There are many examples of how publishers are using personalization to increase engagement and revenue. For instance, some publishers are using personalization strategies to recommend articles or products to readers based on their on-site browsing history or previous purchases. Others are using personalization to send targeted emails and push notifications to readers based on their interests and behaviors.
However publishers decide to utilize personalization, implementing a strategy requires a robust understanding of audiences. Since a personalization strategy is only as good as the data backing it up, personalization starts by collecting data on reader interests and behaviors.
By using intelligent paywalls and registration forms, first-party data can be progressively collected directly from users, and then stored, managed and analyzed in customer relationship management software (CRM). As mentioned in section one above, this data can then be analyzed with the help of AI to gain granular insights that would otherwise be impossible to identify through other means.
Ultimately, personalization means optimizing user journeys and content, ensuring that readers can not only access and navigate content on desktop, mobile and other devices, but also ensuring that the messaging they see makes sense. E.g. A student who enjoys political content sees student discounts, content related to their demographic and interests, and offers for political packages that they’re likely to find enticing.