4 How one retailer unlocked growth by launching targeted offers One North American retailer used a traditional calendar-based approach to promotions, offering mass discounts to all customers during holidays, and created a tiered discount program for different types of members of its loyalty program at other times. Recently, the company embarked on a mission to pivot toward personalized and data-driven marketing. It wanted to know how to increase demand during the times of year with softer sales. The company explored how to deploy mass promotions in a personalized way—with offers to the full customer base during specific days, but with different depths of discounts to different customer segments. It also experimented with offers targeted only to specific customers who fit certain criteria. To start, the marketing team developed analytical models to assess the likelihood that a customer would respond positively to an offer—called “promotion propensity”—based on past purchases. The retailer then engaged in A/B testing for each model, using data analytics that allowed agile, cross-functioning teams to test the value of various targeted offers (made primarily via email) over a series of two-week sprints. After learning that its customers felt overwhelmed by too many promotions, the company pared back some of its offer frequency and simplified the customer experience. After three months of using this more targeted approach, the retailer saw a boost of about 3 percent in annualized margins during initial tests. The company now plans to expand targeted promotions at scale. and use cases; they are not automating or integrating to reduce the bottlenecks of operational inefficiencies, nor are they evaluating content performance. Smarter use of gen AI can help unlock more cohesive personalization opportunities with touchpoints and interactions more tailored to what customers want. Currently, there is no consolidated suite that does this end to end. But with thoughtfully integrated solutions that align people, processes, and platforms, marketers can deliver more rigorous content creation, smoother orchestration across teams, and seamless implementation of targeted promotions and content. While content creation today is highly manual, gen AI can accelerate and magnify the entire process, helping channel operators, creators, and analysts become more productive. As more marketing material gets fed into a robust, foundational content data model, gen AI can learn from a feedback loop and create more copy that can be tailored for personalization. One important thing to note: As organizations increasingly use gen AI, it is critical that they build models to validate and govern gen AI–created content in order to establish guardrails against bias, toxicity, and hallucinations, and to ensure that content is in accordance with enterprise standards and design systems. To see what’s possible for better workflow in the future, it helps to look at how the ecosystem operates—both today and in the future—across the three stages of content production (table).