Retailers see it every day: customers expect smoother, more relevant experiences that are tailored to their preferences. But in reality, few organizations have the tools, coordination, and especially the data needed to meet this demand.

Personalization cannot be decreed; it must be built through interactions, weak signals, and the ability to activate data at the right time, on the right channel. This means going beyond classic segmentation or generic campaigns.

Some approaches already in use in retail demonstrate how to leverage data without complicating the customer journey. Through four key trends, new, more refined, reactive, and behaviorally aligned personalization methods are emerging.

Data for personalization: an underused potential in retail?

Why is personalization becoming a top priority for customers?

Expectations for personalized experiences keep growing. Today, 73% of customers expect brands to understand their needs and offer tailor-made interactions. And this demand isn’t just a marketing “plus”: it directly influences purchasing decisions. Nearly 78% of consumers are more likely to repurchase from a company that personalizes their experience.

But this desire for personalization is not always met. Only 15% of CMOs believe they are on the right track. This gap creates a huge opportunity for retailers. And the stakes are strategic: relevant personalization increases customer loyalty by 49%.

Conversely, poorly targeted efforts can damage brand image. Nearly a quarter of consumers report receiving recommendations for products they’ve already bought. This misstep creates a counterproductive effect by reinforcing the sense of disconnected marketing. In other words, an impersonal customer experience makes a brand invisible to the consumer.

What role does data play in enhancing the customer experience?

To meet these expectations, retailers now have a key lever: data. Every interaction (browsing, purchases, campaign responses, etc.) feeds a base of information that, when used correctly, helps refine customer journeys without adding friction.

En amont, elle aide à comprendre les intentions : pages consultées, recherches, interactions sociales… Ces signaux permettent d’adapter les contenus ou les offres, parfois dès la première visite. 

Upstream, it helps identify intent: pages viewed, searches made, social interactions… These signals allow content or offers to be adapted sometimes from the first visit.

During the purchase, it simplifies the journey: product suggestions based on preferences, anticipated delivery options, integrated advice online or in-store.

After the purchase, it enables more relevant engagement: useful recommendations, targeted follow-ups, content tailored to context or history.

The goal is not to personalize everything, but to do so where it makes sense. It’s not the amount of data that creates value, but the right activation at the right moment.

Behavioral segmentation: from traditional profiling to real-time activation

Segmenting customers by age or average basket size is still useful… but far from sufficient. Such static segmentation doesn’t reflect behavior evolution or real-time signals.

Today, the most agile brands are shifting to dynamic segmentations fueled by real-time data. Every click, search, or cart addition becomes a usable signal. It’s no longer about static profiles, but continuously updated observed actions.

Tools like Customer Data Platforms (CDPs) unify data from various channels (web, mobile, in-store). The result: a customer can automatically join a “high intent” segment after viewing a product multiple times, or be removed from an active segment after a period of inactivity.  

Conversational AI: from chatbots to self-learning smart agents

In retail, conversational agents have made major strides. We’ve moved far beyond basic scripts offering canned responses. Today, powered by AI, some bots understand natural language, detect customer intent, and tailor responses based on history and preferences.

This adaptability depends directly on data. Every question asked, every choice made, every past interaction enriches their learning base. The richer and better-structured the data, the more precise and relevant the conversational AI becomes.

Brands use these bots for both customer service and sales support. Order tracking, product choices, cart help: these bots handle more and more interactions especially during busy periods or outside business hours while still offering a satisfying level of personalization.

Smart automation: when CRM data meets the right signals

Effective marketing automation hinges on one thing: connecting what you know about a customer to what they’re doing now. This is exactly what happens when CRM data is combined with behavioral signals.

CRM data provides structure: loyalty status, declared preferences, purchase history. Add to this recent behaviors: browsing categories, abandoned carts, prolonged inactivity. Combining both enables personalized actions to be triggered without human intervention. These automations go beyond email: brands activate mobile push notifications, SMS, personalized web content, even in-store messages. Each channel is used based on context and customer profile.

When CRM and behavioral data are synchronized across channels, the result is a unified customer view. The ability to activate the right data at the right time, with the right message, is what makes real-time behavioral segmentation meaningful. CDPs help by collecting and unifying data from various sources to ensure optimal personalization. And the performance results speak for themselves: open rates, clicks, and conversions all rise significantly when messages are triggered by real behavior—not a calendar.

Selling without pushing: product recommendations put to the test

Recommendation engines analyze shopping and browsing behavior to suggest the most relevant products based on a customer’s profile. Two methods are often combined: suggesting what similar customers bought, or showcasing items related to previously viewed products.

These systems draw on diverse data: past purchases, viewed products, cart additions, declared preferences… plus broader trends from all users. Some models even include predictive logic: if a customer replaces their sneakers every 18 months, the algorithm can anticipate a new purchase around that time.

In e-commerce, these recommendations appear directly on pages: personalized carousels, post-purchase suggestions, targeted emails. In-store, some retailers test systems via tablets, sales associates, or interactive kiosks connected to the customer profile, replicating the personalization logic in a physical space.

But the benefits go beyond single transactions. A recommendation aligned with the customer’s tastes strengthens the sense of brand closeness. Over time, this relevance drives loyalty, especially if suggestions are integrated into the relationship journey: post-purchase emails, mobile app alerts, notifications about new collections aligned with preferences.

The more accurate the recommendations, the less they feel intrusive and the more they are perceived as a service rather than a sales tactic. This balance is key to the long-term effectiveness of these tools.

Retail & Data: what constraints should be anticipated during implementation?

These innovations require some precautions. First, from the customer experience perspective: if personalization is too obvious or poorly explained, it can be poorly received. It is essential to inform users and let them manage their preferences.

From a regulatory standpoint, the phasing out of third-party cookies is pushing brands to build their own databases: customer accounts, mobile apps, loyalty programs. These sources will be crucial to maintain individualized relationships.

Finally, to use these tools daily, teams must be trained, engaged, and properly equipped. The challenge isn’t technological it’s organizational: ensuring data flows freely, uses are clear, and every role sees a tangible benefit in their work.

AI, a new strategic advantage to improve company performance

Why educate your company about artificial intelligence? Far from being a mere tool, artificial intelligence is now a vital strategic advantage that improves a company’s performance. For senior management, AI has the potential to become a new strategic resource for rapid and better-informed decision-making, based on data and in-depth analysis.

How AI optimizes performance and strengthens the competitiveness of companies that understand the technology

Artificial intelligence provides a major advantage in terms of optimizing operational performance through the automation of complex processes, far beyond traditional automation.

Let’s take the claims handling process of a health insurance company as an example. This process comprises several stages, including checking the customer’s personal file and analyzing their request, and can lead to various measures, such as recovering missing documents and estimating the amount to be reimbursed. Generative AI can fully automate this process, because the tool interprets requests, has knowledge of contracts, is familiar with procedures and can resolve issues at each stage of the process in a logical way. The result? More accurate, more reliable and considerably faster management that improves customer satisfaction.

Automation is just one of the advantages offered by AI. Through predictive analysis, it can anticipate customer needs and improve competitiveness. As part of the claims handling process, predictive analysis can, for example, identify a customer who frequently claims reimbursements for alternative medical care. In this case, AI can suggest a customized insurance policy with better reimbursement for alternative medicine. By combining this ability to anticipate with the automation of tasks including report generation, AI enhances competitiveness and improves the customer experience.

Ultimately, this combination leads to optimized performance, better management of internal processes and increased customization of services in a constantly changing environment.

Educating senior management about artificial intelligence to improve strategic decision-making

Generative AI is a valuable strategic asset in the decision-making process. Senior management can rely on AI systems to rapidly provide precise and actionable insights. The large-scale data mining of internal data (sales history, stock, marketing performance indicators, turnover rate, etc.) and external data (global events, new environmental regulations, etc.), provides precise analysis and recommendations, based on real information. By cross-referencing this data, AI helps identify trends and detect opportunities that humans couldn’t spot in such a short space of time. 

These tools help make companies more agile as they can make decisions that are based on data and are better aligned with market realities. They offer invaluable support to senior management when it comes to anticipating future challenges and maximizing current performance.

Threats posed by a lack of education about AI

Companies that fail to recognize the importance of artificial intelligence are exposed to significant risks, including technological lag, a loss of competitiveness and a growing divide between business and technology. These threats can limit their ability to innovate and adapt to fast-changing market conditions.

Educating your company about AI: the key to avoiding technological lag and staying competitive

Companies that are slow to adopt artificial intelligence risk falling behind in terms of technology. While some organizations have already implemented AI use cases and are reaping the initial benefits, others that are reluctant to take the plunge could soon be left behind. This inertia can lead to reduced competitiveness, particularly in sectors in which agility and innovation are crucial to be able to meet customer expectations and keep pace with market trends. AI makes it possible to adjust strategies in real time, adapting to consumer needs and industry innovations. Companies that leverage its potential therefore enjoy a significant competitive advantage. Conversely, ignoring these technologies can result in significant opportunity costs. Companies risk missing out on new growth opportunities and limiting themselves in comparison with more agile competitors.

In retail, for instance, specifically in e-commerce, generative AI has a wide range of applications, including optimizing marketplace visibility, accelerating launches and fine-tuning delivery routes for greater speed and lower CO2 emissions. Retailers can use these technologies to improve customer satisfaction while increasing their return on investment (ROI).

The digital divide: how education about AI can reconnect business and technology

Artificial intelligence (AI) is profoundly transforming companies, but a lack of familiarity with this technology can create a digital divide between businesses and tools. Without a clear understanding of the challenges and opportunities that AI presents, teams will struggle to integrate these new technologies in their day-to-day processes. This will hamper the adoption of artificial intelligence and, consequently, lead to a technological lag within the organization. What’s more, this unfamiliarity can lead to mistrust among employees, exacerbating internal resistance.

This disconnect is primarily caused by a lack of adequate training, leaving employees out of step with market expectations. A company’s Executive Committee has a key role to play in this digital transformation. By educating senior management first, followed by operational teams, companies can ensure that their business objectives align with technological innovations and can also better anticipate market developments.

Raising senior management’s awareness of AI: key to the digital transformation

Educating a company about artificial intelligence (AI) is not just about implementing cutting-edge technologies. It is vital to understand that the successful adoption of artificial intelligence requires a thorough understanding of its implications, particularly in terms of security, ethics and compliance.

Protecting the company from cyberthreats and ensuring regulatory compliance

AI can strengthen cybersecurity by actively detecting potential threats. By analyzing vast volumes of data in real time, it identifies suspect behavior and enables companies to act before a cyberattack occurs. However, integrating these technologies also presents risks if they are not properly understood. For example, vulnerabilities can appear if the data processed by AI is poorly protected or if the processes for the storage and use of data to train AI models aren’t transparent.

In addition to its role in threat detection, AI must be managed to ensure strict compliance with data protection regulations, including the European Union’s AI Act. This regulatory framework establishes stringent requirements as to how AI systems must process and store data, particularly sensitive data. Companies that fail to manage these aspects are exposed to cyberthreats and also run the risk of financial penalties and reputational damage in the event of non-compliance.

It is therefore vital for senior management to understand that although AI is a powerful tool that can enhance security, it can also lead to a security breach if security protocols are not rigorously enforced. Educating senior management teams about the specific risks associated with AI, including data management and algorithm transparency, is vital to ensure the responsible use of this technology, while maximizing its performance in the fight against cyberthreats.

Identifying relevant use cases

Educating people about artificial intelligence involves more than just understanding the technology and should include an exploration of its practical applications. AI provides a wide range of possibilities in terms of automation, analysis, customization and prediction. By focusing on these capabilities, companies can identify use cases that are suited to their needs. Involving management teams from the earliest stages of a company’s AI transformation is vital to speed up the process and create momentum within the organization.

Senior management must play a key role in this process by launching initiatives to familiarize all teams with AI. Brainstorming sessions, group workshops and AI hackathons can be organized to encourage innovation and identify specific solutions. Executive management must demonstrate leadership to instill this dynamic and ensure the consistent adoption of AI at all levels of the company.

Ensuring an ethical and responsible approach

Despite its benefits, artificial intelligence can unintentionally introduce biases into algorithms. To prevent this, it is vital to ensure that every recommendation produced by AI is justifiable and understandable. Educating people about AI helps identify and correct these biases to ensure ethical and transparent use. This ensures that processes and decisions are managed in a transparent way and align with the company’s objectives.

The education of senior management teams plays a key role in the adoption of AI. By understanding the risks linked to algorithmic bias and the improper use of data, senior management can better manage the use of these technologies. Senior management must ensure that processes using AI are understandable and that automated decisions can be justified at any time. Not only does this strengthen employee trust in this tool, it also ensures that AI is a well-managed strategic resource, rather than a source of biased or misinterpreted decisions.

Artificial intelligence is a strategic advantage for companies, but different kinds of AI fulfill different needs. With so many solutions available, a strategic choice often has to be made between the creativity of generative AI and the analytical precision of predictive AI.

How do they work? What practical uses do they have? What are the criteria for making the right choice? Analysis.

Definition and how it works

Generative AI is a technology that can produce original content using the data available to it. More specifically, it can write texts, create images, generate videos and compose music.

It works using deep learning models, trained with huge volumes of data to identify patterns and generate consistent results. For example, these models can understand how words string together to form coherent sentences and how shapes and colors come together in an image to determine its structure.

Principle characteristics of generative AI:

• Automation of content creation.
• Use of deep neural networks.
• Production of results from specific instructions (prompts).
• Improvement through continuous learning (fine-tuning and human feedback).

Generative AI is a major step forward in the automation of content creation, but companies must develop a well thought-out approach when using it. Although it has significant potential in terms of productivity and customization, it also raises questions with regard to the quality of its results and the biased nature of its models.

Beyond these technical aspects, there are also important ethical issues. Given that generative AI can produce incorrect or discriminatory results, it raises questions of security and trust. These issues have led to the development of legal frameworks, including the AI Act in Europe, to provide a framework for these promising but potentially sensitive technologies.

What use cases for generative AI?

Much more than just a technical tool, generative AI is part of a strategic approach that can transform business processes.

Increased productivity

By automating time-consuming tasks, generative AI frees up time for staff to work on other things, while enhancing process reliability. For example:

• In insurance, it can automatically generate customized contracts, reducing human error.
• In marketing, it can provide tailored campaigns for each audience, accelerating strategy implementation.

In addition to speeding up tasks, it also ensures better quality and provides measurable results.

Large-scale customization

Generative AI transforms customer interactions by providing bespoke content and seamless communication that is perfectly tailored to the specific needs of each user. For example:

• In the luxury and e-commerce sectors, in which customization is vital, it can generate highly targeted product recommendations, strengthening customer engagement and satisfaction.
• For health insurance companies, an AI-enhanced chatbot can respond to customers 24/7, with natural, accurate communications, reducing waiting time and operational costs.

These solutions improve the user experience while improving conversion rates to combine efficiency and differentiation, particularly in competitive markets.

Support for innovation

Generative AI plays a key role in innovation cycles, quickly generating prototypes, designs and even functional models. Companies can use it to test various iterations of a product or service before moving on to the production phase, reducing lead times and associated costs.

• In manufacturing, it can design bespoke parts and optimize manufacturing processes, such as the design of complex industrial components and innovative packaging.
• When it comes to services, it can support the development of bespoke solutions by automating the creation of training scenarios and generating user paths for digital tools.

By providing more flexibility and considerable time savings, generative AI improves a company’s ability to provide innovative products and services that reflect customer expectations.

Employees focused on added value

Generative AI frees teams from the most repetitive tasks, such as data entry and the production of standardized content, so that they can focus on tasks with significant added value.

• For example, marketing teams can focus on strategy and performance analysis, while AI produces basic content or initial creative suggestions.
• In human resources, it can automate the processes of drafting job advertisements and answering candidates’ frequently asked questions, leaving more time for staff to engage in more substantive interactions.

This reduces their operational workload and makes their work more meaningful. These solutions help improve employee engagement and well-being.

Definition and how it works

Unlike generative AI, predictive AI doesn’t create new content; instead, it analyzes existing data to anticipate trends, detect risks and optimize resources.

Like generative AI, predictive AI is based on automatic learning models, but it uses data to identify patterns and relationships. It uses statistical and automatic learning models that can identify recurring patterns in large-scale data sets.

Once the model has been trained to recognize patterns in data, it can generate predictions using new information. These models are regularly updated with new data to ensure they remain relevant and accurate.

Principle characteristics of predictive AI:

• Generates predictions using historical data.
• Based on algorithms including regression, random forests and neural networks.
• Structured data analysis to identify correlation.
• Generates predictions based on new data sets.

Predictive AI: a powerful strategic tool that presents challenges

Predictive AI can be used to anticipate events, identify trends and adapt responses accordingly. Companies can use it for recommendations that are based on precise analysis to facilitate decision-making.

Although predictive AI is a powerful driver of performance, it requires a thorough approach and its results must be interpreted in a transparent way. These weaknesses highlight the importance of transparency. To ensure trust and accountability, it is vital that people can approve and justify decisions taken by AI. This requires a perfect understanding of the tools used.

What use cases for predictive AI?

With its analytical and anticipatory capabilities, predictive AI gives companies a competitive advantage by helping them make more informed decisions. This approach, focused on analysis and forecasting, is a strategic asset for many sectors.

Optimized resource management

By using predictive models, companies can anticipate their needs and adjust their resources accordingly:

• In the logistics sector, it can analyze variables including customer demand, the weather and major events (sales, holidays, sports competitions). AI can help adjust stock levels and avoid shortages or overstocking.
• In human resources, it can anticipate staffing needs and optimize schedules, particularly in the health sector and industry, where poor team management can impact service quality.

En structurant la gestion des ressources sur des prévisions fiables, les entreprises gagnent en efficacité et réduisent les coûts liés aux déséquilibres d’approvisionnement ou de main-d’œuvre.

Reduced costs through prevention

Predictive AI identifies recurring patterns to detect anomalies and prevent incidents, thereby limiting financial losses caused by breakdowns, fraud and other risks:

• In industry, it is used for predictive maintenance. By analyzing data from IoT sensors, it can anticipate machine failures and facilitate intervention before a breakdown causes a production shutdown.
• In insurance, it can identify high-risk profiles and suggest suitable coverage. It is also useful in the detection of fraud, identifying suspicious behavior when claims and requests for reimbursement are made.

By integrating predictive AI, companies strengthen their ability to prevent incidents and reduce the costs associated with failures and fraud.

An enhanced customer experience

By analyzing consumer behavior, predictive AI can improve the customer experience and strengthen engagement, particularly in the retail sector:

• Detecting churn (customer disengagement) enables companies to identify the weak signals that suggest that a customer is at risk of leaving. Targeted initiatives, such as customized promotional offers, can then be implemented to retain these customers.
• Predictive recommendations improve product suggestions, based on consumer habits and preferences, thereby increasing customer satisfaction and sales.

By anticipating customer expectations, predictive AI enables companies to adopt a proactive approach and improve customer loyalty.

A tool for the energy transition

Predictive AI also contributes to environmental performance by optimizing resource management and reducing waste:

• In manufacturing, it can be used to adapt machinery use, based on spikes in energy consumption, and anticipate unsold items to adjust production, thereby limiting overproduction and waste.
• In the transport sector, it can plan more efficient routes, thereby reducing the fuel consumption and the carbon footprint of these journeys.

Combining economic performance and environmental responsibility, predictive AI is a strategic asset for companies committed to the energy transition.

Although they share the same principles (data analysis, machine learning), generative AI and predictive AI meet different needs.

• Generative AI is designed to automate content creation and customize interactions with customers. It is ideal for companies that want to produce quality content quickly, improve customer engagement or accelerate innovation.
• Predictive AI, meanwhile, analyzes existing data to anticipate trends, detect risks and optimize resources. It is vital for companies that need to make strategic decisions based on reliable forecasts.

Strategic complementarity

Rather than choosing between these two approaches, many companies take advantage of their synergies.

Case in point: An e-commerce company can use generative AI to automatically write engaging product descriptions, while using predictive AI to anticipate purchasing trends and optimize its inventory accordingly.

How to make the right choice?

A comparison of generative AI vs. predictive AI

Rather than being competing solutions, generative AI and predictive AI are complementary tools. Depending on its objectives, a company can opt for one or the other, or even combine the two to maximize performance and competitiveness.