# Emily Kramer

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.

- Profile: https://b2binfluencer.marketing/creators/emily-kramer/
- LinkedIn: https://www.linkedin.com/in/emilykramer
- LinkedIn followers: 53,775
- Median of last 8 posts: 50 reactions, 48 comments (checked 2026-11-02)
- Topics: artificial-intelligence, b2b-marketing, creator-marketing
- Based on 8 dated posts, latest 2026-10-10

## Summary

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.
- Check first: Ask for recent results on comparable posts and any audience survey or demographics export.

### 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.

## Main ideas

- **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. 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.
- **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. 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.
- **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. 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.
- **Change a recommendation when the numbers change**: Kramer's post on Meta for B2B is framed as a public reversal, not a new topic. 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.
- **Publish the method, not only the result**: Kramer treats the research process as worth explaining on its own. 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.
- **Ship the research as a tool people can query**: Kramer's pattern after a report is to turn the data into something an audience can interact with. 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.
- **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. 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.

## Work to read

- [OpenAI and Anthropic pay on a different scale: 1,100+ marketing job posts](https://www.linkedin.com/posts/emilykramer_i-analyzed-1100-marketing-job-posts-and-activity-7511541929872945152-KCW_)
- [Do growth roles pay more than content and brand roles?](https://www.linkedin.com/posts/emilykramer_do-growth-marketing-roles-pay-more-than-content-activity-7511868857939058689-jhwP)
- [Turning a salary report into an app and an AI connector](https://www.linkedin.com/posts/emilykramer_i-built-this-salary-report-in-lovable-but-activity-7520887553219235840-cUR3)
- [How the salary research was actually produced in a week](https://www.linkedin.com/posts/emilykramer_ad-activity-7519114458338574336-1P27)
- [Reversing a standing recommendation on Meta for B2B](https://www.linkedin.com/posts/emilykramer_primerpartner-activity-7521621853308874752-hyn-)
- [Asking in-house marketers how creator partnerships actually run](https://www.linkedin.com/posts/emilykramer_whats-your-best-advice-for-other-marketers-activity-7523085443077287936-UuII)

## Brand relationships

- **MKT1** (owned_business)
- **Claude Code** (sponsor)
- **Primer** (disclosed_partnership)
- **Lovable** (disclosed_partnership)
- **Dear Marketers Podcast** (mention)

## Partnerships

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. 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.

## Alternatives

- Rebecca Nolan: 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. 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.
- Josh Madakor: 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. 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.
- Whitney Blankenship: 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. 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.

---
AI-assisted summary of public posts. Audience demographics are not available. Engagement appears only where we checked recent posts. Method: https://b2binfluencer.marketing/methodology/