Martin Kilp shares LinkedIn commentary on ecommerce and digital advertising, especially Snapchat campaigns, incrementality measurement, and emerging agentic commerce.
Useful for LinkedIn posts explaining agentic commerce, Snapchat campaigns, and incrementality measurement to ecommerce and paid-media practitioners.
Best for
B2B ecommerce software or measurement tools running a short LinkedIn post series.
Not for
Consumer skincare or fashion short-form video campaigns needing on-camera demos and guaranteed consumer reach.
Key takeaways
A four-point UCP readiness diagnostic for Shopify stores.
Last-click reporting stops describing what happened once an agent completes the sale.
Snapchat is an underpriced, measurable channel for ecommerce and retail advertisers.
Check first: Confirm audience analytics, sponsorship terms, and independence from Snap.
Main ideas
AI agents are becoming a buying surface, and the buy button is moving off the retailer’s site · Last-click reporting stops describing what happened once an agent completes the sale · Judge channels by incremental return and halo, not by the sale they appear to have taken
Martin Kilp shares LinkedIn commentary on ecommerce and digital advertising.
Their posts focus on Snapchat campaigns, incrementality measurement, and the shift toward shopping through AI assistants.
For readers following agentic commerce, their work connects changing buying habits with practical questions about product data, checkout readiness, and how brands measure sales that may not register as clicks.
Martin Kilp’s distinctive strength is turning advertising and commerce changes into concrete explanations and actions.
They frame agentic commerce as both a measurement problem and a technology shift: if an AI assistant completes a purchase without a conventional website visit, last-click reporting may miss the paid media that helped create demand.
A four-step Shopify and UCP readiness checklist makes the issue tangible.
In other posts, they use reported Snapchat case-study figures and incrementality research to show how measurement can change a brand’s view of a channel.
The set also includes a recruiting post for Snap Inc. sales roles, so ask about their perspective on the company’s campaigns if independence matters to your decision.
Martin Kilp’s posts trace one shift and its consequences: buying is moving into AI assistants, so a machine completes the sale without seeing the retailer’s landing page or tracking pixel.
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The recurring priorities are measurement and readiness.
A store that cannot accept an agent’s order loses the sale regardless of the ad or page; last-click reporting no longer describes what happened.
The useful questions are which channels created incremental demand and whether the store’s product data is structured for machines to parse.
The posts return to incrementality, halo effects across a media mix, and Snapchat as a channel whose measured returns appear better than its share of spend suggests.
The method is consistent: translate the technical term into plain English, then reduce it to a short numbered list of actions a brand can take today.
AI agents are becoming a buying surface, and the buy button is moving off the retailer’s site
Martin Kilp treats AI assistants as commerce surfaces, not just places to discover products.
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When a shopper tells an assistant to buy something, the assistant must complete the transaction through the store’s checkout.
A store that cannot accept the order loses the sale, no matter how good the ad or how fast the page loads.
A related post reads OpenAI selling ads inside ChatGPT, measured on impressions rather than clicks, and a direct-to-consumer brand closing its own channel as further evidence that agent-mediated shopping optimises for price, speed and convenience, often sending shoppers to large retailers.
The opportunity, the post argues, is for retailers to intercept that demand before it defaults to Amazon.
On Google’s Universal Commerce Protocol, the post walks through a store scenario: a shopper asks an assistant to buy skin cream, but if the store has not implemented the protocol handshake, the agent cannot complete the transaction.
The remedy is four ordered checks, including switching on Business Agent in Merchant Center so inventory is write-ready rather than read-only, and replacing generic product taxonomy—such as filing men’s supplements under “All Products”—with conversational attributes.
Last-click reporting stops describing what happened once an agent completes the sale
These posts identify an attribution gap as the counterpart to agentic buying.
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An agent can bypass the landing page and pixel entirely, making a purchase driven by paid media appear in analytics as plain direct traffic.
Martin Kilp argues that the question is no longer which channel took the last click, but which channels created incremental demand and improved the rest of the plan.
The proposed response is to measure intent before the agent takes over and prepare product data so agents can find and transact.
The funnel post contrasts the old path—click, land, buy—with a new one: a user sees an ad, tells an AI assistant to buy, and the agent executes the purchase.
Because the agent skips the landing page, pixel and last-click attribution, the sale registers as direct traffic.
The post recommends tracking who saw the ad before the agent took over, using a clicks-plus-deterministic-views approach and incrementality partners such as Northbeam and WorkMagic.
It closes by asking readers whether they are measuring the right thing.
Judge channels by incremental return and halo, not by the sale they appear to have taken
Across these posts, the measurement priority is to surface value that last-click reporting hides, including the halo effect one channel creates across the rest of a media plan.
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Martin Kilp labels figures by what they actually measure rather than presenting them as universal benchmarks.
One post cites an early-2026 statistic that 68% of Google searches ended without a click, noting that it is a search statistic, not a paid-media benchmark.
The point is that the path from exposure to purchase is harder to observe.
The post then cites a study by Measured, which analysed 130 ecommerce brands and found Snapchat took 5% of social ad spend while delivering a median incremental ROAS of $2.84, 19.3% above the blended social return.
Brands investing there saw 12.9% higher incremental returns across search and social.
Snapchat is an underpriced, measurable channel for ecommerce and retail advertisers
A cluster of posts argues that Snapchat delivers returns its share of spend does not reflect, and that the case can be proved with the right measurement.
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The claim is that the channel reaches trend-conscious audiences where they discover and buy, and that incremental results—not click counts—are the fair test.
A short case study describes Steve Madden combining Dynamic Product Ads, creator collaborations and a custom Bitmoji collection.
It reports 3x higher incremental ROAS, 2.2M Snapchatters reached at 44% lower CPMs, and a 24% reduction in cost per click.
The post concludes that Snapchat is a must-have channel in an ecommerce media mix.
The Snapchat evidence comes from close to the platform: another post promotes the Snap Inc.
Performance Summit with registration links, while one lists open Snap sales roles across New York, Los Angeles and London and invites introductions.
Buyers weighing the channel claims should ask for comparable case studies from other channels; the recruiting post provides professional context, not analysis.
Explain the platform change plainly, then reduce it to a short checklist
Across these posts, the method is a teaching sequence, not commentary: open with a blunt statement that something has changed, translate the jargon into plain English, give two or four numbered implications, point to further reading, and close with a question.
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The deliberately short lists leave brands with actions they can take that week.
The Universal Commerce Protocol post turns a platform change into four same-day checks and states the underlying rule plainly: agents do not browse; they parse.
The funnel post offers two steps: measure intent rather than transactions, and build product data so agents can find it.
Both close by asking readers how they are handling the shift.
It answers a newcomer’s first question—“what actually changes for brands if AI agents do the buying?”—in one short, structured post.
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Martin Kilp contrasts traditional commerce—click, land, buy—with agentic commerce, where a buyer tells an AI assistant to purchase and the agent executes.
The central claim: the agent skips the landing page and tracking pixel, so a sale driven by paid media can appear in analytics as plain direct traffic.
The proposed response is to measure intent rather than transactions, using a clicks-plus-deterministic-views approach and incrementality partners such as Northbeam and WorkMagic, and to structure product data so agents can parse it.
For brands planning a campaign
The clearest starting point for a series on agentic commerce: it states the problem, frames the before and after, and points to a measurement fix. A SaaS vendor could sponsor a post that opens with this attribution gap and closes with a concrete measurement workflow.
It shows how Martin Kilp turns an abstract platform shift into steps a store operator can take the same day—the most reusable format in the set.
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This post turns a platform change into a checklist.
It claims Google has switched on the Universal Commerce Protocol (UCP) and argues that stores that are not compliant cannot be bought from by assistants such as Gemini or ChatGPT.
The four steps: turn on “Business Agent” in Merchant Center; implement the UCP handshake so an agent can place an order directly into the Shopify backend; rewrite product taxonomy with conversational attributes so agent queries match; and pilot Direct Offers if running Google Ads.
The framing is deliberately alarming (“you might be invisible right now”), but the actions are specific and testable.
For brands planning a campaign
The diagnostic format translates directly into a short post series: one post per step, each with a screenshot or before-and-after example and a single measurable action. A vendor selling feed or catalog tooling could use the taxonomy step as a natural entry point.
It’s the best example of Martin Kilp’s evidence handling: a specific, sourced number used to change a media-planning decision rather than decorate a post.
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Martin Kilp separates a search statistic from a paid-media benchmark to build an argument about measurement.
The stat: 68% of Google searches ended without a click in early 2026.
The study: Measured analyzed 130 ecommerce brands and found Snapchat at 5% of social ad spend delivered a median incremental ROAS of $2.84, 19.3% above the blended social return, with brands on Snapchat seeing 12.9% higher incremental returns across search and social.
The conclusion: better measurement surfaces value that last-click reporting hides, including a halo effect across the media mix.
The post invites readers to a Snap Inc.
Performance Summit in New York and Los Angeles.
For brands planning a campaign
These are the strongest proof points in the corpus for a pitch about incrementality testing. A brand or platform could ask Martin Kilp to build the same structure around its own study: one external stat, one named measurement study, one practical implication, and one event or call to action.
It answers “is this a real shift or just a trend?” with two concrete commercial signals rather than opinion.
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Martin Kilp reads the introduction of ads inside ChatGPT, measured on impressions rather than clicks, as a signal that AI interfaces are becoming commerce surfaces, not just discovery tools.
They pair this with a retail example—a DTC brand shutting down direct-to-consumer operations—to argue that more shopping will route through agents optimizing for price, speed and convenience, often landing at large retailers.
The opportunity they identify is for retailers to intercept demand before it defaults to Amazon.
The challenge: click-based attribution will not show the full picture, so the halo effect across channels must be measured.
For brands planning a campaign
A useful “why now” piece in a series: it frames the shift in buyer behaviour before the practical checklist. A vendor could pair it with the UCP diagnostic to move readers from context to action.
It shows what a finished result looks like, helping a brand judge whether the same treatment would suit its own numbers.
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A short case-study post on how Steve Madden combined Dynamic Product Ads, creator collaborations and a custom Bitmoji collection on Snapchat.
Reported results: 3x higher incremental ROAS, 2.2M Snapchatters reached at 44% lower CPMs, and a 24% reduction in cost per click.
Martin Kilp reads the outcome as evidence that Snapchat is a must-have channel for ecommerce and retail advertisers, as well as trend-conscious audiences.
For brands planning a campaign
The post demonstrates the case-study format: named brand, named products, four comparable metrics, one conclusion. A campaign aimed at a different advertiser could use the same structure, leaving the metrics and mix of formats visible for readers to check.
It gives general readers context for the professional world these posts come from, so they can read the advertising arguments as industry-insider positions rather than neutral reviews.
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Not analysis, but a useful signal about where Martin Kilp’s posts point.
They list open Snap Inc. roles across sales operations, revenue growth, product marketing, business development and account management in New York, Los Angeles and London, and invite direct messages and introductions.
The post shows an active professional network around Snap’s advertising business, not a purely editorial voice.
For brands planning a campaign
Relevant when assessing fit for employer-adjacent work: Martin Kilp moves candidates into platform sales roles, not just writes commentary. Confirm any employer relationship before asking for channel-neutral commentary.
Recurring post shape and how the creator addresses an audience5
Agentic commerce and the Universal Commerce Protocol3
Measurement when the click disappears3
Snapchat as an underpriced, measurable channel3
Store-level readiness checklists2
Agentic commerce and the Universal Commerce Protocol
Several posts build the same argument: AI agents are becoming a real buying surface, and the buy button is moving off the retailer's own site.
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Martin Kilp describes Google's Universal Commerce Protocol as letting an assistant such as ChatGPT or Gemini complete a purchase on the shopper's behalf.
They argue that a store that cannot hand an order to an agent loses the sale, no matter how good the ad or how fast the page loads.
A companion post picks up OpenAI selling ads inside ChatGPT on impressions rather than clicks and connects it to more shopping flowing through agents that optimise for price, speed and convenience.
For brands planning a campaign
Good raw material for an explainer series aimed at DTC teams: what changes when an agent completes checkout, and why a storefront's job shifts from persuading a person to handing a machine a clean, machine-readable order. The creator frames the stakes sharply, which suits short, argument-led posts.
The flip side of agentic buying, in these posts, is that last-click attribution stops describing what happened.
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Martin Kilp points out that an agent can skip the landing page and the pixel entirely, so a paid-media-driven sale shows up as direct traffic.
They argue that the useful question is which channels created incremental demand, not which one took the last click.
One post cites an early-2026 figure that 68% of Google searches ended without a click, explicitly labelling it a search statistic rather than a general benchmark, and calls for tracking intent before the agent takes over.
The halo effect one channel creates across the rest of a plan is a recurring point, with Northbeam, WorkMagic and incrementality partners named as ways to make that visible.
For brands planning a campaign
Strong fit for a measurement-led series: why click-based reporting understates paid media once agents transact, how halo effects work, and what a brand should instrument first. The creator already speaks in the vocabulary of incrementality, so briefs can skip the basics and go straight to method.
A cluster of posts makes the case for Snapchat specifically.
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One cites Measured's analysis of 130 ecommerce brands: Snapchat took 5% of social ad spend but delivered a median incremental ROAS of $2.84, about 19.3% above the blended social return, and brands investing there saw 12.9% higher incremental returns across search and social. Another walks through Steve Madden combining Dynamic Product Ads, creator collaborations and a custom Bitmoji collection to reach 3x higher incremental ROAS, 2.2M Snapchatters at 44% lower CPMs and 24% lower cost per click.
One of the Snapchat posts is a recruiting notice listing open sales roles, not buyer-facing analysis, and carries no performance evidence.
For brands planning a campaign
Ready-made proof points for a retail or ecommerce advertiser pitch, and a clear signal that the creator will push past cheap clicks toward incremental return. Because Snapchat gets the most airtime, ask for the argument to be tested against other channels.
The most operational post is a four-point diagnostic for Shopify stores: switch on Business Agent in Merchant Center, enable direct checkout through the protocol handshake, rewrite product taxonomy with conversational attributes rather than dumping everything under a generic category, and pilot direct offers if running Google Ads.
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The post argues that agents parse rather than browse, so messy product data makes a store invisible to them.
Technical updates are translated into plain English, then reduced to numbered actions framed as something to run today.
For brands planning a campaign
Useful shape for a practical series: each post gives a brand a finite checklist it can act on this week, then explains why each item matters in commercial terms. This is the creator's most concrete format and the easiest to replicate with a sponsoring SaaS product, provided the steps are real and testable.
Recurring post shape and how the creator addresses an audience
The posts follow a consistent pattern: a blunt hook about a recent change, a plain-English translation of the technical term, two or four numbered implications, a link to further reading in the comments, and a closing question to the reader or a direct question about how others are handling the shift.
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Hashtags mix category terms with platform names. Several posts promote named measurement tools and events, and one includes registration links for an advertising summit.
For brands planning a campaign
Low friction for a sponsored series: the slots for a product mention, a closing question and a linked resource are already there. A brand should plan the sponsored angle so it does not compete with promotion of third-party tools and events in the same feed.
Martin K.’s posts point to a recurring commercial affiliation with Snap Inc.: they promote its open sales roles and describe its performance summit with “we.”
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They also repeatedly promote WorkMagic, an incrementality measurement tool, without explaining their relationship with the company. Ask about the nature of these affiliations; the posts don’t state sponsorship, affiliate, ownership, investment or client terms, or include a dated or paid-partnership disclosure.
Snap Inc.
Disclosed partnership
Martin K. promotes Snap Inc. openings in sales, client partner and product marketing, and invites people to message about them.
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They describe the Snap Performance Summit as “the conversation we are bringing to Snap Inc.” The posts don’t state their job title or employment status, so ask about their relationship with Snap Inc. A separate post presents Steve Madden’s Snapchat campaign results as a brand success story.
Martin K. names WorkMagic twice as a way to measure incremental impact and channel halo effects, including a suggestion that brands “work with incrementality partners like WorkMagic.”
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The posts promote the tool but don’t explain their relationship with the company, so ask about it.
The fastest way into Martin Kilp's work is the four-point store diagnostic on Universal Commerce Protocol readiness.
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It explains what changes when an AI assistant rather than a person completes a checkout, then turns that into checks a brand can run the same week.
A companion post on the same shift covers why that sale lands in analytics as direct traffic, and a third examines a study of 130 ecommerce brands, comparing Snapchat's share of spend with its measured incremental return.
One Steve Madden case study is the only extended campaign example in the material; a recruiting notice for sales roles is professional context, not analysis.
Before relying on the measurement claims, ask for comparable case studies across other channels and longer-form analysis beyond the numbered action checklists.
For adjacent reading, Tom Shivers covers the same protocols from the product-data side, Reeto M. tackles the measurement problem through experiment design, and Joe P. builds the trust and compliance layer.
Shivers approaches the same subject from a neighbouring angle: agentic commerce protocols, machine-readable product data, and what a store must do to be recommended and checked out by an AI agent.
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The central claims overlap closely: feeds and structured data now decide visibility, and agents skip the storefront as people know it.
Shivers focuses on protocol mechanics and entity data, framing the structured feed as the new homepage and recommending feed fixes, brand entities and citable content. Ask for attribution, incrementality and channel-level performance evidence.
Shares the measurement half of the beat: incremental attribution, why click and platform-reported numbers mislead, and how to test a change against real ecommerce spend rather than attribution claims.
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The posts tell an experiment narrative from inside a measurement vendor: predictive CAPI against an incumbent, a two-cell A/B test, blended new-customer CAC, Meta's incremental attribution reporting. They predate the agentic-commerce wave and focus on measurement rather than agents completing purchases, where Martin Kilp starts.
Addresses the same Universal Commerce Protocol shift and the premise that fragmented, poorly structured product data makes a brand invisible to agents — the problem Martin Kilp frames as agents parsing rather than browsing.
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The frame is compliance and trust infrastructure rather than media measurement: silently throttled feeds, listings suppressed without warning, a proposed Universal Compliance Protocol, and a named company building that layer. One post is a memoir-style account of early manual product-data work.
Martin Kilp’s LinkedIn profile lists 6,272 followers, observed 9 October 2026.
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That establishes a real, modest audience on LinkedIn.
It does not show who those followers are, where they work or what roles they hold, so it cannot confirm that they include the DTC ecommerce decision-makers a B2B ecommerce SaaS campaign needs to reach.
Martin’s six complete posts show whom the content addresses: store operators and performance marketers.
They cover a four-point UCP readiness diagnostic for Shopify stores; the shift from last-click attribution to incremental measurement, framed around Shopify and agentic commerce; a Snap Inc.
Performance Summit invitation built around incremental ROAS data; and a retail case study for ecommerce and retail advertisers.
Another post advertises sales roles at Snap.
The specific calls to action — run the diagnostic, measure intent rather than just transactions, request to attend — point to in-house ecommerce and paid-media practitioners, not general consumers or executives outside marketing.
Martin’s LinkedIn account is a reasonable candidate for a short series on agentic commerce and incrementality aimed at ecommerce and paid-media practitioners who use LinkedIn.
But the available data does not establish reach by company size, seniority or geography, or show whether a sponsorable share of the audience works at DTC brands rather than agencies, sellers or adjacent roles.
Ask Martin or their team for LinkedIn follower analytics by industry, company size, seniority and location for the same period as the observed count, plus reach, engagement and click or lead figures from at least one sponsored post on agentic commerce or measurement.
If those data are unavailable, ask whether Martin runs audience surveys that document professional attributes.
Test two materially different campaigns against the results before committing: one aimed at DTC brand marketers and the other at agency media buyers.
Martin’s posts also cite a Measured analysis of 130 ecommerce brands reporting Snapchat at 5% of social ad spend with a median incremental ROAS of $2.84, and a Snap-backed Steve Madden case study reporting 2.2M Snapchatters reached at 44% lower CPMs.
These figures describe advertiser-side outcomes on Snapchat, not Martin’s readership.
LinkedIn followers
6,272
Follower count on the LinkedIn profile that published the six reviewed posts, observed 9 October 2026. No demographic, geography, or seniority breakdown was available with it. · 2026-10-09
Martin Kilp's posts show channel-side promotion and opinion, but no partnership or sponsorship offer, format specifications, rate card, media kit, disclosed sponsored placements or official inquiry route.
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Four posts explain or promote Snapchat-adjacent commercial themes: agentic commerce and Universal Commerce Protocol readiness, incremental measurement, and a Steve Madden results story.
Two posts recommend measurement tools (WorkMagic, Northbeam) without disclosing sponsorship.
A post dated 25 September 2025 lists open Snap Inc. sales roles and invites direct messages to learn about them; a post dated 24 August 2026 points to attendance requests for a Snap Inc.
Performance Summit.
Both are recruitment and event routes, not creator partnership routes.
Before planning a campaign, ask directly whether Martin Kilp accepts sponsorships and what terms apply.
A B2B ecommerce software company selling catalog, feed-management or incrementality-measurement tools to DTC brands and retail advertisers wants a short LinkedIn post series explaining agentic commerce readiness and measurement to in-house ecommerce and paid-media practitioners.
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This is the strongest fit.
Martin K.'s posts already do this job without a sponsor: a four-step Universal Commerce Protocol readiness diagnostic for Shopify stores, a post on how agentic buying breaks last-click attribution and what to measure instead, an incrementality argument built on a cited 130-brand study, and a read on ads appearing inside ChatGPT as a sign that AI assistants are becoming commerce surfaces.
The recurring format is directly reusable for a sponsored series: a blunt hook about a change that just happened, the technical term translated into plain English, two to four numbered actions, a link in the comments and a closing question.
Those slots already exist, so a sponsor's product mention can fit the creator's usual format rather than fight it.
The audience argument is plausible but unproven.
Kilp's LinkedIn account had 6,272 followers as observed on 9 October 2026, and the writing addresses store operators and performance marketers directly.
That makes it a reasonable place to put a series aimed at practitioners, but does not establish reach into a particular company size, seniority band or geography.
Check three conditions before committing.
First, the posts say nothing about sponsorship terms, formats, rates or an inquiry route, so agree those from scratch.
Second, the creator is closely associated with Snapchat advertising, which gets the most airtime in the posts; a sponsor selling channel-neutral measurement should expect that association to shape the tone and agree in advance how other channels will be treated.
Third, the UCP and agentic-checkout claims are the creator's own assertions.
The sponsor should fact-check them before publication because it carries the reputational cost if a platform detail is wrong.
When to choose a specialist
A consumer skincare or fashion brand wants a named creator to front a short-form video campaign on TikTok or Instagram Reels, featuring on-camera product demonstrations, lifestyle storytelling and a guaranteed reach figure for a consumer audience.
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This campaign is a poor fit on several counts.
Every post here is written LinkedIn text: no video, on-camera appearance or visual or lifestyle content.
The posts do not demonstrate an ability to hold a consumer audience through short-form entertainment or handle a skincare, beauty or apparel product on camera.
They address marketing practitioners, not shoppers, so the creator's trade-explainer style would be out of place in a general consumer feed.
The audience information does not support a consumer reach guarantee.
The only measured figure is a follower count of 6,272 on one professional platform; the posts publish no demographics, geography or seniority data.
A consumer brand would be accepting a reach promise the available evidence does not support, and the followers are likely concentrated in advertising and ecommerce roles rather than among beauty buyers.
Two further mismatches matter.
The creator's visible activity points toward the Snap advertising business, including a recruiting post for open Snap sales roles and a promotion of a Snap Performance Summit.
That is a platform and industry-world association, not consumer-category authority.
The posts also say nothing about sponsorship mechanics, leaving a consumer brand to negotiate pricing, usage and approval terms without a reference point from prior deals.
Ask for evidence of prior consumer assignments and details of sponsorship terms before reconsidering the fit.
A worked campaign example
Worked angle for a B2B ecommerce software campaign: a three-post LinkedIn series on UCP readiness and measurement, published over one to two weeks, with each post ending in one action the reader can take that day.
Post one, the context piece.
Open with the claim that the buy button is moving off the retailer's own site and that a store unable to hand an order to an assistant loses the sale regardless of ad quality or page speed.
Explain what changes when an agent completes checkout, why the agent never sees the landing page or pixel, and how that affects a sale that looks like direct traffic in analytics.
Use the ChatGPT-ads-on-impressions signal as a second commercial proof that this is a real shift rather than a trend.
Position the sponsor's product as the tool that keeps a brand's intent data visible.
Post two, the readiness checklist.
Use the creator's existing four-step diagnostic as the spine: turn on the merchant-side business agent setting, enable direct checkout through the protocol handshake, rewrite product taxonomy with conversational attributes so agent queries resolve, and pilot direct offers if running search ads.
Expand each step with the sponsor's own setup screenshot or a before-and-after catalog example, then ask readers which of the four they have not done.
Keep the claim that agents parse rather than browse, so messy data makes a store invisible; the sponsor should confirm the platform mechanics before publication.
Post three, the measurement argument.
State plainly that last-click attribution stops describing what happened once agents transact, and that the useful question is which channels created incremental demand.
Feature two external proof points from Martin K.'s posts, clearly attributed to the advertiser-side study rather than the sponsor: a Measured analysis of 130 ecommerce brands finding Snapchat at 5% of social ad spend with a median incremental ROAS of $2.84, about 19.3% above blended social return, and 12.9% higher incremental returns across search and social; and the Steve Madden result set of 3x higher incremental ROAS, 2.2M people reached at 44% lower CPMs, and 24% lower cost per click.
Then hand off with a named measurement process and one call to action, such as a catalog-readiness audit or demo request.
Before launch, agree whether a sponsorship offer exists, the format and price, and whether the sponsor's product can be named alongside the third-party tools the creator already promotes.
Follower analytics by industry, company size, seniority and location would determine whether to target DTC brand marketers or agency media buyers.
Post-level reach, engagement and any click or lead figures from one sponsored post would help set realistic expectations.
If the creator's relationship with the Snap advertising business is an employer relationship, write the series as channel-neutral commentary rather than platform-adjacent promotion, and settle that in the campaign plan.
Choose Tom Shivers over Martin K. when a campaign needs a patient protocol explainer rather than a fast campaign hook.
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Tom Shivers walks through both the Universal Commerce Protocol and the older OpenAI-Stripe Agentic Commerce Protocol, explains how each is meant to keep the retailer as merchant of record, and lists concrete retailer setup steps such as structured product feeds, ACP checkout endpoints, delegated payments, and clean pricing, inventory, and fulfillment logic.
That makes Tom Shivers a better fit for a sponsor selling feed management, checkout enablement, or catalog infrastructure: the argument is built around implementation detail rather than an ad-platform case study.
Tom Shivers also offers a feed-readiness checklist as a working resource, which suits a lead-generation series that ends in an audit or demo.
The tradeoff is measurement.
Tom Shivers's posts offer no incrementality proof, named third-party study, or retail-performance case study like Martin K.'s.
A sponsor selling measurement or incrementality software would get clear protocol education, but not the attribution argument that makes the pain concrete for paid-media buyers.
Before committing, ask for audience figures, sponsorship history, and engagement data; none is published in Tom Shivers's posts.
If the series must cover both agentic-commerce mechanics and incrementality proof, Martin K. is the more balanced starting point.
Tom Shivers is stronger when the focus is protocol and feed implementation.
Choose Reeto M. when the sponsor's product is incrementality measurement, attribution, or predictive CAPI, and the audience is growth and performance marketers who already run paid media.
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Reeto M.'s posts work through a live measurement problem in detail: a brand with over 50% returning-customer orders trying to isolate new-customer acquisition, with seven-figure monthly spend across Meta, Google, and affiliates; a two-cell A/B test of a predictive CAPI against an incumbent; and Meta's incremental attribution reporting as a way to optimize campaigns on estimated incrementality rather than platform-reported conversions.
That is a closer match than Martin K. for a campaign that must demonstrate why blended new-customer CAC, marginal CAC, and LTV:CAC are the right scoreboard.
The posts also name the operational setup, helping a sponsor show where its tool sits in the stack.
The tradeoff is agentic commerce.
Reeto M.'s posts do not explain UCP, agentic checkout, or AI-assisted buying, so a sponsor would lose the timely commerce-shift narrative that makes Martin K.'s posts easy to pitch as a short series.
Reeto M. is also tied to Angler AI in the posts and promotes its predictive CAPI and pLTV signals, creating a conflict to check if the sponsor sells competing measurement or attribution tooling.
Before committing, ask for follower count, audience breakdown, and sponsorship terms; none is published in Reeto M.'s posts.
Use Reeto M. when measurement rigor is the main proof and agentic commerce is background; use Martin K. when the agentic shift is the hook and measurement is the second act.
Choose Joe P. when the sponsor needs a focus on product-data trust, feed compliance, and catalog operations inside agentic commerce.
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Joe P.'s posts make that case directly: one lays out the Google I/O 2026 alignment around the Universal Commerce Protocol, AP2, and Universal Cart, then argues that the missing layer is machine-readable trust and automated validation across Google, Meta, Amazon, and Shopify, and names FeedZip.ai as the infrastructure being built for it.
The other is an operator story from iHerb in the early 2000s, describing how manual scanning, typing, and double-checking of roughly 20,000 SKUs built an accuracy system that still matters when the tools change.
For a sponsor selling feed management, product-data quality, catalog compliance, or retail media integrity, Joe P. can speak to the exact failure mode—how inconsistent data redirects demand to a cleaner competitor—with both a current market argument and a firsthand operations anecdote.
The tradeoff is measurement.
Joe P.'s posts offer no incrementality tests, blended CAC, or attribution results, so a sponsor pitching measurement software would need another creator or a different argument.
Joe P. is also developing FeedZip.ai, a compliance and trust engine for agentic commerce, creating a direct conflict risk for sponsors selling overlapping feed or catalog-compliance products.
One post is a career story rather than current market commentary, making the posts uneven for a consistently forward-looking series.
Use Joe P. when the decision hinges on product data and machine-readable trust; use Martin K. when it hinges on channel measurement and a fast, blunt explanation of the agentic shift.
Martin Kilp's six complete LinkedIn posts cover agentic commerce and Universal Commerce Protocol readiness for Shopify stores in three posts, incrementality measurement and Snapchat campaign results in two posts, and open sales roles in one recruiting notice.
The posts do not include publication dates or analysis records, so this page describes what those six posts show and makes no claim about wider or earlier work.
The only audience figure available is a LinkedIn follower count of 6,272, observed on 9 October 2026.
No sponsorship terms, rates, formats, video work, or audience demographics appear in the posts here.