Kim Stiefel shares practical ideas for ecommerce teams on prioritizing experiments, improving business operations, and using AI workflows. Their posts also promote IntelliJAMS discussions about ecommerce testing and operations.
Kim Stiefel offers practical ecommerce experimentation and operations advice, including AI workflows and peer-led discussions.
Best for
DTC ecommerce brands seeking sharper experimentation and stronger operational resilience.
Not for
Brands seeking bookable sponsored posts or impression-based consumer reach.
Key takeaways
Test a big bet at the smallest size that can answer the question.
With AI, the hard part is choosing which tests to run.
The biggest testing levers sit outside the page.
Check first: Confirm sponsorship availability and recent reach and engagement on comparable posts.
Main ideas
Test a big bet at the smallest size that can answer the question · With AI, the hard part of experimentation is choosing which tests to run · The biggest testing levers sit outside the page
Kim Stiefel shares practical ideas for ecommerce teams, especially on choosing worthwhile experiments, testing large bets at small scale, and building stronger operations.
The posts also promote IntelliJAMS discussions about ecommerce testing and operations, and describe a practical, peer-led approach to sharing AI workflows.
Follow them for advice that connects experimentation to business fundamentals—not only page changes or advertising—including pricing, logistics, margins, product mix, and post-purchase experience.
Kim’s work links practical testing methods with a broader view of how ecommerce businesses operate.
In a step-by-step post, they suggest defining a big idea, testing a minimum viable version, and using the results to decide whether to invest.
Examples include trying AI-generated lifestyle images before commissioning a shoot and using a landing page to gauge product demand before buying inventory.
Other posts extend that thinking from individual tests to business resilience: they frame prioritization as the new experimentation bottleneck, identify operational levers beyond the website, and describe volatility as something a business can learn and adapt from.
The IntelliJAMS posts are episode descriptions, not full transcripts; ask for the transcripts to assess the complete discussions.
Elsewhere, they share an engineering-role referral and promote an operator gathering built around exchanging usable AI workflows.
Together, these posts offer concrete testing ideas and an operations-minded perspective on ecommerce, alongside peer learning and occasional community or recruiting posts.
Kim Stiefel's posts return to a few positions: decide which experiments are worth running before producing more, look for testing levers beyond the page, treat volatility as design input rather than something to absorb, and treat AI as practical operational tooling that operators should share openly.
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The arguments draw mostly on ecommerce experimentation and operations work. Several come from IntelliJAMS episodes rather than Kim's own voice; those are attributed below.
Test a big bet at the smallest size that can answer the question
Kim Stiefel lays out a three-step method for validating a major change before committing real money: define what you want to do and why, build the cheapest version that can produce directional data, then read the results and either make a confident bet or save the time and money for something else.
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The goal is not to avoid big investments but to gather evidence before going all in. The same sequence works across products, funnels, imagery, and channels.
For more lifestyle imagery in product shots, create it with AI instead of paying for a photo shoot. For a new product, launch a landing page to gauge demand before sourcing and procurement tie up cash in inventory.
With AI, the hard part of experimentation is choosing which tests to run
In the IntelliJAMS episode Kim Stiefel describes, Alex McEachern and Adam Kitain argue that building tests used to be the bottleneck, but AI has removed much of that constraint, making prioritization the real competitive advantage.
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They frame the episode around a shift: test production is no longer the limiting step; deciding what deserves to run is.
The post uses the shift itself as an illustration: when AI lowers the cost of producing tests, a team's backlog of potential tests grows faster than its ability to judge them. Ranking the options, not building them, becomes the constraint.
Alex McEachern and Adam Kitain argue in the IntelliJAMS episodes Kim Stiefel summarizes that most experimentation programs are too narrow.
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They point to pricing, logistics, margins, and offer structures as the biggest levers, and the post-purchase experience as one of ecommerce's most overlooked optimization opportunities. Their rule of thumb: the closer a test sits to the transaction, the bigger its impact.
Expand a program beyond on-site page changes to pricing, margin, and offer structure. Treat checkout and post-purchase as areas worth testing rather than assuming the work ends at conversion.
AI agents are becoming the operational layer for ecommerce
In a post describing an IntelliJAMS episode, Kim Stiefel sets out four jobs for AI agents in an ecommerce team: analyze tests, prioritize opportunities, automate optimizations, and free people to focus on higher-level strategy.
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The post frames this as a shift in where teams spend attention, not as a product pitch.
The concrete claim: agents handle analysis and routine optimization, freeing operators to focus on strategy.
This extends the prioritization argument in Kim Stiefel's earlier post on test bottlenecks: once choosing tests becomes the constraint, agents are the proposed way to handle ranking and routine work.
Kim Stiefel distinguishes resilience, which survives shocks, from antifragility, which uses volatility as raw material.
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The post names the pressures ecommerce businesses face: tariff shifts overnight, logistics cost surges, tightening privacy rules, and consumer behavior swinging with every economic report.
They argue that successful brands rethink, retool, and rebound rather than simply endure.
They describe some of their most painful professional moments as pivotal because those experiences changed how they built.
A companion post makes the market-level case: DTC has entered an era of operational excellence rather than advertising excellence, with margins, product mix, supply chain, and sales channels underpinning resilient businesses.
The post cites overnight tariff changes, logistics cost surges, and privacy regulation as normal operating conditions for ecommerce, not exceptions to plan around.
Practical AI skills should be traded in the open, not gatekept
Kim argues against gatekeeping AI skills because a single workflow, prompt, or habit can save hours every week and unlock far more output.
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They favor peer exchange: a small room of ecommerce founders, operators, and marketers where each person brings an AI workflow, prompt, or system they actually use and leaves with the others' examples. Kim says the fastest way to learn is to build alongside other people, and rules out slides, pitches, and AI thought leadership in favor of material people can put to work the same day.
Claude & Coffee NYC, co-hosted with Grace Clarke: about ten operators, two hours, Tuesday June 30, 10:00 AM to noon at The Farm House SoHo. The exchange format: bring one workflow and leave with nine more.
See how Kim turns a vague desire to test something big into a small, low-cost experiment.
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Kim outlines a three-step method for validating a major change before committing real budget: define what you want to do and why, test a minimum viable version, then evaluate the results.
The examples are concrete: use AI-generated lifestyle imagery before paying for a real shoot, or launch a landing page before sourcing inventory.
The goal is not to avoid big investments, but to gather directional data first.
For brands planning a campaign
For a DTC brand weighing a major creative, product or channel bet, this offers a framework for sequencing experiments and avoiding an all-in commitment before testing demand.
Read Kim's clearest statement on where AI is changing experimentation most.
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Kim summarizes an IntelliJAMS discussion with Alex McEachern and Adam Kitain: the old bottleneck was building tests; the new one is knowing which tests are worth running. The post says AI is changing ecommerce experimentation and that prioritization is becoming the real competitive advantage.
For brands planning a campaign
For a brand with more test ideas than capacity, this argument shifts the focus to decision-making and prioritization systems rather than producing more tests.
See how Kim frames AI agents as practical tools for ecommerce operations, not abstract technology.
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Kim describes AI agents as an operational layer for ecommerce: analyzing tests, prioritizing opportunities, automating optimizations and freeing teams for higher-level strategy. The post is a short episode description, not a full transcript; ask for the episode to hear the detailed argument.
For brands planning a campaign
Use this as a starting hypothesis for how agents could fit into an ecommerce operating model, then listen to the linked episode for more context.
See what Kim thinks belongs in a serious ecommerce experimentation program.
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Kim highlights an IntelliJAMS conversation with Alex McEachern and Adam Kitain about expanding experimentation beyond page changes to pricing, logistics, margins and offer structures. The post calls these the biggest levers in an experimentation program.
For brands planning a campaign
For a brand whose testing roadmap focuses mostly on on-site copy and layout changes, this is a prompt to broaden the program to commercial and supply-side variables.
Find a specific area where Kim thinks ecommerce teams are leaving value on the table.
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Kim shares the argument that the closer a test is to the transaction, the bigger its impact. In the IntelliJAMS discussion with Adam Kitain and Alex McEachern, the speakers describe post-purchase experiences as one of ecommerce's most overlooked optimization opportunities.
For brands planning a campaign
This offers a concrete campaign angle for retention or customer-experience work: focus on what happens after checkout, not only on acquisition or product pages.
Start here to understand why Kim talks about operations, not just marketing or acquisition.
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Kim says DTC has entered an era focused on operational excellence rather than advertising excellence. The IntelliJAMS episode description points to margins, product mix, supply chain and sales channels as fundamentals brands use to build resilient businesses.
For brands planning a campaign
This connects Kim's content directly to a DTC brief about resilience and fundamentals, not just ad efficiency.
Routing opportunities through a founder and operator network1
Testing a big bet before making it
Kim Stiefel's most concrete teaching post lays out a three-step way to test a big idea cheaply: define what you want to do and why, build the smallest version that can answer the question, then read the results and decide whether to invest.
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The examples are specific: AI-generated lifestyle imagery before paying for a real photo shoot, and a quick landing page before sourcing inventory and tying up cash. The same sequence applies to new products, funnels, imagery, and channels.
Name the goal, build a minimum viable test, gather directional data, then make a confident bet or bank the savings.
For brands planning a campaign
A natural fit for a workshop, guest post, or webinar built around a real business decision. Stiefel's approach asks the audience to bring a proposed change, not a generic framework.
Across the IntelliJAMS episode summaries, the biggest levers sit beyond standard page-level A/B tests.
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The posts point to pricing, logistics, margins, and offer structures as underused areas for experimentation; describe post-purchase experience as one of ecommerce's most overlooked optimization opportunities; and argue that as AI makes tests easier to build, the real bottleneck is knowing which ones are worth running. The through-line is prioritization, not test volume.
For brands planning a campaign
Useful for briefs about experimentation programs and margin improvement, where the value lies in deciding what to test. A brand selling testing, pricing, or post-purchase tooling could frame content around prioritization rather than product features.
Stiefel describes AI agents as becoming the operational layer for ecommerce teams: analyzing tests, prioritizing opportunities, automating optimizations, and freeing people for higher-level strategy.
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The claim is about shifting where teams spend their attention, not pitching a product.
Treat agents as a layer that handles analysis and routine optimization, freeing operators to focus on strategy.
For brands planning a campaign
Fits brands marketing automation or AI tooling to ecommerce operations teams, particularly when the message is about reallocating operators' time rather than replacing them.
Two posts make a broader argument about how ecommerce businesses should be built.
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One says that anyone launching another company should design for antifragility first: systems that do more than survive volatility and instead use it.
Tariffs, logistics costs, privacy rules, and swings in consumer behavior are described as normal conditions of ecommerce.
The other announces that DTC has entered an era centered on operational excellence rather than advertising excellence, pointing to margin, product mix, supply chain, and sales channels.
For brands planning a campaign
Strong fit for operations, supply chain, or margin-focused brands, and for campaign angles about resilience and business fundamentals rather than acquisition efficiency.
Stiefel co-hosts small New York gatherings for ecommerce founders, operators, and marketers.
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The stated format: about ten people, two hours, no slides and no pitches.
Each attendee brings one AI workflow, prompt, or system they actually use and leaves with the others' examples.
The invitation pairs this format with an explicit dislike of gatekeeping AI skills, arguing that a single workflow can save hours a week.
Small-room peer exchange built on working material, not presentations.
Ask about sponsorship slots, fees, and terms for brand participation.
For brands planning a campaign
An intimate operator event format that could suit a brand wanting direct access to ecommerce practitioners in a working-session setting. The posts describe the format and audience but not brand participation, and do not describe any sponsorship arrangement.
Routing opportunities through a founder and operator network
One post is a hiring call rather than a teaching post: it endorses a venture led by Sarah Wissel, describes the backing behind it, and seeks a zero-to-one engineer for a remote role starting mid-October.
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It shows the account also moves people and openings through a network of founders and operators.
An outlier in the reviewed set, this post does not establish an ongoing recruiting or talent focus.
For brands planning a campaign
Evidence of reach and credibility inside founder and operator circles. Weaker evidence for product or campaign marketing, since the post is a recruiting ask rather than commentary.
Kim Stiefel's posts revolve around IntelliJAMS, the ecommerce experimentation show they post about using 'we', and they point to experience building Repeat.
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The posts disclose no sponsorship, affiliate, or client relationships, and no post states a formal role at any company.
IntelliJAMS
Disclosed partnership · 2026-05-26
Kim Stiefel posts about IntelliJAMS episodes and describes the show's analysis with “we”—for example, how AI is shifting which ecommerce tests are worth running, how AI agents are becoming an operational layer for ecommerce, and why post-purchase experience is an overlooked optimization area.
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Some posts credit Alex McEachern and Adam Kitain as the voices in an episode, while others use “we.” Ask what their relationship to the show is; the posts do not specify it.
Kim Stiefel's posts are easiest to explore by theme rather than in order.
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Start with the short method for testing a big idea at small scale: define the goal, build the cheapest version that can give directional data, then decide whether to invest.
The recurring argument is that as AI makes tests cheaper to build, the real constraint becomes choosing which tests are worth running—and that the largest levers sit outside page-level changes, in pricing, offers, margin, and post-purchase.
A second strand treats AI as practical operational tooling, with agents handling analysis and routine optimization so operators can spend time on strategy.
Several posts summarize episodes of IntelliJAMS, where Alex McEachern and Adam Kitain make the underlying argument.
The posts point to the original conversations; follow the credited speakers for the uncondensed version.
Event posts describe a small-room peer-exchange format for ecommerce operators trading AI workflows, a source of hands-on material.
Like Kim Stiefel, this creator starts from the observation that after working across dozens of consumer brands, the same problems repeat—and the fix is a better operating system, not more output.
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Both write for product and marketing teams at DTC-style companies, draw on their own client work, and argue that the production model, not effort or headcount, is the constraint.
Weyman focuses on product content itself: building an accurate digital product once and reusing it across images, video, 360s, and sales tools.
The stated obstacles are the friction of change, acquisitions, and scattered product data.
Kim Stiefel focuses on which experiments to run and how AI tooling changes where operator attention goes.
The two overlap on the diagnosis but address different parts of the work.
Like Kim Stiefel, Mandic takes one operational problem, breaks it into a named list of conditions or requirements, and lays out an order of operations with rough timelines.
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Both treat ecommerce problems as systems to fix rather than channels to work harder, and point readers to a fuller asset behind the post: Mandic to a question bank and audit, Stiefel to the IntelliJAMS episode.
Mandic focuses on retention and customer data: the conditions that make repurchase possible and zero-party data collected through email and Klaviyo profiles. Kim Stiefel works upstream, on experimentation design and prioritization, making Mandic the stronger follow for lifecycle and CRM questions and Stiefel the better fit for testing and operational tooling.
Both use the same format for a similar audience: long-form LinkedIn posts built on numbered lists, named client examples, and specific numbers from accounts they work on, aimed at DTC brands and the operators running them.
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Both argue that the winning move is a structural change, not a tactical tweak to the existing setup.
Voynov focuses on paid media and creative: top-of-funnel creative economics, running an in-house team alongside agencies, subscriptions as an LTV play, and founder-story ads.
Kim Stiefel stays out of ad creative and media buying.
Readers looking for testing and operations should follow Stiefel; those focused on acquisition and creative scaling should follow Voynov.
For a DTC campaign focused on experimentation and operational resilience, Kim Stiefel's posts speak to practitioners.
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They cover IntelliJAMS episodes on how AI is shifting ecommerce experimentation and making test prioritization a competitive advantage; post-purchase experience as an overlooked optimization area; expanding experiments beyond page changes into pricing, logistics, margins, and offer structure; DTC's shift from advertising excellence to operational excellence; and AI agents becoming the operational layer for ecommerce teams.
Two posts recruit for and describe an in-person NYC gathering explicitly limited to ecommerce founders, operators, and marketers, with ten operators each bringing one working AI process.
A practical how-to on validating a big swing before committing budget and an essay on antifragility and building for volatility point to the same readership: people running or staffing ecommerce operations and testing programs.
That content aligns with a DTC brand's need for experimentation and operational resilience, reaching operators, founders, and marketers who own testing, merchandising, and fulfillment decisions.
The only measured audience figure provided is the LinkedIn profile's 9,178 followers, observed 10 October 2026.
There are no audience demographics, survey results on job function or employer type, per-item reach or engagement figures, platform demographics, or video performance summary.
Before committing to a placement, ask for recent reach and engagement on comparable items—such as a few IntelliJAMS episode posts and process or operations posts of similar depth—and whether any audience survey covers employer type, job function, and seniority.
Kim Stiefel’s posts describe no paid partnership format, rate card, sponsored-content disclosure, media kit, or brand-facing inquiry route, so buyers can’t treat a sponsored post, episode placement, or product integration as bookable.
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The recurring format is a small, in-person working session: the Claude & Coffee NYC gathering, co-hosted with Grace Clarke and capped at roughly ten ecommerce operators.
Each attendee brings one AI workflow, prompt, or system and leaves with several more.
The session is built for practitioners swapping tactics, not brands buying reach; the only entry route named in the posts is a direct message asking to join the room.
The posts also promote IntelliJAMS episodes—conversations with named guests such as Alex McEachern and Adam Kitain about experimentation, pricing, logistics, and post-purchase optimization.
They describe the discussion topics but don’t say who produces the show or pays for it.
For a brand placement, ask who produces the show and whether sponsorships are available.
Strong fit for a DTC ecommerce brand that wants to sharpen experimentation and build operational resilience.
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Kim Stiefel's posts address the decisions a campaign targeting that need puts on the table.
The clearest entry point is their argument that the constraint has shifted: building tests is no longer the bottleneck; choosing which tests are worth running is.
That reframes experimentation from volume to triage, where teams with mature testing programs often get stuck.
The second is scope: they point beyond page changes to pricing, logistics, margins, and offer structures as larger levers, and call post-purchase experience one of ecommerce's most overlooked optimization opportunities.
A resilience campaign also aligns with their view that DTC has shifted from advertising excellence to operational excellence, measured in margin, product mix, supply chain, and sales channels.
Their antifragility argument is that surviving shocks is less useful than building systems that use volatility, treating tariffs, logistics cost swings, and privacy changes as normal conditions rather than exceptions.
Their AI thread fits the same audience: agents act as an operational layer that analyzes tests, prioritizes opportunities, and automates routine optimization so teams can focus on strategy.
Why the fit holds.
The posts speak practitioner to practitioner, and the two event posts are explicitly limited to ecommerce founders, operators, and marketers.
That subject matter and assumed readership align with a brand-side testing and operations team.
The teaching style also suits a working engagement: one post turns a vague ambition into a three-step test, with AI-generated lifestyle imagery before a real shoot and a landing page before sourcing inventory as examples.
A brand can hand that format to a team on Monday.
What to confirm before budgeting: the posts establish subject-matter alignment, but not a verified commercial offer.
They include no rate card, sponsorship disclosure, media kit, or brand-facing inquiry route; the only entry point named for the NYC gatherings is a direct message asking to join the room.
The only published audience figure is 9,178 LinkedIn followers, observed 10 October 2026.
Ask for demographics, job function, employer type, per-item reach and engagement data, and commercial terms.
When to choose a specialist
Poor fit for a national consumer packaged goods brand buying paid social reach, where the deliverable is a disclosed sponsored post or short-form video distributed to a large measured consumer audience and priced against expected impressions.
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Three mismatches would remain even with a tighter creative brief.
First, there is no documented format to buy.
The posts contain no sponsored-content disclosure, rate card, media kit, or brand-facing way to start a commercial engagement; the only named route into the operator gatherings is a direct message to join a small room.
Ask whether Stiefel accepts sponsorship rather than assuming either way.
Second, the published audience information cannot support an impression-based buy.
The only measured figure is 9,178 LinkedIn followers, observed 10 October 2026, with no demographic, job-function, or employer-type breakdown and no per-item reach or engagement figures.
The posts address ecommerce operators, founders, and marketers, not consumer shoppers, so even strong creative would reach the wrong audience.
Third, the content model works against reach-based formats.
The gatherings are capped at roughly ten people, run without slides or pitches, and ask each attendee to bring one working AI workflow.
Stiefel's position is that a single workflow can save hours a week and that keeping those workflows to oneself is unhelpful.
A campaign measured by impressions or volume of consumer views requires something the posts do not describe as an offering.
An official sponsor page, disclosed paid collaboration, rate card, or performance data on comparable placements could change this judgment; none appears in the posts.
If the buyer needs access to ecommerce operators rather than consumer reach, the picture changes substantially, and the good-fit campaign need above is the better starting point.
A worked campaign example
Worked campaign angle for the good-fit campaign need: a DTC brand that wants to improve experimentation and operational resilience.
Angle in one line: a small working session plus a portable prioritization playbook, built around the idea that the scarce resource is deciding which tests are worth running, not producing more of them.
Why this shape.
It draws on Stiefel's stated preference for small rooms where practitioners bring working material instead of slides.
It turns their recurring argument—that prioritization has become the competitive advantage as AI lowers the cost of building tests—into something the brand's team can use after the session.
The resilience element applies the same discipline to non-page variables such as pricing, logistics, margins, and post-purchase experience, using an operational-excellence framing the brand is likely already buying into.
The session.
Eight to ten people, for two hours, drawn from the brand's growth, merchandising, and operations teams, plus a few outside operators.
Each attendee brings one live test they are considering, using Stiefel's format: state what you want to do and why, describe the smallest version that could answer the question, then say what result would justify the full investment.
The brand supplies two or three real candidates from its roadmap.
Examples from their post translate well: an AI-generated lifestyle image set before booking a shoot, or a landing page before committing to inventory and cash.
What comes out of it.
A one-page prioritization template the brand owns: the proposed change, the cheapest test that produces directional data, the decision threshold, and the owner.
Delivered blank and filled in during the session, it serves as a repeatable artifact rather than a recap.
Optional extension: a short recording or written summary for the wider team, focused on which tests the group would run first and why.
Use the post-purchase and pricing examples from Stiefel's episode discussions to prompt a broader program beyond on-site copy and layout changes.
Sequencing and practical notes.
Define the campaign need and candidate tests before making contact; the format depends on real material.
Start with the direct-message route named in the event posts, since they mention no commercial booking page.
Ask for recent reach and engagement on comparable items, plus audience data on job function, employer type, and seniority; none is published.
If the brand wants distribution rather than a working session, this is the wrong vehicle; see the poor-fit campaign need above.
Expected effort and risk.
Low production cost, small room, no slide build.
The main risk is mistaking a session for reach.
Set the success metric as decision quality: how many of the brand's candidate tests get a cheaper first version agreed on and an owner assigned within two weeks.
Choose Weyman when a brand sets out to improve experimentation and discovers the real bottleneck is content production, not test selection.
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Weyman founded MarketThrive in 2018 after leading ecommerce for a manufacturer with thousands of SKUs, and describes the same patterns repeating across 80+ brands: teams that resist changing a working-but-failing process, production models that cannot keep up with more products, channels, and formats on flat resources, acquisitions that mix asset standards and teams, and organizations that cannot reliably locate their own current product specs.
The pitch: build one accurate digital product and reuse it across lifestyle, video, 360s, installation, training, ecommerce, and sales material, with a system for controlling the whole pipeline instead of producing one asset at a time.
For a brand whose tests keep stalling because no clean, reusable product data or imagery exists to test with, this is a more direct answer than a prioritization framework.
The tradeoff is scope.
Weyman's posts cover the production system and product-information control, not measurement design, test triage, or how to choose between candidate experiments.
They say nothing about pricing, margin, or post-purchase experience as levers.
A brand that already produces content fast enough but cannot decide what deserves a test gets less from this route than from Stiefel's argument that prioritization has become the competitive advantage as AI lowers the cost of building tests.
The account of a working engagement rests on posts from 26 August and 16 September 2026; ask for a longer program history before assessing the depth of their engagement experience.
Marko Mandic
Choose Mandic when a brand reframes its problem from experimentation to retention economics.
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The post sets out eight conditions for retention campaigns to work: a reason to repurchase, a purchase experience worth remembering, real product education, repurchase timing based on survey and repeat-purchase data, a usage habit, cross-sells drawn from what customers actually buy together, discount math grounded in margin and acquisition cost, and loyalty tiers that feel worth chasing.
It separates week-long fixes such as packaging inserts from three-to-six-month projects such as adding a product line, and tells teams to build a dated roadmap and treat retention like a product launch.
The second post describes 400 zero-party-data questions across ten DTC categories, assembled from auditing 150+ email accounts, with a method for mapping answers into customer-profile properties.
A brand whose repeat rate, not its test pipeline, is the constraint should start here.
The tradeoff is that this is a retention and data-collection lane, not the experimentation lane Stiefel works in.
Mandic's posts do not treat pricing, logistics, margin, or post-purchase experience as test variables, or offer a method for deciding which tests are worth running.
A brand buying a resilience-focused campaign would still need the prioritization piece from elsewhere.
The work also leans on email and lifecycle tooling, which assumes a stack and data practice the brand may not have.
The posts are from 26 March and 8 April 2026.
Lachezar Voynov
Consider Voynov when a brand wants creative volume and paid acquisition growth rather than a method for choosing tests.
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The posts claim oversight of $25M in monthly Meta spend and describe what scales: top-of-funnel creative that needs a higher cost-per-acquisition tolerance than mid- and bottom-funnel ads; tracking new-visitor share per creative and budgeting separately for new customers rather than judging everything on blended return; an internal creative team focused on iterating what already works while two or three agencies chase bigger swings; and subscriptions as the mechanism that lets a brand outspend competitors on high lifetime value.
One worked example claims founder-story ads cut cost per acquisition by half and new-customer cost per acquisition by 60% within seven days after UGC, AI UGC, and static discount ads had stalled.
A brand hiring for ad account throughput and founder-led content should look here first.
The tradeoff is that this is media buying and creative production, not test design or operational resilience.
The posts do not address pricing, logistics, margins, or post-purchase experience as variables, and the new-visitor and subscription arguments solve a different constraint than deciding which experiments deserve investment.
A brand engaging on this basis should expect account-scaling advice, not a reusable prioritization playbook.
The two programs may pull in different directions if both run at once.
The posts are from 20 March 2025 and 1 October 2026.
Ten LinkedIn posts published between May 2025 and September 2026 cover practical testing advice, IntelliJAMS episode descriptions, AI workflow posts, event invitations and a hiring referral.
Episode points reflect the posts’ descriptions, not full transcripts.
The posts do not say that Kim holds a formal role at IntelliJAMS or Repeat; Kim refers to experience building Repeat.
Kim’s posts do not say anything about earlier signature work.
