Likes are a vanity metric. They feel good, they are visible to everyone, and they have almost no predictive relationship with account growth or business outcomes. The algorithm weights likes less than saves, shares, and comments — which is why posts with huge like counts sometimes stall in reach while quieter posts with high saves keep circulating for weeks.

Signal metrics are the ones that predict whether your content will attract the right audience and convert them into followers, leads, or customers. Track those instead.

Reach tells you how many unique accounts saw your content — it is the clearest indicator of distribution health. If reach is declining week over week, the algorithm is distributing your content to fewer people, which is a signal to test different formats or hooks.

Profile visits from content tell you how many people were interested enough after seeing a post to visit your profile. This is one of the best leading indicators of follower intent — and it connects post quality directly to account growth.

Engagement rate normalises performance relative to how many people actually saw the post. A post seen by 500 people with 50 interactions is a 10% engagement rate — significantly better than a post seen by 10,000 people with 200 interactions (a 2% rate), even though the second post has more absolute engagement.

This matters when comparing posts across different time periods, format types, or when your account is growing rapidly. Absolute numbers mislead; rates reveal.

Tracking data is only useful if you do something with it. A monthly review does not need to be complex — it needs to answer three questions: What was my best-performing content and why? What format or topic is gaining momentum? What should I drop or change next month?

Document the answers in a running note. After three months, patterns become obvious — and obvious patterns are the foundation of a content strategy that reliably grows.