6 min.
Improve customer retention through data and segmentation
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Improve customer retention through data and segmentation


Faced with the gradual withdrawal of third-party cookies, the digital marketing landscape is gradually reshaping, refocusing the attention of decision-makers on customer retention. 

This technological change, and the evolution of the legal context surrounding the protection of personal data, makes it all the more necessary to develop an appropriate proprietary data strategy: a strategy aimed at reducing the dependence of organizations on media giants by improving the performance of their own channels. 

In this context, the success of marketing strategies depends more than ever on the ability of companies to understand customer data and generate insights that will allow them to target priority audiences and initiatives in relation to their objectives.

This is what two specialists at Adviso,  Roger Kamena , Principal Data Scientist and Head of Innovation, Research and Development, and  Maxime Philippon , CRM Strategist, discussed during a webinar held last month.

We summarize the main points of this interview here, and the webinar remains available for free in replay.

Watch the webinar


With the paradigm shift brought about by the withdrawal of third-party cookies, the use of proprietary data (  0 ,  1st  and  2nd  party data) will become essential for companies in 2023, because it is this data that will allow them to properly target their audiences. and personalize their communications.

Proof of this interest in the acquisition of proprietary data, a  2020 study published by Winterberry Group  reveals that 60% of Canadian companies plan to invest in the use of primary data to adapt to future changes.

Since the email address is the main identifier used to access online services, customer relationship management (or CRM, for  Customer Relationship Management ) and its flagship channel,  e-mailing , are becoming essential in a post-cookie world. Companies that excel in retaining their audiences stand out from their competitors when it comes to valuing their customer base.

In addition, numerous surveys conducted among digital marketing  professionals , in North America and Europe, underline the importance for companies to invest in their e-mailing channel  to adjust to the disappearance of third-party cookies.


The effectiveness of targeting marketing initiatives depends on segmentation. If common sense encourages us to personalize communications by taking into account the diversity of customer profiles, prioritizing these initiatives without data is a blind bet whose results are unpredictable.

To take advantage of e-mailing , which can generate returns on investment of up to 40 times the amount invested, segmentation and personalization are essential. 

In  e-mailing , for example, the segmentation and personalization of initiatives make it possible to generate higher revenues per recipient. Indeed, they promote the achievement of higher engagement and conversion rates, as well as more efficient email delivery (lower SPAM and unsubscribe rates). 

To send a correctly positioned message at the right time, likely to motivate a purchase (or any other commitment), the identification and definition of the segments are prerequisites.


To build effective data-informed segmentation, companies must perform a continuous iterative process, following these main steps:

  1. Collect different types of data about their customers: online and in-store transactions, online visit behavior, marketing engagement, industry and competitive data informing about market trends, data about products and services offered , etc.;
  2. Unify customer data, transform and clean it as needed. Then, enrich the profiles with the relevant attributes according to the business objectives (examples: the value of the average shopping cart, the total number of transactions, the category of the most purchased products, etc.); 
  3. Target audiences by prioritizing promising segments (in terms of volume or business opportunities);
  4. Deliver these segments to activation channels such as email , website, or media platforms (including for retargeting initiatives or acquisition of prospects who share similar characteristics to the company's current customer base);
  5. Analyze the results of marketing campaigns to improve segmentation.

The process of centralizing, unifying and delivering data can represent an operational challenge for companies that do not have marketing technologies that allow them to have a 360 view of their customers. 

These operations can be carried out using Reverse ETL (Extract, Transform, Load), a process that allows data to be copied to a central warehouse and then synchronized with operational recording systems (ERP, CRM, media platforms, etc.) . 

To learn more about this, you can also  consult our article on the Reverse ETL .


To meet the needs of marketing (and B2B sales) teams, segmentation models must take into consideration business objectives as well as available customer data. For this reason, each organization will have unique data segmentation.

Several models have been shown to be effective in maximizing customer retention. To maximize the usefulness of a segmentation strategy, it is important to combine various models and adapt them to the needs of each company. 

Here are some examples of segmentation models:

  1. The RFM model (Recency, Frequency, Monetization)  is based on recency, frequency of purchase (or engagement with the brand) and monetary value associated with users. This model, which we present in more detail in the webinar, makes it possible in particular to build groups of customers that can be easily activated and classified according to their ability to generate income for businesses.


  1. Segmentation according to LTV ( lifetime value  or customer value),  groups customers according to an estimate of the profit that they could generate in the long term, ie over the entire duration of the commercial relationship with the company.
  2. Segmentation by stage of the buying journey and behavior  (online and/or offline) is the model for targeting customers at the right time. It is essential to guarantee the relevance of the messages.
  3. Demographic and/or psychographic segmentation , which is more widely used, relies less on transactional data. It is faster to implement, but in return, it generates less impact.
  4. Segmentation according to life moments  (birthday, wedding, buying a house, etc.) is a particularly popular model in targeting media campaigns (example: Facebook Meta). This is ideal when combined with a segmentation carried out according to the stage of the purchase journey and the behavior (described in point 3).
  5. Rather than being used only during limited periods,  seasonal customer segments  can be reused at other times of the year to encourage the public to a new buying behavior.
  6. Models based on artificial intelligence and machine learning  are perfectly suited to the needs of mature companies that aim for excellence in personalization.

Segmentation strategies are specific to each company. It is therefore important to remember that this ongoing reflection must make it possible to maximize the financial and human investments of companies. 

So all you have to do is (re)discover the model that suits you: the one that will allow you to build lasting relationships with your communities, in this  post-cookie era  where customer retention is shaping up to be a competitive advantage. determining. 

To learn more about the benefits of successful segmentation, the different aspects of these models, and the process of implementing segmentation tailored to your business, watch the free webinar recording  "  Improving Customer Retention through email  marketing .