Customer Insights in E- retail in the Era of Agent-Based AI

Predictive and Agent-Based AI: What Role Does Customer Knowledge Play in Marketing- retail ?

• Customer insights form the operational foundation for predictive AI (scores, probabilities) and agent-based AI (orchestration).
• The challenge is no longer just about better segmentation, but about making better decisions and acting more quickly across every channel.
• Today, data (transactional, behavioral, and omnichannel) should be used to power predictive models and AI agents.
• AI enables us to predict churn, customer engagement, and customer value, and to trigger appropriate actions.
• Key benefits in retail personalization, customer loyalty, marketing efficiency, and enhanced customer service.

Why is the role of customer insight changing with AI?

Retail marketing is undergoing a subtle yet decisive shift. At the heart of this transformation lies customer insights, but its role is changing. It is no longer merely an asset for better segmentation and personalization. It is becoming the operational foundation that powers predictive models and AI agents capable of helping teams (marketing, CRM, customer service, sales) make decisions and take action faster, more precisely, and at scale.

Moving away from traditional marketing strategies, the focus is shifting toward an approach where data analysis is used both to understand and to anticipate. Predictive AI transforms signals into probabilities (churn risk, product affinity, promotional sensitivity, value), and agent-based AI transforms these predictions into orchestrated actions: recommendations, channel selection, timing, content, and sometimes even the execution of supervised scenarios.

This growth is rapid: according to Gartner, 40% of enterprise applications will incorporate specialized AI agents by the end of 2026, up from less than 5% in 2025.

What is “active” customer insight?

Every interaction—whether it’s an online click, an in-store purchase, a response to a campaign, or a conversation with customer service—becomes actionable data. This data feeds into a “living” customer profile that is continuously updated, enabling the delivery of a more relevant experience by blending the physical and digital worlds.

But how can companies effectively leverage this customer insight when volumes are skyrocketing and customer journeys are becoming increasingly fragmented? How can they turn it into a real driver of growth in an environment where AI can predict and take action? These questions are now at the heart of business strategies, as companies seek to stand out in an increasingly competitive market.

1. Know your customers to predict and take action

For the experienced marketer, customer insights go beyond simply collecting data. It's a dynamic, evolving system that integrates diverse information to provide an accurate picture of the consumer. But these insights are no longer limited to describing a customer; they must enabls us to predict what will happen and trigger the most appropriate action through automation.

This holistic approach helps us understand purchasing habits, motivations, and preferences, as well as intentions and probabilities: the probability of making a purchase within 7 days, the probability of churn, and the probability of being receptive to a new product or, conversely, of feeling overwhelmed by excessive marketing pressure.

The Fundamentals of Customer Insights Enhanced by AI

• Demographic data: age, gender, geographic location… This basic information forms the foundation of customer insight.
• Behavioral data: browsing patterns, purchasing preferences, interactions with the brand… All of these provide clues that reveal signals of intent.
• Transaction data: purchase history, average basket size, frequency… These figures provide insight into the relationship and are used to estimate future value.
• Data by channel: store, e-commerce, social media, email, app… Each touchpoint provides a unique perspective.
• Predictive signals (new layer): scores and probabilities generated by models (appetite, churn, CLV, promotional sensitivity, next best action).
• "Agent-ready" context (new requirement): business rules, constraints (inventory, sales pressure, consent), objectives (margin, repurchase, store traffic), and customer preferences, so that AI agents can act in a controlled manner.

The value of data becomes apparent when it is combined to provide a clear, dynamic, and actionable view. This is how raw data transforms into valuable customer insights, ready to power predictive AI and guide AI agents.

Predictive, generative, or agent-based AI: What are the differences?

2. How does customer insight improve marketing performance?

The effective use of customer data remains a key differentiator in retail. But the stakes have been raised: it’s no longer just about refining marketing targeting; it’s about making better decisions on an ongoing basis and, at times, executing those decisions more effectively with assistance.

In-depth customer insights enable us to better serve existing customers, refine customer acquisition, and, above all, optimize decision-making: who to contact, when, through which channel, with what message, and with what objective (conversion, reactivation, margin, loyalty).

The Challenges of Customer Insights for Marketers in the Predictive and Agent-Based Era

• Personalization: delivering a tailored experience, now guided by scores, intentions, and constraints (marketing pressure, inventory, value).

A top priority: According to Deloitte, 26% of executives at retail are already using AI-powered personalization, and an additional 35% plan to roll out AI-powered personalized recommendations within the year.

• Customer Loyalty: Understanding early warning signs and taking action before a breakdown (Risk Detection, Proactive Prevention).
• Anticipating needs: offering the right product at the right time using predictive models and orchestrated customer journeys.
• Marketing productivity: Delegate some of the day-to-day decisions to supervised AI agents to improve speed and consistency.

Customer insights also play a central role in omnichannel strategies. In the future, customer journeys will not only be unified; they will be orchestrated by systems that learn and adapt the experience in real time, while adhering to established safeguards.

3. How can we collect and manage customer data to feed it into AI models and agents?

For marketers, the quest for customer insights begins with collecting data from multiple sources.

Data: What are the sources?

• Physical retail locations: loyalty cards, customer interactions, in-store behavior…
• e-commerce sites: navigation, wish lists, abandoned carts…
• Social media: mentions, interactions with posts…
• Marketing campaigns: opens, clicks, conversions, unsubscribes…

To optimize this data collection, marketers are implementing increasingly innovative strategies: attractive loyalty programsmobile wallets, short and useful surveys, and sometimes in-store recognition technologies. The goal remains the same: to encourage customers to share their information in exchange for tangible added value.

This data is essential for predictive and agent-based AI. Managing and analyzing it therefore requires sophisticated tools. CRM platforms are increasingly integrating AI capabilities to process massive volumes of data, generate scores, detect signals, and transform these insights into actionable levers. This trend is accelerating: according to Deloitte, 67% of executives at the retail expect to have AI-driven personalization capabilities within the year to enable tailored experiences, targeted campaigns, and loyalty programs.

As such, Splio now natively integrates predictive, generic, and agent-based artificial intelligence into the core of its platform.

4. How can we turn customer insights into action?

The true value of customer insights lies in translating them into concrete actions. Savvy marketers know how to turn data into actionable strategies: more granular segmentation, timing adjustments, and large-scale personalization.

The business impact is measurable: according to McKinsey, the AI-driven “next best experience” increases customer satisfaction by 15 to 20 percent, boosts revenue by 5 to 8 percent, and reduces service costs by 20 to 30 percent.

With agent-based AI, we’re taking things to the next level: we don’t just make recommendations—we guide the execution. An AI agent can, for example, propose an action plan for a segment, suggest message variations, recommend a channel, or trigger a scenario based on specific rules, as is the case with Ask My CRM, the new partner for CRM marketers.

Personalization: The Key to retail Marketing retail the Age of Predictive Analytics

With a deep understanding of the customer journey, marketers can offer:

• Highly targeted product recommendations, enhanced by appeal scores
• Personalized discounts based on preferences, purchase history, and promotional sensitivity
• Content tailored to every touchpoint (email, website, mobile app, etc.)
• Customer ourneys adjusted in real time based on the probability of conversion or churn

Customer service enhanced by AI agents

Customer insights, and CRM more broadly, must become the focal point of interactions in the age of agent-driven services, enabling, for example, an elevation in the standard of customer service to ensure a smoother experience that builds customer loyalty and turns customers into brand ambassadors.

• Proactive anticipation (detection of friction, delays, and potential dissatisfaction)
• More relevant and context-aware responses (quick access to history and preferences)
• Proactive monitoring of satisfaction and triggering of reassurance workflows

Turning customer insights into decisions

Customer insights remain the cornerstone of effective and innovative retail marketing. But They're also becoming the foundation of predictive and agent-based marketing —that is, marketing capable of anticipating, orchestrating, and personalizing on a large scale.

The era of AI-driven marketing raises a key question: How can we balance the power of data and algorithms with the authenticity of human relationships? The future of retail will be shaped at the intersection of technology and people. The challenge is no longer simply to predict purchasing behavior, but to do so in an ethical, controlled manner that is truly beneficial to both the customer and the brand.