Segmenting your customer database is a crucial step in optimising your understanding and thereby building a winning marketing strategy. By grouping your contacts (customers, prospects, collaborators, etc.) according to relevant criteria, you can personalise your offers, content, or campaigns in a truly effective way for an optimal user experience. In this article, we present five proven methods that can be easily deployed from your Customer Data Platform and will allow you to move to a data-driven marketing approach. We will particularly cover RFM segmentation and PMG segmentation, two powerful tools for identifying your most valuable customers.

 

RFM Segmentation

RFM segmentation is based on three key indicators:

• Recency When was the last interaction or purchase?
• Frequency How often does a customer buy your products or services?
• Amount What is the turnover generated by this customer?

This approach makes it possible, among other things, to quickly identify several types of customers:
✓ The VIPs Regular, high-value clients, to be prioritised in your loyalty strategies.
✓ Occasional customers Lower value clients with low purchase frequency, to work on transforming them into regular customers.
✓ At-risk customers Those whose purchase frequency is decreasing, requiring re-engagement actions.
✓ Dormant customers Underactive, but with potential to be exploited.

By applying RFM segmentation to your database, you will be able to select the most suitable contacts for each of your marketing campaigns or personalise your relationship programme to maximise engagement and profitability.

2. Segmentation PMG

The PMG (Petits, Moyens, Gros - Small, Medium, Large) segmentation is a simple and effective method for classifying your customers or prospects based on their economic weight or commercial potential. This method allows you to divide your base into 3 segments.

• The little customers : often numerous, they represent a small share of the turnover
• Average customers Soft belly of your database, they represent an average turnover brought back to your total sales.
• The big clients corresponds to your high-value customers who generate the largest part of your turnover (up to 80%)

Pourquoi choisir la segmentation PMG ?

This approach allows for the optimisation of resources, for example:

✅ Personalised support for major clients
✅ An automation approach for the smallest clients
✅ A cross-selling strategy for medium-sized clients

 It also offers better readability for managing activity, refining offers, or prioritising loyalty efforts. Simple to implement, PMG segmentation is a powerful tool for making fairer and more profitable decisions.

3. Clustering

Cluster segmentation is a method based on the analysis of your customer data that allows for the automatic grouping of individuals (customers, users, etc.) into homogeneous segments according to their behaviours, characteristics, or preferences. Unlike traditional segmentations, often based on predefined criteria (such as age or revenue), clustering adopts a data-driven approach: it is the data itself that reveals hidden structures within a dataset.

Thanks to algorithms like Machine Learning (K-means, etc.), this method identifies often unsuspected, yet highly relevant segments, which allow for better personalisation of offers, anticipation of needs, or optimisation of marketing campaigns. The clustering approach is fully in line with a performance-driven logic: it allows us to move beyond preconceptions to build strategies truly centred on real behaviours, thereby increasing the effectiveness and relevance of actions.

More complex to implement, this segmentation is useful for:
✔️ Personalise your marketing messages based on very specific business objectives
Optimise your advertising campaigns with more relevant audiences based on data
Adapt your offers to the specific needs of each cluster.

💡 Tip Combine this segmentation with RFM, for example, for a more refined approach.

4. Behavioural Segmentation

Behavioural analysis relies on customers' actions and interactions with your brand:

• Purchase history
• Reactions to Marketing Campaigns
• Activity on your site or app

This approach allows To understand better :
The most popular products with certain segments.
The most engaged customers with your brand.
✔️ Upselling and cross-selling opportunities.

Example application: A customer who frequently views a product category without completing a purchase can be targeted with a specific campaign featuring a tailored offer.

5. Customer Lifecycle Segmentation

The Customer journey evolves over time. Lifecycle segmentation allows each stage to be addressed with appropriate communication:

• Prospect : They discover your brand → Acquisition campaigns & lead nurturing
• New client : He has just made his first purchase → Onboarding & welcome offers
• Loyal customer He buys regularly → Loyalty programmes
• Declining client : He buys less often → Re-engagement actions (special offers, personalised reminders)

💡 Why is it effective?

✅ You maximise customer retention by anticipating their needs.
✅ You tailor your campaigns to each stage of the journey to optimise your conversion rate.

Conclusion

Segmenting your customer database is a strategic step that allows you to better understand and target your audiences. By combining approaches such as RFM, PMG, clustering, behavioural, and lifecycle segmentation, you optimise the relevance of your marketing campaigns and strengthen customer loyalty. At Smartprofile, we offer our expertise to mid-sized and large businesses, providing a modular platform that integrates Web and Mobile Analytics, a Customer Data Platform, and a marketing automation module, in order to transform your data into performance drivers. Adopt these proven methods and position your company as a leader in marketing innovation.