Real-time personalization for a unique shopping experience

How real-time personalization works technically

To enable personalization, technology based on artificial intelligence is required. This so-called AI engine comprises a network of intelligent algorithms and uses artificial intelligence methods such as machine learning and reinforcement learning.

Machine learning as the fuel for the AI engine

Machine learning is a process that generates knowledge from experience. Based on a corresponding database, the AI engine can recognize patterns and regularities and thus make predictions for the personalized display of products and content. The required database consists of the shop's product catalog, the click and purchase behavior of shop customers, and the expert knowledge of the shop operator and personalization provider. This data is combined in a so-called knowledge base. The AI system processes it into knowledge and ultimately uses it to make predictions about the purchasing behavior of shop customers.

Real-time personalization requires a knowledge base consisting of four data categories: product data, clicks and purchases, open data, and expert knowledge.

Reinforcement learning as a trainer for the AI engine

Reinforcement learning is an artificial intelligence method in which a so-called agent learns to interact with its environment as effectively as possible. Real-time analytics is required to ensure that this happens in real time. The incoming data is analyzed and processed directly.

Automated decisions can be made within seconds or milliseconds using real-time analytics. This could be a decision about which content or products to display to a customer who is currently visiting a store. By making automated decisions in real time, the agent can provide this store customer with suitable recommendations within milliseconds.

When the page is accessed, a request is sent to the server, which interprets the request and creates a numerical vector containing information about the request for the agent. Based on this vector, the agent can decide on one or more actions. These decisions influence the rule chain used to determine the products that are actually recommended. These products are then returned to the user's browser.

The AI engine develops through self-learning with every click and purchase, allowing personalized real-time communication to be continuously refined. By using real-time personalization, you can deliver personalized content to each individual shopper at various touchpoints along the customer journey.

Stay up to date on personalization: Sign up for the epoq newsletter. Register now!

You can find out more about real time and how to collect, enrich, and interpret as much data as possible in order to extract the truly meaningful information from the mass of data in our blog article Reinforcement Learning Process: How to Increase the Probability of Purchase with Real-Time Analytics (Part 2).

What role AI plays in this

Real-time personalization therefore goes hand in hand with AI. Only systems with artificial intelligence are capable of analyzing large amounts of customer data in real time, creating a profile while the shop visitor is browsing, and using this information to display personalized content and products.

The profile generated in this way can be taken into account, for example, by using an intelligent search function for search queries. If your customer enters "parka" in the search field, for example, the AI automatically adjusts the order of the jackets displayed to suit their taste. Is elegance and quality more important to the user than a low price? Then high-quality brand-name products will appear on their screen first, followed by the less expensive parkas.

The genius of it: with every click, more data is generated that can be used immediately. With every interaction, the AI system learns more about what works and what doesn't for your shop visitors and adjusts the products or content accordingly.

This allows you to create a personalized customer experience that significantly improves customer loyalty and conversion rates. AI is therefore the "driving force" behind real-time personalization—without it, nothing works.

How real-time personalization and the customer journey are connected

To take full advantage of real-time personalization and create tailored shopping experiences, you need to understand your customers' journey. You track and analyze all of a user's touchpoints until they perform a specific action, such as making a purchase or signing up for a newsletter. This provides a 360-degree view of customer engagement in your store.

Shop operators can use real-time personalization to optimize the entire customer journey for their customers.

You analyze your customers' customer journey by using detailed web tracking and evaluating the individual click behavior of each customer. This gives you in-depth information about their purchasing behavior. Ideally, the system gets to know the customer so well based on data that it can even predict what they expect from certain touchpoints. You can then use this knowledge to optimize the customer journey by displaying positive, personalized interactions in real time at each of their touchpoints.

These include, for example

• personalized search results through the use of intelligent search
• LLM-based consulting with an AI shopping assistant
• personalized product recommendations on various shop pages
• personalized emails with individual product recommendations

Jeans-direct.de offers every customer a personalized shopping experience in the form of personalized emails using real-time personalization.
(Source: Screenshot of email from jeans-direct.de)

If you coordinate real-time personalization and the customer journey well, customers will get the impression when visiting your online shop that you know their wishes, value them, and care about them. We bet that won't be the last time they shop with you!

1. "Others also bought"

Actually, we humans are quite simple creatures: we always want what others have. Or at least we are interested in what "others" think is great. The "others also bought" product recommendations take advantage of this habit. To make the whole thing even more effective, it is important to match these recommendations with the individual interests of each customer and only recommend products that the shop customer might potentially like and that are also available in their size, for example.

2. "This could be a good fit for your product."

Your customer has added a set of nails to their shopping cart. Now you have the opportunity to offer them matching additional items, e.g. , in the form of a shopping cart layer with products that go well with them: hammers, picture frames, DIY sets. How likely is it that your customer will suddenly remember that they still need this and that? Right—quite high. Here, too, the relevance of the products displayed is crucial. The product recommendations must be consistent, i.e., they must be compatible with each other in terms of content or technology. If you also take into account the personal preferences of each shop customer, you can make even more targeted recommendations that suit their individual tastes.

3. "Now reduced"

Your customer is browsing around their favorite store, not really looking for anything in particular. However , during one of their last visits, your customer took a closer look at some great clothes but didn't buy them, perhaps because the price was too high. If some of these items are currently on sale, you can follow up with personalized recommendations in the style of "Now reduced." If a customer has already had their eye on certain products that interest them, they may now decide to buy them at a lower price.

4. "You may also like"

Based on the click and purchase behavior of your shop customers, you can act asa "digital sales assistant"and make targeted, personalized product recommendations that your customers will like. By tailoring further product suggestions to the individual online shopper (favorite brand, preferred color, correct size, etc.), you can draw their attention to products that might also suit their taste. This allows you to offer good service and, in the best case scenario, benefit from increased shopping cart values at the same time.

In the Görtz online shop, customers receive product suggestions in the form of similar items that they might also like.
(Source: Screenshot from goertz.de)

So, did you have déjà vu? Only when you learn more about real-time personalization in e-commerce do you realize how ingenious its mechanisms are and how much they influence you. Find out how well-known online shops use personalization effectively in different ways in our blog article 5 examples of successful personalization.

Stay up to date on personalization: Sign up for the epoq newsletter. Register now!

It's high time to start using real-time personalization for your customers too! Because even better than its mechanisms are its results: strong customer loyalty, high conversion rates, and unique shopping experiences.