Market Scan · personal project

What 1,261 demand gen job posts call the job.

About 95 new demand gen job posts appear each week. My tool gathers them from 33 sources and scores each against a checklist of job titles.

The first version quietly dropped most of the good posts. The lesson: a short list only means something if you check what disappeared.

In one picture: a pile of job posts goes in, a checklist sorts it, a short stack comes out.
The result Only about 1 in 13 posts called the job “demand gen.” 99 of 1,261 titles used the words, July to October 2026. Most said manager, growth, or paid media.
On this page · 5 sections · 4 min read
Why I built it

Scoring is cheap. Reading is the expensive part.

Reading every post is slow, and skimming misses the pattern. A written checklist can do the first pass on every post, so the reading goes to what the whole pile says.

  1. 01 · Sources33job boards and feeds
  2. 02 · Collect1,261job posts gathered during the tracked window
  3. 03 · Score1,193posts scored against the written checklist
  4. 04 · Top score123posts that reached the top score
Most posts match only part of the checklist. Of 1,193 scored, 1,070 landed below the top score. Each post’s source, date, and score stay logged, so the short list can’t hide what dropped out.
What broke first

The first run found six ways to lose a good posting.

The first run searched 117 job boards and kept only four jobs. There were plenty of postings; six separate filters were quietly throwing good ones away. The tool now reads 33 sources.

I traced and fixed each one before trusting the list.

The six filters that dropped good posts
  1. How each board’s fields were read
  2. How locations were sorted
  3. The age cutoff
  4. The job-title match
  5. Which boards were covered
  6. Duplicate removal
Every one of these looked reasonable on its own. They only showed up as a problem once I counted what never reached the list.
What the checklist is

Fifteen job titles. One point each.

The checklist is a written list of 15 job titles, from demand generation manager to paid media, PPC, SEM, and growth marketing manager. A post earns one point for each title phrase that shows up in its title or description, and posts from the one board I trust most get a small bonus. A post that matches several phrases outranks one that matches a single phrase in passing.

An invented example, not a real posting
B2B Demand Generation Manager

Own the budget for paid search and paid social, build weekly reports, and work alongside our PPC Manager on lead quality. Remote within the U.S.

  • In the titledemand generation manager
  • In the descriptionPPC manager
  • Not countedbudget, paid search, and remote aren’t titles on the list
  • Score2 points, out of a possible 10
The score counts what a post actually says. This example would score 2, the middle score in the tracked window. It counts the words a post uses; it doesn’t guess at what they mean.
A top score is rare. Real scores ran from 0 to 4 (the scale allows up to 10). The middle score was 2.0, and 123 of 1,193 scored posts reached 4. Blue marks the top score.
Why that is enough It’s deliberately simple: a fast first pass through 1,261 posts, so the reading starts with the closest matches.
What the titles say

The same job goes by many names.

Counted across all 1,261 job titles. The tool keeps U.S. remote posts only, so this describes that slice, not the whole market. The surprise: only 99 titles used the words “demand gen.”

60% of titles say “manager”
32% say senior, lead, or principal
17% name paid media, search, PPC, or SEM
1% are director level or above
The lead-scoring lesson A checklist that ranks posts is a lead-scoring model with a different input. The six filters that dropped good posts are the same leaks that drop good leads: a field mapped wrong, a location rule set too tight, duplicate removal that eats real records.
What it took

A useful list needed more than a score.

How long
Built between early July and mid-August 2026, going by the project’s file dates.
Do differently
Log every post a filter throws away from day one, so a short list never looks good before the missing ones are explained.
Evidence record

Every number on this page, with its source.

Open the full evidence record

This public record contains totals only. Company names, links, and salaries are deliberately left out, and job titles appear only as word counts. The checked summary stays public; the larger working record remains private.

One row is one job posting, counted once. Postings gathered, postings scored, and postings at the top score are narrowing stages of the same pile, so the smaller numbers are always inside the larger ones.

The claim

Posts gathered, posts scored, and posts at the top score.

1,261 job posts gathered
1,193 posts scored
123 posts at the top score

The checks

How the scores were spread across the scored posts.

10.3 % of scored posts reached the top score
2.0 middle score

The scale

Where posts came from and when tracking ran.

33 distinct sources
31 automated collectors
July 14, 2026 tracking started
October 14, 2026 latest post counted
Compare notes

Do these patterns match what you see?

If you work in demand gen, I’d like to hear how your team describes the job and which parts of these posts ring true.