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My Approach
When managing paid advertising campaigns, I focus first and foremost on the business metrics that matter most—whether that means revenue, profitability, customer acquisition cost, lead quality, or another core objective.
My work is built around two key principles:
Hypothesis generation and experimentation
I continuously develop and test hypotheses aimed at improving campaign performance. Successful experiments help unlock meaningful growth, while inconclusive or unsuccessful tests reveal potential weaknesses, challenge assumptions, and provide insights that support better decisions in the future.
Continuous campaign optimization
Ongoing optimization is an essential part of my workflow. This includes reviewing and excluding irrelevant search terms, refining geographic targeting, improving keyword selection, adjusting bids and budgets, and identifying inefficient traffic segments.
This combination of structured experimentation and detailed campaign management allows me to continuously improve performance while keeping advertising activity aligned with broader business goals.
About the client
Overgear is a global e-commerce marketplace for digital gaming services, connecting players with professional service providers across multiple games and international markets. PPC activity supports multiple game ecosystems, service lines and purchase journeys, requiring disciplined segmentation, rapid experiment cycles and close efficiency control.
Letter of Recommendation by Konstantin Sukhachev, CMO of Overgear
These results were achieved during my work at the PPC agency adgasm.io, where Overgear was my primary account. I was responsible for campaign strategy, execution, optimization, and performance reporting.
Letter of Recommendation by Rasul Tnaliev, Co-founder of Adgasm.io
NDA naming policy
Game names are anonymized as game-*; direction names are anonymized as service*. Operational campaign suffixes—including _all, _retention, _test and _control—remain visible because they identify the test group, not the NDA entity.
Used Metrics
The case studies listed below contain data from GA4 and Google Ads related to the respective experiments.
For simplicity and consistency, several similar GA4 metrics—such as transactions, purchases, and total purchases—have been consolidated under the term “Transactions.”
The Google Ads “Cost / conv.” metric has also been renamed “CPA.”
List of conducted experiments
Bid increase tests
Main goal: to increase share of voice and amount of clients.
- Bid increase — game-3Hypothesis: Increase Search impression share, top-of-page visibility, CR and AOV while maintaining or reducing CPO. Traffic split: 70/30 for service3; 50/50 for other directions.
- Bid increase — game-2Hypothesis: Buy more impression share, improve CTR and increase conversions while lowering CAC. Traffic split: 50/50.
- Bid increase — game-4 service5Hypothesis: Raise Search impression share and top positions to increase traffic and purchases despite higher CPC. Traffic split: 50/50.
- Bid increase — game-2 service3 C1Hypothesis: Increase impression share and top positions, adding traffic and purchases despite CPC growth. Traffic split: 50/50.
RSA-related tests
Experiments with ads for best texts optimization.
- Pinned headlinesHypothesis: Pinned USP headlines improve CTR and CR, reducing CPA despite potential CPC growth. Traffic split: 50/50.
- Ad quality upliftHypothesis: Improve Top %, Abs. Top %, CTR and CR while reducing CPA through better ad relevance. Traffic split: 50/50.
- RSA simplification — game-3 service7/service8Hypothesis: Reduce RSA headline count and tighten pins to lift relevance and CR without materially hurting position. Traffic split: 50/50.
- RSA simplification — game-3 service3Hypothesis: Use fewer, more relevant headlines to improve query fit, CTR, CR, CPO and CAC. Traffic split: 50/50.
Other cases
- Exact match keyword expansionHypothesis: Exact Match improves CTR and CR through more precise intent, while Phrase Match maintains reach and lower CPC. Traffic split: None; Exact and Phrase ran together for four weeks.
- Low-bid split for high-volume keywordsHypothesis: Separate high-frequency terms into low-bid campaigns to cut CPC by 30–50% while preserving traffic for lower CPA. Traffic split: None; pre 08.01–12.02 vs post 13.02–20.03.
- Generic service semanticsHypothesis: Reach new non-game-specific audiences and generate additional purchases at acceptable ROAS, CPO and CAC. Traffic split: 50/50.
- Retention split into separate campaignsHypothesis: Separate warm audiences from broad acquisition to lift CTR, CR trans, CR FP and ROAS. Traffic split: None; retention vs all-traffic baselines across 2 projects × 2 directions.
Mirach SEM Extension
Role: Product Manager
I initiated the development of a browser extension for faster negative keyword management in Google Ads and Microsoft Advertising.
The product was built together with my brother, Alex, who handled the technical development. I defined the concept and requirements, designed the core user flows and interface logic, and tested the extension in real PPC workflows.
The extension reduced the time required for routine search-term optimization and has attracted more than 600 PPC specialists worldwide.