Chapter 6
Measure audience quality
audience-quality scorecard is the practical output of this chapter. Complete it in one focused work session so you can decide which leading and lagging signals determine the next week’s content decision, without optimizing follower count while qualified actions decline.
What you’ll get
- A completed audience-quality scorecard you can use in the next chapter.
- A documented decision about which leading and lagging signals determine the next week’s content decision.
- A pass or fail signal: weekly review distinguishes reach, qualified response, store movement, list growth, and sales.
- A concrete first action: Track one signal at each stage and write the next test before opening the analytics again.
Use a ladder of signals
Review activity from weakest to strongest:
- Qualified reach: the content reached people who fit the topic.
- Saves, shares, and substantive comments: the idea was useful enough to keep or discuss.
- Profile visits: the creator promise created curiosity.
- Link clicks or store visits: interest became action.
- Lead-magnet claims or replies: the person exchanged attention for a deeper step.
- Checkout starts, purchases, bookings, or referrals: intent became commercial behavior.
Do not compare unrelated posts by one metric. A broad opinion post may earn reach; a product demonstration may reach fewer people yet create more store visits. Evaluate each post by its job.
Use a weekly decision table
For each pillar, record the content, intended action, result, audience questions, and next test. Repeat formats that bring relevant people deeper into the journey. Rewrite or retire posts that attract the wrong audience, even when their view count is high.
Apply this chapter to your store
Start with the evidence you already have: the current Tapmy setup, the exact customer path, the messages people see, and the records created after an action. Do not redesign the entire system while you are still trying to locate the constraint.
Implementation move: Track one signal at each stage and write the next test before opening the analytics again.
Write the decision beside the evidence that supports it. If a teammate cannot reproduce the conclusion from the same evidence, the work is not yet operational. The deliverable should be specific enough to hand to another person without a meeting.
Decision checkpoint
| Check | Pass condition | If it fails |
|---|---|---|
| Evidence | The current state is visible and reproducible | Capture the real route, screen, record, or message before changing it |
| Decision | which leading and lagging signals determine the next week’s content decision is written in one sentence | Reduce the scope until one owner can choose |
| Execution | Track one signal at each stage and write the next test before opening the analytics again. | Assign a time and owner, then complete the smallest end-to-end version |
| Verification | weekly review distinguishes reach, qualified response, store movement, list growth, and sales | Return to the failed boundary and retest only after the cause is changed |
Common failure mode
Comparing every post by views even when posts have different commercial jobs. This hides the actual constraint and creates false confidence. Use the chapter output as a gate: do not move forward until the decision is explicit and the pass signal is visible.
20-minute implementation sprint
- Open the current Tapmy path and capture the starting state.
- Complete the smallest version of the audience-quality scorecard.
- Record the decision, owner, and next review date.
- Test the pass signal once from the customer’s point of view.
- Save the evidence where the person running the next chapter can find it.