Check first: Confirm scope, fees, deliverables and whether they offer sponsorships before contacting them.
Main ideas
Set acquisition targets from the backend, not from the ad platform · Attribution flatters campaigns, and some of the budget buys customers who already purchased · Treat BFCM as a margin event, not a revenue event
Valentin Kuznetcov is a DTC ecommerce adviser known for connecting paid-media results to the financial health of the business.
Their posts question whether platform-reported ROAS and CPA reflect genuinely new customers, profitable growth and available cash.
They also write about BFCM planning, customer-acquisition payback and how marketing choices interact with inventory, retention, debt and operations.
Follow their work for practical ways to test whether growth decisions make sense beyond the ad dashboard.
Kuznetcov’s distinctive contribution is tracing a marketing metric back to the business outcome it is supposed to represent.
In one audit example, they contrast a platform CPA of $49 with a backend new-customer acquisition cost of $59; in another, they describe a campaign where excluding existing customers lowered ROAS slightly while profit contribution improved.
These cases show why they ask who actually bought and what acquisition cost the business incurred, rather than treating attributed revenue as proof of success.
They apply the same profit-and-cash-flow lens beyond advertising: the BFCM analysis follows discounts, media, logistics, partner fees and financing costs, while the CAC-payback discussion brings inventory sell-through, retention and debt into the picture.
They argue that incrementality models can be unreliable when data is thin or conditions change, and recommend starting with breakeven analysis when that better fits the available information.
The result is a connected, practical view of DTC growth—useful to readers who want to understand the financial consequences behind marketing performance, not just improve a platform score.
Kuznetcov’s central position is that DTC growth decisions should be based on backend economics — margin, repeat purchasing, retention and cash — rather than numbers reported by ad platforms.
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The LinkedIn posts develop that position through audit findings, a BFCM cost breakdown, a CAC-payback diagnostic, scepticism about incrementality modelling, a test for judging decisions and a framework-first argument about AI.
A related priority is reducing dependence on paid platforms by building owned demand; another is broadening operators’ skills across marketing and finance.
Some posts point to a video walkthrough or Lucris, a tool Kuznetcov says they built.
Ask for evidence of the tool’s performance before relying on its claims.
Set acquisition targets from the backend, not from the ad platform
Their most repeated argument is that a brand can hit its in-platform CPA target and still lose money on every new customer: the platform number leaves out gross margin, repeat purchase behaviour and contribution margin.
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The method starts with those backend inputs, works out affordable LTV:nCAC or payback, and only then determines what Meta and Google need to report for the brand to hit the real number. Revenue and ROAS can both look healthy while the gap persists, which is why they say it does not show up in a standard agency report.
In audits they describe for an 8-figure skincare brand and a 7-figure pet supplement brand, the agency was optimising toward a $42–$49 CPA, while backend reconciliation put new-customer acquisition at $54–$59. The skincare audit found roughly $125,000 of one month's budget, about 47%, in spend they judged unoptimized or borderline waste; the pet supplement audit put about $80,000 in one month in the same category.
The same diagnosis recurs across brands of different sizes and categories. In later posts, they attach a named service—the Marketing Efficiency Audit System—to it, making this a standing position rather than a single case study.
Attribution flatters campaigns, and some of the budget buys customers who already purchased
They argue that campaigns defended as top performers often pay to reach people who already bought, while platform attribution lets several channels claim the same sale.
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The fix is to exclude existing customers from acquisition reporting and re-measure, rather than protect a blended ROAS figure that everyone is afraid to touch. They treat the campaign as a test subject: isolate it and watch what happens to new-customer cost, not platform-reported return.
At an 8-figure brand, they say nearly 20% of a Meta budget was going to existing customers; after the exclusion, ROAS dropped only marginally and profit contribution improved by 18% overnight.
In a separate case, they describe an 8-figure wellness brand during BFCM where conversion and PMax reporting both looked strong but nCAC was rising.
Switching PMax off as a test dropped nCAC by roughly 25% overnight, with the same brand, site, offer and period.
In a post built around a hypothetical brand's holiday sale, they walk through five costs that a revenue headline hides: discounting, advertising, logistics, partner fees and the cost of capital financing inventory.
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They urge brands to approach BFCM from a profit-first standpoint, rather than chase short-term revenue at the cost of sustainable scale, and expect the post-sale liquidity squeeze to be worse the following Q1. The percentages are an illustrative best-case reconstruction, not one brand's measured results.
The post's arithmetic: gross margin down 10–15% because markups did not price discounts in and both winners and losers were discounted; CPMs up about 45%; fuel surcharges up 10–20%; agency and affiliate fees taking another 3–7% as spend and coupon volume rose; and 1–3% lost to faster repayment on borrowed inventory financing. Together, they put the change at about −11 percentage points of net profit margin versus the prior October.
A long CAC payback period is a whole-business problem, not a media problem
They argue that when payback runs beyond 90 days, the strain shifts to inventory, retention and debt.
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Inventory sell-through has to carry the funding gap, retention beyond 90 days has to hold, and borrowing adds repayment pressure after a short window of positive cash flow. They stress that revenue payback is not the same as profit payback, and close with questions about inventory efficiency, repeat purchases, operating expenses and debt servicing.
Their rough benchmarks: sell through 45% or more of purchased stock within 30 days on a profit basis, and treat 25% or more of customers repurchasing beyond 90 days as the level that supports an extended payback. They also note that borrowing gives only about four weeks of positive cash flow before repayments begin.
Where the audit posts argue about where the money went, this one asks whether the business can survive the timing of when it comes back: the same economics applied to cash flow.
Prefer breakeven analysis to incrementality modelling
They say incrementality modelling is a waste of time for most brands, including many 8-figure ones, for three reasons: it predicts the future from historical data, so it cannot account for new creative, site changes or product launches; supply-chain shocks invalidate the model; and most brands do not have enough data to train it, while those that do have to keep revising it.
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They are not against the technique in principle: their view is that it has its time and place and is simply being over-weighted. The substitute is profit breakeven analysis: work out the profit contribution the business needs, then set a range of losing, breakeven and profitable aMER from retention, margins and average order value.
They name COVID, the Suez blockage and tariffs as events that make a historical model irrelevant, and ask what happens to the forecast when a core product goes out of stock and traffic fails to convert.
Judge decisions by their second- and third-order effects
Outside media buying, they repeatedly argue that decision quality matters more than execution quality because some choices have asymmetric consequences.
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They evaluate opportunities across inventory, marketing efficiency, supply chain, team capacity, customer behaviour and cash flow, rather than asking only whether something will grow revenue. A decision that improves one metric while quietly damaging four others is rarely a good one.
Months of gains—contribution margin up 2%, CAC down 5%, inventory turns up 10%, 30-day repeat up 15%—can be wiped out by overspending on unprofitable marketing, over-ordering slow sellers, hiring too quickly, launching a discount that trains customers to wait for sales, or expanding into a new market before the core one is solid. They ask clients to consider the second- and third-order impact of a decision across the whole business.
They argue that brands spending around $5,000 a month on AI still struggle with growth decisions because the pool of possible answers is too large, not because the models are weak.
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A framework that narrows a revenue decline to traffic, conversion rate or average order value, then to a channel, then to the specific metric, removes most of the options before AI starts investigating, making the analysis cheaper and easier to roll out.
The illustration is a revenue-decline question: rather than letting AI range across traffic, conversion, AOV, CAC, retention, creative, pricing, product mix, attribution, cohorts and seasonality, the framework narrows to a shortlist first and only then lets the model work inside it. They say the product, Lucris, was built framework-first with AI inside it—their own description, not something the posts verify.
Paid media should support acquisition, not carry it alone
They describe paid platforms as accumulating customer intelligence that the brand pays to create but never owns, leaving it unable to leave without losing a large share of acquisition—a situation they compare to vendor lock-in.
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They do not advocate abandoning Meta and Google. Instead, they argue for building organic demand, an owned audience, partnerships, retention and leverage alongside them, so the goal is to be able to leave rather than actually stop advertising.
They frame it through a founder's line—'we have to pay them more this year and we can't leave, they have our data and customers'—and argue that the same behaviour from an agency or SaaS vendor would be treated as a reason to fire them or call out lock-in.
Operators gain leverage by learning the other side of the business
A recurring career-side argument is that the fastest way to earn more is to build leverage by expanding beyond a core discipline: marketers learning finance, finance people learning marketing, and operations people learning either.
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Kuznetcov argues that understanding both the frontend and backend of a business is shifting from a luxury to a requirement, and that the advantage lasts while competitors chase attribution hacks and complex planning instead.
Kuznetcov relays feedback from a community member who, according to Kuznetcov, raised the member's marketing prices a month after joining — that reported outcome comes from the member and is Kuznetcov's account of it, not an independently checked result.
This post shows how a big BFCM revenue result can still be a bad outcome, offering newcomers a practical introduction to Kuznetcov’s profit-first approach.
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Kuznetcov breaks a holiday sale into five costs: discounts, advertising, logistics, partners and capital.
They estimate that the combined effects could reduce net profit margin by 11 percentage points compared with October 2023, even if revenue rises.
Track the sale through the profit-and-loss statement instead of treating a revenue spike as proof it worked.
For brands planning a campaign
For a profit-focused BFCM campaign, plan around contribution and cash costs as well as sales. Ask the brand to model discount depth, media costs, shipping surcharges, partner fees and inventory financing before approving the promotion.
This post explains what Kuznetcov’s paid-media audits look for and why a healthy-looking dashboard may not show whether acquisition is profitable.
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Start here when deciding whether to investigate ad spend before increasing it.
In a skincare-brand audit, Kuznetcov says about $125,000 of a monthly ad budget—roughly 47%—went to spend they judged unoptimized or borderline wasted.
They identify repeat-customer spend counted toward acquisition, search-term spend with no sales, creatives above breakeven CPA, and budget going to average-performing products.
They also contrast an agency target CPA of $49 with backend new-customer acquisition cost of $59.
The example shows why they check who bought and what the business actually paid, rather than accepting platform metrics alone.
For brands planning a campaign
Before scaling an audit or paid-media campaign, reconcile platform reporting with backend customer and margin data. Treat the figures as this post’s case example, not a guaranteed result for another account.
This post explains what else a brand should examine when CAC payback is slow.
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It’s a useful starting point when a campaign needs to account for cash flow, not just customer acquisition.
Kuznetcov argues that a long customer-acquisition-cost payback period puts pressure on more than advertising: inventory sell-through, retention and debt servicing all affect whether the business can keep funding growth. They warn that revenue payback is not the same as profit payback, and offer questions about inventory, repeat purchases, operating expenses and debt as a diagnostic checklist.
For brands planning a campaign
For a brand with slow payback, a campaign should not promise growth based only on ad efficiency. Evaluate inventory cash requirements, repeat-purchase behavior and financing alongside acquisition targets; establish the brand’s own thresholds before applying the example figures.
This post explains when a sophisticated measurement model may be more effort than help, and why Kuznetcov favors decision-ready fundamentals before complex modeling.
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Rather than dismissing incrementality outright, Kuznetcov says forecasting can be undermined by new creative, site changes, product launches, supply disruptions and insufficient data.
They recommend starting with profit breakeven analysis and setting a range of losing, breakeven and profitable aMER targets using retention, margins and average order value.
The post shows how the choice of measurement complexity depends on how stable and data-rich the business is.
For brands planning a campaign
Before commissioning a complex measurement project, check whether the brand has enough consistent data and whether major business changes make historical forecasts unreliable. A breakeven framework may be a more practical first step, but its inputs need to reflect the specific business.
This post explains how Kuznetcov frames business decisions beyond a single growth metric, offering a starting point for readers weighing a cross-functional profitability problem.
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Kuznetcov describes how one decision—such as overspending on unprofitable marketing, over-ordering weak products, hiring too quickly, or discounting—can erase months of incremental improvements.
Their proposed test is to consider second- and third-order effects across inventory, marketing, supply chain, team capacity and customer behavior, all the way through to cash flow.
That systems view explains why a campaign can improve one metric while harming the business overall.
For brands planning a campaign
Use this as a planning prompt when a campaign proposal optimizes a single outcome. Ask what it changes for stock, margin, operations and customer behavior, and compare those tradeoffs with the expected gain.
In-platform CPA versus the real cost of a new customer4
Existing customers inside the acquisition number4
Performance Max and borrowed credit2
Profit-first BFCM1
CAC payback beyond 90 days1
Scepticism about incrementality modelling1
Decision quality over execution1
AI needs a narrower question1
Platform dependency and owned demand1
Cross-domain leverage as a career and pricing argument1
In-platform CPA versus the real cost of a new customer
Kuznetcov repeatedly finds a gap between the number a paid-media agency optimises and what acquiring a customer actually costs the business.
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In audits of an 8-figure skincare brand and a 7-figure pet supplement brand, the agency reported a $42–$49 CPA, while backend reconciliation put new-customer acquisition at $54–$59. The same audits found 47%–50% of spend was unoptimised or borderline waste, 58%–67% of Google search-term spend produced zero sales, and roughly 20%–23% of budget went to repeat customers while being used to judge acquisition performance.
Set the acquisition target from the backend first—gross margin, repeat purchase behaviour, LTV:nCAC or nCAC payback, contribution margin—then work backwards to the number Meta and Google need to report for the brand to hit it.
Kuznetcov notes that ROAS, CPA and revenue can all look fine while the gap persists, keeping the problem out of standard agency reports.
For brands planning a campaign
For a profitability brief, make the reconciliation the campaign angle: show how in-platform CPA and backend nCAC diverge, and where the money actually goes. It’s a concrete, inspectable argument, not a general claim about wasted spend.
Campaigns judged as top performers can partly be buying from people who have already purchased.
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In one 8-figure brand, nearly 20% of a Meta budget went to existing customers.
Excluding them slightly lowered ROAS but improved profit contribution by 18% overnight.
The same pattern appeared in the skincare audit, at roughly 20% of budget, and in the pet supplement audit, at roughly 23%.
Exclude existing customers from acquisition reporting and remeasure instead of defending a blended ROAS figure.
For brands planning a campaign
Ask for a new-buyer view of the account before scaling. The before-and-after exclusion makes an effective teaching example: a modest ROAS change, but a meaningful profit change.
Kuznetcov argues that Performance Max often takes credit for demand created by other campaigns, and that Google's incentive is to maximise its own attributed performance, not the brand's profit.
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During BFCM, switching PMax off as a test at an 8-figure ecommerce wellness brand dropped nCAC by about 25% overnight—with the same brand, site, offer and period—even though conversion had looked strong beforehand.
Treat the campaign as a test subject: isolate it and track its effect on nCAC, not platform-reported ROAS.
For brands planning a campaign
A useful angle for a campaign questioning attribution claims. It also offers a low-cost diagnostic before the brand commits more budget to a campaign everyone is afraid to touch.
One post works through five hidden costs of a BFCM sale: discounting (gross margin down 10–15% when markups did not account for discounts and both winners and losers were reduced); advertising (CPMs up around 45%); logistics (fuel surcharges up 10–20%); partners (agency and affiliate fees taking another 3–7% as spend and coupon volume rose); and capital (faster repayment of borrowed inventory financing raising the annualised cost).
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Combined, the illustrative net profit margin change is -11% against the prior October, and Kuznetcov expects the Q1 2026 liquidity squeeze to be worse.
The margin percentages are an illustrative reconstruction of a best case, not measured results from a single named brand.
For brands planning a campaign
A ready-made seasonal angle for a campaign focused on BFCM profitability: a profit-first plan that treats revenue spikes as a cost event and measures discount, media, logistics and partner decisions against margin rather than top-line growth.
Kuznetcov sets out what breaks when a brand runs CAC payback above 90 days: inventory sell-through must be strong (45%+ of purchased stock sold within 30 days, measured by profit rather than revenue payback); retention beyond 90 days must hold (they use 25%+ repurchase as a rough benchmark); and debt servicing adds pressure because borrowing gives only about four weeks of positive cash flow before repayments start.
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The closing diagnostic lists four checks: inventory sell-through efficiency, retention beyond 90 days, operating expense bloat and debt servicing load.
Measure CAC payback accurately, then test whether inventory, retention and debt can each support the extended window.
For brands planning a campaign
This framework gives a campaign a practical structure: calculate payback, then test whether the four supporting systems can sustain it instead of assuming slow payback is acceptable because LTV looks adequate.
Kuznetcov argues incrementality modelling is a waste of time for most brands, including many 8-figure ones, for three reasons: it predicts the future from historical data and cannot account for new creative, site or product launches; supply-chain shocks (they cite COVID, the Suez blockage and tariffs) invalidate the model; and most brands lack the data volume to train it, while those with enough data must keep revising it.
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They recommend starting with profit breakeven analysis instead: find the profit contribution the business needs and a range of losing, breakeven and profitable aMER based on retention, margins and AOV.
For brands planning a campaign
Positions Kuznetcov against over-engineering measurement. For a brand being asked to fund an incrementality programme, this offers a usable counter-argument and a substitute method.
A recurring theme outside media buying is that one bad decision can erase six months of operational gains.
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Kuznetcov lists the repeated culprits — overspending on unprofitable marketing, over-ordering slow-selling stock, hiring too quickly, launching discounts that train customers to wait for sales, and expanding into a new market before the core one is solid — and evaluates decisions by their second- and third-order effects across inventory, marketing efficiency, supply chain, team capacity, customer behaviour and cash flow.
Ask what a decision does to the other systems before asking whether it will grow revenue. A decision that improves one metric while quietly damaging four is rarely good.
For brands planning a campaign
A campaign angle on decision review rather than acquisition tactics, useful for founders and operators who already know the media basics.
Kuznetcov argues that brands spending around $5k a month on AI still struggle with growth decisions because the pool of possible answers is too large, not because the models are weak.
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A framework that narrows a revenue decline to traffic, conversion rate or AOV, then to a channel, then to the specific metric (spend, CPM, CTR), removes most of the options before AI investigates and makes the analysis cheaper. They tie this directly to how Lucris was built: decision framework first, AI inside it.
The claim about product design is Kuznetcov's own description of Lucris. Ask for independent evidence before relying on it.
For brands planning a campaign
Relevant if the campaign touches tooling or analytics. The argument that cheaper, narrower AI beats broader AI gives a brand a concrete reason to work with someone who brings a framework rather than a data access plan.
Kuznetcov describes paid platforms as accumulating customer intelligence bought with the brand's money that never becomes the brand's asset, leaving the brand unable to leave without losing a large share of acquisition.
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Their position is not to abandon Meta and Google but to make paid media one part of acquisition rather than the whole strategy, building organic demand, owned audience, partnerships, retention and leverage alongside it. The goal is to be able to leave.
For brands planning a campaign
Strong angle for an audience worried about rising acquisition costs: a campaign can pair paid-media efficiency work with building channels the brand controls, a more saleable first step than simply cutting spend.
Cross-domain leverage as a career and pricing argument
One post argues that the fastest way to earn more is to gain leverage by expanding beyond a core discipline — marketers learning finance, finance people learning marketing, operations people learning either — and cites a community member who raised his prices a month after joining.
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Kuznetcov argues that knowing both the backend and frontend of a business is moving from a luxury to a requirement.
For brands planning a campaign
An education and community angle rather than a media-buying one. It suits a brand or platform targeting operators who want to broaden commercial skills, and shows the audience Kuznetcov speaks to most directly.
The posts are almost entirely LinkedIn posts in a few consistent formats: audit write-ups built from percentages and dollar figures, dialogue posts in which hypothetical brand objections ('But Val, our PMax is our best-performing campaign') are answered in turn, and posts that end with a short video breakdown and a call to join a community or contact Kuznetcov about an audit.
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Engagement routes are an audit offer, a D2C community and the Lucris product. One post is personal — a light note about a LEGO collection with a joke about needing help with ecommerce — and carries no commercial argument.
No post in this set documents a sponsorship or branded partnership. Ask for examples of sponsorship formats before planning one.
For brands planning a campaign
Expect the first step of any engagement to be an audit or community contact rather than a content collaboration. Plan the campaign around the audit as the entry point and the community as the ongoing audience.
Valentin Kuznetcov presents Lucris as their SaaS product for DTC business analysis, combining decision frameworks with tools for examining customer acquisition and business performance.
Lucris
Owned business
Valentin calls Lucris “my SaaS” and says they are building it around DTC decision frameworks, including a decision tree for evaluating business decisions.
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They also promote it for measuring CAC payback. These posts describe their product and offer, not an external sponsorship or partnership.
Kuznetcov's material is best followed for one recurring exercise: checking what an ad platform reports against what a new customer actually costs the business.
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Start with a single audit write-up, read end to end: one sets an agency's reported $42–$49 CPA beside a backend figure of $54–$59 and shows where a month's budget went.
Framework posts explain the reasoning behind those numbers: setting acquisition targets from gross margin, repeat purchasing and contribution margin; a CAC-payback diagnostic that treats a long payback as an inventory, retention and debt problem rather than a media problem; and a five-part breakdown of what a BFCM sale costs in discounts, advertising, logistics, partner fees and financing.
Shorter argument posts cover Performance Max taking credit for demand other campaigns created, the case for breakeven analysis over incrementality modelling, judging decisions by their knock-on effects, and reducing dependence on paid platforms by building owned demand.
Read the paid-media audit case posts together: the same findings recur across brands of different sizes, and they point to the same named service.
Three comparators below approach the same economics from other angles: contribution and cohort measurement, retention and lifetime gross profit, and contribution-margin budgeting.
Ramseyer tackles the same question as Kuznetcov — what a customer is actually worth once costs are subtracted — but frames it as a measurement model rather than an audit: revenue lifetime value versus CAC:LTV versus contribution LTV, and why blended CAC and blended AOV hide what is really happening.
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The argument that a cohort's contribution, not top-line revenue, should sit against acquisition spend echoes Kuznetcov's reconciliation of in-platform CPA against backend nCAC. Both insist on separating new-customer metrics from blended ones instead of averaging everyone together.
Ramseyer's centre of gravity is TikTok Shop and social commerce: main-profile content feeding GMV Max, affiliate sampling, creator efficiency, and the mechanics of a marketplace channel still being learned.
Kuznetcov focuses on auditing Meta and Google spend and rebuilding acquisition targets from the P&L.
For cohort-level measurement advice on a channel like TikTok Shop, start with Ramseyer; to assess whether a paid search and Meta account is being reported honestly, start with Kuznetcov.
Perdriau makes the same case that acquisition targets should be built from lifetime gross profit rather than a platform CPA, and is similarly sceptical of Performance Max and of attribution-only tools.
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A year-end prediction scorecard covers much of Kuznetcov's ground from a portfolio view instead of one account at a time: Meta gaining wallet share, spend moving out of PMax into standard shopping, attribution tools having to widen into platforms, agencies building their own software, and finance-led marketing mattering more while mostly failing in execution.
Perdriau focuses on retention — lifetime gross profit, first-to-second-order conversion, Klaviyo flow structure, RFM segmentation — in long-form YouTube masterclasses and a yearly scorecard of predictions he grades himself.
Kuznetcov approaches the same economics through written paid-media audits and cash-flow decision reviews.
For a full retention framework, go to Perdriau; to find out where a media budget is going, start with Kuznetcov.
Peters applies the same contribution-margin-first logic to budgeting: rebuild landed costs each year, distrust comfortable gross margin assumptions that ignore discounts and channel fees, right-size SG&A before planning growth, sanity-check inventory turns against real constraints, and rationalise SKUs by plotting turn against margin.
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Kuznetcov's CAC-payback post asks nearly the same questions — inventory sell-through efficiency, retention beyond 90 days, operating expense bloat and debt servicing. Both treat a single bad decision as capable of erasing months of gains.
Peters delivers this through podcast episodes and founder interviews for mid-market CEOs, CFOs and ownership teams, including guest case studies such as a debt-free, IP-protected founder.
Kuznetcov publishes written audit findings and dialogue-style posts for DTC operators.
For a budget cycle, read Peters; to diagnose acquisition spend, start with Kuznetcov.
On LinkedIn, Valentin Kuznetcov reaches a small, specialist audience: roughly 5,374 followers as of October 2026.
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Their posts target DTC ecommerce founders, operators, and the marketers and agencies who serve them.
The writing assumes readers own or advise a brand's profit and loss: posts break down named-scale brands ("8-figure DTC brand"), explain how to reconcile in-platform CPA against backend nCAC, share CAC payback and inventory sell-through frameworks, and invite readers to join a paid community or request an audit.
For a campaign aimed at DTC operators, treat this as narrow B2B reach suited to a tool, audit, finance, retention or media-efficiency offer, rather than a broad consumer reach play.
Ask for recent results on comparable content (paid-media teardowns and CAC payback or BFCM profitability frameworks) and whether any audience survey covers respondents' job role and seniority.
LinkedIn followers
5,374
Public LinkedIn profile follower count · 2026-10-09
Kuznetcov’s posts promote paid offers for DTC operators, not brand sponsorships or collaborations: a community they run with Nick Miller, a financial program, the “Beyond ROAS” manual, and Lucris, a SaaS tool they describe as applying a DTC decision framework to AI to narrow the questions it investigates.
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They also offer direct engagements, including a Marketing Efficiency Audit and paid-media audits, and invite interested brands to DM them or comment on a post.
To explore a sponsorship, ask directly on LinkedIn; the posts publish no rate card, deliverable list, audience specification, or sponsorship policy.
They present consulting and audit engagements, not creator sponsorships.
A 7- to 9-figure DTC ecommerce brand whose growth has stalled on profit and cash flow rather than demand. It wants a paid-media and BFCM plan built around contribution margin, new-customer cost and payback, and is open to an audit-led engagement rather than a sponsorship.
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Reasons
The brand’s problem is the exact one Kuznetcov writes about: the gap between the CPA an agency optimises and what a new customer actually costs. One audit puts the agency target at $49 against $59 backend nCAC; another puts it at $42 against $54. The posts also describe 47% to 50% of spend as unoptimised or borderline waste and 58% to 67% of Google search-term spend producing no sales.
Kuznetcov offers usable frameworks, not just opinions. The CAC payback diagnostic covers inventory sell-through, retention beyond 90 days, opex and debt servicing. The judgement test asks what a decision does to stock, margin, operations and customer behaviour before asking whether it grows revenue.
Kuznetcov has a ready seasonal angle. Their BFCM post works a holiday sale through five cost centres — discounts, advertising, logistics, partners and capital — and lands on an illustrative 11-point net profit margin decline against the prior October. It’s a concrete way to reframe a revenue-first promotion.
Kuznetcov argues against over-engineering measurement. They make the case for profit breakeven analysis and a range of losing, breakeven and profitable aMER targets instead of an incrementality model, which suits a brand seeking a decision this quarter rather than a measurement programme.
Kuznetcov’s diagnostic approach offers a low-risk test: isolate a campaign and watch nCAC rather than platform ROAS. In one described case, switching Performance Max off dropped nCAC by about 25% overnight with the same site, offer and period.
The commercial route is clear, though it isn’t a sponsorship: the audit offer, the D2C community run with Nick Miller, the financial programme, the 'Beyond ROAS' manual and the Lucris tool all target operators buying this kind of help. The natural first step is a direct LinkedIn approach about an audit.
Conditions
Confirm scope and fees directly, since the posts give no rate card, deliverable list or sponsorship terms.
Ask for named results on comparable accounts. The audit figures come from Kuznetcov’s own posts; ask to see the brand’s reconciliation before relying on them.
Roughly 5,374 LinkedIn followers as of October 2026 points to narrow specialist reach, suited to a tool, audit, finance, retention or media-efficiency offer for DTC operators rather than a broad awareness push.
Ask directly for transcripts of the video breakdowns referenced in several posts.
Verdict
Strong fit, provided the brand expects consulting or audit work rather than a sponsored content slot.
When to choose a specialist
A consumer skincare brand launching a new mass-market product line wants a paid creator partnership for top-of-funnel awareness: Instagram and TikTok video content, a published rate card, defined deliverables, and reach measured in impressions against a broad consumer audience.
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Reasons
The posts promote Kuznetcov’s own paid offers to DTC operators — the community, financial programme, manual, audit services and Lucris — not brand collaborations. Ask directly whether they offer sponsorships and, if so, for a rate card and deliverable list.
The channel shown is LinkedIn, not Instagram or TikTok. The posts address DTC founders, operators, marketers and agencies, not a mass consumer audience on visual-first platforms.
The audience is narrow and specialist rather than broad. Roughly 5,374 LinkedIn followers as of October 2026, on a profile written for people who own or advise a brand's profit and loss, is a B2B operator audience. Follower count alone also cannot establish geography, age, job role or seniority, because no demographic or engagement data appears in the posts here.
The content format works against an awareness brief. Long analytical teardowns with percentages, dollar figures and dialogue-style rebuttals of brand objections teach and persuade; they’re not built for a fast visual impression campaign.
Kuznetcov offers diagnostic expertise, not reach. Their argument is that scale without first-purchase profitability, retention and inventory control is the problem — a poor match for a campaign aimed at top-of-funnel impressions.
A brand seeking a sponsorship would instead encounter an audit or community relationship, a different budget line and internal owner.
Verdict
Poor fit: the brief calls for broad consumer reach, while the posts show a specialist LinkedIn presence aimed at DTC operators.
What would make it work
Re-scope the brief to a B2B objective aimed at DTC operators — for example, a media-efficiency or finance tool with an audit-led offer — and treat it as a consulting or thought-leadership engagement rather than a consumer sponsorship.
If the brand still wants a documented sponsorship format, confirm directly with Kuznetcov that they offer one before proceeding.
A worked campaign example
Brief
A DTC brand entering its first profit-first BFCM wants a concrete campaign angle and working plan it can approve internally, built around contribution margin and new-customer cost rather than revenue.
Campaign angle
'What the sale costs before you approve it' — a profit-first BFCM teardown that walks a brand through the five ways a holiday promotion quietly removes margin, then shows how to set media and discount decisions against a backend new-customer target instead of a platform ROAS number.
Deliverables
A short teardown series on LinkedIn: the cost model, the reconciliation gap, the backend target-setting method and the two tests.
A BFCM profit checklist the brand can fill in before approving discount depth, media spend, shipping terms and partner fees.
A follow-up video walkthrough of the reconciliation, in the recorded-breakdown format used in their posts.
An audit as the entry point, with the community as an ongoing resource for operators who want to keep using the frameworks.
Honest gaps
The video breakdowns referenced in several posts are not in the posts here, so the printed arguments here rest on post text alone.
No sponsorship or branded-partnership format is documented. For an audit, consulting or community relationship, discuss fees directly.
The audit percentages and margin figures are case examples from their posts and an explicitly illustrative reconstruction, not guaranteed outcomes for another brand.
Plan
Detail
Lay out the five cost centres of a holiday sale — discounts, advertising, logistics, partners and capital — and the illustrative decline in net profit margin against the prior October. Frame it as a best-case reconstruction of one promotion, not a measured result from a named brand, so the brand reads it as a model, not a claim.
Step
Open with the hidden-cost teardown
Detail
Use the audit pattern: roughly a fifth of budget going to repeat customers while being used to judge acquisition, most Google search-term spend producing no sales, and creatives running above breakeven CPA. The teaching point: ROAS, CPA and revenue can all look fine while the gap persists.
Step
Show where acquisition budget actually goes
Detail
Set the acquisition target from gross margin, repeat purchase behaviour, LTV to new-customer cost or payback, and contribution margin. Then work backwards to the numbers Meta and Google must report for the brand to hit it. This shifts the focus from dashboard health to business health.
Step
Replace the platform target with a backend one
Detail
First, exclude existing customers from acquisition reporting and re-measure: the described outcome is a modest ROAS drop, with profit contribution up around 18% overnight. Second, treat the campaign everyone is afraid to touch as a test subject: isolate it and watch nCAC rather than platform ROAS.
Step
Run the two cheap tests
Detail
If payback runs beyond 90 days, work through four checks: inventory sell-through on profit rather than revenue payback, repurchase behaviour beyond 90 days, operating expense bloat, and debt servicing load. Slow payback is supportable only when those systems are strong.
Step
Check whether payback can be carried at all
Detail
Ask what the promotion changes for stock, margin, operations and customer behaviour before asking whether it grows revenue. A decision that improves one metric while damaging several others can undo months of disciplined execution.
Step
Close on decision quality, not tactics
Why this angle
Lead with their strongest, most repeatable evidence: the gap between reported CPA and backend nCAC, and the finding that a chunk of acquisition budget goes to people who already bought.
Reframe the seasonal decision the brand is actually about to make. The posts support following the sale through the profit and loss rather than treating a revenue spike as proof it worked.
End with a doable test, not a promise. The brand can exclude existing customers from acquisition reporting and watch nCAC during the period itself.
Choose Athens Ramseyer when the brand's problem is how it defines the metric.
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Their writing separates revenue lifetime value, CAC-to-LTV and contribution LTV, and argues that mature brands should compare acquisition spend with the contribution a customer actually produces after product cost, payment processing, discounts, refunds and other variable costs.
They also insist on splitting new-customer CAC and conversion from blended figures, segmenting by offer, discount, channel, SKU, subscription status and acquisition cohort, and tracking contribution at 30, 60, 90 and 180 days.
A second post covers TikTok Shop growth: daily shoppable brand posts feeding GMV Max while an affiliate program matures, partly to protect contribution margin.
Ramseyer is a better fit when the brand needs to rebuild how it defines LTV and CAC, or when social commerce is part of the profit problem.
Kuznetcov's posts show framework and metric design.
Their audit findings include dollar figures: an agency target of $49 against $59 backend new-customer cost, and roughly half of a budget described as unoptimised.
Ramseyer's posts teach the model and segmentation logic but don't document a reconciliation on a named account.
The social-commerce item describes one brand's relaunch and focuses on platform mechanics.
Choose Ramseyer when metric architecture is the blocker; choose Kuznetcov when the immediate need is an audit showing where the media budget is being misread.
Perdriau is the stronger choice when retention, rather than acquisition, is the binding constraint.
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The retention masterclass description covers building CAC targets from lifetime gross profit; treating email and SMS as downstream of product, customer experience, acquisition strategy and unit economics; structuring Klaviyo flows; improving first-to-second-order conversion; segmenting customers; and using paid media to buy higher-quality customers.
The other item is a public scorecard of 2025 ecommerce predictions, arguing that structure — forecasting, incrementality thinking, financial alignment and systems — mattered more than new tactics, and that finance-led marketing is harder to execute than to talk about.
Choose Perdriau when weak repurchase is straining cash and the brand wants CAC targets anchored to lifetime gross profit and a retention system to fix, and when a video and consultancy presence fits the engagement.
The masterclass is available only as a video description with a timestamp list, not a transcript; ask for the transcript to assess the framework's depth.
The second item is written commentary rather than a working method.
Perdriau's work is teaching and foresight, while Kuznetcov's is a media-efficiency audit pattern with case figures attached.
Choose Perdriau when retention and lifetime-gross-profit target-setting are the priority; choose Kuznetcov when the brand first needs to reconcile platform CPA against backend cost.
Luke Peters fits decisions one level above the campaign: the annual budget and the profit and loss.
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The 2026 budgeting episode works through contribution margin rather than revenue as the driver, rebuilding landed costs, keeping gross margin realistic after discounts, promos and channel fees, right-sizing SG&A when sales are declining, sanity-checking inventory turns, rationalising SKUs by turn and margin, and pressure-testing costs finance teams skip.
The second item is a founder interview with the Nodpod founder on debt-free, category-creating growth: protecting the core mechanism with IP, choosing a hero SKU with clean economics, treating negative reviews as a roadmap, and forecasting inventory against realistic sell-through rather than hopes.
Choose Peters when the profitability decision is the yearly plan — margin structure, cost base, SKU mix and working capital — or when the brand wants a founder-level perspective on cash-first scaling.
Both items are episode descriptions rather than transcripts; the material covers CEO and CFO coaching and founder interviews rather than hands-on paid-media work.
A brand seeking a campaign-quarter diagnostic — where the ad budget is going and why the platform number disagrees with the P&L — should look to Kuznetcov, not Peters.
Choose Peters for budget and cost-structure planning; choose Kuznetcov when the fix has to happen inside the media account.
Valentin Kuznetcov has fourteen LinkedIn posts covering paid media audit findings and the gap between platform-reported CPA and backend new-customer acquisition cost, BFCM cost breakdowns, CAC payback diagnostics, scepticism about incrementality modelling, decision review, Performance Max, platform dependency, AI-assisted analysis and cross-domain operator skills, plus one personal post with no commercial argument.
The posts show roughly 5,374 LinkedIn followers as of October 2026.
Each argument is attributed to the post it comes from; audit percentages and margin figures are presented as Valentin's own case examples or an explicitly illustrative reconstruction, not as measured results for a named brand.
Several posts point to video breakdowns whose transcripts are not in the posts here, so those arguments rest on the post text alone.
No post documents a sponsorship or brand-collaboration format, so no commercial formats were inferred and none are described here.
The posts carry no publication dates; October 2026 is when the follower count was observed, not a post date.