# Andrew Daniv

Andrii Daniv works on paid media and SEO, with a focus on improving Google Ads campaigns, conversion tracking, and ecommerce performance. Their posts also discuss practical ways to use AI in marketing.

- Profile: https://b2binfluencer.marketing/creators/andrew-daniv/
- LinkedIn: https://www.linkedin.com/in/andrii-daniv
- Topics: artificial-intelligence, ecommerce, paid-ads, seo, google-ads, conversion-tracking, performance-max
- Based on 19 dated posts, latest 2026-10-08

## Summary

They are most useful for diagnosing paid search and SEO conversion definitions, tracking, and ecommerce performance.

- Best for: B2B SaaS brands with considered purchases wanting Google Ads and SEO to produce measurable pipeline.
- Not for: Sponsored-post or influencer awareness campaigns needing reach, follower-count justification, or documented paid partnership formats.
- Check first: Confirm one client-side decision owner and one-round feedback before starting.

### Takeaways

- They split high-intent demo requests from webinar and event registrations before optimizing Google Ads.
- For ecommerce, they separated brand demand from non-brand to judge Performance Max acquisition results.
- They use AI for repetitive execution but keep approval with a person.

## Main ideas

- **Count valuable actions, not just form fills**: Daniv argues that ad platforms should optimize for actions that reflect real sales intent. Grouping different actions together can raise reported conversions without helping the sales pipeline.
- **Separate brand demand before judging acquisition**: Daniv warns that blended return on ad spend can make a campaign look more effective by including people already searching for the brand. The useful question is whether non-brand activity is bringing in demand, not just whether the combined number looks strong.
- **Fix the inputs before scaling automation**: Daniv’s PMax examples emphasize conversion quality, product structure, and budget controls over simply giving an automated campaign more spend. The key is understanding what the system was measuring and promoting.
- **Use AI for repetitive execution, but keep approval with a person**: Daniv presents AI as a way to speed up operational work, not replace the person responsible for budget decisions. The distinction is between preparing and applying an edit—and deciding whether it should happen.
- **Make SEO work understandable and actionable**: Daniv criticizes audits and dashboards that create complexity without a clear next step. They propose a small set of visible tasks tied to what buyers search for and the pages they need.
- **Campaign progress depends on clear client decisions too**: Daniv argues that good agency execution cannot overcome slow access, scattered feedback, or a missing decision-maker. The client-side process affects how much time remains for testing and learning.

## Work to read

- [Four forms, one conversion—and $350 per lead](https://www.linkedin.com/posts/andrii-daniv_four-different-forms-one-conversion-and-activity-7428489670033952769-HvVR)
- [When HubSpot and Google Ads disagree](https://www.linkedin.com/posts/andrii-daniv_hubspot-said-leads-were-missing-google-ads-activity-7450867585761542144-nk-t)
- [A PMax rebuild that separated brand from non-brand](https://www.linkedin.com/posts/andrii-daniv_pmax-lost-to-a-cleaner-version-of-itself-activity-7497019773373980672-8hx0)
- [Four practical SEO workstreams](https://www.linkedin.com/posts/andrii-daniv_boring-is-sexy-some-seo-pitches-lead-with-activity-7497389881195794432-V5Sd)
- [An ecommerce PMax turnaround, including the measurement work](https://www.linkedin.com/posts/andrii-daniv_one-store-rebuilt-pmax-system-roas-from-activity-7495200765389737984-6f-3)

## Brand relationships

- **Etavrian** (mention)

## Partnerships

Andrii Daniv’s posts focus on agency and in-house paid media and SEO projects, including Google Ads rebuilds, conversion-tracking cleanups, PMax structure changes, and AI-assisted workflow tips. Several describe client outcomes and link to fuller case studies; these are service-provider case studies, not sponsored placements. A post offering a downloadable Codex skill in exchange for a comment is a lead-generation giveaway, not a paid partnership product. The posts do not specify partnership formats, pricing, deliverables, audience guarantees, disclosure practices, or a dedicated inquiry route. Brands interested in a collaboration should ask directly via the LinkedIn profile linked in the identity section.

## Alternatives

- Martin Davies: Choose Davies when sales have dropped and the team can’t tell whether the market shrank or a competitor took the auctions. Their diagnostic is a day-by-day impression share report: steady impression share alongside falling sales points to lower market demand; slipping share points to a competitor sale or aggressive push. It answers the first question a B2B SaaS team asks when paid search wobbles, before anyone changes the CRM or conversion setup.
- Jordan Fry: Choose Fry when an account is large enough for Google’s incrementality test and the team needs to know how much brand search would have converted anyway. Fry explains that Conversion Lift now runs on Search and Performance Max without a Google representative: it withholds ads from one group of users and compares them with users who saw the ads. The gap shows sales caused by the ads, rather than sales credited to them. Fry also works through Google’s Target ROAS calculator, which turns a profit margin into a break-even ROAS (a 15% margin gives 6.67x). Treat that figure as a floor, not a target: it’s only as good as the margin entered. Both tools help when an account sets or defends a ROAS number that no one can tie to the P&L.
- Robin Tesselaar: Choose Tesselaar when the dashboard says the account is profitable but the team isn’t sure it really is. Tesselaar explains how ROAS misleads: it ignores margin, rewards warm audiences and returning customers, and averages away diminishing returns. A 4x campaign can lose money while a 2x campaign is the account’s most profitable. They propose gross profit divided by ad spend, break-even ROAS of 1 divided by gross margin, new-customer acquisition cost and new-customer share in Performance Max. They also outline five structural moves for Performance Max: a dedicated exact-match brand campaign to force it into cold traffic; asset groups split by margin; CRM and lifetime-value audiences; conversion value rules so a large sale outweighs a small one; and search themes used as a diagnostic.

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AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/