Emily Kramer creates practical B2B marketing research and tools, including salary analyses based on job-posting data, and shares them through the MKT1 newsletter and other formats.
Useful for practical B2B marketing salary research and tools from public job posts.
Best for
B2B SaaS brands needing credible marketing compensation benchmarks by role and level.
Not for
Short-form video campaigns with guaranteed demo signups and hard conversion targets.
Key takeaways
Growth roles did not consistently pay more than content and brand roles in 1,100+ posts.
AI labs pay on a different scale, with widest gap at Director and Head level.
Salary report became a benchmark app and an MCP connector for Claude or ChatGPT.
Check first: Ask for recent results on comparable posts and any audience survey or demographics export.
Main ideas
Compensation benchmarks come from public job posts, reported as medians by role and level · AI labs pay on a different scale, with the widest gap at Director and Head of level · Test the assumption that growth roles pay more, because the data may not support it
Emily Kramer is a B2B marketing researcher and creator who turns public job-posting data into salary comparisons, practical tools, and advice shared through MKT1.
Her work is useful to marketers because it tests familiar assumptions with specific numbers: in one analysis of 1,100+ marketing job posts, growth roles did not consistently pay more than content and brand roles.
She also discusses creator partnerships from the creator’s perspective and has referred to earlier work as a co-host of the Dear Marketers podcast.
Kramer connects analysis to something readers can use.
She breaks salary findings down by role and seniority, points out when a comparison rests on a small sample, and explains limits such as relying on salaries listed in job posts.
She has also described turning a salary report into a benchmark app and an AI-assistant connector.
In another post, she revisits her advice to B2B marketers to skip Meta and lays out the figures behind that change of view, attributing the supporting report and explanations to Primer.
The reviewed posts show a mix of data-led analysis, practical formats, and willingness to update a position; they do not establish the full range of her earlier work or rule out other signature projects.
Emily Kramer's recurring priorities are first-party research built from public data, a method stated openly with its limits, and shipping the output as something readers can use rather than only read.
Read more
The subject matter is mostly B2B marketing pay and channel economics; lighter posts are community prompts. Across the retained posts, Kramer reverses a standing recommendation when the numbers move, and treats their own creator-side experience with 60+ companies as material for asking in-house marketers how those partnerships should run.
Compensation benchmarks come from public job posts, reported as medians by role and level
Kramer's most consistent argument is that useful pay research does not require proprietary salary data.
Read more
Public job posts, collected in volume, can support benchmarks when the analysis separates role families and levels and reports medians instead of one blended average. Kramer states the limits inside the posts rather than leaving them to the reader: the figures come from publicly listed salaries in September job posts, and at least one comparison rests on a very small sample.
In the analysis of 1,100+ marketing job posts, 794 of them from private B2B startups, mid-level content and brand roles show a median of $165K against $160K across all roles, mid-level growth roles $156K, and Director/Head of content and brand $235K against $225K across all areas. The post also points paid newsletter subscribers to a tool for slicing the same dataset.
AI labs pay on a different scale, with the widest gap at Director and Head of level
A specific claim Kramer makes from the same dataset is that OpenAI and Anthropic are not just another startup employer for marketing hires.
Read more
The argument is about how far apart the medians sit, not just that AI companies pay well, and Kramer flags where the evidence is thin rather than presenting the whole comparison as settled.
A Head of role at OpenAI or Anthropic shows a median of $381K against $300K for VP or CMO roles at other startups.
At Director or Head of level the gap is widest: $381K against $225K, a $156K difference.
Kramer notes that comparison comes from five job posts, that no early-career or VP/CMO openings appeared at either company, and that 45 of the 63 postings found there were senior roles.
A separate finding is that startups which raised within the last year paid about 17% more.
Test the assumption that growth roles pay more, because the data may not support it
Kramer describes holding the belief that growth marketing pays more and never finding it logical, then testing it against the job-post dataset.
Read more
The point is not only the result but the habit of naming a working assumption and checking it, since the finding cuts against what many marketers expect.
At mid-level, content and brand roles show a median of $165K against $160K across all roles, with growth at $156K.
At Director or Head of level, content and brand leaders lead at $235K against $225K across all areas.
Growth only moves ahead at senior level, six-plus years excluding director and above, where it pays $178K against $169K.
Kramer's post on Meta for B2B is framed as a public reversal, not a new topic.
Read more
The argument is that the earlier advice to skip Meta rested on match-rate economics that have since shifted, and the post walks through the evidence before restating the recommendation. Reasons for the shift are attributed to a named outside source rather than presented as Kramer's own measurement.
The post works through Primer's State of B2B Paid Advertising report, described as data from 379 B2B companies' real ad accounts rather than a survey: Meta's average share of paid budget rose from 20.8% to 24.9% in about six months; 63% of companies in the report advertised on Meta, up from 54%; Google fell from 43.6% to 40.9%; and LinkedIn cost about five times more than Meta to reach the same number of people within the same company's accounts.
Keith Putnam-Delaney at Primer is credited with the explanation: linking work emails to personal emails lifts Meta match rates to about 80%, against about 50% on Google and 70% on Reddit, and Meta CPMs are roughly $7 to $15 against $31+ on LinkedIn.
The post carries a Primer partner tag.
Kramer opens by saying they have told a lot of B2B marketers to skip Meta and may owe them an update, so the post documents a changed position rather than a first take on the channel.
Kramer treats the research process as worth explaining on its own.
Read more
The argument is that a small newsletter team can produce data-heavy reports from public sources with an AI-assisted pipeline, and that the pipeline should be documented and reused so each run improves. The post carries an ad disclosure for a Claude Code sign-up link while describing the workflow as the real process.
Kramer reports reviewing 2,250+ job posts and shipping a two-part salary report with 30+ graphs in a few days, and lists seven steps: run Claude Code in the Mac desktop app, using Opus 5.5 for the main work and Sonnet and Haiku for smaller jobs; start from public data; run research in parallel with subagents, 16 at one point; push data to a database as it goes; build graph templates in Claude Design first so one polished sample per style carries through to the charts; keep asking why behind the numbers; and save the process as a reusable research skill.
Reports that used to take a quarter were made two in a week, and Kramer says the second report in the series went faster than the first because the saved skills compounded.
Kramer's pattern after a report is to turn the data into something an audience can interact with.
Read more
The argument is that benchmarks are more useful when someone can check their own salary or query the dataset inside the assistant they already use, rather than only reading a long newsletter.
Kramer built the salary report in Lovable and described it as more than a report: an app where anyone can check their B2B startup marketing salary against the benchmarks, then an MCP connector built in Lovable with a prompt so the same data can be queried in Claude, ChatGPT, or another assistant.
Kramer co-hosted a Buildathon with Lovable on Wednesday, October 28, with ready-made prompts, build time, a question-and-answer session with named Lovable team members, and credits for live attendees.
A separate post shows the same-day setup for hosting it, including two live app and MCP builds.
The same dataset moves from report to interactive app to AI connector, with the newsletter as the narrative layer around the tools.
Ask the buyers how creator partnerships should work before writing the guide
Kramer states having worked with 60+ companies as a creator and turns that into a research question for in-house marketers rather than a set of conclusions.
Read more
The argument is that guidance on creator programs should be shaped by the people buying and managing the work, so the post asks specific operational questions and offers to quote the answers.
The post asks whether working with many creators or a small few is the better approach, how teams manage the process and all its pieces, and, for those who have not done much here, why not.
Kramer says commenters will be quoted in the newsletter if their comment is used and that the piece should be out later that week.
The post is a working call for practitioner input, not a finished argument, and it does not name clients or list the companies.
This answers the most common benchmarking question a job-posting analysis can settle: do AI labs pay differently from everyone else?
Read more
It is also the clearest example of how Emily Kramer reports pay numbers — medians by role level, with the sample size stated.
Kramer analyzed 1,100+ marketing job posts, 794 of them from private B2B startups, and reported medians by level rather than a single average.
A "Head of" role at OpenAI or Anthropic shows a median of $381K against $300K for VP or CMO roles at other startups.
At Director or Head of level the gap is widest: $381K against $225K, a $156K difference in the medians.
At senior level (six-plus years, excluding director-and-above), OpenAI and Anthropic medians are $288K against $178K elsewhere.
Kramer flags the limits directly: the Director comparison rests on five job posts, she found no early-career or VP/CMO openings at either company, and 45 of the 63 postings she found there were senior roles.
A separate finding is that funding recency mattered more than any other company factor — startups that raised within the last year paid about 17% more.
For brands planning a campaign
For a compensation brief, the role-level medians are usable as a first-pass benchmark, and the stated caveats tell you which comparisons are too thin to quote. The funding-recency finding is the more portable one for a company that cannot match AI-lab pay: timing of your last raise may matter more to candidates than industry label.
This is the section of the salary research that most often contradicts what people assume.
Read more
It answers whether growth specialists command a premium over content and brand marketers.
Using the same 1,100+ B2B marketing job posts, Kramer tested an assumption she says she had always held — that growth roles pay more.
The data did not support it.
At mid-level, content and brand roles show a median of $165K, above the $160K median across all roles, while mid-level growth shows $156K.
At Director or Head of, content and brand leaders lead at $235K against $225K across all areas.
Growth only moves ahead at senior level, at $178K against $169K.
Kramer also notes a paid-subscriber tool built on the dataset, so readers can cut the numbers by their own criteria, and adds a plain-language limitation: the analysis uses publicly listed salaries from September job posts, which has shortcomings.
For brands planning a campaign
If a brief assumes growth experience should command a premium, this is direct evidence to re-check that assumption by level. The role-level splits give a defensible starting band for content and brand hires in particular.
This shows what happens after the research: Kramer ships the data as something people can query, not only as an article.
Read more
It answers how the salary benchmarks reach an audience that will not read a long newsletter.
Kramer built the salary report in Lovable and then described it as more than a report — an app where anyone can check their own B2B startup marketing salary against the benchmarks.
Kramer then turned it into an MCP, described here as a connector, so the same benchmarks can be queried inside Claude, ChatGPT, or another assistant.
The post promotes a Buildathon co-hosted with Lovable on Wednesday, October 28, including ready-made prompts, build time, and a question-and-answer session with named Lovable team members.
It shows a working tool and an event, not only a data summary.
For brands planning a campaign
A data-led creator partnership can include a tool your audience uses directly, with the newsletter as the narrative layer around it. The connector format is worth noting if your audience works inside AI assistants and would rather query benchmarks than read them.
This answers how a small newsletter team produces research that used to take a quarter.
Read more
It is the process record behind the salary numbers, written as a repeatable seven-step method rather than a results announcement.
Kramer reports reviewing 2,250+ job posts and shipping a two-part salary report with 30+ graphs in a few days, and lays out the method in seven steps: run Claude Code in the Mac desktop app, with Opus 5.5 for the main work and Sonnet and Haiku for smaller jobs; start from public data rather than proprietary sources; run research in parallel using subagents, with 16 running at one point; push everything to a database as it goes; build graph templates in Claude Design first so one polished sample per style carries through to 30+ charts; keep asking why behind the numbers — the example given is what drives higher marketing salaries at AI-native startups; and save the process as a reusable research skill so each run improves. Kramer states the post carries an ad disclosure for a Claude Code sign-up link while describing the workflow as the real process.
For brands planning a campaign
This is the evidence for judging production speed and reproducibility: the method is documented, partly packaged as a reusable skill, and the second report in a series went faster than the first. It also sets a realistic expectation that a data-heavy deliverable is assembled from public data plus a defined pipeline, not from expensive proprietary research.
This answers why a marketer who had been telling people to skip Meta changed position.
Read more
It is a documented reversal, with the numbers that caused it and the source they came from.
Kramer opens by saying she has told a lot of B2B marketers to skip Meta and may owe them an update.
The post works through Primer's State of B2B Paid Advertising report, described as data from 379 B2B companies' real ad accounts rather than a survey.
Figures cited include Meta's average share of paid budget rising from 20.8% to 24.9% in about six months; 63% of companies in the report advertising on Meta, up from 54%; Google falling from 43.6% to 40.9%; and LinkedIn costing about five times more than Meta to reach the same number of people within the same company's accounts.
Reasons given, attributed to a named source at Primer, include improved match rates when work emails are linked to personal emails (about 80% on Meta against about 50% on Google and 70% on Reddit), CPMs of roughly $7–15 on Meta against $31+ on LinkedIn, work-email requirements on instant forms, and algorithmic matching from supplied customer lists.
The post carries a Primer partner tag and links to the full report.
For brands planning a campaign
This is a model for how a creator can carry a vendor's report without flattening it into a pitch: the reversal, the counter-numbers, and the named source all stay visible. It also signals that Kramer will publicly change a recommendation when the data moves, which matters to a brand considering a paid-channel brief.
This answers what Kramer's own creator-side experience looks like in scale, and it shows the questions being asked of the in-house marketers who buy this work.
Read more
Kramer states having worked with 60+ companies as a creator and says a newsletter on that perspective is in progress.
The post asks in-house marketers three concrete questions: whether working with many creators or a small few is the better approach, how the process and all its pieces are managed, and — for those who have not done much of this — why not.
Kramer says commenters will be quoted in the newsletter if their comment is used, and that the piece should be out later that week.
The post is a working call for practitioner input, not a finished argument.
For brands planning a campaign
A brand evaluating Kramer for a creator campaign gets a stated operating history of 60+ company collaborations and an open invitation to shape the guidance that will describe how those collaborations should run. It is a reasonable opening contact point for a marketer who wants their own constraints reflected in the advice.
Emily Kramer publishes compensation research built from public job posts.
Read more
In one analysis of 1,100+ B2B marketing job posts, mid-level content and brand roles had a median salary of $165K, above the $160K median across all roles, while mid-level growth roles had a median of $156K.
At Director/Head of level, content and brand leaders had a median of $235K versus $225K across all areas.
Growth pay sat at the overall median at every level except senior, where it was $178K versus $169K for content and brand.
The work is presented with charts and a newsletter.
Uses public job posts, separates roles and levels, reports medians, and acknowledges that public salary listings have limits.
Findings are based on public job posts, not verified internal payroll. A company should not treat them as its own compensation audit.
For brands planning a campaign
A B2B SaaS company can use this as a starting point for pay-band conversations and content. A brand could invite Emily Kramer to turn its own role and salary data into a benchmark explainer, or to compare its bands with the public job-post market.
Emily Kramer analyzed 1,100+ marketing job posts and found OpenAI and Anthropic paying far above other startups.
Read more
A Head of role at those companies had a median of $381K versus $300K for VP/CMO at other startups.
The largest gap was at Director/Head of, $381K versus $225K, a $156K or 69% increase on a small sample of five job posts.
Senior roles at those companies had a median of $288K versus $178K elsewhere, $110K or 62% higher.
Most OpenAI/Anthropic posts were senior roles, and no early-career or VP/CMO roles appeared.
Startups that raised in the last year paid about 17% more.
The OpenAI/Anthropic Director/Head of comparison came from five job posts, so the gap is directional, not a stable market rate.
For brands planning a campaign
Useful for AI-native B2B companies that need to explain or justify senior marketing pay, and for employers competing with OpenAI and Anthropic for the same candidates. A campaign angle is to benchmark a client's roles against AI-native pay and show where it can compete on scope, equity, or mission instead of cash.
Emily Kramer has described making two research reports in a week after prior reports took a quarter.
Read more
For a two-part salary report, they reviewed 2,250+ job posts and shipped 30+ graphs in a few days.
The process includes running Claude Code in the Mac desktop app, using public data, running subagents in parallel, pushing data to a database, building graph templates in Claude Design first, using the model as a research partner to ask why behind the numbers, and saving the process as a reusable research skill.
The post is marked as an ad but states the process is real.
Public data, parallel subagents, database staging, reusable prompts and skills, and iterative questioning.
The workflow names Claude Code, Opus 5.5, Sonnet, Haiku, and Claude Design, and the post carries an ad disclosure. A brand should separate tool sponsorship from the underlying method.
For brands planning a campaign
A brand can evaluate Emily Kramer for a research build that needs a visible method, not just a final report. A practical campaign could ask for a public-facing methodology post or a workshop that shows a marketing team how to run a job-post or market study with AI assistance.
Emily Kramer built a salary report in Lovable and turned it into an app where people can check their B2B startup marketing salary against benchmarks.
Read more
They then turned it into an MCP connector in Lovable with a prompt so people can check salary data in Claude, ChatGPT, or another assistant. They co-hosted a Buildathon with Lovable to show the process, with prompts, build time, and credits for live attendees.
Builds the report as an app, packages the data as an MCP connector, and teaches the process in a live event.
The build and event were tied to Lovable, and the live session offered credits. Brand fit depends on whether the sponsor relationship is disclosed clearly.
For brands planning a campaign
A company can use this as a model for turning a static report into a lead or engagement tool. A campaign could commission an interactive benchmark, an assistant-ready connector, or a live build session that teaches an audience how to use the data.
Emily Kramer published an update on Meta for B2B after previously telling B2B marketers to skip it.
Read more
The post uses Primer's State of B2B Paid Advertising report, based on 379 B2B companies' real ad accounts.
It reports Meta's average share of paid budget grew from 20.8% to 24.9% in about six months, more B2B companies advertised on Meta (63%, up from 54%), Google dropped from 43.6% to 40.9%, and LinkedIn cost about 5x more than Meta to reach the same number of people within the same company's accounts.
It also covers higher match rates, lower CPMs, instant forms with work email requirements, and algorithm performance after feeding ICP and customer lists.
The post carries a #primerpartner tag.
Uses real ad-account data rather than a survey and compares channels within the same company's accounts.
The post is partner content for Primer, and the figures come from Primer's report. A brand should check the underlying report and its sample before applying the numbers.
For brands planning a campaign
A brand can use Emily Kramer for channel-mix analysis that challenges default B2B assumptions. A campaign could ask for a paid media readout that compares Meta, Google, LinkedIn, and Reddit against the client's own account data, or a sponsored explainer that turns a partner report into a practical recommendation.
Emily Kramer has worked with 60+ companies as a creator and is writing a newsletter about that experience.
Read more
In a post aimed at in-house marketers, they ask whether working with more creators or a small few is better, how teams manage the process and all the pieces, and why some teams have not done much here. They offer to quote responses in the newsletter.
Solicits in-house marketer input for a newsletter, with attributed quotes.
The post describes experience working with 60+ companies but does not list them or name a client. A brand should ask for specific examples before treating that as proof of fit for its category.
For brands planning a campaign
A brand evaluating Emily Kramer for creator-led marketing can use this as a signal of firsthand experience and a collaborative research style. A campaign could commission a creator-marketing guide that combines in-house interviews with Emily Kramer's creator-side view, or a workshop on managing a creator program.
Emily Kramer also posts lighter industry observations and community prompts.
Read more
One post asks what a mysterious billboard in San Francisco means and references a former Dear Marketers Podcast co-host.
Another shares AI and tech Halloween costume ideas, including group and solo options, and hints that one image features four GTM tools.
A third shows behind-the-scenes preparation for a Lovable Buildathon with ring lights and wired headphones.
These posts are not research or compensation analysis. They are useful for reach and cultural relevance, not for a data-backed buying decision.
For brands planning a campaign
These formats can carry a brand into a less formal conversation. A campaign could use a billboard-teardown post, an AI-culture prompt, or a live-event backstage format to get engagement without making a hard product pitch.
Emily Kramer's brand relationships in the retained material are mostly tool and platform companies she has promoted or built with as a marketer and creator.
Read more
One post carries an explicit #ad disclosure for Claude Code (Anthropic's coding tool), one carries a #primerpartner hashtag promoting Primer's B2B paid-advertising report, and two cover a Lovable Buildathon she co-hosted and advertised.
The newsletter and data tools she calls hers sit under MKT1.
She also says she has worked with 60+ companies as a creator, but those posts do not name any of those companies, so no additional brands can be listed.
All posts and the underlying account were recorded in October 2026; individual post dates were not supplied.
MKT1
Owned business
Kramer refers to the MKT1 newsletter as her own — "my latest newsletter" — and to a paid-subscriber data tool as "my MKT1 MCP," where readers can slice the job-post salary data themselves.
Read more
She built a salary analysis from 1,100+ marketing job posts and published it as the newsletter's 20+ graphs.
A sponsored post walking through how Kramer produced two salary research reports in a week using Claude Code, naming Opus 5.5, Sonnet, Haiku, and Claude Design.
Read more
The post carries an ad disclosure and a sign-up link for Claude Code's Max plan.
Kramer promotes Primer's State of B2B Paid Advertising report and quotes Primer's Keith Putnam-Delaney on Meta match rates and CPMs, linking to the full report.
Read more
The post is tagged as a Primer partner post; no dollar terms or contract length are stated.
Kramer co-hosted a Lovable Buildathon, built an app and an MCP live, and promoted registration with Lovable credits for people who attended live.
Read more
A related post advertises the salary-benchmark app and MCP she built in Lovable. She describes the event as a co-hosting arrangement; no payment terms are stated.
Kramer refers to Jenny Thai as her former co-host of the Dear Marketers Podcast while crediting Thai for a billboard photo, and jokes about reviving the show.
Read more
The post indicates a past co-hosting role; it says nothing about sponsorship or current activity.
Emily Kramer's published work clusters around B2B marketing pay and around showing how the numbers were produced.
Read more
To get the benchmarks, start with the analysis of marketing job posts and the follow-up on whether growth roles really pay more than content and brand roles, then the comparison of OpenAI and Anthropic against other startups.
To get the method, the post walking through the AI-assisted research workflow is the most detailed account, and a later post shows the same salary data rebuilt as an interactive app and an assistant connector.
The B2B paid advertising post is where Kramer changes a standing recommendation about Meta after new ad-account data, and the shorter posts mix community prompts with industry observations.
For adjacent reading in the same spirit, namely public data turned into pay and career guidance, the creators listed below cover social media management income, cybersecurity job data, and influencer marketing salaries.
Nolan publishes pay figures for a marketing job family so readers can place their own income against a scale.
Read more
One post lays out social media manager income stage by stage, from starting out at €500–€2,000 a month through established at €3,000–6,000, and a second gives 2026 figures from entry level to head of social in the tech sector. Like Kramer, the argument is that the jump between stages comes from positioning and pricing rather than from doing the craft better.
Nolan's numbers are stage-based ranges tied to freelance and agency income, and the retained posts describe no sample or collection method. Kramer derives medians from collected job posts, separates role families and levels, and states the limits of the data inside the post.
Madakor takes routinely available job-market data and turns it into career decisions, which is the same move Kramer makes with marketing job posts.
Read more
Both also name the gap between what the market promises and what the listings show: Madakor writes that only 7% of entry-level cyber jobs are actually entry-level and that the data does not care what bootcamp ads promised, while Kramer tests a long-held belief about growth pay against the dataset and reports that it does not hold consistently.
Madakor's subject is cybersecurity hiring, certifications, and entry routes, not marketing, and the retained posts are short list-style video scripts rather than charts or a written report. The advice is aimed at people changing careers into security, including leaning into security work inside a current job before moving.
Blankenship analyses pay data inside a marketing discipline to help people benchmark and negotiate.
Read more
Drawing on survey data, the report identifies geography, years of experience, scope, and niche as the recurring drivers of influencer marketing pay, and lands on the same actionable conclusion Kramer's work points toward: move from execution to ownership, because owning budgets, teams, and programs is what pays.
The data is survey-based rather than collected job posts, and the discipline is influencer and creator marketing within ecommerce, not B2B SaaS marketing. The second retained post is seasonal BFCM commentary rather than analysis or method.
The only measured audience figure in this review is a LinkedIn following of 53,775 on Emily Kramer's profile, recorded October 8, 2026.
Read more
No platform demographics export or audience survey was supplied, so the composition of that following — geography, age, gender, employer, job function, and seniority — is unmeasured.
Treat the follower count as scale, not as evidence that most readers are B2B marketing leaders who set compensation, What the reviewed posts show is content appeal rather than composition.
Two dated posts present salary research built from marketing job postings: one covers 1,100+ posts (published October 1, 2026) and argues that pay at OpenAI and Anthropic runs well above other startups; the other compares growth, content and brand roles from the same data set (published October 2, 2026).
A later post describes turning the salary report into a Lovable-built benchmark app and an MCP connector anyone can query in Claude or ChatGPT.
Another post asks in-house marketers how they handle working with creators, framed around Emily Kramer's own experience working with 60+ companies as a creator.
These are the posts most likely to matter for a compensation-benchmarking brief: they mix original job-posting data with tools a marketing team could actually use.
Lighter posts — a billboard spotted near San Francisco, group Halloween costume ideas, a livestream buildathon — suggest the following is broader than a compensation-research audience, though nothing in the reviewed evidence shows who engages with which post.
For this brief, two questions decide relevance.
First, ask for recent results on comparable posts — a salary-research data analysis and a tool or app post — alongside a lighter post, so the reach and engagement of the heavier material can be compared rather than assumed from total followers.
Second, ask whether there is an audience survey or another documented source covering employer, job function and seniority; a platform demographics export would cover geography, age and gender but cannot answer those professional attributes.
Emily Kramer's brand work in the reviewed material takes three concrete shapes: a sponsored tool walkthrough marked #ad, a partner-data explainer carrying a partner hashtag, and a co-hosted live buildathon with a software brand.
Read more
Each is built on research or a report she has already produced, not on a standalone ad unit, so a partner usually supplies data, product access, or event infrastructure that she then works into a longer narrative.
She also states she has worked with 60+ companies as a creator, without describing the terms of those engagements.
Her salary research is distributed through the MKT1 newsletter, which includes a paid subscriber tier with a tool that lets subscribers work with the underlying salary data, so a campaign aimed at that audience sits alongside an existing paid product rather than a neutral feed.
No published rate card, sponsorship policy, media kit, or inquiry route appears in the reviewed posts, so no official partnership contact is verified and the inquiry link is left empty.
A buyer has no documented way to open a conversation from this evidence, and no post states pricing, exclusivity, turnaround, usage rights, or approval process.
Sponsored tool walkthrough with an #ad disclosure
Buyer requirement
Expect the post to read as a numbered, step-by-step account of the creator's own process using your product, with the sponsorship disclosed as #ad and a signup link near the end rather than at the top.
Plan to supply product access and a trackable link, and expect the value to come from the detailed workflow and the creator's own commentary, including hedges such as saying a claim reflects her real process.
A brief that only asks for feature mentions will not match this format.
Partner-data explainer under a partner hashtag
Buyer requirement
Expect your report data to be quoted with named sources, specific figures, and a link to the full report, framed against the creator's earlier public position on the topic.
The reviewed example carried a partner hashtag rather than an #ad disclosure, and payment should not be inferred from the hashtag alone.
A buyer needs to supply the underlying data and a spokesperson the creator can attribute, and should accept that she will restate her own prior recommendation and reversal as part of the piece.
Co-hosted live buildathon
Buyer requirement
Expect a joint live session rather than a single sponsored post: the creator builds in your product on camera, named brand staff handle questions, and registration runs through the brand's event page with an incentive such as credits for people who attend live. Plan for a fixed scheduled slot, ready-made prompts for attendees, a recorded version for later reuse, and brand staff booked for the session.
Brief: a B2B SaaS company wants credible public benchmarks for marketing compensation by role and level, and wants them in a form its own hiring managers and its marketing audience can both use.
Read more
This is the clearest fit in the reviewed evidence.
Kramer has already done the work twice from the same base: an analysis of 1,100+ B2B marketing job posts that reports medians by level rather than one average, and a second cut of that dataset comparing growth with content and brand roles.
The pay angles are concrete enough to build a campaign around.
The AI-lab comparison puts a Head of role at OpenAI or Anthropic at a median of $381K against $300K for VP or CMO roles at other startups, with the widest gap at Director or Head of level, $381K against $225K.
Kramer also states the weak points in the same breath: that Director comparison rests on five job posts, no early-career or VP/CMO roles appeared at either company, and startups that raised within the last year paid about 17% more, which is the most portable finding for a client that cannot match AI-lab cash.
A buyer who wants numbers quoted honestly should read that as a feature, not a problem.
The delivery pattern also matches a compensation brief.
The salary work was shipped as a newsletter issue with 20+ graphs, a paid-subscriber tool for slicing the data, then an app that checks an individual salary against the benchmarks, then an MCP connector so the same data can be queried inside Claude or ChatGPT.
Kramer documents the production method in seven steps, including using public data rather than proprietary sources, running subagents in parallel, staging results in a database, and saving the process as a reusable research skill so later reports move faster.
That means a client is buying a repeatable pipeline, not one lucky report.
The commercial formats already in evidence suit it too: a sponsored tool walkthrough disclosed as #ad, a partner-data explainer carrying a partner hashtag and a link to the full report, and a co-hosted live buildathon with brand staff answering questions and registration running through the brand's page.
A compensation campaign can take any of those shapes without asking Kramer to work outside a demonstrated format.
Two expectations should be set before the brief is signed.
First, the only audience figure in evidence is a LinkedIn following of 53,775 recorded October 8, 2026; there is no demographics export, no audience survey, and no per-post engagement data, so scale is not proof that most readers set pay.
Second, there is no published rate card, sponsorship policy, media kit, or inquiry route, and the reviewed posts state nothing about pricing, exclusivity, turnaround, usage rights, or approval.
Terms start from a blank page.
Disclosure will need agreeing explicitly: the Claude Code post carried an #ad line, while the Primer report post carried a partner hashtag, and payment should not be read into that hashtag.
What would make this a poor fit inside an otherwise good match: a brief that treats the job-post medians as a verified internal payroll audit.
The data comes from public listings, and anyone asking Kramer to certify a client's own bands from it is asking for a claim the method does not support.
When to choose a specialist
Brief: a brand wants ten short vertical videos in three weeks, each ending on a hard demo-signup call to action, with a guaranteed number of qualified signups attached to the spend.
Read more
Almost none of that is supported by the reviewed material.
Every item in evidence is a written LinkedIn post.
There is no video transcript, no short-form video, and no performance data for any format, so there is nothing to compare a ten-video commitment against.
The reach evidence is a single follower count with no engagement per post, and four of the reviewed posts supplied no audience metrics at all.
Promising conversion volume on top of that would be a claim neither the creator evidence nor the audience evidence can carry.
Frequency is the second problem.
Kramer's heavier posts are the output of a research process, not a publishing cadence: 2,250+ job posts reviewed and 30+ graphs shipped across two reports in a few days, with the second report faster than the first because the method had been saved as a reusable skill.
Speed came from a defined pipeline over a dataset, not from turning out volume on demand.
The lighter posts that do exist, a billboard spotted on the drive home from San Francisco and a set of AI-themed team costume ideas, show range and a conversational tone, but they carry no performance data either, so a brand cannot plan a direct-response slate from them.
The format expectation is a mismatch as well.
The commercial work in evidence is built on something Kramer has already made or is making alongside the brand: a sponsored walkthrough of their own process with the product, a partner report explained with named figures and a link to the source, a co-hosted live session with ready-made prompts, brand staff on the call, and attendee credits.
A brief that asks for feature mentions in a short format and nothing else asks for a shape that does not appear in this record.
And with no rate card, media kit, or stated turnaround, a three-week guaranteed-volume commitment would be negotiated from nothing, with all the risk placed on promises the evidence does not support.
One adjacent mismatch is worth flagging for a buyer who is deciding between briefs.
A company that wants its own pay bands certified as market-accurate is also a poor fit, because the underlying method reads public job posts and says so, including where a headline gap rests on a handful of postings.
A worked campaign example
A worked campaign angle for the compensation brief, from the client's position.
The client is a Series C B2B SaaS company preparing 2027 marketing hiring.
It cannot match AI-lab pay and keeps losing senior content and brand candidates.
It also wants something its own audience will read.
The brief: produce a public marketing-salary benchmark for 2027, and use the same analysis to show the client's own bands against the market.
Scope the data first.
The client supplies its live marketing job postings and, if it will, anonymised internal bands by level.
Kramer runs the method already on record: public job-post data, medians split by role and level rather than one average, sample sizes stated next to every comparison, and a plain statement that public listings are not payroll.
The client should agree in advance which comparisons are too thin to quote, using Kramer's own precedent that a Director-level gap can rest on five postings.
The angle: where AI-lab pay is real and where it is a five-post rumour.
That framing earns attention from the strongest number in the dataset, the OpenAI and Anthropic premium, then spends it on the more useful finding for most employers, that funding recency moved pay about 17% more than industry label.
The client's differentiator becomes the honest cut, not the biggest headline number.
Deliverables, in the shape Kramer has already shipped: a newsletter issue with the charts; one interactive benchmark tool that lets a reader check a role and level against the medians; the same data packaged as a connector so a hiring manager can query it inside an AI assistant; and one live session where the client's hiring leads walk through the tool with Kramer. Prompts and a recording come out of the session so the client can reuse it, mirroring the buildathon format with attendee credits and brand staff handling questions.
Decisions the client has to make before signing.
Disclosure form, either an #ad line as in the sponsored walkthrough or a partner tag as in the report explainer, agreed in writing rather than assumed from either.
Placement, because the salary data already sits alongside a paid subscriber tier that includes a data-slicing tool, so the client should decide whether its benchmark version is public or gated with the existing research.
Terms, because nothing in evidence states price, exclusivity, turnaround, usage rights, or approval, and there is no published inquiry route, so a direct approach is the only path and the negotiation starts cold.
What to test before scaling.
Pilot one deliverable and measure it, since no per-post engagement data exists for any format and the 53,775 follower figure says nothing about who sets compensation.
Check that the benchmark tool works for someone who never reads the article, and check that every quoted comparison carries its sample size.
If the client wants the internal band read, keep it as a separate private analysis so a thin public comparison is never presented as the client's verified position.
This angle uses only evidence already on record and does not require Kramer to claim a payroll audit or a conversion guarantee.
Choose Nolan when the brief is about what independent social media managers earn and how that changes across career stages, rather than what employers post for B2B SaaS marketing roles.
Read more
Two posts lay out monthly income bands from €500–€2,000 at the start through €2,500–€4,000 getting traction, €3,000–€6,000 established, and €8,000–€10,000+ for a senior or specialist, with a 2026 version adding an agency-owner range of €10,000–€30,000 a month and a head-of-social figure of €80,000–€120,000 a year.
A campaign aimed at freelancers, solo practitioners, or people deciding whether to go independent sits closer to this material than to job-post medians.
The argument is explicit and usable: the jump between stages is almost never about posting better content, it is about positioning, pricing, and the confidence to charge what the work is worth, and the 2026 post adds that a skill, proof, and a findable personal brand matter more than a degree or years of experience.
The figures are practitioner income ranges in euros, not medians pulled from a documented dataset, so there is no sample size, no collection window, and no method statement to defend if a client's finance team questions them.
Nothing in the two posts covers B2B SaaS marketing functions such as growth, content, or brand at listed-day rates, so a client cannot line the numbers up against role-level benchmarks.
There is no interactive benchmark tool, no connector inside an AI assistant, and no AI-lab or startup-funding comparison.
A client that needs defensible employer benchmarks keeps Kramer; a client that needs to speak to freelancers about their own take-home can use Nolan, and should present the figures as one practitioner's ranges rather than market data.
Choose Madakor when the audience is career changers making a first move into a technical field and the client wants short-form video rather than written analysis.
Read more
The two videos run a credential-versus-pay argument in a compact format: CCNA teaches the most useful fundamentals and shows up in the most listings; Security+ opens doors but grinding it before touching real tools is backwards; learn Active Directory because helpdesk work uses it every day; get practice in Sentinel, Defender, and Azure NSGs, which are free.
The second video sharpens the pay angle: the highest-paying entry roles want Linux, which costs nothing to learn; A+ is the most common certification and the lowest-paying; Python is nearly invisible at entry level and then becomes the line between $85K and $200K; and only 7% of "entry-level" cyber jobs are actually entry-level.
The advice is deliberately non-quitting, lean into the security parts of your current job and jump when ready.
A campaign about retraining, certifications, or the gap between what course ads promise and what job data shows has a closer fit here than a marketing-compensation brief.
The subject is cybersecurity, not marketing, so none of the pay figures transfer to marketing roles and a client comparing marketing salaries cannot use them as an alternative benchmark.
The evidence is two short videos, so there is no long-form analysis, no published dataset behind the placed numbers, no method statement, and no tool or report a reader can open and interrogate.
A brand wanting reach into a technical job-seeker audience through video trades away the written-research depth, the reusable research pipeline, and the interactive assets that Kramer's record shows.
A brand wanting marketing pay benchmarks trades away relevance before it trades anything else.
Choose Blankenship when the brief needs to reach influencer and creator-marketing practitioners specifically and when the pay story should be framed around what moves pay rather than a table of role-by-level medians.
Read more
The survey-based post names four drivers: geography, where North America averages about $86,947, nearly double the global average; years of experience, with a visible jump around the three-to-four year mark that tends to coincide with a know-how jump; ownership scope, meaning leading a team, cross-functional work, owning budgets, and running bigger programs; and niche, which can shift the ceiling even inside ecommerce and DTC.
The practical instruction is a sequence, move from execution to ownership to scope, which gives a campaign a behavioral call to action rather than a chart.
The audience is the influencer-marketing function that B2B SaaS brands hire into, so a brief aimed at that job family, or one organized around geography and ownership rather than function, lands closer here than with Kramer.
The data is described as survey data without a published sample size, collection window, or methodology in the post, so a client gets a direction of travel rather than benchmarks it can cite line by line.
The pay frame is influencer marketing and the creator economy, not B2B SaaS marketing by function, so growth, content, and brand comparisons do not appear and the AI-lab premium has no equivalent.
There is no interactive benchmark tool and no connector inside an AI assistant.
Neither reviewed post shows a disclosed commercial format, so a sponsored-data brief would be built without a precedent to point to.
Kramer offers the role-by-level cuts and a repeatable research pipeline; Blankenship offers the audience fit and the ownership argument, and a client should pick on which of those two the campaign depends.
This page is built from nine LinkedIn posts written by Emily Kramer's account, each read as complete post text rather than as a headline or preview.
The two posts carrying publication timestamps are dated 1 and 2 October 2026; the remaining seven had no date metadata attached, so timing for those is drawn from internal references, including marketing salaries listed in September job posts and a Lovable Buildathon scheduled for Wednesday, 28 October 2026.
Her account's follower figure, 53,775, was recorded on 8 October 2026.
The salary figures, research method, and paid-advertising positions on this page come from the posts themselves.
The MKT1 newsletter issues, the salary report, the benchmark app, the assistant connector, and the Primer paid-advertising report are named in those posts but were not supplied, so descriptions of them rest on how Kramer describes them.
Comparator creators were used only to place her work alongside similar pay-and-data research, not to support claims about her.
Several gaps are worth stating because they limit what can be concluded.
No engagement or conversion data exists for any post or format, no audience demographics or survey was supplied, and the follower count says nothing about who the readers are or whether they set pay.
No rate card, media kit, sponsorship policy, or inquiry route was available, and none of the reviewed posts states price, exclusivity, turnaround, usage rights, or approval terms.
The posts also do not name the 60+ companies Kramer says she has worked with as a creator, so no further brand relationships can be listed.
Where a comparison in her own analysis rests on a small sample, such as the Director and Head of level gap between AI labs and other startups, that is noted alongside the figure rather than left out.
Coverage here is a record of what was reviewed; it is not a claim that her full body of work, earlier career, or any book or show was examined.
Recent engagement
Across their last 8 LinkedIn posts, the median post got 50 reactions and 48 comments. Checked 2026-11-02.