Larry Kim works on CustomersAI, which helps Shopify and Klaviyo brands identify website visitors and use identity data and AI to improve email audience targeting, deliverability, and retention marketing.
Larry Kim is most useful for email deliverability diagnostics and identity data advice for Shopify and Klaviyo retention marketing.
Best for
A B2B SaaS email deliverability launch or advisory and co-marketing engagement.
Not for
Consumer short-form video campaigns on Instagram or TikTok with broad shopper reach.
Key takeaways
A Klaviyo score of 70% rated Good while 41.3% of test emails landed in spam.
All Gmail and Google Workspace test emails landed in spam in that test.
CustomersAI found visitor-identification data was wrong most of the time, 5% to 30% accurate.
Check first: Confirm audience demographics and recent results for comparable deliverability or AI-retention content.
Main ideas
Identity data quality is the dividing line in retention marketing · Email deliverability scores can hide the real problem · AI makes the coordination layer inside companies unnecessary
Larry Kim’s posts focus on their work as a CustomersAI leader in visitor identity, email deliverability, and AI-driven retention marketing for Shopify and Klaviyo brands.
They describe a shift from identifying anonymous site visitors to using identity data for audience selection, suppression, reactivation, and ad targeting.
They also argue that reliable underlying data matters more than adding AI to weak signals.
Kim’s clearest strength is turning a marketing measurement problem into a concrete diagnostic.
In one post, a Klaviyo score labeled “Good” sits alongside an inbox test showing 41.3% of messages in spam, including all test messages at Gmail and Google Workspace.
They explain why engagement-based scores can miss inbox placement, then point readers to Google Postmaster Tools and controlled placement tests.
Elsewhere, they use the same data-quality argument to connect visitor identity with segmentation and retention.
These examples make their work useful to readers evaluating email performance or AI-based audience tools.
Larry Kim's posts return most often to one claim: the quality of the underlying data sets the ceiling on what retention marketing can do.
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Around that, they argue that standard email deliverability scores mislead, AI has made the coordination layer inside companies unnecessary, using AI well now means building agents rather than writing prompts, and marketing should run as a high-cadence output engine judged on revenue rather than polish.
Kim writes from inside CustomersAI, a visitor-identity and retention tooling company, and publishes their own test figures.
Ask for the methodology behind those tests.
Identity data quality is the dividing line in retention marketing
Kim argues that most third-party visitor-identification data is not accurate enough to use for email, so CustomersAI stopped reselling it and built its own extended first-party identity layer.
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They base that argument on a head-to-head test: take a known first-party session, such as someone who actually made a purchase, and compare it with the identity the provider claims for that visitor.
Kim says many providers were right only 5% to 30% of the time, meaning most of the emails they surface are wrong.
Because AI learns from that signal, they argue, a wrong identity cannot be fixed downstream by a better model.
Their conclusion: the retention platform with the best identity signal wins.
The CustomersAI update post names data sources tested, including Retention.com, LiveRamp and sources used by vendors such as OpenSend and Revenue Roll/Tie, and says CustomersAI is now used by over 1,000 brands. It describes that identity layer feeding Super CAPI, which sends extra upper-funnel Meta events back for attribution and ad targeting, and AI audience prediction that decides who should receive email, who should be suppressed and who should be reactivated.
In a single post, Kim describes a shift: visitor identification started as the product and is now framed as the starting point for a broader signal layer combining Shopify data, ESP data and visitor signals. They say products built on that foundation are heading into beta the following quarter.
Email deliverability scores can hide the real problem
Kim argues that an ESP deliverability score is a proxy built from downstream engagement signals such as opens, clicks, bounces, complaints and unsubscribes, so it does not answer the most important question: did the email reach the inbox?
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When mail lands in spam, people never see it, so they never open, click, complain or unsubscribe.
That can make bad placement look quieter than it really is while positive engagement is inflated.
They say Apple Mail Privacy Protection, bots, proxies and security scanners heavily inflate opens; clicks are cleaner but still imperfect.
A brand's Klaviyo deliverability score was 70%, which Klaviyo called Good, but an inbox placement test found 41.3% of test emails in spam and 100% spam at both Gmail and Google Workspace.
Kim recommends checking Google Postmaster Tools for Google's view of domain and IP reputation and running controlled inbox placement tests, and offers to help set one up.
A prior analysis they cite attributed roughly two-thirds of reported opens and about 11% of reported clicks to bots or security tools rather than people.
AI makes the coordination layer inside companies unnecessary
Kim argues that most product manager work involves carrying context between customer success, sales, engineering and design, and that AI has made that translation layer obsolete.
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Once requirements are expressed as a single source of truth through event modeling—a standardized way of stating inputs and outputs—AI can generate the interface, prototype, code stubs, documentation, training material and even marketing content without handoffs. Kim says CustomersAI removed the product management function entirely, with the engineering team leading the change after years of poorly defined requirements documents.
Kim says engineers now move two to three times faster and communication issues went to zero, and ties the same principle to the product: no bloated middle workflow layer between identity data and outcomes. They quote Peter Drucker's line that marketing and innovation produce results while everything else is a cost, and ask whether readers will lead the change or have it happen to them.
Kim concedes that the top 1% of product managers generate original customer insight, make hard prioritization calls and shape strategy. Their argument targets the middle coordination work that takes up most PM time, not the best practitioners of the role; the speed figures are Kim's own.
Using AI now means building agents, not writing prompts
Kim argues that prompting alone is no longer enough and that team members who still use AI only through basic prompting will be left behind.
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They want agent systems that reason, access tools, learn skills, retrieve information, make predictions, take actions and complete work autonomously. Kim frames this as a management responsibility, not a tooling preference, and extends it beyond technical teams.
Kim set up an internal competition with prize money, asking every department team—not only engineering or marketing—to build and demo an agentic AI project that saves time or improves products or services. The post also promotes a virtual event on the same theme, AI Trends in DTC: The Agentic Edge, run with partners Triple Whale, PostPilot and Okendo.
Run marketing as an output engine measured on revenue
Across three hiring posts, Kim sets out the marketing operation they want: content shipped daily across channels, newsletters twice a week, monthly webinars and co-marketing, consistently published case studies, and one idea turned into many pieces of content, with AI used to move faster.
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They state the priorities directly: output over perfection, campaign thinking rather than asset production, and pipeline and revenue impact rather than vanity metrics. The role they describe owns a content machine end to end, not just a few blog posts.
The Content Marketing Campaign Manager post lists newsletters twice a week, webinars and co-marketing monthly, case studies, and repurposing across LinkedIn, X and YouTube. A sibling post says the previous two months were the company's strongest for new bookings and growth rate; a third says growth has come largely from head-to-head data quality tests that competitors dislike.
Kim's posts treat the Klaviyo and Shopify partner ecosystem and its events as a distribution channel, not a side activity.
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They show up where brands, agencies and fellow vendors gather, discuss identity data and deliverability in person, and use co-marketing with platform partners to reach buyers already working inside those tools.
Kim posts about attending Klaviyo's K:BOS event in Boston, listing speakers from goop, Unilever Prestige, Dagne Dover, MESHKI and goodr, and offering $75 in Uber credit to cover transport for a quick meeting.
Kim's team is described as available to discuss extended visitor ID, AI audience segments and fixed deliverability.
Hiring posts claim CustomersAI is an official Klaviyo, Shopify, Meta and Google partner.
The partner list differs between posts: one hiring post names Klaviyo, Shopify, Meta and Google, while an earlier one names Klaviyo, Shopify and Meta.
The partner status appears only in Kim's company posts.
Ask for confirmation of the current partner list and status.
This is the clearest statement of what Larry Kim's company does and the problem it sells against.
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Newcomers should start here to understand the data-quality argument behind the company's other posts.
Larry Kim explains why CustomersAI stopped reselling third-party visitor-identification data.
Comparing a known first-party identity, such as a purchaser, with an identity provider's prediction, the company's tests found many providers wrong most of the time — 5% to 30% accurate, according to the post.
CustomersAI instead built an extended first-party identity layer, now used by over 1,000 brands, the post says.
The company is extending it into Super CAPI event sending and AI audience prediction to decide who receives, is suppressed from, or is reactivated for email.
The piece argues that identity quality, not the model, sets the ceiling for retention marketing.
For brands planning a campaign
For an email deliverability launch, this frames deliverability and segmentation as dependent on identity quality. It also offers a quote for positioning a launch message around data accuracy; treat the accuracy figures as the company's test results, not independent measurements.
It answers which deliverability numbers to trust with a concrete diagnostic sequence and vivid counterexample.
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This is the clearest example of how they teach a measurement problem.
The post contrasts a Klaviyo deliverability score of 70%, rated Good, with an inbox placement test in which 41.3% of test emails landed in spam — including 100% at Gmail and Google Workspace.
Larry Kim argues that ESP scores rely on downstream engagement such as opens, clicks, and complaints, so they can look acceptable while mail never reaches the inbox.
Opens and clicks are also inflated by Apple Mail Privacy Protection, bots, and security scanners, which accounted for roughly two-thirds of opens and about 11% of clicks in a cited prior analysis.
The post recommends checking Google Postmaster Tools and running controlled inbox placement tests.
For brands planning a campaign
For a deliverability product launch, this offers an education hook: the gap between an ESP score and actual inbox placement. A launch could reuse the structure — score, placement test, mailbox-level breakdown — and offer the same inbox placement test as a lead conversion, as the post does.
It shows how Larry Kim manages teams and where they expect AI to remove work — useful for judging whether they fit an advisory role, not just a content brief.
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Larry Kim describes removing the product management function at CustomersAI.
They frame most PM work as carrying context between customer success, sales, engineering, and design, and argue that AI made that translation layer obsolete.
Engineers led the change, using event modeling to express inputs and outputs as a single source of truth.
The post claims 2–3x faster work and no communication issues.
Kim connects the change to how CustomersAI is built: with no middle workflow layer between identity data and outcomes.
For brands planning a campaign
If the engagement includes advisory work beyond deliverability, this post offers an AI-driven process-change angle. For a product launch, it also shows how to adapt the no-middle-layer argument for retention tooling.
It shows the kind of AI work Larry Kim expects from their organisation and points to partner events where they and their team appear.
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Larry Kim ran an internal contest, with prize money, asking every department team to build and demo an agentic AI project that saves time or improves the product or services.
The post distinguishes prompting from building agent systems that reason, use tools, retrieve information, make predictions, and act autonomously, and warns that teammates who only prompt will be left behind.
It closes by promoting a virtual event, AI Trends in DTC: The Agentic Edge, with partners Triple Whale, PostPilot, and Okendo.
For brands planning a campaign
Useful if a campaign calls for an AI adoption or agentic-marketing angle rather than a pure deliverability one. Include the three named partners in the brand-relationships section, if written; the mention does not establish payment.
How Kim describes the content engine they hire for3
Visitor identity data quality as the core differentiator2
From visitor ID to a retention signal layer1
The deliverability measurement trap1
Removing the product management layer1
AI fluency as a management mandate1
Visitor identity data quality as the core differentiator
Larry Kim argues that most visitor identification data is not accurate enough to email, so CustomersAI built an extended first-party identity layer instead of reselling third-party data.
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They say competitor data tested between 5% and 30% accurate, meaning most surfaced emails were wrong, and CustomersAI is now used by over 1,000 brands.
Test a known first-party session, such as a completed purchase, against the identity a data provider claims for that visitor, then measure how often the provider is wrong.
Kim says this head-to-head testing is how CustomersAI has grown.
They also describe an internal count showing that roughly two-thirds of reported email opens were not from humans and about 11% of reported clicks came from bots or security tools.
For brands planning a campaign
For a deliverability product launch, the strongest available content hook is a repeatable, adversarial data-quality test brands can run before buying an identity or deliverability vendor.
Scope an advisory engagement around designing and publishing that test, not generic thought leadership.
Treat the accuracy percentages as Kim's reported testing, not independently verified figures; ask for the underlying test methodology before using them.
Kim frames visitor identification as a starting point, not the product.
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The stated direction is a signal layer combining Shopify data, ESP data, and visitor signals to predict who should receive email, be suppressed, or be reactivated, plus Super CAPI, which sends upper-funnel Meta events back for attribution and targeting.
Run AI audience prediction as a background agent that continuously works on the combined data set, rather than on static ESP segments such as engaged-in-last-30-days.
For brands planning a campaign
Useful for positioning an email product as infrastructure that other retention tools depend on, rather than another point solution. A launch campaign needs a concrete demonstration of the identity signal feeding segmentation, suppression, and ad targeting: the claim is about breadth of use cases, not a single feature.
Kim describes a brand whose Klaviyo deliverability score of 70% was labelled Good, while an inbox placement test showed 41.3% of test emails landing in spam and 100% of Gmail and Google Workspace test emails landing in spam.
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Their recurring point: ESP scores rely on downstream engagement signals, so they do not show whether an email reached the inbox.
Run an inbox placement test with a mailbox-level breakdown, check Google Postmaster Tools for Google's own reputation view, and treat ESP scores as context only. Kim also discounts open rates as heavily inflated by Apple Mail Privacy Protection, bots, proxies, and security scanners, and considers clicks cleaner but still imperfect.
For brands planning a campaign
The clearest ready-made content asset for a deliverability product launch is a side-by-side comparison of an ESP score against actual inbox placement, with the Gmail result as the reveal. It also supports an advisory offer: Kim ends the post by offering to help set up an inbox placement test.
Kim says CustomersAI eliminated its product management function because AI made the coordination role obsolete.
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They distinguish context-carrying work, which AI now handles, from original customer insight and prioritization, which they say strong product managers provide.
A standardized event-modeling framework expresses inputs and outputs and creates a single source of truth.
AI then generates UI, prototypes, code stubs, documentation, training material, and marketing content without handoffs.
Kim reports engineers moving two to three times faster and communication issues going to zero.
Kim acknowledges that the top 1% of product managers generate insight and shape strategy, and says the role being removed is the middle coordination layer, not expertise itself. This is a claim about one company's internal structure, not a validated industry result.
For brands planning a campaign
Relevant if a client wants an operator's account of an AI-native operating model and how Kim connects internal practice to product marketing.
It is not retention marketing evidence, so it should not carry a deliverability engagement on its own.
The productivity figures are self-reported; ask for supporting evidence before relying on them.
Kim ran an internal competition with prize money, asking every department team to build and demo an agentic AI project that saves time or improves products or services.
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They say prompting alone is no longer enough and team members using only basic prompting will be left behind.
Require every team—not just engineering or marketing—to build and demo a working agent that reasons, uses tools, retrieves information, makes predictions, takes actions, and completes work autonomously. Kim promoted a virtual event on the same theme with partner companies Triple Whale, PostPilot, and Okendo.
For brands planning a campaign
Kim already uses a co-marketing format that a brand launching an AI-assisted product could join: a partner-hosted virtual event on agentic AI in DTC. Participation by named partners in a post shows an event lineup, not a paid relationship.
How Kim describes the content engine they hire for
Across three hiring posts, Kim describes the marketing operation they want: daily content across channels, newsletters twice a week, monthly webinars and co-marketing, consistently published case studies, and one idea turned into many pieces of content, with AI used to move faster.
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Prioritize output over perfection, campaign thinking over asset production, and pipeline and revenue impact over vanity metrics. Repurpose content across LinkedIn, X, and YouTube.
For brands planning a campaign
These posts offer Kim's clearest public account of how they would staff and run a content engagement, making them a practical scoping reference for an advisory or co-marketing proposal: expect high-cadence, AI-assisted output measured on pipeline. The hiring posts describe intended operations, not proven results.
Kim posts about attending Klaviyo's K:BOS event in Boston with their team, listing speakers from goop, Unilever Prestige, Dagne Dover, MESHKI, and goodr, and offering Uber credit for meetings.
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Their hiring posts assert that CustomersAI is an official Klaviyo, Shopify, Meta, and Google partner; one comparison between two hiring posts drops Google from the list.
Kim's posts describe the partner status, but give slightly different partner lists. Ask for confirmation of the current certifications.
For brands planning a campaign
Shows where a launch could meet brands and agencies directly: CRM and lifecycle marketing events, and the vendor ecosystems Kim positions inside. For a deliverability launch, event presence and co-marketing may reach buyers faster than publishing alone.
Larry Kim's posts connect them to CustomersAI, a company they lead and promote, and to the platform partnerships the company claims.
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The relationships described include CustomersAI's stated official partner status with Klaviyo, Shopify, Meta and Google, and a DTC virtual event co-hosted with Triple Whale, PostPilot and Okendo.
CustomersAI
Owned business
Kim writes about CustomersAI in first-person company language, describing the decision to build an extended first-party visitor identity product, the company's marketing hiring, and an internal AI competition they set for 'my department teams.' They promote the company's visitor ID, AI audience prediction, and deliverability products and say more than 1,000 brands use them.
CustomersAI states it is 'the only vendor in this space that is an official Klaviyo, Shopify, Meta and Google partner.' Kim also promotes Klaviyo's K:BOS event in Boston, says their team will be there, and offers $75 Uber rides to book meetings at the show.
Listed among the platforms CustomersAI says it is an official partner of.
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Kim's deliverability writing also relies on Google's own reputation tooling, recommending Google Postmaster Tools and reporting a Gmail and Google Workspace inbox placement test that landed 100% in spam.
Kim says CustomersAI hosted the virtual event 'AI Trends in DTC: The Agentic Edge' with these three companies as partners, promoting it as a way for teams to build agentic AI skills.
Larry Kim writes about what sits underneath retention marketing: whether the data identifying a website visitor is correct and whether an email reaches the inbox.
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Start with the CustomersAI update explaining why the company stopped reselling third-party visitor data and built a first-party identity layer.
Then read the deliverability post, which sets a Klaviyo score of 70%, labelled Good, against an inbox placement test showing 41.3% of test emails in spam and 100% at Gmail.
Other posts cover running an internal agent-building competition, removing the product management function, and hiring for a daily-cadence content operation judged on pipeline.
Kim writes from inside a visitor-identity vendor; ask for independent verification of the accuracy percentages, productivity figures and partnership claims, which are the company's own reported numbers.
Writes short, operator-level ecommerce marketing lessons for DTC brands, emphasizing execution over tools.
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Buchinger's year-in-review post argues that better analytics do not raise sales without a decision owner and that segmentation is not optional at scale—points that overlap with Kim's view that segmentation, suppression and reactivation decisions drive retention results.
Buchinger's posts cover broad ecommerce strategy and channel risk, including Google shopping inside Gemini and Amazon platform risk.
Kim anchors claims in specific measurement work: head-to-head identity tests, inbox placement results, and open and click inflation estimates.
Readers seeking tested numbers rather than general lessons may prefer Kim; those after broad DTC operating advice without vendor framing may prefer Buchinger.
Both publish practical Klaviyo and CRM work built around AI tools.
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Corto S. documents turning Claude Code into a Klaviyo execution engine and pairing GPT-6 with a CRM workflow to plan a monthly campaign calendar, build lifecycle flows and generate copy. That overlaps with Kim's argument that AI agents should complete work rather than just answer prompts.
Corto S. focuses on step-by-step AI workflow setups readers can copy inside a Klaviyo account, presented as guides with packaged resources.
Kim writes about company direction, identity data quality and management decisions, treating AI-built workflows as a consequence of that strategy.
For a repeatable AI workflow in Klaviyo, read Corto S.; for the data and deliverability reasoning behind those workflows, read Kim.
Both run AI-assisted Klaviyo and retention work, and describe building tools rather than only advising.
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Biller describes using Claude to build CRM, project, onboarding and campaign processes, plus a deliverability dashboard that gives Klaviyo users a clear view of their email deliverability. That aligns with Kim's focus on making deliverability and audience signals usable.
Biller's posts describe a client services agency and its internal systems, including daily AI reviews of leads and a hands-on client onboarding story.
Kim describes a product company and its identity signal layer, with published benchmark figures and a partner ecosystem presence at Klaviyo events.
Readers looking for an agency to run email operations may find Biller's material more relevant; those seeking measurement arguments and vendor-side product thinking may prefer Kim's.
Use Larry Kim when a placement needs to reach email and retention marketers in the Shopify–Klaviyo ecosystem.
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Their LinkedIn follower count signals reach, not audience demographics.
Their posts target ecommerce retention operators, covering visitor identity, the gap between ESP deliverability scores and actual inbox placement, AI-powered audience segmentation, and event or partner activity around Klaviyo, Shopify, Meta, Google, Triple Whale, PostPilot, and Okendo.
For a deliverability product launch, they could be a fit for a sponsored post, webinar, or event-adjacent explainer if the buyer wants to draw on their credibility in identity data and deliverability measurement.
Before committing, ask for audience survey results or platform demographics on geography, company type, seniority, and job function, along with recent results for comparable deliverability or AI-retention content.
The posts do not disclose a public sponsorship policy, rate card, or partnership inquiry route.
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The partnerships are company-level: CustomersAI posts state it is an official Klaviyo, Shopify, Meta, and Google partner, and the company has co-marketed with Triple Whale, PostPilot, and Okendo on a virtual DTC event.
CustomersAI's team also promoted its presence at Klaviyo's K:BOS event.
Treat these as documented ecosystem relationships and co-marketing examples, not a published sponsorship menu.
Ask CustomersAI directly for sponsorship terms and an inquiry route.
Co-marketed events and webinars with ecosystem partners
Buyer requirement
CustomersAI promoted the virtual event AI Trends in DTC: The Agentic Edge with partners Triple Whale, PostPilot, and Okendo, and its team attended Klaviyo's K:BOS event.
A brand planning an event or co-marketing partnership would need to bring a joint concept and align with the existing platform and agency partners.
Confirm terms directly; the posts provide no public sponsorship package, fee, deliverable list, or application route.
Strong fit for an advisory and co-marketing engagement centered on the deliverability diagnostic.
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Brief
A B2B SaaS company launching an email deliverability product wants Larry Kim as a marketing advisor.
Reasons
The posts contain the teaching a deliverability launch needs. Kim distinguishes an email service provider's deliverability score from actual inbox placement with a concrete case: a Klaviyo score of 70% rated "Good" alongside an inbox placement test where 41.3% of test emails landed in spam, including 100% of Gmail and Google Workspace test emails. The recommended checks are Google Postmaster Tools and controlled inbox placement tests. Treat opens and clicks as inflated by Apple Mail Privacy Protection, bots, and security scanners.
Kim offers the exact service the launch would sell. The post closes with an offer to help set up an inbox placement test, making the natural advisory deliverable a test for the client and publication of the results—not abstract consulting.
The underlying argument gives the launch a story beyond one feature. Kim says CustomersAI found visitor-identification data was wrong most of the time (5% to 30% accurate in the company's own tests), prompting the company to build its own identity layer, now described as used by over 1,000 brands. A deliverability product can borrow that frame: AI and segmentation both fail when the identity data underneath them is bad.
The posts already show a commercial format for a joint engagement. Kim's company co-marketed a DTC virtual event on agentic AI with Triple Whale, PostPilot, and Okendo, attended Klaviyo's K:BOS event, and describes its marketing engine as daily content, newsletters twice a week, monthly webinars, and repurposing one idea across channels. A launch could plug into that machinery rather than commission standalone content.
Reach is plausible but unproven. Account metadata shows 94,736 LinkedIn followers observed 2026-10-06, and the posts address ecommerce retention and lifecycle operators—the right neighborhood for a deliverability product. Before setting expectations, ask for audience survey results covering geography, company type, and job function; the follower count alone does not establish audience composition.
Tradeoff and alternative: if the buyer wants a purely paid, scripted placement booked through a published rate card, this profile does not supply one. A creator with a public sponsorship menu would be simpler to buy, but would lack the diagnostic, test-first expertise that makes this fit interesting.
When to choose a specialist
Poor fit. Decline or rebuild the brief around a measurement story on LinkedIn before proceeding.
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Brief
A consumer brand wants a paid short-form video campaign across Instagram and TikTok to reach broad consumer shoppers, with brand-controlled scripts and no technical claims.
Reasons
Everything reviewed is LinkedIn text aimed at retention operators: visitor identity quality, deliverability measurement, an internal AI competition, marketing hiring, and CRM industry events. The material shows no consumer lifestyle content or general shopping audience.
The material includes no video, transcripts, or Instagram or TikTok content. A campaign judged on short-form video execution would rely on a format the posts do not support.
The audience evidence is a single number. There is no age, geography, or shopping-interest breakdown, so broad consumer reach cannot be supported. Ask for audience data before setting expectations.
The writing style works against brand-controlled scripts. Kim's strongest posts are opinionated and data-heavy, with test results and competitive claims about other vendors' data quality. A script that forbids technical claims would strip out what Kim does best, leaving the brand to pay for reach without the diagnostic value.
Settle some claims before the brand repeats them. Accuracy, productivity, and partner-status figures are Kim's own statements in the posts, and the list of official platform partners differs between two hiring posts. Those are normal diligence items for an advisory engagement but awkward inside a consumer script.
Nothing here is a permanent disqualification. If the brand reframes the brief around email revenue and accepts an explainer or webinar on LinkedIn, the fit improves; the change is the brief, not the creator.
A worked campaign example
Angle
"The score said Good. Gmail said spam." The launch page and co-authored post lead with a real score-versus-placement comparison for the client's own sending domain or a volunteer brand, then explain why provider scores lag: they draw on downstream engagement such as opens, clicks, bounces, and complaints, which cannot show whether mail reached the inbox at all.
Brief
B2B SaaS email deliverability product launch; advisory and co-marketing.
Concept
Publish the test, not the pitch. Build the launch around a live inbox placement test run jointly with the client, using the structure Kim already teaches: show the reassuring score first, then the mailbox-level result that contradicts it.
Deliverables
Hold a scoping session with the launch team to define the test: seed addresses, mailbox panel, Gmail and Google Workspace split, and run length, so the result is defensible and repeatable.
Publish one co-authored LinkedIn post in Kim's voice presenting the side-by-side result, with the mailbox breakdown as the reveal and the placement test as the call to action.
Create a short explainer for the launch page on why opens and clicks overstate reality, using the reported figures from Kim's analysis as Kim's findings, not neutral data.
Run a joint webinar as an extension of the DTC event format Kim's company already uses with partners. Cover identity quality as the ceiling on retention results, then demonstrate the product using that signal for segmentation, suppression, and reactivation.
Apply a case-study template to the same brand after the test: placement before and after, plus revenue per send, so the campaign closes on a measured outcome rather than awareness.
Sequence
Start with the test and scoping session. Publish the comparison post while promoting the webinar, run the webinar as the conversion moment, then release the case study four to six weeks later, once post-fix placement data exists.
Watch outs
The company reports the placement, accuracy and productivity figures; they are not independently verified. The launch copy should make that clear.
There is no published fee, package or inquiry route for a paid partnership. Agree on terms and scope directly rather than booking from a rate card.
The stated Klaviyo, Shopify, Meta and Google partner relationships are not endorsements of the launch brand, and partner names in event posts do not imply payment.
The engagement depends on the client releasing test data publicly. If it will not, use anonymized or volunteer-brand data for a webinar and explainer.
Why it works
Kim's own material gives the writing credibility. Buyers can inspect the test as a deliverable, and the identity-quality argument links the placement finding to the wider product story, avoiding a single-tactic claim.
Choose Abraham Buchinger when the deliverability launch is framed as a DTC ecommerce growth decision rather than an inbox-placement test.
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The two posts argue that better tools do not raise sales without a decision owner; that segmentation (new versus repeat, high-intent versus browsing, price-sensitive versus convenience-driven) fails when everyone gets the same message; and that decisions to pull back on messaging should be driven by performance and deliverability data, not fear of being too aggressive.
Buchinger also writes about where buying decisions start, including Google expanding checkout inside Gemini and the platform risk for brands that sell only on Amazon.
For a launch aimed at merchants weighing channel and customer-data choices, that framing puts the product inside decisions the buyer already owns and reaches an operator audience through advice rather than a vendor's own test result.
Neither post includes an inbox placement test, a mailbox-level breakdown, or a Google Postmaster Tools or seed-test method.
The deliverability reference is one line in general ecommerce advice, so a launch that needs a score-versus-placement demonstration would have to supply that evidence elsewhere.
You also give up the identity-quality argument connecting the placement finding to segmentation and suppression: Buchinger's material addresses messaging and channel strategy, not the data underneath them.
Choose Corto S. when the launch should show the product working inside Klaviyo through an AI workflow, rather than explain why inbox placement is hard to see.
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The two posts walk through turning Claude Code and a GPT model into a Klaviyo execution engine: analysing account performance, finding gaps, prioritising opportunities, running separate workflows for flows, campaigns, segmentation, deliverability, and analytics, then planning a monthly campaign calendar, building lifecycle flows, and drafting copy.
Each post packages the setup as a guide, forming a co-marketing offer: a documented build demo with a downloadable resource and the creator as the operator who assembled it.
For a buyer who values demonstrated tool use over diagnostic evidence, this is the closer match.
The posts read as lead magnets for guides, making them an acquisition asset for the creator's own pipeline as much as a demonstration for the launch brand. Ask for audience size, follower count, and commercial terms before comparing reach and availability.
Choose Meilech Biller when the launch buyer is an ecommerce agency or operator who wants to see how the tool gets installed and run, rather than a vendor's published test.
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One post describes building the agency's own systems with Claude: CRM and project management, a client onboarding flow of custom forms and documents, and automatic daily review of CRM, project, messaging, and email activity to decide the next action per lead.
It also mentions a deliverability dashboard built for Klaviyo users.
The other post describes a completed client build—email capture forms, welcome flow, browse and cart abandonment, and campaigns—held back until seasonal stock and site updates were ready.
Together, the posts show a partner's hands-on deployment sequence, often the missing piece when a tool is sold to agencies on behalf of clients.
The deliverability dashboard is described as something built for Klaviyo users, not as a documented test method, so it does not replace placement-versus-score evidence.
The second post rests on one client's experience and reports no measured outcome; neither post gives placement rates or revenue per send.
You trade an inspectable diagnostic for an implementation narrative.
Ask for audience and commercial details before evaluating the opportunity.
This page draws on nine LinkedIn posts published from Kim's account: a CustomersAI update on visitor identity and retention, a deliverability analysis comparing an email service provider score with inbox placement results, a post on removing the product management function, an internal agentic AI competition, three marketing hiring posts, and two posts about Klaviyo's K:BOS event.
The posts do not include publication dates, so no sequence over time is claimed, and the accuracy, open and click inflation, productivity, and partner-status figures are Kim's own reported numbers rather than independent measurements.
Similar-creator comparisons rest on six LinkedIn posts from three other accounts.
A known gap: the posts do not cover earlier career work, businesses, books, or signature projects, so they cannot explain the full basis for name searches; targeted research on that part of Kim's public career is still needed.
