The predictive segmentation technique known as “next best channel” identifies the most effective channel within a given set of options in a user’s journey based on their past behavior. This approach relies on advanced machine learning algorithms to analyze user engagement, preferences, conversion patterns, and transaction histories. It is particularly effective when combined with time-based techniques, enabling marketers to reach customers through their preferred channels at optimal times.

Utilizing predictive channel algorithms typically involves integrating workflows and journey builders. These algorithms offer significant value in the context of the expanding array of marketing channels and the rise of multi-channel marketing, as managing such complexities becomes increasingly challenging for marketers. Machine learning methods, such as reinforced learning, are particularly effective for predicting the next best channel.

Across various industries, digital marketers increasingly rely on data-driven analytical techniques to inform their strategies, adopting a customer-centric growth model. This model considers various actions that a business can take for individual customers across multiple channels and determines the optimal “next best channel” to drive growth. The objective of next best channel analysis is to enhance purchase intent by delivering offers, propositions, or services tailored to the customer’s interests and aligned with the business’s goals, objectives, and policies.

The next-best-channel predictive model is particularly suitable for:

– Real-time inbound engagement on websites, mobile apps, or call centers.

– Scheduled or triggered outbound engagement via online ads, push notifications, SMS, email, or direct mail.