Measuring the Impact of Generative AI in CRM, Loyalty, and Personalisation: A Data-Driven Approach

The promise of Generative AI (GenAI) in marketing is immense, particularly in CRM, loyalty, and personalisation; In particular if you listen to the plethora of vendors suggesting as such. Yet, as highlighted in recent insights, the economic value of GenAI remains difficult to quantify, and confidence in its impact often rests more on optimism than rigorous measurement. To bridge this gap, controlled experiments and careful metrics are critical to understanding the true impact of GenAI. Below are five key ways to measure GenAI’s impact in CRM, loyalty, and personalisation, rooted in a framework of experimentation and evidence:

1. Campaign Performance and Content Quality

  • What to measure: Compare performance metrics like email open rates, click-through rates (CTR), and conversion rates across three groups: one using GenAI to create content without human review, another with human review, and a control group with no GenAI involvement.

  • Why it matters: While GenAI can generate content faster, the quality and relevance of this content must be assessed. Measuring engagement metrics and user feedback provides a clear picture of whether GenAI is truly adding value.

  • Example KPI: Improvements in email open rates, CTR, or conversions by X% compared to the control group.

2. Customer Lifetime Value (CLV) and Retention

  • What to measure: Track the CLV and retention rates of customer segments managed by GenAI-driven personalisation versus those handled traditionally.

  • Why it matters: Personalised experiences powered by GenAI promise to deepen loyalty and increase retention, but without measurement, these gains remain anecdotal.

  • Example KPI: X% increase in average CLV or retention rate over a defined period.

3. Operational Efficiency and Productivity Gains

  • What to measure: Quantify the time saved in creating campaigns, segmenting audiences, and generating reports between GenAI and traditional methods.

  • Why it matters: The 2025 AI & Data Leadership Benchmark Survey highlights that many leaders perceive GenAI as a tool for exponential ( that’s meant to signify big numbers) productivity gains, yet few are quantifying these improvements rigorously. Measuring actual time and cost savings reveals whether those perceptions hold true.

  • Example KPI: Reduction in campaign setup time by X hours or cost savings by Y%.

4. Impact on Personalisation Effectiveness

  • What to measure: Analyse the success rates of personalised offers, recommendations, and messages generated by GenAI versus manually curated ones.

  • Why it matters: If GenAI leads to improved personalisation outcomes—such as higher redemption rates for offers or better customer satisfaction—this demonstrates tangible value in loyalty and CRM contexts.

  • Example KPI: X% increase in offer redemption rates or customer satisfaction scores.

5. Customer Sentiment and Engagement

  • What to measure: Use sentiment analysis and feedback tools to assess customer reactions to GenAI-driven marketing efforts.

  • Why it matters: Generative AI might generate content faster, but if customers find it irrelevant or disengaging, the effort is wasted. Monitoring sentiment provides a qualitative lens to complement quantitative metrics.

  • Example KPI: X% improvement in Net Promoter Score (NPS) or sentiment scores in customer feedback surveys.

The Experimentation Imperative

As noted, the best way to demonstrate the value of GenAI is through controlled experiments. Establishing A/B testing frameworks where one team uses GenAI while another does not—or varying levels of GenAI involvement—can provide actionable insights. For instance:

  • Test content generation speed versus quality. If GenAI creates blog posts faster but engagement drops, the trade-off may not justify the investment.

  • Measure productivity at the worker level. If marketers save time using GenAI, understand how they’re reallocating those hours and whether it drives additional value.

Final Thoughts

The challenges of quantifying GenAI’s impact highlight the need for companies to move from optimistic assumptions to rigorous measurement. In CRM, loyalty, and personalisation, the potential is there—but only by carefully measuring both the benefits and unintended consequences can organisations realise its promise. If companies fail to measure, they risk missing the opportunity to make informed decisions about where GenAI truly adds value.

AI, CRM, Datagianfranco cuzziolAI, loyalty, CLV, Measurement, dataComment