# Andrei Haret

Andrei Haret writes about B2B paid media, focusing on lead quality, measurement, and using customer-acquisition economics to guide campaign decisions.

- Profile: https://b2binfluencer.marketing/creators/andrei-haret/
- LinkedIn: https://www.linkedin.com/in/andreiharet
- LinkedIn followers: 7,438
- Topics: artificial-intelligence, paid-ads, ai-in-marketing, conversion-tracking
- Based on 17 dated posts, latest 2026-09-17

## Summary

Andrei Haret is most useful for B2B paid media advice on lead quality, measurement, and CAC payback.

- Best for: B2B SaaS brands seeking better paid media lead quality and CRM feedback loops.
- Not for: Consumer broad reach campaigns or booked sponsored placements with media kits and rate cards.
- Check first: Confirm partnership route, pricing, scope, and audience data before assuming a creator deal.

### Takeaways

- Report CAC payback, not just ROAS, because payback decides whether scaling is affordable.
- Google Ads produces the leads it was told to produce, so change the signal.
- Bad tracking does not just produce wrong numbers; it makes you scale wrong things.

## Main ideas

- **Report CAC payback, not just ROAS: payback decides whether scaling is affordable**: Haret argues that ROAS describes a revenue-to-spend ratio at a moment in time, while CAC payback shows how long money stays out before a customer returns it. In B2B, the gap decides whether growing spend is safe: a strong ROAS on a nine-month deal means nothing if cash runs out at month four. Before touching a campaign, the method is to run three numbers: monthly ad spend budget, target CAC and average deal size, then calculate the implied payback window. When payback exceeds six months and there is no recurring revenue model, scaling paid media simply accelerates the cash problem, they argue. Fix the math first.
- **Google Ads produces the leads it was told to produce. Change the signal you feed it**: Smart bidding is a feedback machine: if the only signal it receives is a form submission, a bot, a student, a competitor and a real buyer all look identical to the algorithm. The account is not broken; the definition of success is. Haret’s fix is to attach the Google click ID to the lead in the CRM, have sales mark the stage that actually matters (qualified or opportunity), and import that stage back into the ad account as the conversion bidding optimises toward. Cost per lead goes up while cost per qualified lead goes down. That trade is the whole point, they argue, so tell the client in advance: a rising CPL reads as failure to anyone who doesn’t know the plan. Meta uses the same approach with different plumbing.
- **Bad tracking doesn’t just produce wrong numbers; it makes you scale the wrong things**: Haret describes auditing B2B accounts where CPL looked clean, campaigns were hitting targets and budgets were scaling, only to find roughly half the leads were phantom: form submissions firing twice, thank-you page visits counted as conversions, no Enhanced Conversions and no offline upload. The algorithm was optimising toward noise. The costs: pausing winners because they look expensive, scaling losers because they look cheap, and losing months of learning that cannot be recovered. The suggested fix is specific: start with Enhanced Conversions in Google Ads to match hashed user data to actual pipeline, then layer in offline conversion uploads tied to the CRM so campaigns optimise toward deals rather than form fills.
- **Stop reconciling GA4, Google Ads and the CRM. Pick one bidding signal instead**: Haret argues that the three systems will never agree because they measure different things: GA4 is last-click, deduped and session-based; Google Ads uses its own attribution window and includes modelled conversions; the CRM records what actually closed. Founders who spend weeks reconciling them are doing the wrong job while their bid strategy starves. The practical recommendation is to pick Google Ads as the single bidding signal, import offline conversions from the CRM, and use GA4 and CRM totals for context rather than bidding. One source drives the machine; the others show whether it’s working.
- **AI accelerates execution, but judgment stays with the people who know the account**: Haret uses AI tools daily to spot anomalies faster, generate headline variants and turn raw data into readable summaries, and says that works well. The judgment calls stay with people who know the account: what to test next, which segments to push harder, when to push back on a client’s campaign goals, and whether a CPL that looks bad is actually fine. A CPL that looks bad in isolation may be acceptable given a 90-day sales cycle, and an audience that appears to underperform may be the warmest segment, just slower to convert. The risk of over-automating, Haret argues, isn’t moving too fast but making faster decisions that are still wrong. They also describe automated processes as delivering about 95 percent quality on average, with the last 5 percent falling exactly where client reports, legal, finance and communications live. Every process keeps a human as the final checkpoint.
- **Reason backwards from a broken result, and screen hires for the same habit**: When results look fine but outcomes are poor, Haret starts outside the ad account. Given a campaign with decent CTR and in-range CPL where sales says lead quality is bad, the average answer stays inside the account (adjust targeting, test new creative, tighten the audience). The operator asks what sales is actually seeing, what the form-fill path looks like, and whether the right conversion event is tracked. The same preference shapes hiring: Haret screens for mathematical thinking and process orientation rather than certifications or platform speed. Running ads is learnable in six to twelve months, they argue, while reasoning about CAC, LTV and payback as business decisions takes years.
- **Qualify clients before the work: paid media cannot save a broken funnel**: Haret writes that taking on unqualified clients early in their career cost nearly two years. Bad client fit costs more than the contract is worth: opportunity cost, mental overhead and slow damage to team morale when a client is never satisfied. Their current filter has three checks in the first twenty minutes: can the client define success in concrete terms; do they own their funnel, with an early insistence that the landing page converts fine treated as a yellow flag; and can they approve a creative or budget change within 48 hours? The filter, Haret says, protects the team, not revenue.

## Work to read

- [Wrong metric. Every time. (ROAS vs. CAC payback)](https://www.linkedin.com/posts/andreiharet_cac-payback-carouselpdf-activity-7472293655630954496-GnAR)
- [Google Ads is not producing bad leads. It is producing exactly the leads you told it to produce.](https://www.linkedin.com/posts/andreiharet_google-ads-is-not-producing-bad-leads-it-activity-7486073630326587393-TlE3)
- [Three conversion numbers. Three different totals.](https://www.linkedin.com/posts/andreiharet_three-conversion-numbers-three-different-activity-7457074571423211521-86ud)
- [Bad tracking doesn't give you wrong numbers. It makes you scale the wrong things.](https://www.linkedin.com/posts/andreiharet_bad-tracking-doesnt-give-you-wrong-numbers-activity-7455647922068742144-iqe5)
- [Interview question: CPL is in range but sales says lead quality is bad](https://www.linkedin.com/posts/andreiharet_most-paid-media-candidates-can-describe-what-activity-7477738313018372097-Qo1I)
- [AI won't tell you when a bad CPL is actually fine](https://www.linkedin.com/posts/andreiharet_ai-wont-tell-you-when-a-bad-cpl-is-actually-activity-7480275720544509952-Ix-a)

## Brand relationships

- **MarketinGO** (disclosed_partnership)

## Partnerships

Andrei Haret's posts do not describe a partnership offer. They write from the perspective of running paid media in-house and for clients at MarketinGO; none describe sponsored placements, paid collaborations, media kits, rate cards or booking terms. The posts also provide no partnership enquiry route. Their writing supports three narrow, judgment-based positions: screening paid media people for business math rather than platform buttons, defining success in concrete numbers during a sales conversation, and building CRM feedback so bidding optimises toward qualified pipeline rather than form fills. A brand with a paid-media product or service could approach this as a commercial or consulting conversation rather than a sponsorship booking. Before proceeding, ask whether that work is offered, how it is scoped and what it costs.

## Alternatives

- Muhammad Anas Raza: Consider Muhammad Anas Raza when a Meta lead-form campaign is filling the CRM with cheap, unqualified leads and you want someone who has made the qualification fix, not just described it. In one post, he walks through the change: turning off Meta's one-click auto-fill form, adding two mandatory gates on monthly budget and buying timeline, and requiring prospects to type their phone numbers manually. He reports a blunt result: cost per lead rose from $3 to $16 and lead volume dropped about 70%, after which prospects answered calls, recognised the brand and had budget ready. A second post explains the reasoning: cheap leads ruin businesses faster than expensive ones, a bad offer will break a good ad, and a short diagnostic of a founder's conversion funnel builds more trust than a long generic deck. For a SaaS team struggling with junk volume from lead forms, this is a directly applicable account of the trade-off: accept a higher cost per lead to buy intent.
- Scott Gelber: Consider Scott Gelber when the real obstacle is internal rather than technical: paid media is working, but the marketing leader cannot get finance to release more budget. His material reframes the buyer's decision: an external partner's value often lies in mental bandwidth, not campaign execution alone. The clearest example compares two clients. One went from three to eighteen sales opportunities a month on the same $10,000 budget, a sixfold drop in cost per opportunity, yet could not scale because finance held strict spending limits. The other aligned with finance before launch on the cost-per-opportunity numbers needed to hit revenue goals, then won budget approval the following month. Gelber reduces the agency standard to four things — stability, clarity, initiative and ownership — and describes a VP of Marketing at a PE-backed SaaS company who moved from exhausting weekly calls and untrustworthy reporting to consistent account management and reporting that reduced friction. For a SaaS company likely to seek more spend after a successful pilot, that framing is closer to the internal decision the buyer will have to make.

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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/