32 signals
Signals
A signal is one instrument configured to watch something: a tag, a tracked property, a platform conversion, a report setting.
What you can do with a signal
- tableMeasurement Plan · Measurement Model
- tableInstrumentation Plan · Data Sources
- tableInstrumentation Plan · Identity
- tableEvidence Plan · Settings
Its signals— 32
- Site VisitA session start on the owned site.
- Internal SearchA query typed into the site's own search.
- Global ClickAny tracked click on an owned surface, carrying the element and page it fired on.
- Form SubmitA completed form submission on an owned surface.
- Phone Number ClickA tap or click on a phone number that places a call.
- Chat StartA conversation started with a chat or messaging widget.
- Conversion StartThe first step of a conversion flow: a cart opened, a booking begun, an application started.
- Conversion CompleteA confirmed conversion at the end of a flow: an order placed, a booking made, a sign-up finished.
- Account Registration FunnelThe steps from registration start to an activated account.
- Video FunnelPlay, progress and completion events on owned-site video.
- Resolved Lead InputA lead input matched to a person and a campaign key.
- Fulfilment Outcome FeedFulfilled, cancelled, returned and undone outcomes from the system that owns delivery of what was agreed.
- Email EngagementDelivery, open and click events for each send.
- SMS Delivery & EngagementDelivery, click and reply events for each SMS send.
- Paid Media DeliveryImpressions and clicks delivered against a tactic.
- Paid Search Cost & Conversion TrackingSpend and platform-reported conversions at keyword and campaign grain.
- Media Cost at TacticGross media cost recorded against a tactic in the period.
- Offline Delivery VolumeThe delivery counts a print, mail or broadcast supplier reports back for placements run.
- Organic Search VisibilityRankings and impressions for tracked queries.
- AI Citation TrackingWhether and where AI assistants cite the brand's pages.
- AI Response MonitoringA tracked query set run against AI assistants, capturing each rendered response: whether the brand appears, where in the answer, and in what terms.
- Brand Mention TrackingMentions of the brand across the open web.
- Video Answer TrackingVideo results shown for tracked queries.
- Schema Coverage AuditA crawl that checks which governed properties are published as structured data.
- Page Experience TrackingField Core Web Vitals per page template.
- Social ListeningPublic posts matching monitored topics.
- Consumer SentimentSentiment scored on public conversation and reviews.
- Lapse DetectionThe scheduled job that closes a lookback window against every active relationship and records each one with no qualifying activity inside it.
- Propensity ScoringThe model run that scores every active relationship for likelihood to disengage and records each one that crosses the declared threshold.
- Referral TrackingReferral codes and links resolved to the person who issued them, so that bringing someone else in is attributed to the one who did it.
- Partner Handoff FeedThe partner feed that records each person a partner's surface hands to the brand's, with the partner and the moment of handoff.
- Consent RecordThe record of permissions granted and withdrawn, per person, purpose and channel, as the consent platform, the preference centre and the one-click unsubscribe header report them.
Why it matters
What goes wrong without it, as the commitments that license this kind say it, each standing on its sources.
- Declare measurement grain before calculationA rate means nothing unless everyone agrees what one instance of its numerator and its denominator represents. Marketing reporting fails less often because a number is wrong than because two numbers computed at different grains were divided — sessions over people, clicks over sends — and nothing in the output says so. Declaring the grain first is what makes the calculation reproducible by someone who did not write it.
Declaring the Grain - Separate executable actions, observed events and consumer actor grainWhat a system did and what a person did are different facts, and the two are easily merged — which is why event models separate what was explicitly triggered from what was implicitly observed. A send is not an open; a delivery total is not a set of people. Once an execution record and a behavioural observation are the same kind of thing, every count downstream quietly mixes them, and an audience figure stops being checkable.
XDM ExperienceEvent Class · Declaring the Grain - Make outcome quality, calculation method and attribution inspectableAn outcome number is only as good as the inputs, exclusions and window that nobody wrote down. Media measurement standards exist because outcome claims circulate detached from their method, and a figure with no stated method cannot be challenged, reproduced or compared against anyone else's. Keeping what a measure means apart from how it is computed is what makes either one reviewable.
Outcomes and Data Quality Standards · Declaring the Grain · Measuring Visibility in the AI Era · Data-driven multi-touch attribution models · Causally motivated attribution for online advertising · A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook · Why Three Exposures May Be Enough · Digital Audience-Based Measurement Standards - A metric exists because a question needs itGoal-Question-Metric was formulated against a specific failure: measurement chosen because a system already produces it, rather than because a stated question needs it. The order it insists on is goal, then question, then measurement. Without the middle term a dashboard grows without limit, and which of its numbers would change a decision stops being answerable.
The Goal Question Metric Approach · Outcomes and Data Quality Standards · Declaring the Grain · Aligning Organizations Through Measurement: The GQM+Strategies Approach - A declared path must be walkableEvery other check asks whether a relationship is well formed: typed, bounded, licensed, inverted. None asks whether anything travels it. So the model could pass every check with a verb declared for thirty-eight kinds and asserted by none, and say nothing - which is what happened. A reader meeting that has no way to tell a deliberate absence from an oversight, and neither does the author six months on. The distinction is schema completeness against population completeness: the first was already enforced here and the second had no instrument at all. It matters most where a claim is a chain rather than a link. Performance asserted from delivery alone, with no step that reaches a person, is the specific failure measurement standards exist to prevent, and it is invisible to a check that only reads one edge at a time.
Shapes Constraint Language (SHACL) · Outcomes and Data Quality Standards - A move that cannot be counted is a pictureA journey that names its moves and cannot count them is a funnel with arrows: it can be drawn and not measured, which is the failure a state machine was adopted to end. Customer-migration models make the point exactly. A transition rate is the moves out of a condition over the people in it, and the people in it are the fact that put them there; so the events added to give every move a trigger are not extra instrumentation but the missing denominators of rates the model already carried and could not derive. A law that every move has a trigger, satisfied by facts nothing observes, would be the same picture with more arrows.
Modeling customer relationships as Markov chains · Declaring the Grain · The Goal Question Metric Approach · Counting Your Customers the Easy Way: An Alternative to the Pareto/NBD Model