Customer personnalization with AI for a tailored experience

Transforming customer experience through AI-assisted personalization is a major advancement for companies aiming to optimize interactions. Beyond targeted marketing, this approach offers contextualized interactions, anticipates expectations, and adapts messages, offers, and journeys in real-time. At Sofrecom, this revolution relies on synergy between data quality, technology, and rigorous governance to ensure relevant, measurable, and compliant customer experiences.

NBO NBA : what are they ?

Definition and challenges

NBO (Next Best Offer) and NBA (Next Best Action) are the foundation of this new approach. NBO involves recommending the most relevant product or service for a customer at a specific moment, considering their context and behavior. NBA aims to determine the optimal action to enhance the customer experience or maximize commercial performance, whether it’s a promotional gesture, reminder, tutorial, or ticket opening.

Evolution of Approaches

Since the emergence of machine learning, these approaches have significantly evolved. Early generations relied on static rules and manual segmentation, limiting real-time adaptation. Today, integrating predictive models like propensity or churn scores refines recommendations. The main limitation of previous methods was asynchronous scoring, done in batch, unable to consider immediate interaction context. Transitioning to real-time scoring, incorporating immediate data, has made recommendations more relevant and responsive.

The convergence of real-time scoring and generative AI

Online scoring: a necessity for instant interaction

Real-time scoring is now central to customer interactions. When a customer opens a page, clicks, or calls, models compute and update scores instantly. This immediate adaptation enhances recommendation relevance and customer satisfaction.

The contribution of generative AI: semantic understanding and content production

The contribution of generative AI: semantic understanding and content production Parallelly, approaches like large language models (LLMs) and Retrieval-Augmented Generation (RAG) provide fine semantic understanding and content generation. Combining natural language processing (NLP) and embeddings, systems grasp varied intents like «I’m traveling abroad» or «I’m leaving France,» triggering actions such as activating a pass or proposing a suitable plan.

Controlled generation: relevance and compliance

Generative AI, guided by templates and business rules, creates contextually appropriate messages across channels, respecting price, legal, and brand standards. It also facilitates rapid testing (A/B testing) to optimize scripts and improve communication continuously.

A hybrid, controlled, and measurable decision architecture

Components of a modern engine

A robust NBO/NBA engine relies on a hybrid architecture with three complementary components:

• Logical Decision: Incorporates company policies, priorities, and regulatory constraints, e.g., avoiding promoting unavailable services or exceeding discount caps.
• Machine Learning Scoring: Uses models to estimate propensity, churn risk, product affinity, or expected value, aiding in ranking offers/actions.
• Controlled Generation: Language models produce messages/scripts within strict templates, with variables, compliance filters, and safeguards, maintaining control over content and tone.

Governance and compliance

Managing risks and ensuring compliance are vital to secure the use of LLMs. This involves traceability of consents, data protection via minimization, anonymization, encryption, and strict access controls. Explainability through detailed logs facilitates audits and transparency. Monitoring biases, human review of sensitive cases, and RAG use to limit hallucinations strengthen trust.

Use cases and feedback

Optimized marketing targeting: more relevant and effective campaigns

Using NBO/NBA engines, Sofrecom has helped clients transform multichannel targeting (SMS, email, telemarketing). Personalized recommendations increased engagement, improved offer relevance, and reduced acquisition costs. Real-time message adjustments based on interactions enhanced satisfaction and conversions.

Results: A significant increase in engagement rate, improved relevance of the offers proposed, and a reduction in acquisition costs. This approach also allowed for real-time adjustment of messages based on interactions with customers, which enhanced customer satisfaction and conversion.

Reducing churn and upsell development: targeted strategies for sustainable growth

For clients facing high churn, predictive models identified at-risk customers early. Targeted retention offers and personalized communications reduced churn significantly. Conversely, identifying high-potential upsell clients allowed repositioning toward premium offers.

Results : Increased revenue, better customer value, and more sustainable growth. These examples show how proactive personalization powered by AI optimizes relationships and maximizes commercial value.

Roadmap for effective implementation

Key steps

• Phase 0 – Diagnosis & framing : Assess data maturity, define priority use cases, and KPIs.
• Phase 1 – Quick ROI Cases: Deploy real-time scoring in pilot scopes, measure performance.
• Phase 2 – Omnichannel xtension: Generalize scoring, incorporate new events and channels.
• Phase 3 – Generative AI Integration: Implement RAG, deploy controlled generation
• Phase 4 – Industrialization & Governance: Deploy MLOps/LLMOps, monitoring platforms, continuous improvement processes Success factors: performance measurement approach

A structured KPI framework ensures effectiveness and ROI

It tracks NBO/NBA impact, identifies regressions, and secures compliance.

Categories of KPIs:

• Business: conversion rate, average basket, acceptance rate, cross-sell/up-sell
• Customer Experience: satisfaction (CSAT), Net Promoter Score (NPS), taux de churn, volume et nature des réclamations. 
• Technical & AI: latency, error rate, endpoint availability, hallucination rate.
• Compliance & Governance: GDPR requests, audit logs, explainability, security incidents

Summary

NBO/NBA evolves from simple business logic to an intelligent platform capable of learning, orchestrating rich dialogues, and making real-time decisions, all while ensuring trust. Modern architectures enable modular, scalable, and resilient systems. Success depends on clear governance, human-in-the-loop feedback, and a data-driven approach. Sofrecom supports clients in this transformation with robust, explainable, and compliant solutions, turning AI promises into sustainable operational gains benefiting both users and businesses.