Customer Experience enriches with multiple AI-linked dimensions

Strategy and segmentation

AI facilitates the design of marketing, sales, or customer relation strategies by identifying and analyzing high-value segments. It projects these segments across the entire market, identifies promising prospects, and retains high-value customers. Its analytical capabilities avoid static segmentation by integrating a dynamic, real-time dimension, allowing adaptation. The DEF ProPME strategy illustrates this approach: it decomposes into multi-criteria segments, enabling targeted interventions without disrupting the market or devaluing offers. This relies on optimal data use, promoting «surgical» actions to maximize impact

Visibility and prospecting

AI makes online prospecting more precise and efficient. It allows fine targeting by geographic criteria, even down to IRIS codes, and enables localized promotions. It also facilitates intelligent redirection of customers to nearby points of sale based on their catchment area. This personalization can be adapted to various sectors: artisans, retailers, healthcare professionals, or hospitality. For example, for a plumber, mobility is key; for a retailer, communication with customers; for healthcare, teleconsultation solutions; for hotels or restaurants, local connectivity. AI also accelerates content production (texts, photos, videos), improving natural referencing. Search evolves toward longer and voice queries, changing the shopping experience. For example, a newly opened bakery in Orléans can benefit from optimized content to promote its new activity.

NBOA – Next Best Offer

or Action This predictive recommendation engine uses data to suggest relevant offers, respecting data sovereignty. It automates the recommendation and orchestration of messages based on customer context, using affinity or fragility scores for fine personalization. Previously, segmentation was based on cross-analyses or broad campaigns, requiring extensive time and wide targeting for profitability. Today, NBOA refines these actions by proposing precise recommendations, in real-time, tailored to each customer or prospect. It embodies brands’ desire to be more proactive and responsive.

Digital demoyenization of promotions

Personalization is deployed through 120 differentiated scenarios, enriching the customer experience. Internal banners are adapted based on customer data, context, and NBOA parameters. Tools like Djingo Pro assist sales by querying the customer and integrating data for tailored responses, generating around €2.5 million in annual gains. For external communication, audience segmentation enables effective redirection of ProPME clients to dedicated sites, avoiding channel cannibalization. Precise segmentation and intelligent audience management create a virtuous circle, enhancing campaign relevance.

AI assistance for sales teams

One of the first concrete applications is preparing sales appointments, generating about €1 million in annual gains. By combining internal and external data, AI helps salespeople better understand the customer context, easing contact initiation. Beyond recommendations, AI Gen also listens to and analyzes conversations to understand call motives, train or coach teams, and improve interaction quality. In real-time, it can suggest content or commercial proposals, even automate their sending, enriching the customer file. The main challenge remains managing customer preferences and consent, as well as convincing salespeople of AI’s added value, especially regarding their ego or compensation systems. Success depends on perceived relevance by users. 

Bots and 24/7 customer service

Since eight years, bots for technical assistance have saved millions annually. These early tools, based on conventional AI, offer precise responses and avoid hallucinations. The rise of generative AI now allows handling a broader range of topics in natural language, coupled with traditional AI to ensure reliability. Chatbots are evolving toward voice interfaces, especially for mobile use. Their role is to ensure constant availability, simplify language-based interaction, and personalize responses (e.g., PUK code). Intelligent transfer management to human agents is essential to avoid customer frustration while maintaining context. The future may see increased disintermediation by large language models (LLMs) from GAFAM, which could respond directly to customer requests. However, trust and relevance still need strengthening.

Conclusion and perspectives

The success of AI integration depends primarily on data quality and richness, especially First Party data. Building a robust technical foundation for optimal exploitation is essential for large-scale deployment. By expanding information sources, companies can better understand their clients and competitive environment, creating a «single source of truth.» This will enable them to integrate AI into their segmented acquisition strategy, while avoiding exclusive dependence on Google queries. Data thus becomes the key lever for a more proactive, personalised and sovereign customer relationship