’s about matching interactions between users and articles. Then, based on the number of views, to recommend those who have the most success. This approach can easily be implemented. However, it will very quickly reach its limit, on several points.

At first, only the most viewed articles will be recommended, creating a self-sustaining loop. Thus, it will be difficult to integrate new products into the system. As for the commercial data, it will be biased, making the whole process useless. This approach also excludes the exploratory part making it possible to bring up so-called “catalog background” articles that are relevant for the user.

It also involves regular updating and maintenance of the relational model leading to a waste of time for the teams.

Predictive models are now more powerful than the behavioral models used in the past.

This is where Big Data and Machine Learning come into play. In recent years, with the increasing simplicity of technical implementations, machine learning and cloud platforms, it is becoming easier and easier places a model with much more relevant results.

Where the approach was limited to upstream creation and analysis (creation of profiles, drafting of user paths and implementation), machine learning will, for its part, have a data-centric approach whose path is as follows : definition of data, creation of user segments, creation of models and implementation of machine learning.

An example that is used in e-commerce is that of language and syntactic construction models for suggesting articles that may be of interest to users. The method is similar to the text generators that we have been seeing for a few years, where thanks to deep learning it is possible to “predict” the next word in a sentence. By writing for example on Gmail “How are you”? or “Did you have a good vacation?” the text editor will prompt the user with “did you have a good vacation?” “. In other cases, the words you might use are scored. “The screwdriver is…” will give a score of 70% to the most probable word, then 50% to another proposition… By using these methods for product recommendation, these approaches lead to better relevance of the results, unlike a solution based only on statistical data.

Predictive models are now more powerful than behavioral models used in the past.

The predictive model takes on its full meaning thanks to cloud platforms which now offer (for the most part) a range of services that can collect data and exploit it in an optimal way, but also which make it possible to build and deploy AI and machine solutions. learning with all the advantages of the cloud (costs, scalability, high availability).

For personalization, this is also a considerable advantage. Some public clouds such as AWS and GCP offer managed services that are easy to set up and require little maintenance (such as “Personalize” at AWS, which uses the recommendation engine of Amazon and Prime Video, or “Recommendation AI” , its equivalent at GCP).

And tomorrow ? A home page in your image!

With the right tools and the right experts, hyper-personalization can happen quickly. Some large groups are already using it and using extremely powerful tools to define hyper-targeted communication strategies.

For example, the StockX site added a simple “Recommended products for you” line to its home page (it uses AWS Personalize to process the data). This simple extra line quickly became the most successful area of ​​their homepage!

One of the major ongoing projects is the creation of ultra-personalized user experiences. The goal is to offer spaces that fully match the user’s profile.

Amazon, for example, completely adapts its interface according to the user’s profile. For example, the list of categories is sorted differently depending on the purchase history.

But the challenge today to differentiate is greater. Currently, hyper-personalization naturally focuses on transforming site visits, but new approaches will emerge. As mentioned earlier, using predictive models can have other impacts: what if we used the predictive model to manage inventory? What happens if we use an e-commerce solution on written content? The playing field is immense and the evolutions are certain.

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Big Data and Machine Learning: how to achieve real hyper-personalization in e-commerce?

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