Perplexity
GrokThe useful return is measurement clarity: the team can see search, analytics, and AI visibility activity for every sub-brand in one place.
Back to case studiesMulti-brand measurement baseline
Pet breeding group: turning scattered brand data into a measured baseline
This pet breeding group had several public brands, separate websites, and uneven tracking. New Reward assembled the stored GSC, GA4, AI visibility, and sales-report rows into one plain-English baseline the team could act on.
Multi-brand baseline boardThis image shows the multi-brand case as a practical measurement problem: several brands, one baseline board, and the search and analytics activity in a single view.
Visual boundary: New case-specific illustration of the baseline board, drawn from the stored numbers on this page.
Stored May 2026 Search Console totals for one sub-brand, with GA4 sessions and AI visibility rows alongside them.
April to June 2026 is the first measured window; later windows compare against it.
The journey
Problem to proof.
01
Problem
The business had activity across several brands, but the proof was scattered across separate accounts, separate sites, and separate ledgers.
02
Action
New Reward reviewed the stored search, analytics, AI visibility, sales-report, package, and social ledgers, then put the measurable activity for all three sub-brands into one readout.
03
Result
The readout showed one sub-brand with 3,484 stored GSC clicks and 35,025 impressions in May 2026, a June 1 GA4 snapshot with 1,369 sessions for one brand and 386 sessions for another, and AI visibility rows across all three sub-brands.
04
Why it matters
A multi-brand company cannot improve what it cannot measure. This baseline gave the team one place to see search and AI visibility for every sub-brand at once.
Sales use
How to use this proof in a buyer conversation.
Buyer question
Can New Reward make scattered multi-brand proof readable without overclaiming?
Use this for multi-location or multi-brand operators that have partial GSC, GA4, social, and AI visibility data.
Objection handled: The buyer has activity everywhere; the case shows how to separate measurable visibility from blocked inquiry or revenue claims.
Best fit
What we measure next
- Non-PII inquiry and booked-call export
- Consistent GA4/GSC mapping by brand or location
Client context
Anonymized pet breeding group
- Industry
- Pet breeding and multi-brand reputation
- Timeframe
- April to June 2026 stored GSC, GA4, and AI visibility readout
- Starting point
- The group had three active sub-brand records with partial GA4 and Search Console evidence spread across separate accounts.
Proof boundary
This proves stored measurement and visibility baseline data across the group's three sub-brands, from April to June 2026.
Verified resultThe verified result is a measurement baseline the team can act on: search, GA4, and AI visibility activity for all three sub-brands in one view.
Inspectable work
What shipped and where the proof comes from.
What shipped
- Reviewed stored Search Console ranking and aggregate rows across the sub-brands.
- Reviewed June 1 GA4 overview and channel snapshots where exact properties were selected.
- Reviewed AI visibility snapshots and sub-brand sales-report score history.
- Mapped the source exports each brand still needs to extend the baseline.
Source notes
Performance history ledger
Internal anonymized pet-breeding performance-history CSV dated 2026-06-03.
Google tracking weekly ledger
Internal anonymized pet-breeding weekly GSC CSV dated 2026-06-03.
GA4 channel summary
Internal anonymized pet-breeding GA4 channel summary CSV dated 2026-06-03.
AI visibility readout
Internal anonymized pet-breeding AI visibility and sales-report ledgers dated 2026-06-03.
Next step
Turn this proof into the right offer lane.
Start from the evidence, then choose the work that matches the buyer path.