AI Skills Marketplace / ABM & Enrichment

Account Reach & Frequency Saturation Modeler

The Account Reach & Frequency Saturation Modeler is a free Claude Skill that models effective reach across a target account's buying committee using the standard diminishing-returns exposure curve from media-planning theory, calibrated from your own historical engagement data, flagging under-reached committees and oversaturated ones.

↗ View Source ⬇ Download Full Repo (.zip)
MIT
License
4 functions
Decision branches
oversaturation + underreach classification thresholds
Escalation rules
Yes
Multi-tool support
Aug 17, 2026
Last updated
21/21 passing
Tests
README.md View raw ↗

What this is (and isn’t)

A reach-curve modeling engine, not a live ad-platform or CRM integration. You supply impression counts and, optionally, historical engagement data to calibrate the model; this computes real, exact reach probabilities and saturation classifications.

Files

FilePurpose
modeler.jscalibrateAttentionProbability(), cumulativeReachProbability(), marginalReachGain(), modelAccountReach() — zero dependencies
test.js21-test suite, including hand-verified reach-curve math and a round-trip calibration check
SKILL.mdFull skill definition, loadable in Claude Code/Desktop

The two real guardrails

The per-impression attention probability is calibrated from your own observed engagement data rather than an assumed constant, and round-trips exactly through the reach formula. And reach across a multi-person buying committee is combined with the mathematically correct “at least one person reached” formula, not a naive sum that can exceed 100 percent, under the standard independence assumption stated explicitly.

FAQ

Is this skill free to use?

Yes. Free to download and run yourself. MV3 charges $175/hr only for implementation help wiring this into your real ad platform's actual delivery and engagement data.

Does this connect directly to my ad platforms?

No. It is a pure function over the impression and engagement data you supply. You still need to pull that data out of your ad platform and feed it in yourself.

Where does the attention probability come from?

You calibrate it from your own historical data: an observed engagement rate and the average number of impressions it took to get there. The function solves for the probability that reproduces your real observed rate.

Why not just sum reach probabilities across a buying committee?

Summing individual probabilities can exceed 100 percent and is not the correct way to combine them. This uses the mathematically correct formula for the probability that at least one committee member was reached.

Does my account or impression data get sent to MV3?

No. All calculations run entirely in your own environment.

JR
Jordan Reeves
ABM & Outbound Pipeline, MV3 Marketing