Getting to know your customers better without knowing everything: first-party data in 6 lessons

It's tempting for marketers to believe that the more data they collect, the better they'll understand their customers. But the reality is quite different: over-collection breeds complexity, silos and CRM fatigue. According to the AFCDP barometer, 65% of French companies admit to collecting more information than they actually use. This finding illustrates an imbalance: customer knowledge based on accumulation rather than relevance.

The key is not to multiply data, but to make intelligent use of the data that counts: first-party data, derived directly from interactions between the brand and its customers. Observed through behaviors and customer engagement, it enables us to build more reliable segmentations and fuel activatable campaigns. Rethinking segmentation around first-party data means aiming for efficiency and consistency. It also paves the way for a more sustainable customer relationship, where data becomes a lever in the service of the experience, and not an end in itself.

1. Over-collection or the illusion of control

For a long time, the promise of data-driven marketing has long been confused with the idea that you need more and more data to better understand your customers. This logic of over-collection creates an illusion of control, when in fact it often leads to the opposite (wasted time, increased complexity and technical silos).

At a time of RGPD and the end of third-party cookiesdata overkill no longer makes sense. What counts today is not volumes, but the quality and relevance of data collected directly from customers. This is where first-party data becomes essential: it is reliable, actionable and, above all, derived from the relationship of trust between the brand and its customers.

2. Marketing pressure, symptom of a relational imbalance

Customers are saturated with marketing solicitations. Repetitive newsletters, poorly targeted campaigns, redundant messages: all symptoms of an unbalanced relationship between brands and consumers. So-called "CRM fatigue"is not a passing whim, but a gradual disengagement fueled by an overabundance of poorly addressed messages.

Consumers no longer expect marketing to be omnipresent, but relevant. For good reason: 64% of customers say they are weary of messages they consider too frequent or poorly targeted (according to KPAM's Customer Trends 2023 white paper). What they object to is not the commercial intention itself, but its execution when it becomes mechanical, redundant or disconnected from their expectations. It's better to have fewer, but better-calibrated solicitations: today, accuracy takes precedence over repetition.

Relying on first-party data can rebalance this relationship. Data derived from direct engagement (purchases, visits, interactions) reflect customers' true areas of interest. By refocusing campaigns on these tangible signals, we can limit over-solicitation, improve the quality of interactions and restore the value of every conversation.

3. Engagement: a more reliable compass than declarative data

Historically, marketing segmentation has been based on declarative data: age, gender, place of residence, family or professional situation. This data, while useful for targeting, says nothing about an individual's real level of interest in a brand or product at a given moment.

Today's most successful marketers prefer a more dynamic reading: engagement signals. These signals reveal appetence, receptivity and intention. Among the most exploitable indicators :

• Recency of interaction: a customer who clicked on a campaign 24 hours ago does not have the same potential as one who has been inactive for 6 months.
• Navigation depth: a visit to a product page is less engaging than an add-to-cart or simulation.
• Frequency of contacts initiated: opening an e-mail, interacting with an app, calling customer service...

First-party data derived from actual behavior (site visits, repeat purchases, interactions with campaigns) therefore provides a more accurate and up-to-date measure. Engagement then becomes a reliable compass to guide customer relations. It's by analyzing what customers do, not just what they say, that we can adapt marketing pressure, propose relevant offers and build a fluid, coherent experience.

4. Weak signals: anticipating with predictive AI

Observable engagement - clicks, opens, purchases - is only part of the story. Beyond visible behavior, first-party data conceals a more subtle richness: weak signals. Weak signals can be the frequency of visits to a site without conversion, repeated consultation of the same product category, or a gradual decline in interactions in a loyalty program.

A customer who regularly visits the same category without buying, who slows down the rate at which he opens emails, or who returns to consult a specific product, suggests intent or risk. Individually, these signals are difficult to interpret. But combined and put into perspective by predictive AI, they become powerful indicators.

Thanks to AI, marketers can detect potential churn, predict purchase intent, spot upsell or identify key moments to trigger interaction. Rather than waiting for the customer to show up, AI makes it possible to act at the right moment, relying solely on the value of existing first-party data.

Far from encouraging over-collection, this approach maximizes what already exists, transforming data into actionable intelligence.

5. Towards a more relevant approach to segmentation

Segmentation has long been based on socio-demographic or declarative criteria. But at a time when data is abundant, this approach is showing its limitations.

First-party data provides a more solid basis: it reflects actual behavior and enables the construction of dynamic segmentations. The most telling example is RFM (Recency, Frequency, Amount)segmentation, which classifies customers according to their transactional engagement. This approach can already be used to identify key profiles (VIP, inactive, recent) and adapt strategy accordingly.

AI-generated targeting, fed by first-party data, enhances message relevance.

6. CDP: the conductor of customer data

Given the complexity of customer journeys, the Customer Data Platform ( CDP) has become an indispensable tool. It centralizes and deduplicates first-party data from all sources (website, e-commerce, points of sale, CRM, loyalty programs) to build a single vision of the customer.

This consolidated view not only makes it easy to activate segmentations such as RFM, but also to fully exploit weak signals via predictive AI. Instead of multiplying scattered databases, CDP transforms data into actionable intelligence, reducing CRM fatigue and optimizing campaign relevance.

By making data more accessible and intelligible, CDP also frees up time for marketing teams, who can focus on strategy and creativity rather than on processing data silos. CDP thus becomes the pivot between data and its intelligent activation.

The right balance as a requirement

Knowing your customers better no longer means knowing everything about them. The challenge of data-driven marketing is not to collect more and more, but to find the right balance between quantity and relevance.

Too little data limits personalization, but too much data leads to complexity and over-solicitation. First-party data, enriched by CDP and informed by predictive AI, embodies the right balance. It anchors marketing strategies in a sustainable logic, where every interaction is relevant, targeted and respectful of the customer relationship.

By choosing to focus on value rather than volume, brands can transform data into a genuine lever of trust, loyalty and performance.