Customer Segmentation

Leading retailers are realizing substantial gains from implementing AI-driven dynamic segmentation. Brand Collective, a multi-brand apparel and footwear group based in Australia, customized its digital campaigns using the customer data platform (CDP) from Lexer. The initiative produced a significant increase in return on ad spend, doubled new customer acquisition, and generated five times more revenue from paid channels.

In the fashion sector, Slazenger used automated workflows across email, push notifications, and text messaging to enhance engagement. Within 12 weeks, the company achieved a significant increase in customer acquisition and a good return on investment.

Consumer packaged goods (CPG) companies are also benefiting. According to Boston Consulting Group, personalized marketing can deliver five to eight times higher returns on investment for CPG firms. Nike’s Hong Kong division, working with SAP Emarsys, reported a 110% increase in conversion rates and a 33% rise in website traffic after implementing AI-driven personalization.

Adoption of AI in retail and CPG continues to expand. As of early 2024, 42% of companies in these sectors were actively using AI technologies, with another 34% in the testing or pilot stage. Analysts estimate the global AI-in- retail market at $2.46 billion.

AI-driven segmentation is also gaining ground in business-to-business (B2B) commerce. Industry research reveals 19% of B2B sales teams are already deploying generative AI for customer engagement, and data-driven organizations that integrate generative AI into personalization efforts are 1.7 times more likely to gain market share.

The financial sector demonstrates similar gains. McKinsey estimates that AI-driven customer service accounts for 24% of the total value created in insurance and 18% in banking.

Return on investment data shows that the most successful AI leaders take a focused approach. Rather than pursuing every opportunity, these organizations concentrate on high-value use cases—typically about half as many as their peers—yet achieve more than double the average return. Strategic prioritization, combined with strong data foundations and clear governance, distinguishes organizations that turn AI experimentation into measurable business outcomes.