Traffic Intelligence
Made the keyword estate understandable as patterns of demand rather than an undifferentiated inventory.
Global ecommerce / Paid search intelligence
Making one million keywords intelligible to machines and useful to people.
A fast-growing global fashion business needed to reorganize paid search at extraordinary scale without discarding the history that helped its accounts perform.
conversion rate
cost per conversion
impressions
01 / Business context
The scale made the opportunity, and the problem, larger.
The company was one of the fastest-growing eCommerce businesses in its category, operating several leading fashion brands across global markets.
Its Google Ads estate contained more than one million keywords across several accounts. That scale represented substantial reach and accumulated learning, but it also made the system difficult to understand, restructure, and improve without introducing performance risk.
02 / The acquisition problem
Activity was visible. Meaning was not.
The company needed to improve account structure quickly while preserving valuable historical performance. The challenge was not simply moving keywords between ad groups. It was determining which keywords expressed related intent, which audiences they represented, and how structure and content could communicate that meaning more clearly.
When large keyword estates are organized primarily around inherited structures, the relationships among search language, intent, ads, and landing experiences can weaken. Publisher AI still sees activity, but the signals describing relevance become fragmented.
03 / The intelligence questions
Before changing the system, MotiveMetrics needed to understand it.
- 01
Which keywords represented the same underlying purchase intent?
- 02
How could more than one million keywords be reorganized without sacrificing valuable account history?
- 03
Where were ad-group structure and creative obscuring relevance?
- 04
What structure would give Google clearer signals while improving the customer’s experience?
04 / The intelligence applied
Different intelligence capabilities. One connected decision.
The work predates today’s MotiveMap architecture. Viewed through the current model, the outcome came from connecting these forms of intelligence, not applying an isolated optimization.
Purchase Intent Intelligence
Identified the meaning beneath search language and grouped activity around coherent reasons for buying.
Structure & Targeting Intelligence
Translated intent relationships into an account architecture designed to preserve history and strengthen signals.
Content Intelligence
Aligned ad language with the intent represented inside each newly coherent group.
Publisher Intelligence
Considered how Google would interpret the new relationships among keywords, structure, ads, and response.
Performance Intelligence
Measured whether the new signal environment produced more reach and stronger conversion economics.
05 / What changed
Intelligence became a clearer signal.
Understanding alone does not change performance. MotiveMetrics translated it into a more coherent acquisition environment, giving people greater control and publisher AI better inputs.
Mapped a massive keyword estate into more coherent intent relationships.
Reorganized account structure without discarding accumulated historical value.
Created tighter ad groups in which keywords and creative represented the same underlying demand.
Aligned ad copy with the intent communicated by each group.
Measured reach, conversion rate, and cost efficiency after deployment.
06 / The outcome
Better signals produced better business outcomes.
The new environment expanded reach while simultaneously improving conversion efficiency. Impressions increased 60%, conversion rate increased 63%, and cost per conversion declined 14%.
Those results are important together. The company did not merely trade efficiency for scale or scale for efficiency; a clearer acquisition structure supported both.
+63%conversion rate
−14%cost per conversion
+60%impressions
07 / Why it worked
The system became easier for machines to interpret, and more useful to people.
The documented change was an intent-informed account structure with more coherent ad groups and aligned creative. Our current interpretation is that these changes improved the consistency of the signals Google used to understand relevance.
Keywords expressing related demand lived in a more appropriate environment. Ads communicated a message consistent with that demand. Publisher automation received stronger relationships among query, keyword, group, and creative, and customers encountered advertising more aligned with what they were trying to accomplish.
At enterprise scale, structure is not administration. It is signal architecture.
The larger story
Performance is the outcome.
Intelligence is how we get there.
MotiveMap connects people, market, acquisition, publisher, and governance intelligence to identify opportunity, improve signals, and guide governed action.
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