Personalising the customer experience is no longer an option, it's a standard set by giants like Amazon or Netflix. For marketing teams, the challenge is now clear: it's no longer just a «nice to have,» but an essential lever for boosting revenue and loyalty.
However, moving from theory to practice remains a challenge. Dependence on IT teams, scattered data, time-consuming manual processes... the obstacles are numerous. The solution to overcome this hurdle? Automation via an AI-driven recommendation engine backed by a Customer Data Platform (CDP).
In this article, we explain how to turn your raw data into incremental revenue using a proven methodology.
Why switch from manual customisation to AI?
While business rules (top sellers, new arrivals) have their uses, they quickly show their limitations: lack of relevance for the individual and difficulty in industrialisation.
An AI-based recommendation engine is a game-changer. It analyses the context and history of each visitor in real-time to suggest the most relevant content. The results are tangible: according to McKinsey, personalisation is a lever capable of generating +15% in revenue
To function, the engine draws on several types of data that your CDP allows you to unify:
Explicit data Declared preferences, notices.
Behavioural data Page views, time spent, clicks
Transactional data Purchase history and frequency.
Contextual data Seasonality, trends, device
The 3 algorithmic approaches to know
To orchestrate these recommendations, the engine must select the most relevant algorithm for each visitor. Three main families can be distinguished
- Collaborative Filtering It is based on similarity between users («People like you also liked...»).
- Content-Based Filtering It recommends items similar to those the user has already viewed, ideal for active profiles.
- The Hybrid Approach It is often the most performant. It combines several strategies to circumvent the «Cold Start» problem (new users without history) and improve the diversity of suggestions.
Methodology: 6 steps to deploy your bespoke engine
At Smartprofile, we advocate a circular and iterative approach to ensure your project's success. Here's how to structure your approach:
1. Data exploration and preparation
Everything starts in your CDP. You need to make the «raw material» reliable: unify contact data, the product catalogue, and transactional history. This is the essential foundation for feeding the algorithms.
2. Definition of the business context
AI doesn't do everything on its own. You need to set the «rules of the game» by integrating your specific constraints: seasonality, RFM score importance, or stock rules.
3. Choice of algorithms and initial tests
It's time to implement «basic» (unsupervised) algorithms such as collaborative filtering or content-based filtering, and to launch A/B tests to validate their relevance.
4. Supervised Learning (Machine Learning)
To inject real predictive intelligence, we use «Learning-to-Rank» algorithms. The model trains on real historical data (clicks, purchases) to rank recommendations by conversion probability. The system then becomes hybrid and much more effective.
5. Omnichannel deployment
Once the model is validated, it is industrialised. Via API or MCP server, recommendations are pushed in real time across all your channels: website, emails, pop-ins, SMS.
6. Maintenance and continuous improvement
A recommendation engine needs to be alive. It is crucial to retrain the model regularly (ideally weekly) and to monitor precise KPIs to correct any potential drift.
Tangible results for your sector
The power of this approach lies in its ability to adapt to each profession.
- Retail : By suggesting a complementary shirt for a jacket seen (cross-selling), our customers are observing an increase of 10 to 15% in the average basket value.
- Tourism Suggesting additional activities or services before a trip helps to increase a customer's «Lifetime Value» (LTV) while also improving their satisfaction.
- B2B Recommending a white paper related to a blog post that has been consulted allows the transformation of an anonymous visitor into a qualified lead for sales representatives.
If this article interests you and you wish to delve deeper, find the Smartprofile teams' Webinar on this topic of recommendation engines: I'm watching the webinar!
Do you want to harness the potential of your data to boost your revenue? To succeed, you need reliable data, clear governance (GDPR), and robust industrialisation. The Smartprofile teams are here to support you in setting up your tailor-made recommendation engine.
