SAP Emarsys has been embedding targeted AI's capabilities into our systems for over a decade. Today, we help customers leverage this technology across key focus areas.

First is data—using AI to quickly process and analyze large volumes, reducing manual work and human error. AI automates data integration and updates customer data platforms as new information arrives from multiple sources, converting data on products, sales, and customers into valuable insights and actions to enhance engagement.

Global sportswear brand PUMA gained insights into customer behaviors, creating personalized journeys across online and offline touchpoints, using AI, automation, and first-party data within the Emarsys platform. This led to five times revenue growth and 50% database growth within six months.

We also help retailers engage customers across channels with the right message at the right time, addressing the challenge of complex customer journeys. Our unique send time optimisation algorithm learns and optimises on user level, for billions of individuals over the globe uniquely, personalising not just the content but the sending time as well.

AI-powered segmentation allows marketers to interact with customers in real-time, providing timely and relevant messages. When a campaign goes live, it targets customers based on their latest behaviors and actions.

SAP Emarsys partner Mention Me, a customer advocacy platform, uses AI to predict advocacy based on previous interactions. This approach enabled luxury retailer BrandAlley to achieve a 23% increase in engagement rates and a 25% increase in repeat rates without extra effort from the marketing team.

AI-driven personalization has been a focus in 2024. This approach uses detailed customer data, including browsing and purchase history, to offer tailored recommendations and dynamic content, leveraging predictive AI to anticipate needs and create seamless cross-channel experiences.

For example, Swedish sock retailer Happy Socks keeps customers satisfied with AI-driven personalization that makes them feel like store regulars. They use AI analysis and regional data for personalized product recommendations, like a sales assistant in a small store, ensuring relevance with personalization tokens and adjustments for language, currency, and location.