AI-driven product recommendations are an excellent way to both improve your customer experience and drive more sales. They can do what a human team cannot – create personalised offers for every single client. How is this achieved? You will find it out in this article.

Is it easy to implement a personalised product recommendations system powered by AI? Yes and no. While you can work with us at Ailleron to prepare an excellent model, you still need to unify your data and cleanse it. Nevertheless, it is worth the struggle, as it will help you deliver personalised customer experiences.

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How Do AI Product Recommendations Work?

AI product recommendations are based fully on the data your organisation collected on a particular client. The process can be broken down into 2 stages, namely:

Collecting Data

This starts with the first interaction between your bank and the customer. Each piece of information is collected and stored in a central database. Your AI model accesses it and analyses it to find opportunities for product recommendations.

Bear in mind that data collection does not refer only to current information about a given customer – historical data is also crucial. Why? It’s used to train your large language model (LLM). The role of historical data is paramount – AI uses it to spot patterns that indicate purchase intent, hence defining which of your customers are potential buyers who should be provided with a product recommendation and which are not.

Finding Sales Opportunities (Potential Buyers)

With all your data in place, the AI system analyses the customers to find out who they should provide with intelligent product recommendations. If the customer shows signs that they might be interested in an upsell/cross-sell, they are provided with a tailor-made recommendation that they are more likely to buy.

What is so special about personalised product recommendation models is that, unlike traditional AI models, they do not focus purely on the general commercial intent of the customer but rather on the likelihood of them purchasing a particular, customised product. This allows financial organisations to create more sales opportunities and achieve higher conversion rates.

Do you want to invest in AI-powered personalised product recommendations? Check out our AI banking solutions!

Why Do You Need AI-Driven, Personalised Product Recommendations?

Knowing how AI product recommendations work, you might wonder whether you really need them in your bank. Well, you do, and there are several reasons for that.

1. Customers Expect Personalisation

Firstly, personalised customer experience has become an expectation – a demand a financial institution needs to fulfil in order to attract and retain customers. This isn’t exactly a new phenomenon; we could see this trend already in 2017. However, its importance has been rising since then, so nowadays, personalisation is a must, and AI product recommendations will help you achieve it.

2. Higher Sales

The ultimate goal of every business is to drive sales (and to do this cost-effectively). Again, personalised product recommendations will help you achieve this objective.

Due to tailor-made offers, your customers will be much more likely to convert when presented with a product recommendation. As a result, your one-off investment in an AI model will increase sales significantly.

3. Customer Satisfaction

To maintain high customer loyalty, you need to build customer satisfaction. And what builds satisfaction better than personalisation?

Bespoke product (and content!) recommendations are the way to show your customers that you understand their needs. What is more, they are convenient – your clients do not need to spend hours researching your products to find the right one for them. As a consequence of all of this, their satisfaction rises, and so do your customer retention rates.

4. Proactivity

Proactive banking is a trend appreciated by customers in the financial sector. It refers to an approach where you try to understand your customers’ needs and predict their issues or expectations before they occur. Intelligent and personalised product recommendations are an element that will help you build up proactive communication.

The Importance of Preparing Your Data for AI-Driven Systems

Although designing and teaching an AI model is undoubtedly the most important element of an AI product recommendations system, the true cornerstone for it is… data storage. After all, your artificial intelligence needs to be provided with all the relevant information to work efficiently. What does this mean in practice?

  • You need to cleanse your data – select all the information relevant to your product recommendations and eliminate the rest.
  • You have to prevent data bias – ensure that your data is objective and that your system does not mistakenly consider patterns that it shouldn’t.
  • You must integrate your data – create a single customer data platform where all the relevant information is stored.

Potential Risk of Smart Recommendation Models

Naturally, there are some dangers and challenges related to the introduction of smart product recommendations. What are they?

  • Data bias – already mentioned in the previous section, data bias may lead to discrimination, which in turn will increase the existing financial inequalities.
  • Trustworthiness – it’s crucial to invest in solutions that use the finest security measures and to understand how your system works.
  • Liability – what if an AI system makes a mistake, for instance, by offering a product that should not be sold to a given customer (e.g., due to a lower credit score)? Who will take responsibility for this? This is a major issue regarding AI-powered systems. However, there is a way to mitigate this problem – leave business rule checking to project teams and analysts, as well as those working on the first line of customer service.

The Future of AI Product Recommendation Systems

If we want to cover this topic fully, we need to discuss the potential future of such intelligent product recommendations. What will we (probably) bring to the table in the next couple of years?

  • Contextualisation – So far, AI-powered smart recommendation systems analyse data but not the immediate context they will be placed within. In the future, such models should be able to understand real-time factors like mood, location, or current activity and use them to optimise the timing of presenting products to clients.
  • Constant improvement – An AI system is as good as the data it’s trained on. The more information it is provided with, the better it gets. In the next couple of years, the amount of data will skyrocket. This might cause some issues regarding AI development. At the same time, it’s an opportunity since there will be more training material for artificial intelligence. As a result, the systems will get even better than they are now.


AI-powered, personalised product recommendations are a way to boost sales and enhance customer experience. At the same time, they require some preparation, including cleansing and organising your data. Yet, if you invest in a good model, you should quickly see the benefits of implementing such a system and earn a steady return on your investment. Therefore, do not hesitate – contact us to talk about designing your AI model.

You might also read: How Banks Can Use AI to Improve Sales & Marketing

Ailleron - AI Product Recommendations


Ailleron Marketing team includes digital marketers and content creators who provide insights and expertise from across the organization, including #AilleronExperts. For media queries, please get in touch with us via our contact form.

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