What is Follow Recommendation Emails?

In the realm of digital marketing, Follow Recommendation Emails are quite new but have quickly transformed how social platforms and other online services guide their users towards new connections.

Making its rise with the explosion of social media, this tactic harnesses the power of what's known as 'collaborative filtering'. Simply put, Follow Recommendation Emails analyze user habits and preferences and then suggest like-minded or relevant individuals they could connect with.

The strategy is primarily beneficial for retention- the process of keeping users actively engaged and returning to your platform. The principle is straightforward: the more connections a user has within a community, the more likely they are to return and remain involved. Not only does the user build a larger network, but the communally beneficial network effect is also sustained in the process.

Examples of Follow Recommendation Emails

1. A social networking site analyses the interests and friends of its users, subsequently sending an email proposing some new friends they could connect with who have similar interests or mutual connections.

2. An online reading platform observes a user’s reading history and sends a mail proposing authors and readers with similar tastes for them to follow.

3. A fitness application tracks the workout regimes and dietary practices of its user. Consequently, it sends an email suggesting other users with similar fitness routines they could collaborate or compete with.

4. An e-commerce brand studies the purchase history and scrolls of its shoppers. Thereafter it sends them an email recommending other like-minded shoppers to follow, potentially sparking future purchases through shared interests.

5. A business professional platform, after analyzing a user's industry, skills, and connections, sends a recommendation email to follow individuals and businesses that could be beneficial for career growth.

Marketing Tactics Similar to Follow Recommendation Emails

• Personalized Content: Analyzes user behavior to provide tailor-made content that caters to their preferences, which increases engagement and retention.
• Push Notifications: These are instant alerts sent to users recommending new connections, activities, or updates based on their usage pattern. They work similarly to follow recommendation emails but are more spontaneous.
• In-App Recommendations: These are suggestions made within the application, offering users related content or connections based on their preferences and behavior.
• Retargeting Ads: These are targeted advertisements shown to users based on their previous actions or preferences, with the goal of bringing them back to the platform.

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