Skip to content
storewakeStart free

From the founder’s workspace

What changed in Respira’s rankings?

A real Storewake MCP example: compare Respira’s US keyword snapshots, separate observations from assumptions and choose what to investigate next.

A real investigation from the founder’s app.

Respira is Pablo’s pollen tracker. Visit the Respira website · View Respira on the App Store.

This example uses saved US keyword snapshots from September 13–14, 2026, queried through Storewake’s production MCP. It demonstrates a workflow, not a measured increase in downloads or revenue.

“What changed, and what should I look at next?”

A single overall visibility number can hide very different movements. For Respira, the useful starting point was to compare individual terms in one storefront and identify which changes deserved a closer look.

Tool: tracked_keywords
Storefront: US
History: 2 snapshots
Observation dates: September 13 and 14, 2026

The agent can turn this response into a readable comparison while keeping the dates and raw positions attached to each claim.

Some terms climbed. Others fell.

Selected measured changes · US storefront · Lower rank numbers are better.
KeywordSep 13Sep 14Movement
allergy log12685Up 41
allergy forecast169136Up 33
cedar pollen5338Up 15
allergy diary6050Up 10
allergy90124Down 34
allergies110142Down 32

These six terms are a selected excerpt, not the complete tracked keyword set. The same response contained missing ranks for other terms; those are not assigned an invented position.

An observation is not yet an explanation

“Allergy log” improved from 126 to 85 while “allergy” fell from 90 to 124. That is enough to identify a mixed pattern across terms. It is not enough to say a listing edit caused it, or that overall search traffic increased.

Popularity was unavailable for all six terms shown here. We cannot use this excerpt to claim that the gains occurred on high-demand keywords. It also contains no conversion, download or revenue evidence.

A useful next question:

Do these changes persist across later snapshots, and do competitor movements or the timing of a listing edit help explain them?

Turn the comparison into a small investigation

  1. Check persistence. Compare additional available snapshots for these same US terms. Treat a one-day move as something to investigate.
  2. Build the timeline. Bring in the dates and contents of any listing edits. The excerpt does not include that history.
  3. Inspect competitors. Compare relevant search results and available competitor data. A current search cannot reconstruct an earlier result page.
  4. Choose one hypothesis. If the evidence supports it, draft a focused listing experiment and record the expected outcome before changing anything.

Those are proposed next steps. This case does not claim they were executed or produced a lift.

Source and measurement limits

Source: the Storewake production MCP tracked_keywords response captured on September 14, 2026, for Respira’s US storefront, requesting two historical snapshots.

Ranks describe the public App Store search results collected for that storefront. They do not represent every personalized search, an iPhone/iPad split or a universal App Store position. Two daily observations do not establish a sustained trend or causality.

The practical value here is a traceable starting point: the agent has concrete positions and dates to reason about, and the builder can decide what evidence to collect next.

Your app. Your data. Your next move.

Start tracking one app for free. Connect your agent and live integrations with Grow.

Create free account →