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 Milo use?

This post introduces one framework. Two more show up across the Milo blog, and it helps to see all three in one place.

Milo 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 a Google Maps search.
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, opening Google Maps 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 Google Business Profile 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
    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
Scoring rubric per axis (0 to 3)

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

Three axes, each 0 to 3. Act on totals of 5 or more.

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 Milo produce these scores?

Milo pulls Local signals from public map data and business listings, 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.