Buying signals: local, digital, and organisational
A three axis taxonomy for buying signals: local, digital, organisational. Score each 0 to 3, act on totals of 5 or more, and stop guessing which lead is warm.
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TL;DR
Buying signals split cleanly across three axes: local (physical world evidence), digital (web and ad footprint) and organisational (people and structure changes). Score each axis 0 to 3 and act on totals of 5 or more. This piece introduces that rubric as The three-axis signal score.
Why does the category need a taxonomy?
Most "buying signal" content flattens the concept into a single list. That is why teams end up chasing a mix of weak digital breadcrumbs and strong organisational events with the same urgency. A taxonomy fixes that.
Framework
The three-axis signal score. Rate each prospect 0 to 3 on Local signals, Digital signals and Organisational signals. Sum to a 0 to 9 score. Act on 5 and above, watch 3 to 4, ignore 0 to 2.
Which frameworks does ExtrovertAI use?
This post introduces one framework. Two more show up across the ExtrovertAI blog, and it helps to see all three in one place.
- ExtrovertAI FTR Score
- A per-signal quality score on three axes (Fit, Timing, Reachability), 1 to 3 each, 9 total. Anything under 6 goes to a nurture list. Introduced in the buying-signal playbook for local B2B.
- Three-axis signal score
- A per-account intent score across Local, Digital and Organisational axes, 0 to 3 each, 9 total. Act on 5 or more. Introduced in this post.
- Signal-first list build
- A five-step framework for turning a raw local search into a working prospect list: pick market, pull public data, apply signals, qualify by fit, shape outreach. Introduced in How to build a prospecting list from public business listings.
- Stop-on-reply cadence
- A four-message follow-up policy for cold email where the sequence terminates the moment a human replies. Introduced in The stop-on-reply cadence.
Axis 1: Local signals
Physical world evidence about a business. Anything you could in principle verify by walking past the shop, looking up its public listing or reading a local newspaper.
- A new location has opened or is under fit-out.
- The business has changed name or brand on the storefront.
- Review volume has jumped sharply in the last 60 days.
- A permit or licence filing appears in a public register.
- A public business listing has changed category or hours.
Axis 2: Digital signals
Anything the business is doing on the open web.
- A new page appears on the website (services, careers, pricing).
- The business has started running paid search or Meta ads.
- A tech stack change is visible (new booking widget, new CMS).
- A social account posts a launch or hiring notice.
- Website copy changes to mention a new service area.
Axis 3: Organisational signals
People and structure changes.
- A new head of a relevant function is announced.
- A funding round or acquisition is filed publicly.
- A specific role is opened on a jobs board.
- Executive team page changes.
- A parent company or franchise relationship changes.
How does the scoring rubric work?
| Score | Local axis | Digital axis | Organisational axis |
|---|---|---|---|
| 0 | No evidence | No evidence | No evidence |
| 1 | Stable, minor change | Static site, no ads | No visible people change |
| 2 | One clear event in 90 days | One clear change in 90 days | One relevant hire or filing |
| 3 | Multiple compounding events | Multiple compounding changes | Named role change plus one more |
- Score
- 0
- Local axis
- No evidence
- Digital axis
- No evidence
- Organisational axis
- No evidence
- Score
- 1
- Local axis
- Stable, minor change
- Digital axis
- Static site, no ads
- Organisational axis
- No visible people change
- Score
- 2
- Local axis
- One clear event in 90 days
- Digital axis
- One clear change in 90 days
- Organisational axis
- One relevant hire or filing
- Score
- 3
- Local axis
- Multiple compounding events
- Digital axis
- Multiple compounding changes
- Organisational axis
- Named role change plus one more
The score, visualised
Two-axis matrix
The three-axis signal score
Local
Physical world evidence
Digital
Web and ad footprint
Organisational
People and structure
Act now
Score >= 5
Watch
Score 3 to 4
Ignore
Score 0 to 2
What does the score look like on a real business?
A physiotherapy clinic in Bengaluru opens a second location (Local 2), starts running Google Ads on "sports injury" (Digital 2), and posts a job for a lead physio (Organisational 2). Total score: 6. That is an act-now record. The opener writes itself, because all three anchors are already in the record.
Why three axes matter
A single-axis signal is easy to fake to yourself. Three axes filter out coincidences. A business doing one visible thing might be noise. A business doing three at once is almost never noise.
How does ExtrovertAI produce these scores?
ExtrovertAI pulls Local signals from public business listings and reviews, Digital from a public site reads and public ad footprints, and Organisational from public web pages and news. The three-axis score is computed per record before drafting begins.
Related reading
Buying signal examples Internal.
Buying signal playbook for local B2B Internal.
High intent local leads Internal.
LinkedIn Economic Graph mobility research Primary source.
FAQ
Is this the same as intent data?
No. Intent data usually means third-party topic surges from cookie networks. The three-axis signal score is built from first-party public evidence about the business itself, not about anonymous visitor cohorts.
Why 5 as the action threshold?
Because a total of 5 forces at least two axes to be non-trivial. Single-axis signals are too easy to over-index on.
Written by
ArunFounder, ExtrovertAI
Bootstrapped ExtrovertAI out of Coimbatore after a decade of watching teams pay per seat for lists their SDRs never worked. Writes about positioning, category, and the argument for signal-first prospecting. Opinionated, first-person, not neutral.
Writes from the founder chair. Names the tradeoff, picks a side, tells you why.
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