Michael Stratta works on marketing attribution and AI-driven measurement at Arcalea, where Stratta discusses using first-party data, customer journeys, and revenue outcomes to guide marketing decisions. Their posts also describe Galileo, Arcalea’s attribution platform.
Michael Stratta is most useful for attribution and AI measurement explainers tied to revenue outcomes.
Best for
Measurement or analytics vendors selling cross-channel attribution to marketing practitioners and analytics leads.
Not for
Consumer or lifestyle brands seeking short-form video promotion to a guaranteed demographic audience.
Key takeaways
Platform data cannot be its own referee.
Measurement should end at revenue and profit, not clicks or impressions.
AI search visibility needs a why, not just a whether.
Check first: Confirm audience demographics and comparable post results before contacting them.
Main ideas
Platform data cannot be its own referee · Measurement should end at revenue and profit, not clicks or impressions · AI's useful role is prescribing where budget moves, not describing what happened
Michael Stratta writes about marketing attribution and measurement through work at Arcalea, including its Galileo attribution platform.
Their posts focus on connecting channel activity to customer journeys, first-party data, and revenue rather than relying on clicks, impressions, or platform-reported results alone.
In a detailed account of a marketing attribution case at Northwestern’s Kellogg School, they describe students using real data and Galileo to compare channels and consider budget shifts.
More recently, they argued that marketers need to measure not just whether brands appear in AI search results, but why they appear and whether that visibility changes customer behavior.
This work is useful to readers trying to understand how attribution decisions can be grounded in business outcomes as discovery and advertising become more automated.
Stratta connects measurement principles to practical decisions.
In the Kellogg case post, they explain how incomplete data and platform bias can mislead marketers, then describe using ROAS and customer acquisition cost to guide budget allocation; the reported revenue increase is a projection from the students’ scenario, not a general result.
Other posts extend that concern from campaign tracking—such as UTM parameters and data-driven attribution—to AI search visibility.
Across these examples, Stratta insists on asking what drove a business result, rather than stopping at a platform metric.
The posts also show different registers: detailed teaching through a case, short announcements for “Marketing Attribution Decoded” issues, and business context in a hiring post.
Michael Stratta works in marketing attribution and measurement, returning to one argument: a number from inside a platform cannot settle what actually drove a business result.
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They teach attribution as a practical craft, from UTM tagging to machine-learning models, and move the finish line away from clicks and impressions toward first-party revenue and profit. More recently, they have applied the same reasoning to AI search, where the questions are shifting from whether a brand appears to why it appears, which sources shape the answer, and whether any of it changes customer behavior.
Platform data cannot be its own referee
Stratta argues that partial or biased data produces confident but wrong recommendations, and that measurement tools tend to favor the channels they own.
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The fix is an independent reference point: multi-touch or data-driven models, first-party data, and actual business outcomes.
In the Kellogg case write-up, data integrity is called non-negotiable, with Google named as an example of a platform that overweights conversions inside its own ecosystem.
At Hero Conf and brightonSEO, a colleague made the same point: Jesse A.
Sallee's "Dead Reckoning" session argued that platform data is useful but needs first-party data and real business outcomes as an independent reference.
Measurement should end at revenue and profit, not clicks or impressions
Stratta moves attribution's goal toward money.
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Channels should be judged by the revenue and acquisition cost they produce, not by how much activity they generate. Measurement should get as close as possible to first-party revenue and profit.
Students in the Kellogg exercise used Galileo to allocate budget by return on ad spend and customer acquisition cost, shifting spend away from underperformers and toward high-ROAS channels.
The post reports projected revenue growth of over 90% in the scenario.
The conference post closes with a stated task: push measurement closer to first-party revenue and profit.
AI's useful role is prescribing where budget moves, not describing what happened
Stratta treats reporting as the floor, not the point.
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The value of AI-driven analysis, in this telling, is to recommend reallocation and support real-time calibration instead of static annual or quarterly plans.
In the Kellogg post, students run scenarios in Galileo and make strategic pivots on the fly.
The post presents that interactive decision-making, rather than simply reading a case, as the immersive part of the exercise.
The stated aim is AI that highlights where to reallocate budgets dynamically.
AI search visibility needs a why, not just a whether
Stratta reports that AI search has moved past experimentation and that the conversation is shifting from rankings to how brands are represented, cited, and recommended inside AI platforms.
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They call measuring AI visibility table stakes; the harder questions are why a brand appears, which sources influence the answer, and whether it changes customer behavior.
The Hero Conf and brightonSEO post lists three tasks: measure why brands appear in AI answers, not just whether they appear; connect AI visibility to what customers actually do; and push measurement closer to first-party revenue and profit.
The same post notes a tension in paid media: more decisions are automated inside platforms while the customer journey gets harder to follow across channels. In Stratta's framing, more automation means more platform decisions, but neither automation nor extra signals alone shows what drove the business result.
Attribution is a teachable craft, down to tagging hygiene
Rather than treating attribution as a vendor claim, Stratta writes a recurring series, "Marketing Attribution Decoded," that explains one technique or shift per issue in practical terms.
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The through-line: unglamorous groundwork such as campaign tagging is where attribution programs quietly succeed or fail.
One announced issue is a guide to UTM parameters, described as the most comprehensive yet and covering everything from initial setup to tracking cross-device user behavior.
Another makes the case for data-driven attribution as third-party cookies disappear and privacy rules tighten, covering the limits of traditional models, the role of machine learning and AI, and first-party data as the privacy-safe basis.
The university case applies the same instinct: students work through real data scenarios in a co-taught debrief.
The case was co-taught with Mohanbir Sawhney and Pallavi Goodman, and the post says it was submitted to Harvard Business School for global syndication.
The register shifts with the setting.
The UTM post is playful, calling the guide a "doozy" and admitting the theme was bent for fun with easter eggs for readers; the Kellogg case write-up is formal and lesson-structured.
Both make the subject accessible to non-specialists.
Answers the question "what does Michael Stratta actually teach about attribution?"
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It is the clearest single post on how they frame the problem, what they warn against, and what they treat as decisive, so it is the fastest way for a newcomer to understand the substance behind the name.
Michael Stratta describes returning to Kellogg, where they are an alum, to co-teach a marketing attribution case with Mohanbir Sawhney and Pallavi Goodman.
Students worked through real-data scenarios in Galileo, Arcalea's attribution platform.
The post says the case has been submitted to Harvard Business School for global syndication.
Its lessons are concrete: partial or biased data produces misleading recommendations; tools tend to favor their own channels, with Google cited as an example of a platform that overweights conversions within its ecosystem; and budgets should be allocated against ROAS and customer acquisition cost, not clicks or impressions.
Prioritizing high-ROAS channels produced projected revenue growth of over 90% in the students' scenario, the post reports.
It also says AI is used to move from describing what happened to recommending where to reallocate budget.
For brands planning a campaign
A co-created teaching asset would suit this material: a worked scenario or short case following a budget decision from channel data to reallocation, with the platform-bias warning built in. The Kellogg framing also lends academic credibility to a webinar or explainer for marketing leaders, not just practitioners.
Answers "where does Michael Stratta think measurement is heading right now?"
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It is the most current view in the posts here and shows the direction of their thinking on AI visibility, which is the topic a partner would most likely want them to speak to.
Notes from two days at Hero Conf and brightonSEO in San Diego, attended by Arcalea colleagues Gabriela Caballero and Jesse A.
Sallee.
The post argues that AI search has moved past experimentation and that discussion is shifting from rankings to how brands are represented, cited and recommended inside AI platforms.
It calls measuring AI visibility table stakes and says the harder questions are why a brand appears, which sources influence the answer and whether any of it changes customer behavior.
On paid media, it observes that more decisions are automated inside platforms while the customer journey is harder to follow across channels.
It cites Sallee's "Dead Reckoning" session, which argued that platform data needs first-party data and business outcomes as an independent reference point.
The post closes with three tasks: measure why brands appear in AI answers, not just whether they appear; connect AI visibility to customer behavior; and bring measurement closer to first-party revenue and profit.
For brands planning a campaign
This is the strongest hook for a point-of-view piece or panel on measuring AI visibility. A brand could ask Michael Stratta to turn the three closing tasks into a practical framework or run a session that starts with the conference observations and ends with a measurement plan.
Answers "how does Michael Stratta handle the unglamorous groundwork of tracking?"
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UTM tagging is where most attribution programs quietly fail, and this post shows they treat campaign-tagging hygiene as a serious topic rather than a footnote.
Michael Stratta announces an issue of their "Marketing Attribution Decoded" series devoted to UTM parameters, calling it their most comprehensive guide yet.
They say it covers everything from initial setup to advanced strategies for tracking cross-device user behavior, with the goal of precisely optimizing campaigns and getting more from each advertising dollar.
The post also takes an unusually personal tone for the topic: they call the guide a "doozy" and admit they are bending their own theme rules because it was fun, with easter eggs for readers.
For brands planning a campaign
A practical partnership angle is tracking governance: a checklist, tagging standard or onboarding guide sponsored by a martech or analytics vendor.
It suits a technical audience that needs a repeatable process rather than a vision piece.
Ask for the linked guide before assessing its contents.
Answers "how does Michael Stratta introduce data-driven attribution to someone who has not adopted it?"
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It is a compact statement of the case for moving away from traditional models, useful for a newcomer who wants the argument in outline before the detail.
A promotional post for a "Marketing Attribution Decoded" issue on data-driven attribution in a post-cookie, privacy-regulated environment.
The stated contents include the decline and limits of traditional attribution models, what data-driven attribution adds to marketing effectiveness, the role of machine learning and AI in refining models, and real-world success stories.
Listed takeaways include a fuller view of the customer journey, more efficient budget allocation across channels, adaptation to changing customer behavior, and protection of user privacy through reliance on first-party data.
Ask for the article to assess its detail; the post is only an announcement.
For brands planning a campaign
This could support an explainer or webinar taking a marketing team from its current attribution model to a data-driven alternative, with first-party data and privacy treated as constraints from the outset. A vendor with a measurement product could sponsor it as a decision-stage asset.
Answers "what kind of business is this person part of?"
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It is not a work sample, but it is the one available original that describes Arcalea's client work, product claims and team structure, which matters for judging whether a partnership would have delivery capacity behind it.
A hiring post, not an article.
Michael Stratta is recruiting a Senior Partner Success Manager with at least six years of agency experience to manage a portfolio of client accounts and direct specialists across SEO, paid media, content and analytics.
The post says Arcalea builds its own technology, its attribution platform Galileo was recognized by Fortune as one of the most innovative products in the country, clients stay and the business is growing.
The role is fully remote with full benefits.
For brands planning a campaign
Useful for due diligence rather than as a content angle.
It describes a services business with named specialists across SEO, paid, content and analytics, plus an in-house measurement product—a setup that could support joint research or co-marketing from the vendor side.
Ask for evidence of Galileo's recognition and client retention; those claims come from the post and have not been independently verified in this review.
Marketing attribution explainers, from UTM tagging to data-driven models
Michael Stratta writes a recurring attribution series called "Marketing Attribution Decoded."
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One issue walks through UTM parameters, from basic setup to tracking cross-device user behavior.
Another argues that traditional attribution models are fading as third-party cookies disappear and privacy rules tighten, making the case for data-driven attribution built on machine learning and first-party data.
Both treat attribution as a practical, teachable skill rather than a vendor talking point.
A recurring newsletter that covers one attribution technique or industry shift per issue.
For brands planning a campaign
A measurement or analytics vendor can sponsor a tactical explainer built around a specific technique, then make the case for a fuller attribution model.
A recurring argument across Michael Stratta's posts is that biased or partial data leads to confident but wrong decisions.
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In the Kellogg case discussion, data integrity is non-negotiable, and industry tools are flagged for favoring their own channels.
Google over-weighting conversions inside its own ecosystem is the example.
The post concludes that multi-touch or data-driven models are needed to see the full picture.
At Hero Conf and brightonSEO, the same idea appears from a different angle: platform data is useful, but needs first-party data and actual business outcomes as independent reference points.
For brands planning a campaign
A brand selling neutral or cross-channel measurement can build content around a question this audience already recognizes: How do you tell whether your attribution is flattering the platform that reports it?
Michael Stratta consistently pushes measurement toward revenue rather than clicks or impressions.
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In the Kellogg exercise, students used Galileo to shift budget toward high-ROAS channels and away from underperformers, producing projected revenue growth above 90% in the scenario. At the conference, the same priority appears in a stated goal: push measurement closer to first-party revenue and profit.
A worked budget-allocation scenario using ROAS and CAC by channel as the teaching device.
For brands planning a campaign
Content showing an actual reallocation with named metrics will match how the subject already teaches the topic.
Michael Stratta reports that AI search has moved well past experimentation and that the conversation is shifting from rankings to how brands are represented, cited, and recommended across AI platforms.
Read more
Measuring AI visibility is becoming table stakes; the harder questions are why a brand appears, which sources influence the answer, and whether any of it changes customer behavior.
The post also notes a parallel difficulty in paid media: platforms are automating more decisions while the customer journey is getting harder to follow across channels.
For brands planning a campaign
An AI visibility, PR, or search tool could partner on content that moves beyond presence checks to examine source influence and downstream customer behavior.
In the Kellogg write-up, Michael Stratta argues that AI should go beyond describing what happened to recommending where budgets move, with real-time campaign calibration instead of static plans.
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Students run scenarios in Galileo and make strategic pivots on the fly, which the post presents as the immersive part of the exercise.
For brands planning a campaign
Product or case content can follow this sequence: show the analysis recommending a change, then show the change being made, rather than a dashboard screenshot.
Michael Stratta returns to Northwestern's Kellogg School of Management to debrief a marketing attribution case with Mohanbir Sawhney and Pallavi Goodman, running real-world data scenarios with students.
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The post notes that the case was submitted to HBS for global syndication so more MBA programs can use it.
A co-taught case debrief with interactive scenarios rather than a lecture, built around real-world data.
For brands planning a campaign
Educational collaborations—guest case sessions, curriculum partnerships, or course material—fit a track record already oriented this way.
The posts tie Stratta to Arcalea, the marketing agency whose attribution platform Galileo and newsletter content they write about, and to Northwestern University's Kellogg School of Management, where they co-teach a marketing attribution case.
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Ask about sponsorships, affiliate arrangements, or client engagements; the posts state none. Ask for publication dates.
Arcalea
Employment
Stratta writes as an Arcalea insider: the posts feature a job opening for a Senior Partner Success Manager and describe "our clients," "our own technology," and Galileo as "our attribution platform."
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Other posts refer to "our case" taught at Kellogg and to "our own Gabriela Caballero" attending Hero Conf and brightonSEO. The posts do not state Stratta's title or tenure.
Stratta calls it "our attribution platform," notes in a hiring post that Fortune recognized it as one of the country's most innovative products, and describes MBA students running budget-allocation scenarios in Galileo during the Kellogg case session.
Northwestern University Kellogg School of Management
Mention
Stratta, a Kellogg alum according to the post, returns to the school to co-teach a marketing attribution case with professor Mohanbir Sawhney and Pallavi Goodman.
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The case covers data integrity, ecosystem bias, and revenue-based budget allocation. The post says the case has been submitted to Harvard Business School for global syndication.
Michael Stratta's work is easiest to follow through the recurring "Marketing Attribution Decoded" series, where each issue explains one piece of attribution work in practical terms, from UTM parameters to the shift to data-driven models as third-party cookies disappear.
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Notes from Hero Conf and brightonSEO show the current version of the argument: AI search visibility, why a brand gets cited, and whether any of it changes customer behavior.
The Kellogg case debrief shows the same material in a classroom setting, with students reallocating budget by ROAS and CAC inside Galileo.
Start with the series for method, then read the conference post to see where the thinking is heading; the university write-up is the best single example of how they teach a measurement problem.
Smith writes about making marketing decisions from funnel data and the reliability of the data underneath, including a walkthrough of pulling numbers from landing page tools, CRM, and calendar tools into one dashboard.
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Stratta takes the same practical approach to data quality: before arguing about models, establish whether the numbers reflect reality.
Smith's register is operational and small-business oriented, focused on CRM adoption and funnel reporting, while Stratta works on attribution models, platform bias, and budget reallocation. Readers comparing vendors and models will get more from Stratta; readers setting up their first funnel dashboard will get more from Smith.
Marintsch applies quantitative optimization to a business decision and explains the mechanics in public with a mixed-integer model that prioritizes recovery actions by business impact.
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That mirrors how Stratta walks readers through allocating budget by ROAS and cost per acquisition rather than intuition.
Marintsch covers supply chain and logistics: stockout recovery, hub-and-spoke distribution, and trailer utilization.
Stratta covers marketing attribution and AI search measurement.
Both teach similar ways of thinking, but only Stratta covers advertising channels, tagging, and platform-reported numbers.
Yuwen is the closest match on the underlying problem.
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One post unpacks an email program that claimed $1.8M while order data showed $1.1M, tracing the discrepancy to platforms each setting their own attribution rules.
The fix is a warehouse and a deduplication model the executive team agrees on.
That echoes Stratta's argument about platform self-interest and closing the loop to real outcomes, told through a specific client case.
Yuwen writes for ecommerce and lifecycle teams about retention timing, engagement cliffs, and Klaviyo and Meta overlap, often teaching through scripted dialogue between roles. Stratta addresses B2B marketing and analytics audiences through a recurring explainer series and a university case, and also covers AI search visibility.
Michael Stratta's LinkedIn profile shows 8,174 followers as of 7 October 2026, the only hard audience number available here.
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The posts, published from July 2024 to September 2026, focus on marketing measurement: setting up and managing UTM parameters, why siloed click and impression metrics mislead, the shift to data-driven attribution as third-party cookies and privacy rules change, and how first-party revenue outcomes should anchor channel decisions.
They also describe teaching this material: a Kellogg School of Management session with Mohanbir Sawhney and Pallavi Goodman on an attribution case now submitted for wider syndication, and Arcalea's presence at Hero Conf and brightonSEO in San Diego, where AI search visibility and platform-automated paid media were recurring themes.
Two posts (July 2024 and June 2025) explain the work practitioner to practitioner rather than promote it; the June 2026 post advertises a senior partner success manager role aimed at agency-side candidates.
The posts indicate a focus on attribution, paid media, SEO, and analytics practitioners, making them a reasonable fit for an attribution or AI-measurement content partnership aimed at marketing practitioners.
They do not establish whether the followers are B2B marketing decision-makers or reveal their geography, seniority, or industry.
For a B2B marketing technology company considering them as a subject-matter expert or content partner, ask for a LinkedIn follower demographics breakdown (geography, age, gender where LinkedIn reports it, plus any available seniority, industry, or company-size breakdown), documented audience survey results covering occupation or job level, and recent results for posts comparable to the proposed collaboration in topic, depth, and format.
The follower count cannot verify professional attributes, and comparable post results can inform a decision beyond lifetime reach.
Michael Stratta's posts do not mention a partnership or sponsorship offer.
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They read as company-side marketing for Arcalea and its attribution platform, Galileo: promoting a data-driven attribution article series, describing a Kellogg case study taught with Galileo, reporting on Arcalea's presence and speaking sessions at Hero Conf and brightonSEO, and advertising a Senior Partner Success Manager opening at Arcalea.
The job post is for a full-time agency role, not a creator collaboration.
Across the five posts, there is no rate card, sponsored-placement example, disclosure policy, or audience-facing offer.
A B2B marketing technology company should ask directly about collaboration formats, requirements, rates, and disclosure terms.
The posts present Kellogg School of Management, Galileo, and Arcalea in the context of Arcalea's work, not as paid creator-content partnerships.
A measurement or analytics vendor selling cross-channel attribution to marketing practitioners and analytics leads wants a subject-matter expert or co-marketing content partner for a decision-stage explainer and a follow-up webinar.
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This fits Michael Stratta's posts closely.
They write and promote the "Marketing Attribution Decoded" series, moving from the unglamorous groundwork of UTM tagging and cross-device tracking to the case for data-driven attribution as third-party cookies and privacy rules tighten.
Two arguments recur and are exactly the kind a measurement vendor can build a campaign on: partial or biased data produces confident but wrong decisions, with industry tools favoring their own channels; and spend should be judged against ROAS and customer acquisition cost rather than clicks or impressions.
The Kellogg case debrief supplies the strongest teaching asset: a co-taught session that runs students through real data scenarios in Arcalea's attribution platform, Galileo, and reports a projected revenue gain above 90% in the scenario from shifting budget toward high-ROAS channels.
The most recent post points at where the audience is heading: AI search has moved past experimentation, and the open questions are why a brand appears in AI answers, which sources influence the answer, and whether any of it changes customer behaviour.
Deliverable shapes that suit this material: a written explainer with a worked budget-reallocation scenario, a webinar or panel on measuring AI visibility, or a co-created teaching asset with an academic co-sign.
Before negotiating, ask what partnership or sponsorship formats are available, what they cost, and how to inquire.
Also request audience demographics and per-post performance data before promising a specific job-title, seniority, or industry mix among the 8,174 LinkedIn followers.
When to choose a specialist
A consumer or lifestyle brand wants a short-form video creator to promote a product to a guaranteed demographic audience, on a fixed package with per-post performance expectations.
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Michael Stratta's posts do not support this: among the five posts, there is no video, transcript, or sponsored-placement example; the material is LinkedIn text aimed at people who already work on attribution, paid media, SEO, and analytics.
The posts also provide no rate card, disclosure example, audience-facing offer, or sponsorship format, so there is nothing to price or schedule.
Audience composition and per-post performance are unverified, so a reach or engagement promise is unsupported.
A second campaign need fails for a different reason: a brand that needs a measurement commentator who is independent of the tooling being discussed.
Galileo is Arcalea's own platform, and much of the content is company-side marketing for Arcalea and Galileo, including the Kellogg case, which is taught using Galileo.
That is a strength for a vendor collaboration and a conflict for a neutral review assignment.
Decline either campaign or rewrite the brief before committing spend.
A worked campaign example
Campaign angle: "Why your brand shows up in AI answers — and what that appearance is worth."
Take the need for a decision-stage content partnership aimed at marketing and analytics leads who already accept that AI search matters and now need a method.
Open with the shift their posts report: the conversation has moved from rankings to how brands are represented, cited and recommended inside AI platforms, and presence reporting is becoming table stakes.
Then run the three questions the posts frame as the harder ones: why a brand appears, which sources influence the answer, and whether any of it changes customer behaviour.
The middle of the piece is the worked scenario, mirroring the Kellogg exercise: a fictional advertiser allocates budget by channel against ROAS and customer acquisition cost, the analysis reveals that the reporting platform is over-weighting conversions inside its own ecosystem, and the plan is reallocated toward the channels tied to first-party revenue.
Make the platform-bias warning the reason the scenario is necessary, and close on the stated priority of pushing measurement closer to first-party revenue and profit.
Format: one long-form explainer with the worked scenario in full, a companion webinar where Michael Stratta walks the scenario live, and a short summary post.
The teaching track record at Kellogg supports the live format; the series supports the written one.
Before signing, request audience demographics and recent results on comparable posts, and ask directly whether an AI-visibility or measurement vendor collaboration creates a conflict given Arcalea's own Galileo platform.
Set the deliverable as a scoped explainer plus one live session, not an open-ended content retainer, until those answers arrive.
A better choice when the campaign needs a practical explainer on data reliability and funnel plumbing for small teams—the audience that must assemble trustworthy numbers before any attribution model is worth running.
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Michael Stratta's posts argue that decisions are only as good as the data behind them and describe pulling landing-page, CRM, and calendar data into one centralized dashboard.
For a vendor whose product depends on clean inputs from many tools, that setup-first angle reaches buyers earlier in the evaluation than a model-comparison piece.
The free-statistics-guide offer also shows a working distribution mechanic for that audience.
Choosing Smith means giving up the attribution-model argument.
Michael Stratta's posts cover funnel data quality and CRM statistics, not cross-channel budget allocation, platform-ecosystem bias, ROAS and CAC trade-offs, or AI search visibility.
There is no academic teaching asset comparable to the Kellogg case or live-session track record to build a webinar around.
A vendor would need to supply the attribution and AI framing and use the collaborator mainly for the data-hygiene portion.
A better choice when the campaign is about prescriptive decision-support tooling and the buyer wants to see an actual technical build, not just a case for the category.
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Michael Stratta's posts describe a stockout response optimization engine—a mixed-integer linear program built in Python with Gurobi, paired with a lightweight dashboard—presented at the MIT Supply Chain Management Symposium.
They connect that work to agentic automation of recovery decisions.
For a vendor selling optimization or automated reallocation, it is a direct demonstration of the technique applied to a real operational problem, which can carry more weight with technical evaluators than a marketing use case.
The domain is supply chain and logistics, not marketing measurement.
The examples address hub-to-DC transfers, stockout penalties, and trailer utilization, leaving a marketing attribution vendor with a wide gap to bridge before the material speaks to its audience.
There is no marketing-mix modeling, channel-attribution or ROAS discussion, or AI-search visibility content, so the collaborator could not carry the campaign's core measurement argument.
A better choice when the campaign's central point is that platforms overclaim credit and the fix is a deduplication methodology.
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Michael Stratta's posts include a worked brand case: an email platform reporting $1.8M in attributed revenue against $1.1M in actual email-originated orders; an explanation of overlapping attribution windows across email and paid social; and a warehouse-based deduplication model that brought email to about $803K and surfaced a paid channel with a 4.2x blended return.
It is the closest single piece in their posts to a decision-stage explainer for a measurement vendor: it names the problem, shows the numbers, and states the method.
A second post on predictive retention triggers extends the same data-before-action argument into lifecycle decisions.
The content is narrower: one attribution case and one retention automation concept, with no academic credential, co-taught teaching asset, or AI-search measurement content to match the direction of Michael Stratta's most recent material.
The case closes with an invitation to a paid or free walkthrough, so it reads as a practitioner lead-in rather than an independent teaching resource.
Ask whether that commercial framing suits a co-marketing arrangement.
This profile draws on five full LinkedIn posts: two promoting “Marketing Attribution Decoded” issues, a Kellogg case debrief, conference reflections on AI search and paid media measurement, and an Arcalea hiring post.
The linked articles and any video or transcripts were not reviewed.