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.
Andrew Daniv works on paid media and SEO, sharing practical advice grounded in campaign decisions and account examples.
A recurring theme is that marketing results are only as useful as the signals behind them: in a SaaS account, they separated high-intent demo requests from webinar and event registrations before optimizing Google Ads (how Daniv redefined conversions).
For ecommerce advertisers, they argue that blended Performance Max returns can hide whether non-brand acquisition is working, and show how separating brand demand changed the picture (a PMax account rebuild).
Their posts also make SEO more concrete, from improving commercial pages to building relevant internal links and supporting content (four practical SEO workstreams).
They explore AI in marketing operations too, emphasizing human approval before automated changes go live (Codex campaign setup).
Andrew Daniv shows the reasoning behind campaign decisions, not just the reported results.
They connect conversion definitions, CRM data, brand traffic and budget choices to what an account can actually tell you.
In one example, they distinguish Google Ads’ primary conversions for optimization from HubSpot’s revenue reporting, rather than forcing the two systems’ figures to match (how Daniv handles conflicting lead data).
They also share setbacks and counterexamples: tests that underperformed, conversion setups they reversed, and cases where they say they do not use PMax (Daniv’s PPC lessons and reversals).
This gives marketers a practical look at how to diagnose an account while keeping individual case results in perspective.
Across these LinkedIn posts, Andrii Daniv argues for making marketing decisions from useful signals and concrete work, not polished but misleading metrics or broad promises.
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The recurring priorities are defining what counts as a valuable lead, separating brand demand from new acquisition, and keeping human judgment over consequential automation.
They also favor practical SEO and clear client decisions.
One tension: Daniv says “I don’t use PMax” in one post, while other posts describe rebuilding PMax accounts.
The examples suggest a conditional view of the campaign type rather than a blanket endorsement.
Count valuable actions, not just form fills
Daniv argues that ad platforms should optimize for actions that reflect real sales intent.
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Grouping different actions together can raise reported conversions without helping the sales pipeline.
In a US oil and gas SaaS account, demo requests, webinar and event registrations, and general questions all counted as the same conversion.
Daniv says the account was paying about $350 per lead with no sales.
After ranking the actions with the client, the team made higher-intent actions primary and treated the others as secondary.
Reported conversions fell, but the account’s signals better matched pipeline discussions.
Daniv warns that blended return on ad spend can make a campaign look more effective by including people already searching for the brand.
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The useful question is whether non-brand activity is bringing in demand, not just whether the combined number looks strong.
In one ecommerce post, Daniv says a PMax account’s ROAS rose from 3.26 to 9.01 and CPA fell 67% after brand and non-brand demand were separated and the setup tightened.
The campaign type did not change.
In another post, they say excluding brand traffic can make reported performance look worse—and provide a more honest basis for scaling.
Daniv’s PMax examples emphasize conversion quality, product structure, and budget controls over simply giving an automated campaign more spend.
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The key is understanding what the system was measuring and promoting.
For an Australian children’s book store, Daniv describes reconciling GA4 and Shopify tracking, testing Search and Shopping, cutting irrelevant queries, and relaunching PMax with selected products and budget guardrails. The post compares the final 30 days with a baseline and reports ROAS moving from 3.06 to 5.90 as spend rose 37%; average order value stayed nearly flat.
Daniv criticizes audits and dashboards that create complexity without a clear next step.
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They propose a small set of visible tasks tied to what buyers search for and the pages they need.
A post lays out four workstreams: improve commercial product and category pages, link related pages with descriptive text, answer buyers’ questions in supporting content, and earn relevant backlinks. It contrasts targeting “solid oak dining table” on a commercial page with broader searches such as “dining room ideas.”
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.
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The client-side process affects how much time remains for testing and learning.
Daniv lists pending account or website access, feedback sent one message at a time, and repeated approvals as causes of delays. They say stronger working relationships have one person making the final call, feedback consolidated into one round, and leaders who stand behind the agreed trade-offs.
This example shows how conversion tracking can mislead an ad campaign: demo requests, webinar signups, event registrations, and questions were all counted alike, despite differing intent.
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Daniv describes pausing account changes to agree with the client on which actions were most likely to become sales opportunities.
They made those actions primary conversions and treated the others as secondary.
Reported conversions fell, but the account's signals better matched pipeline discussions.
For brands planning a campaign
For a B2B SaaS brief, map conversion events to sales intent with the client before changing bids or keywords. Decide which qualified actions should guide optimization and which should remain observation signals.
A useful next read for anyone deciding which platform's numbers should guide daily decisions when CRM and ad reporting do not match.
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Rather than forcing every number to reconcile, Daniv gives each system a distinct role: Google Ads primary conversions guide bids, budgets, and tests; HubSpot remains the revenue reference, with a lag acknowledged. This follows the earlier decision to define valuable conversions with the client.
For brands planning a campaign
Agree on the decision-making metric and revenue-reporting source before a campaign starts. Set an explicit cost-per-qualified-action target; the post gives US$350 as the target in this case.
A concrete example of why a strong blended return may not show whether a campaign is acquiring new demand.
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Daniv says one account's PMax ROAS had fallen to 3.26 after previously reaching 8.
After separating brand and non-brand demand and tightening signals and budgets, the post reports a 9.01 ROAS and 67% lower CPA after 30 days.
The lesson is to change account structure, not simply switch campaign types.
For brands planning a campaign
For an ecommerce audit, check whether branded demand is flattering reported acquisition performance before recommending more spend. Ask for the comparison period and underlying account data before treating the reported result as a forecast.
The post explains what Daniv means by a practical SEO program and gives newcomers a clearer starting point than a list of technical jargon.
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The post breaks SEO into four recurring jobs: improve commercial product and category pages, link related pages with clear anchor text, build supporting content around buyer questions, and earn relevant backlinks. Its dining-table and standing-desk examples distinguish pages aimed at buyers from broader informational searches.
For brands planning a campaign
For an ecommerce SEO brief, use these workstreams to check whether a proposal connects buyer-intent pages, supporting content, and link acquisition, rather than relying on audits or dashboards without clear next actions.
Read this case to see how Daniv connects tracking quality, product structure, and budget control to a reported ecommerce result.
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For an Australian children's book store, the post describes reconciling GA4 and Shopify tracking, testing Search and Shopping, cutting irrelevant queries, and relaunching PMax with curated products and budget guardrails.
It reports ROAS rising from 3.06 to 5.90 over a 12-month turnaround, comparing the baseline with the final 30 days; spend rose 37% and average order value stayed almost flat.
The post says the reported view excludes the separate brand campaign.
For brands planning a campaign
For an ecommerce performance brief, investigate measurement, query quality, product mix, and brand separation before proposing a budget increase.
Approach pattern: account evidence over platform defaults4
Conversion tracking and lead quality before bid changes3
Rebuilding PMax inputs instead of scaling a blended result3
AI as an operating layer for paid media and SEO, with human approval3
Deciding which system to trust when CRM and ads disagree2
Client-side decision authority and access2
Ecommerce SEO as four practical workstreams1
Failed tests, reversals, and budget discipline1
Vendor messaging: show the work removed, not category claims1
Conversion tracking and lead quality before bid changes
In a US oil and gas SaaS account, every form on the site — demo request, webinar registration, event registration, and question submission — fed the same Google Ads conversion goal called “form submission.”
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The account was paying about $350 per lead with zero sales.
Daniv paused bid and keyword work to rank intent with the client, split conversions into primary and secondary, and set Google Ads to bid only on primary conversions while treating the rest as observation.
Reported conversions dropped for a while, but pipeline reviews began matching what the account showed.
Rank intended actions with the client, split primary and secondary conversions, bid only on primary conversions, and observe the rest.
For brands planning a campaign
For B2B SaaS, define a real opportunity before optimizing bids or keywords.
A campaign can look efficient while counting webinar signups and general questions as leads.
Ask for a primary-versus-secondary conversion model and a target cost per primary conversion.
Deciding which system to trust when CRM and ads disagree
HubSpot said leads were missing while Google Ads counted every form fill.
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The integration did not pass every conversion type cleanly, and the two systems used different attribution models over a roughly three-month decision cycle.
Daniv chose Google Ads primary conversions to steer bids, budgets, and tests, and used HubSpot for revenue, understanding that reporting would lag.
They stopped trying to reconcile every number between the two systems.
Use one system for daily optimization and another for revenue reporting, making the lag explicit rather than reconciling line by line.
For brands planning a campaign
Set clear roles for ad-platform data and CRM data before budget decisions. B2B SaaS teams should agree which source controls bidding and which controls revenue reporting, then track cost per primary conversion against a stated target.
Daniv argues that PMax setups blending brand and non-brand demand hide true acquisition quality.
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In one ecommerce account, separating the two and tightening signals moved ROAS from 3.26 to 9.01 and cut CPA by 67% after 30 days, without switching campaign types.
In another account, excluding branded traffic from PMax produced a deliberately more conservative 5.90 ROAS view.
They say reported performance may look worse after excluding brand, and that is the point.
Run a separate brand campaign, exclude branded traffic from PMax, and judge non-brand acquisition on its own.
The campaign type stayed PMax. Daniv’s argument is about structure and measurement, not abandoning the platform.
For brands planning a campaign
Before accepting a strong blended ROAS, ask whether brand and non-brand demand are separated. For B2B SaaS, request non-brand reporting that shows whether paid search is acquiring new demand or taking credit for existing brand searches.
Rebuilding PMax inputs instead of scaling a blended result
For an Australian children’s books store, Daniv paused PMax while rebuilding inputs: reconciled GA4 and Shopify tracking, used Search and Shopping to relearn demand, and cut roughly 80% of irrelevant queries in one week.
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The relaunch used curated products, audience-informed asset groups, and budget guardrails.
Over a twelve-month turnaround, final 30 days versus baseline showed ROAS moving from 3.06 to 5.90, revenue rising from A$6,863.37 to A$18,112.12 on 37% more spend, conversions increasing from 47.13 to 125, and CPA falling from A$47.59 to A$24.56, with average order value nearly flat.
Brand campaign data was excluded for a conservative view.
Pause automation while fixing conversion signals, product segmentation, and budget controls; relaunch with cleaner inputs.
For brands planning a campaign
When PMax ROAS looks healthy but revenue will not scale, inspect conversion signal quality, brand bleed, and product mix before adding budget. For B2B SaaS, use the same check to test whether automation is getting clear, reconciled conversion data.
Daniv calls boring SEO work “sexy” because it produces action rather than 40-page audits, AI dashboards, and brand-mention reports with no next step.
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The practical program has four deliverables: fix product and category pages built for commercial-intent searches, build internal links between related pages with descriptive anchor text, create supporting content that answers pre-purchase questions, and earn backlinks through monthly outreach matched to the niche. The example targets “solid oak dining table” rather than broad “dining room ideas.”
Put the target phrase where it helps buyers and search engines understand the page — H1, title tag, meta description, opening copy, and product details. Link related categories both ways and point supporting content back to the commercial page.
Daniv says the reader does not need to speak SEO to understand the deliverables.
For brands planning a campaign
For an ecommerce brand, ask for monthly workstreams tied to buyer-facing pages, internal links, supporting content, and link acquisition. For B2B SaaS, request specific page and content actions, not opaque reporting.
AI as an operating layer for paid media and SEO, with human approval
Daniv describes running paid media and SEO from Codex, including live budget moves, pausing ads, technical changes, and reallocating spend across Google, LinkedIn, and Meta.
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Codex reads accounts, recommends how much to move and where, and shows proposed edits; Daniv approves them and Codex applies them live.
Nothing runs without approval.
A separate post shares a JSON cleanup prompt that forces ChatGPT to return only valid JSON for piping into sheets, CRMs, or SEO dashboards.
Let AI read accounts and propose edits, keep human approval on budget calls, and set a hard output rule for machine-facing AI work.
For brands planning a campaign
Evaluate AI-marketing claims by whether the system keeps approval, verifies live changes, and produces clean structured data. Automate repetitive cross-platform edits; leave budget judgment to a human.
Daniv lists failed tests and reversals: DSA and competitor keywords underperformed, broad match added no converting queries, form-only tracking degraded a dental account until click-to-call was reinstated, and large budget increases caused efficiency shocks even when ROAS looked good.
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They also say they do not always use PMax and have cut budgets while ROAS looked strong.
Use smaller budget increases and cuts to protect efficiency, and treat failed tests as useful information.
For brands planning a campaign
Ask prospective partners for failed tests and reversed decisions, not just wins. A B2B SaaS team should know which channel changes were tried, what failed to convert, and what was rolled back.
Daniv says agency delays often stem from pending account or website access, feedback arriving one message at a time, no final decision-maker, pressure to choose the safest option, and internal approvals.
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Strong agency relationships have one person making the final call, feedback delivered in one round, and leaders who stand behind the work once trade-offs are clear. Daniv declines new client work without access to review the site, account, offer, and measurement, and a path to ongoing SEO or paid media work.
For brands planning a campaign
Before hiring, assign one decision owner, arrange fast access and consolidated feedback, and confirm whether the scope covers both strategy and execution. For B2B SaaS campaigns, unclear internal approval can delay tests and shorten the learning window.
Vendor messaging: show the work removed, not category claims
Daniv criticizes CRM pitches that promise more opportunities, faster sales, and more money without explaining why buyers would choose that product.
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Calling a product an “AI revenue platform” or “agentic” does not clarify what sets it apart. They want to see the work it removes from a team’s plate; automated follow-up still needs a buyer willing to pay, and a headline should not require a demo to decode.
For brands planning a campaign
For B2B SaaS messaging, lead with the team work removed and the buyer problem solved. Content should reduce reliance on demos by making the product’s difference clear before a sales call.
Approach pattern: account evidence over platform defaults
Daniv judges systems by what the account actually shows.
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PMax structure should follow account needs, not a comfortable blended ROAS.
AI models should be tested against real constraints such as a $10 CPC cap, noisy form fills, and CRM lead quality.
In one case, qualified MQLs grew sixfold under the same cap after intent groups were tightened, search queries controlled, and qualified-lead data fed back into the account.
Keep constraints visible in every recommendation, separate form fills from qualified leads, use past cases without forcing the same fix, and verify changes in the live account.
For brands planning a campaign
Evaluate recommendations by whether they preserve stated constraints and connect platform data to lead quality. For B2B SaaS, a useful partner should show how changes were checked in the live account, not just what the platform dashboard reported.
Daniv names Etavrian as the setting for a Google Ads case they worked on; ask about their relationship with the firm.
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Google Ads, HubSpot, GA4, Shopify, Meta, LinkedIn, and ChatGPT/Codex appear as platforms and tools inside client accounts, not as sponsorships, client engagements, or businesses they own. Across these 13 posts, no sponsorship, affiliate, investment, or disclosure-tagged partnership is mentioned.
Etavrian
Mention
Daniv describes a Google Ads account they rebuilt as “a real case at Etavrian” and the work as “we.”
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Their relationship with the firm is unclear; ask whether they were an employee, owner, or outside contractor.
Andrii Daniv's posts work best as an operator's notebook: what an account showed, what they changed, and what the result was.
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Start with the Performance Max and conversion-tracking posts, where they argue that reported ROAS and lead counts can look healthy while hiding blended brand demand or form fills that never become sales.
Then follow their threads on ecommerce SEO workstreams, client decision-making, and using AI for repetitive execution while a person still approves budget moves.
Across subjects, they judge a tool, campaign, or vendor claim against account evidence and stated constraints.
Covers the same everyday Google Ads and ecommerce problems from an auditing angle, including the brand-versus-non-brand question in Performance Max and how slow sales can come from market demand rather than account mismanagement.
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Davies also treats account data as the place to settle a disagreement before changing bids.
Davies more often diagnoses why an account moved, pointing readers to outside tools and scripts, such as a Performance Max brand script shared by another practitioner. Daniv tends to narrate one account rebuild in first person, focusing more on conversion definitions and AI-assisted workflows.
Shares Daniv's scepticism about brand-search ROAS and blended numbers, arguing for grounding targets in real margin rather than a platform-reported figure.
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Both explain a Google Ads mechanic step by step and then say what they would do with it in a live account.
Fry writes from an agency-account perspective, citing named campaign figures and covering new or beta Google features such as self-serve Conversion Lift and a margin-based Target ROAS calculator. Fry focuses on Google Ads measurement, while Daniv also writes about SEO deliverables, client approvals, and AI tooling.
Makes the same central case that ROAS is not profit and brand demand flatters reported results.
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Both give Performance Max advice built around separating branded traffic, structuring campaigns by margin, and feeding the system better customer data.
Tesselaar presents numbered frameworks, including POAS, new-customer acquisition cost, and a five-part Performance Max checklist aimed at profit rather than volume. Daniv's posts are usually single-account stories about a measurement or structure fix, and they range beyond paid media.
Consider Andrii Daniv for campaigns targeting marketers who work hands-on with Google Ads, paid media, SEO, or AI-assisted marketing workflows.
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Their posts address practical account problems, including noisy form conversions, brand traffic affecting Performance Max results, and automations producing invalid JSON.
Their dated LinkedIn profile reports 589 followers on 9 October 2026, but gives no details about followers’ jobs, employers, seniority, or location.
Before booking a professional-targeting campaign, request recent documented audience survey results or another source showing followers’ roles and seniority.
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.
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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.
Strong fit for a hands-on paid search and SEO engagement, provided the client wants diagnostic work on conversion definitions and measurement before increasing spend. This is a consulting or execution fit, not evidence of a sponsorship offer.
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Brief
A B2B SaaS company with a considered purchase (roughly a three-month decision cycle) that wants Google Ads and organic search to produce measurable pipeline, not just form fills.
Reasons
Daniv starts by asking what a lead actually is. In a US oil and gas SaaS account, demo requests, webinar signups, event registrations, and question submissions all fed one Google Ads goal called "form submission" at about $350 per lead, with zero sales. They paused bids and keywords, ranked intent with the client, split primary from secondary conversions, and bid only on the primary conversions.
Daniv gives two systems distinct jobs rather than forcing them to reconcile: Google Ads primary conversions steer bids, budgets, and tests, while the CRM remains the revenue reference, with its reporting lag acknowledged. For teams arguing over which dashboard is right, they offer a decision rule rather than another report.
Daniv argues that blended results hide acquisition quality. They present a separate brand campaign and branded traffic excluded from Performance Max as a check worth repeating, warning that reported performance may look worse—and that is the point.
The SEO advice is concrete and buyer-facing: fix commercial product and category pages, link related pages with descriptive anchor text, publish content that answers pre-purchase questions, and earn links monthly. A SaaS team can turn this into a workstream list and check whether a proposal includes real actions.
Daniv publicly lists failed tests, reversed conversion setups, and budget cuts made while ROAS looked strong. Clients get a partner who expects to be judged on account evidence and constraints such as a stated CPC cap, not confident dashboards.
Conditions
One person on the client side must own the final call, with feedback returned in one round. Daniv argues that pending access and rolling approvals stretch launch dates and shorten test windows.
Agree on a clear cost-per-qualified-action target up front. The $350 figure is specific to one account, not a benchmark.
Expect the headline conversion count to fall during the transition as conversion goals are redefined.
When to choose a specialist
Poor fit for a sponsorship based on what Daniv's posts say. Do not book this as a sponsorship.
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Brief
A sponsored-post or influencer awareness campaign where a brand wants reach, follower-count justification, or a documented paid-partnership format from a LinkedIn creator.
Reasons
Daniv's posts focus on client-account work, not a partnership product: they include no media kit, rate card, deliverable list, audience guarantee, disclosure practice, or inquiry route. The one giveaway offers a Codex skill in exchange for a comment—a lead-generation tactic, not a paid placement.
The dated profile snapshot reports 589 followers. That is a small base for a reach campaign, and Daniv's posts provide no breakdown by role, seniority, employer, or location.
Daniv says they pass on one-off work with no path to ongoing engagement and on work outside SEO or paid media. Briefs focused on pure brand awareness, non-search channels, or a single fixed deliverable run against that stated preference.
This profile covers Daniv's LinkedIn post text. The posts provide no video, long-form article, newsletter, or live audience data; ask for examples and performance data if the campaign requires those formats.
Conditions
Brands interested in a paid collaboration must raise it directly through the LinkedIn profile. Whether Daniv accepts sponsorships is undocumented; ask directly.
Reject briefs built on follower count on this evidence; assess briefs built on search and paid media expertise against the posts.
A worked campaign example
Angle
Redefine the qualified action and establish supporting measurement before changing bids, then expand into organic search around buyer questions. Position the work as a paid search and SEO engagement, not a content sponsorship.
Brief
B2B SaaS; Google Ads plus SEO; roughly $10,000 monthly media budget; three-month sales cycle; CRM and ad platform already disagreeing on lead counts.
Steps
Week 1 — Conversion audit. List every form and call action currently reported as a conversion. For each, ask sales which actions routinely become opportunities. In the account described, demo requests usually did; webinar registrations rarely did.
Week 2 — Intent ranking with the client. Split actions into primary and secondary. Set primary conversions as the bidding goal and treat the rest as observation. Expect reported conversions to fall and say so in advance.
Week 2 — Measurement decision. Agree that the ad platform's primary conversions control daily bids, budgets, and tests, while the CRM remains the revenue reference, with its lag acknowledged. Stop reconciling the two line by line. Set one target cost per primary conversion.
Weeks 3–6 — Account work. Keep the agreed CPC cap visible in every recommendation. Tighten intent groups, control search queries, and feed qualified-lead data back into the account. Check brand and non-brand demand separately so branded searches do not flatter reported results.
Weeks 4–12 — Organic search in parallel. Fix commercial product and category pages, add internal links with descriptive anchors between related pages, publish content that answers pre-purchase questions, and conduct monthly outreach for relevant links.
Ongoing — report cost per primary conversion against the agreed target, not total form fills, and review pipeline data against the account view in one recurring meeting.
Ask for the conversion audit, the primary-versus-secondary list, and the measurement rule as deliverables. If Daniv proposes bid or budget changes before those exist, treat that as a mismatch with the approach shown in the posts.
Choose Davies when sales have dropped and the team can’t tell whether the market shrank or a competitor took the auctions.
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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.
Davies’s posts focus on ecommerce and account diagnosis.
They don’t cover conversion definitions for reconciling CRM and Google Ads figures or an organic search workstream.
For a campaign bundling paid search, SEO and a measurement rebuild, Davies can offer a second opinion on the account, but ask who will redefine and count leads and handle SEO.
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.
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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.
The lift test has eligibility requirements: Fry says it needs at least 1,000 observed conversions and a $5,000 campaign budget, and isn’t available to every account.
Most small and mid-size accounts won’t qualify.
Ask whether the account meets those thresholds and, if not, who will redefine the qualified action and rebuild organic search.
Choose Tesselaar when the dashboard says the account is profitable but the team isn’t sure it really is.
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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.
The diagnostics assume ecommerce purchase economics: margin per SKU, repeat buyers, conversion value rules and CRM purchase lists.
A B2B SaaS account with a three-month cycle and a modest number of qualified leads each month won’t generate the volume those tests need; the brand-versus-non-brand split also needs to reflect lead quality rather than order margin.
The material doesn’t cover organic search, so ask who will own SEO if the campaign bundles it with paid search and a measurement rebuild.
