Content recommendation relies on data analysis and algorithms to determine what content will be most relevant to each subscriber. Here are some common approaches:
Behavioral Data: Analyzing past interactions, such as email opens, clicks, and website visits, to understand subscriber interests. Demographic Data: Using information like age, gender, and location to tailor content to specific segments. Purchase History: Recommending products or services based on previous purchases. Explicit Preferences: Allowing subscribers to set their content preferences through preference centers or surveys.