Data segmentation in 2025: towards more precision, privacy and personalisation

Brands and companies constantly seek better ways to engage their audiences through data segmentation, data segments and personalisation. These techniques are essential for maintaining strong customer relationships and driving growth. 

But don’t you feel things have taken an extremely rapid turn these last few years with advancements in AI, analytics, and stricter regulations? 

Let’s be honest: even though it’s sometimes hard to follow, being ahead of the game remains a must for brands. Data segmentation stands at the crossroads of this challenge. It evolved from simple categorisation into a sophisticated strategy driven by precision, privacy, and personalisation.

By 2025, businesses that fail to adapt might fall behind in a world driven by more intelligent use of data and empowered consumers.

In this article, we’ll explore the evolution of data segmentation. From its foundational principles to its role in creating hyper-personalised customer experiences. We’ll discuss the importance of data quality, the transition from data collection to actionable segmentation, and the ultimate goal of personalisation. 

Additionally, we’ll highlight real-world examples from leading brands like Pierre Fabre, Unilever, L’Oréal EMEA, and LolaLiza. We’ll showcase how they’ve leveraged segmentation to enhance their marketing strategies.

1. What is data segmentation?

First things first, let’s understand what segmentation is. 

Gartner defines segmentation as follows:

While this definition should give you an accurate understanding of segmentation, let’s go further and define data segmentation:

You should now better grasp the direction we’re taking here. As data collection methods are getting more and more specialised and varied, data segmentation techniques are also on the rise. Proper data segmentation allows businesses to hyper-focus their marketing strategies and connect with their audiences like never before. Now that consumers demand more relevant and personalised experiences, effective data segmentation isn’t just a competitive advantage; it’s a necessity.

No proper segmentation without data quality

As you can imagine, you first need to collect the data you will use. You need quality data before thinking about data segmentation. Without it, your segmentation will not be qualitative, and your conclusions could be wrong.

Relying on third-party data is no longer a sustainable strategy, as it has been deprecated. Instead, first-party data (information directly collected from customers through their interactions) and zero-party data (data willingly and proactively shared by customers, such as preferences and intentions) have become critical brand assets. These data types are entirely consented to and transparent, giving them much value nowadays.

Brands can adopt many different strategies to collect these data types, and gamification and interactive marketing have proven very effective. For this matter, data collection platforms, like Qualifio, are now trusted by many brands. These platforms provide the tools to collect high-quality, consented and transparent customer data, which is needed for further data segmentation.

From data collection to data segmentation

The journey from data collection to data segmentation is a crucial, multi-step process that allows businesses to turn raw information into valuable insights. This leads to more personalised and effective interactions with their audience.

It all begins with data collection, where companies gather raw data from various touchpoints such as websites, mobile apps, social media platforms, customer surveys, and interactive campaigns like quizzes or polls. This data can range from basic demographic information like age, gender, and location to more detailed insights such as browsing behaviour, purchase history, feedback responses, and even preferences explicitly shared by customers.

However, raw data is rarely ready for immediate use. It must first be cleaned and enriched (often with the help of a Customer Data Platform, CDP, for example). Data cleaning involves removing duplicates, correcting errors, and resolving inconsistencies. Data enrichment then adds information and context to fill in gaps. It provides a more complete view of the customer and improving the understanding of their behaviour.

Once the data has been cleaned and enriched, the next step is data segmentation. This process can involve demographic segmentation (age, income, gender) and behavioural segmentation (purchasing habits, product preferences). Brands may also segment data based on geographic or psychographic factors, such as a customer’s lifestyle or values. The more precise the segmentation, the more effectively businesses can target specific customer needs. It improves the relevance of their communication and offerings. 

The key to successful segmentation? High quality data: accurate, complete, and relevant.

From data segmentation to customer segmentation

Alright, but where is data segmentation taking us, and why is it so important? The ultimate goal of data segmentation is to make it actionable, and that’s where customer segmentation jumps in. While data segmentation focuses on organising data into groups, customer segmentation takes this further. It groups actual customers into more meaningful categories based on shared behaviours, preferences, and needs.

Businesses can better understand their customers, what they want, and how they behave. This insight enables them to create highly personalised marketing campaigns that speak directly to different customer groups rather than using a single approach. And isn’t this the ultimate goal brands are aiming to reach nowadays? 

2. Precision, privacy and personalisation: the ultimate combo

Unlocking hyper-focused insights

The demand for precision in marketing and customer engagement has never been higher. Consumers expect brands to understand their unique needs and preferences and to deliver tailored experiences that resonate with them. 

This precision doesn’t just boost engagement; it also ensures more efficient use of resources. Broad and untargeted campaigns do not usually deliver as expected, negatively affecting marketing budgets. Instead, brands can segment their audiences granularly and allocate resources to the most promising segments. This approach increases conversions and maximises return on investment.

Thanks to accurate data segmentation, predictive analytics can take precision to the next level. By analysing historical data and identifying patterns, businesses can forecast future customer behaviours or purchasing decisions. For example, predictive models can reveal which customers are likely to churn, enabling retention efforts, or which segments are most likely to adopt a new product, enhancing marketing efforts.

Privacy: how segmentation guarantees trust and strengthens customer relationships

Data segmentation further ensures that customer data is used only for relevant and specific purposes, reducing the risk of bad use. It allows brands to personalise interactions, focusing on delivering value rather than invading privacy. 

To achieve this, brands often leverage advanced platforms like Customer Data Platforms (CDPs). CDPs consolidate and manage customer data for personalisation alongside Data Management Platforms (DMPs), typically used for audience targeting in advertising. These tools enable segmentation while maintaining a structured and ethical approach to data usage.

Additionally, regulations such as GDPR have set strict standards for how customer data is collected, stored, and processed. GDPR ensures that segmentation aligns with legal frameworks. By demonstrating respect for privacy through tailored and non-intrusive communication, brands not only meet regulatory requirements but also deepen trust with their customers. When consumers feel their data is safe and ethically handled, privacy becomes an advantage rather than a challenge.

Personalisation: the climax of data segmentation

By segmenting audiences based on detailed criteria (ranging from demographic and behavioural data to psychographics and predictive insights), brands can craft hyper-personalised messages and offers. For instance, a fashion retailer might use segmentation to recommend a winter coat to customers in cold regions or suggest eco-friendly options to sustainability-focused shoppers.

When customers feel understood and valued, they are more likely to engage, convert, and advocate for the brand. Also, personalised marketing based on transparent and ethical data collection and data segmentation ensures the experience feels seamless rather than invasive, the perfect balance between relevance and respect.

Conclusion: data segmentation in 2025 and beyond

Now that we’re entering 2025, data or market segmentation will undoubtedly be a cornerstone of modern marketing, mixing precision, privacy, and personalisation in ways that redefine customer engagement. The rapid evolution of AI, machine learning, and customer data platforms (CDPs) promises to make segmentation even more granular and adaptive, enabling brands to respond to customer needs in real-time. By leveraging high-quality, consented data, businesses can craft meaningful and tailored experiences that resonate deeply with their audiences.

Furthermore, effective data segmentation allows brands to allocate resources strategically, focusing on high-value segments and eliminating wasteful, broad campaigns. This not only improves marketing efficiency but also maximises return on investment. In a landscape where consumers demand relevance and respect for privacy, data segmentation suggests the perfect balance, ensuring trust is maintained while delivering hyper-personalised experiences.

Personalisation, powered by data segmentation, is no longer optional. It’s essential. Brands that embrace the precision of segmentation while prioritising ethical data practices will thrive in 2025’s competitive marketplace. Those who master this balance will stand out and build lasting customer loyalty in a rapidly evolving digital world.