# Alex Rybakov

Alex Rybakov works on AI-enabled creative for consumer brands, with a focus on testing: deciding what to make, what to measure, and what each result should change next.

- Profile: https://b2binfluencer.marketing/creators/alex-rybakov/
- LinkedIn: https://www.linkedin.com/in/alexrybakov-work
- LinkedIn followers: 193
- Topics: artificial-intelligence, ecommerce, dtc-ecommerce, creative-testing
- Based on 11 dated posts, latest 2026-10-07

## Summary

Alex Rybakov is most useful for creative testing frameworks that connect performance data to creative decisions.

- Best for: DTC growth agencies or consumer brands wanting a peer workshop on creative testing frameworks.
- Not for: Paid product placement or reach campaigns based on follower scale and a rate card.
- Check first: Confirm availability, deliverables, and audience or sponsorship data, since no media kit or rate card appears.

### Takeaways

- A creative test should produce a lesson, not just a verdict.
- Write the stop rules before the spend starts.
- AI makes producing variations cheaper, but choosing which ideas deserve spend remains difficult.

## Main ideas

- **A creative test should produce a lesson, not just a verdict**: Rybakov says information gets lost as soon as a test ends. The performance team looks at spend, cost per acquisition, return on ad spend and whether an ad can scale; the creative team looks at the angle, hook, offer, format and execution. Both read the same result differently: one ad wins and gets scaled, another loses and gets switched off, while the harder question goes unanswered — what did the test teach that should change the next creative decision? They press on attribution directly: was the outcome driven by the angle, the offer or the format, or was the test too noisy to draw a conclusion? Connecting performance results back to the creative hypothesis remains work in progress at DTC Performance Lab, with the aim of getting better at finding winners over time, not just identifying them once.
- **Write the stop rules before the spend starts**: Rybakov's most detailed account of deciding whether an idea deserves another dollar comes from a conversation with Konstantin Mitrichenko, described as having 14+ years as a founder, CEO and CFO across ecommerce, marketplaces and consumer products. In one business, roughly 100 product ideas went in and about one launched. Each idea got a landing page, creatives and around $200 of test traffic. A near miss got about a month and a small budget to fix; a bad miss prompted three diagnostic questions rather than more ads: did they misread the price customers expected, fail to show the main benefit, or simply choose the wrong product? The process replaced gut feel with three limits: no more than a month, no more than roughly $500, and a target conversion rate at every stage. Rybakov notes that the right conversion rate depended on category, traffic source and season, and that acquisition cost at around 25–30% of revenue left little room for error at the top of the funnel. They ask: how do you decide when to stop testing an idea?
- **Familiar container, new value**: Rybakov argues that most DTC brands try to be different in too many places at once — new category, language, visual world, creative format and product story — which can feel like work rather than differentiation to customers. They start with an Alex Hormozi line they keep returning to, "match the format that's rewarded them before," and extend it beyond content formats to familiar mental models: what premium looks like, what scientific credibility looks like, what a trustworthy founder looks like, what a product demo looks like, what a transformation story looks like. The practical move is to put new value in a familiar container: use a format people already know how to process, then place the novelty in the product, mechanism, insight, proof or point of view. They reframe the question from "how do we make this unlike anything the customer has ever seen" to "what should feel instantly familiar so the truly new part gets understood."
- **Copy the logic underneath, not the visible output**: Rybakov uses the wearable blanket as a case study: the idea predates Snuggie, which arrived in 2008 with an absurdly memorable infomercial and an offer around $20. Roughly a decade later, Davie Fogarty launched The Oodie around the same fundamental desire — sitting on a couch and being ridiculously comfortable — but with a different machine around it: an oversized hood, sherpa lining, a premium price, DTC distribution, social media, licensing and collaborations. They cite The Oodie's own claim of more than 9 million Originals sold. Rybakov distinguishes copying the output from copying the logic underneath, brings in Oded Shenkar's framing of imitation as a search for cause and effect, and uses Michael Porter to describe the ceiling: an activity can be copied without the system that made it work. Their practical signals are a competitor running the same ad for six months, or five successful companies converging on the same offer. The danger is convergence: everyone sees the same winners, copies the same visible layer and ends up looking the same. Rybakov proposes the sequence copy, observe, understand, modify, invent, and shifts the question from what to copy to why it worked in the first place — separating what was essential from what was contextual and what was an accident attached to a winner.
- **Making things is getting cheap; knowing what to make is not**: Rybakov argues that AI moves the bottleneck rather than removing the need for expertise. They cite three reported examples to argue that AI makes strong people absurdly leveraged — a shift they are betting on for consumer brands and building an AI-native creative business around. The sharpest version is the ecommerce argument: generating far more creative with the same team does not make ad number 1,001 the hard part. The hard part is knowing which ten deserve spend, what is being tested, what was learned and what to make next. Making things is becoming abundant; knowing what deserves to be made is becoming more valuable.

## Work to read

- [On creative testing: the information that gets lost after the test](https://www.linkedin.com/posts/alexrybakov-work_one-thing-i-keep-noticing-in-creative-testing-activity-7508496651381309440-xkG7)
- [A 100-to-1 idea filter and the rules that stopped it drifting](https://www.linkedin.com/posts/alexrybakov-work_creative-production-is-getting-cheaper-every-activity-7513656512041459712-TjPI)
- [Familiar container, new value: where differentiation should actually live](https://www.linkedin.com/posts/alexrybakov-work_most-dtc-brands-try-to-be-different-in-too-activity-7512976304888020992-0r8L)
- [Making things is getting cheap; knowing what to make is not](https://www.linkedin.com/posts/alexrybakov-work_two-people-401-million-in-sales-thats-activity-7511130405111017472-OVGO)
- [Copying the logic underneath, not just the output](https://www.linkedin.com/posts/alexrybakov-work_the-oodie-has-sold-more-than-9-million-wearable-activity-7509254339903823874-O1UW)

## Brand relationships

- **DTC Performance Lab** (disclosed_partnership)

## Partnerships

No sponsorship disclosure, rate card, media kit, or partnership inquiry route appears in the posts, so no public partnership format is described. The posts show how brand-side collaboration has happened: a detailed, recorded conversation with operator Konstantin Mitrichenko about launch thresholds and when to stop funding a test (published October 2026), and ongoing work on creative testing at DTC Performance Lab, described as connecting performance results back to the creative hypothesis so each test gives the next one more context. For an agency proposing a collaborative workshop on creative testing frameworks, the operator interview and working-session style are the closest public examples. Bring a specific testing problem and a defined decision to work through rather than a general sponsorship ask. Ask directly about rates, turnaround, and approval process; the LinkedIn profile linked at the top of the page is the contact route.

## Alternatives

- Lexi Williams: Choose Williams when the agency wants a session that installs a named, repeatable system rather than working through its own test history. Williams publishes a four-step method, INSIGHTS™: find the opportunity by mining customer conversations for untapped angle territories; build the creative into hooks, statics, scripts and briefs across personas and awareness levels; define what each concept is testing and read it against spend, return on ad spend, cost per acquisition, click-through, hook rate and hold rate; then kill, iterate or scale and feed the learning into the next cycle. The published claim is three to five fresh angles and forty-plus testable concepts a month with no extra headcount, and a 10–20% cost-per-acquisition reduction. Customer language and empathy lead the method — credited to direct-response mentor Alex Cattoni — so message mining comes first, not last. An agency that wants something it can pick up and run on day one, and is willing to adopt someone else's structure, gets more from this than from Rybakov's open diagnostic.
- Amir Faizan Malik: Choose Malik when the session should hang on conversion copy and landing pages, and when client before-and-after results are the teaching material the room will trust. Malik publishes audits and outcomes: twenty-three DTC landing pages reviewed with nineteen making the same fault of writing to everyone; a headline rewrite reported at 2.1% to 8.4% conversion on the same product and traffic; and three client cases — an email sequence at 55.6% average open and 8.23% average click-through with 22 calls booked in four weeks, a sales page at 9.7% conversion up from 2.1%, and ad creative at 53% hook rate, 4.33% hold rate and 3.87x return on ad spend. The stated common thread is deep audience research, clear positioning, human-sounding copy and metric-driven iteration. If the agency's real problem is the message and the page, and it wants cases with reported numbers to argue over, Malik is the closer fit.
- Ujla Shakoor: Choose Shakoor when the workshop is about media buying operations — how a winning ad gets scaled, what happens to the account when that winner fatigues, and how to keep a pipeline running — rather than about creative strategy. Shakoor frames the central risk as building an account around one creative, one hook, one angle and one message, so that when it tires the whole account feels it. The answer is to keep asking what is being tested next across different hooks, angles, customer problems and formats. The stated aim is a pipeline of winners rather than a single winner. The posts also carry five years of continuous work, three of them inside DTC brands and Meta advertising, so the session would be a practitioner describing live account conditions. If the agency wants someone to talk through the day-to-day of managing and replacing winners, this is closer than a framework conversation.

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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/