# You're Scoring Leads Like It's 2015 — Common Room > [!question] Lingering Questions > - How do you denoise usage/engagement signals (trials, evaluations, churn risk) so "usage data doesn't lie" actually holds up in practice? > [!tip] Article Ideas > - "Score behavior, not biography" — translate the fit/intent/timing distinction directly into DevRel/community engagement scoring, replacing form-fill/MQL counting. #articleseed > [!link] Cross Reference > - [[3. Resources/Permanent Notes/Readiness Is Predicted by Behavior Over Time Not Static Attributes]] — this source's central claim, already captured as a Permanent note with this article as its origin. #xref > - [[3. Resources/MOCs/DevRel MOC]] — the depth/breadth/expansion signal taxonomy is directly portable to community engagement scoring. #xref > [!quote] Vocabulary > - **Behavioral signature** — the pattern of pre-purchase account behavior (depth of usage, breadth of adoption, unprompted expansion) derived from a company's own closed-won/closed-lost history, as opposed to a theoretical, demographically-derived score. ## Bib capsule Common Room, "You're Scoring Leads Like It's 2015," commonroom.io blog, 2026. https://www.commonroom.io/blog/behavioral-lead-scoring/ ## Author's thesis (Adler Q1+Q2) Firmographic lead scoring answers "who could buy," not "who is ready to buy" — and that gap is where sales pipeline gets wasted chasing accounts that look good on paper but show no buying-intent behavior. For product-led-growth companies that can see inside the account, the fix is to rebuild scoring around what accounts actually *do* — depth of usage, breadth of adoption, unprompted expansion — derived from the company's own recent closed-won and closed-lost deals rather than a theoretical demographic model. The piece is vendor content (Common Room sells this capability), so the specific case numbers should be read as an illustration, not a benchmark. ## Key arguments - Firmographic fit and buying readiness are different questions that produce different account lists. - The actionable reframe: "is this account doing what our best customers did right before they bought?" — a dynamic, behavior-alike question instead of a static, look-alike one. - Behavioral signals cluster into a usable taxonomy: depth of usage (one power user vs. many), breadth of adoption (independent teams), and unprompted expansion (organic growth in seats/usage). ## Claim atoms ### Firmographics show fit, not readiness to buy > [!quote] ID:rw_pending > "Firmographic scoring tells you who could buy. It tells you almost nothing about who is ready to buy. That gap is where pipeline gets wasted." **Where/why**: Core distinction of the piece — fit ≠ intent ≠ timing. Explains why static firmographic models keep resurfacing dormant accounts and wasting reps' time. Already promoted to a Permanent note. **Tags**: #xref #aphorisms ### Score accounts by what your best customers did pre-purchase > [!quote] ID:rw_pending > "It's 'is this account doing the things that our best customers did right before they bought?' Those are very different questions. And they produce very different lists." **Where/why**: The reframe (illustrated with a 70/30 fit-vs-behavior weighting shift at Otter.ai) — reorients scoring around dynamic, daily-changing signals instead of a static profile. **Tags**: #xref #share ### Behavioral signals cluster as depth, breadth, expansion > [!quote] ID:rw_pending > "A single power user is interesting. Three teams using it independently is a buying signal. Ten people added in the last two weeks is a trigger." **Where/why**: The signal taxonomy — directly portable to DevRel/community engagement scoring, which is why this source cross-references the DevRel MOC. **Tags**: #xref #articleseed ### Derive the ready-account signature from your own deals > [!quote] ID:rw_pending > "That analysis gives you the behavioral signature of a ready account — not a theoretical one, but one derived from your actual revenue data." **Where/why**: Build recipe — map closed-won deals (12–18 months) at 30/60/90 days pre-close, contrast against closed-lost. Source's own honest caveat: "It's not perfect," only "significantly more honest" than demographic proxies. **Tags**: #xref #addendum ## Permanent candidates - **Readiness is predicted by behavior over time, not static attributes** — already exists as [[3. Resources/Permanent Notes/Readiness Is Predicted by Behavior Over Time Not Static Attributes]], created from this article. Not duplicated here. - The signal taxonomy (depth/breadth/expansion) and the derive-your-own-signature method are both mechanisms *supporting* that same claim rather than distinct reusable claims of their own — not split into separate Permanent notes. ## Processing checklist - [x] All claim atoms have header + verbatim quote + where/why - [x] Triage callouts populated for every tagged highlight - [x] Checked existing Permanents first — found direct coverage, linked rather than duplicated - [x] Connected into MOC — [[3. Resources/MOCs/DevRel MOC]] ## Links - [[3. Resources/MOCs/DevRel MOC]] — behavioral signal scoring cross-applies directly to community engagement measurement - [[3. Resources/Permanent Notes/Readiness Is Predicted by Behavior Over Time Not Static Attributes]] — because evidence-for; this article is that claim's origin source - [[3. Resources/Readwise/Articles/You're Scoring Leads Like It's 2015]] — source document