Maximizing ROI and productivity means focusing marketing efforts on potential customers who are most likely to convert and existing customers who will bring the most long-term value. By applying AI and machine learning to their unified first-party data for look-alike modeling, retail and consumer goods companies can identify new audiences that resemble their existing customers and target them with tailored marketing efforts. Additionally, they can leverage AI for customer lifetime value (CLV) and propensity models to pinpoint high-value segments or next best action.

These segments can be leveraged in a variety of ways: from prospecting new audiences, to retaining and enriching existing customers. "Next best action" has a wide variety of use cases and deployments, from finding and acquiring new customers and leads to reducing call center volume. Historically, data scientists and BI professionals have been responsible for AI and machine learning execution. However, this process can be time- and labor-intensive and lead to latent, inefficient first-party data utilization. With tools like a customer data platform (CDP), non-technical business users can quickly deploy out-of-the-box models (as is or customized) to identify lookalikes, calculate CLV, and forecast customers’ propensity to buy or churn, and then incorporate those scores in their engagement activities.