In a world where consumers are increasingly demanding, personalization has become a crucial issue for retail companies. To succeed in their personalization initiatives, companies need to adopt a "customer-driven" strategy. This involves collecting data to identify personae, positioning appropriate offers on the most relevant channels, and implementing a powerful content strategy across all channels. Loyalty programs will also play a major role in personalizing offers.
Necessary investments in technology
Companies need to invest in technologies and tools to implement an effective Omnichannel strategy.
Building a 360° customer vision
Retailers possess a wealth of customer data, which they collect when customers create an online account or join an omnichannel loyalty program. This data belongs to them. Indeed, customers are constantly interacting with companies in new ways and via different channels. They leave behind snippets of information - proprietary data - with each interaction.

Third-party cookies and proprietary data
Data linked to online behavior, known as third-party cookies, are more sensitive. These cookies, deposited by third-party sites, make it possible to trace browsing from one site to another. They concern all data linked to online campaigns, particularly retargeting campaigns. With the end of third-party cookies from 2024, retailers must now focus on the customer data they own.
The importance of Customer Data Platforms (CDP)
To optimize the use of data, Customer Data Platforms (CDP) are becoming essential for personalization. They bring together all data collected offline and online. The CDP integrates a unique customer repository, enabling the construction of a customer data base. It goes beyond a simple repository. Customer data platforms are central to customer knowledge. They process, unify and enrich data in real time, making it accessible and usable. As a result, retailers are able to build up highly detailed customer knowledge and dynamic segments.
Collect and unify data
Many of the systems we use, such as e-mail, analytics, CRM (Customer Relationship Management), e-commerce and social networks, operate in silos and don't link data together. This makes it difficult to obtain a complete picture, which in turn complicates analysis. A customer data platform eliminates these barriers by connecting all the tools used by marketing experts and acting as a single source for proprietary customer data. This process is continuous. The customer data platform ingests new data in real time from a variety of sources, maintaining a constantly evolving and updated history of customer interactions.

In-depth customer knowledge
To collect customer data and integrate it into the customer data platform, it is imperative to identify all points of contact and sources of data collection. Retailers' digital platforms (checkout, website, customer service, CRM, etc.) collect this data and distribute it to the customer data platform. It is at this stage that the 360° customer vision is built up, after data unification, processing and cleansing. One of the main advantages of this system is its ability to monitor and analyze a customer base, and to set up personalized reports using datavisualization, updated in real time.
However, the CDP tool does not enable content personalization. The data collected and analyzed is then redistributed to activation tools such as CRM, marketing automation tools, customer solutions, web personalization tools (CMS or A/B testing), digital campaigns and NPS.
What about AI in personalization?
Artificial intelligence (AI) is a key driver of personalization. It enables companies to create unique experiences, tailored to each customer, and optimize interactions across all channels. Thanks to advanced algorithms and the analysis of vast quantities of data, AI refines personalization strategies, making recommendations, offers and services more precise and responsive. Spotify personalizes its playlists and music suggestions based on listening history, user preferences and global trends.
AI processes massive volumes of customer data in real time. It analyzes complex data such as browsing behavior, purchase history, social network interactions, and even contextual data such as location or weather. This provides deep insights into customer preferences and needs. Segmentation becomes much more refined than traditional approaches. By analyzing multiple criteria (behavioral, demographic, transactional), AI creates ultra-precise, dynamic segments, enabling brands and retailers to tailor their marketing approach to each sub-group, or even to each individual.
Personalized predictions and recommendations
Machine learning algorithms predict which products or services are likely to be of interest to a specific customer, based on past behavior and similar profiles.
Platforms like Netflix and Amazon use these algorithms to offer ultra-personalized, real-time recommendations. The more a customer interacts with the platform, the more precise the recommendations become. Customer relations are also improving thanks to chatbots and virtual assistants, powered by AI. Conversations can be personalized by analyzing customer queries and offering responses tailored to their specific needs. They learn from previous interactions to offer more relevant responses, proactively improving the customer experience. Marketing campaigns, thanks to AI, are hyper-personalized in a totally automatic way. For example, e-mails or push notifications can be triggered automatically based on a user's individual behavior (cart abandonment, visit to a specific product page, etc.). AI also adjusts message content according to customer preferences or lifecycle. Some AI systems adjust prices based on demand, buying habits and competitors, creating a personalized price for each customer or customer segment. This maximizes conversions and increases margins.
AI initially benefits the customer by personalizing content and offers. But it is also a powerful tool for anticipating consumer needs and behavior. Thanks to predictive models, AI anticipates customers' future needs. For example, an AI could predict that a customer who has bought a stroller might soon be interested in children's products (car seats, toys, etc.), and propose proactive recommendations or offers.
By offering tailored, relevant and proactive experiences, companies strengthen their relationship with customers, which can lead to increased loyalty and higher conversion rates. In short, personalization is an axis of the customer journey expected by consumers. They receive a large number of messages and offers. To stand out from the crowd, retailers need to put in place a customer-centric strategy that collects customer data and responds to their expectations. Thanks to advances in artificial intelligence, it is now possible to go even further by anticipating customers' future needs.
By Emeline Helion and Inès Montesinos