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Measurement plan
In the admin: Analytics — the screen this page documents (opens in the running app).
The rationale states a set of falsifiable bets. This page is how we test them: the hypotheses we intend to support or refute, the metrics that do the testing, and the end-to-end path that connects a visitor's clicks to a downstream bid outcome.
Everything here is designed to be measurable — most of it can be collected now, from data the app already captures. Where a signal depends on the Eloqua integration or real bid data, that's called out.
Hypotheses → metrics
Each row is a bet from the rationale, the prediction it makes, the metric that confirms or refutes it, and where that metric comes from. The right-hand column ties to the vision success metrics and the behavioral metrics dictionary.
| # | Bet (principle) | Prediction | Metric | Source |
|---|---|---|---|---|
| H1 | Guide, don't filter | Guided sessions converge on a recommendation where open-filter sessions abandon | Recommendation-reached rate across the funnel stages | Session snapshot |
| H2 | Right-size, don't up-sell | A cost-appropriate cheaper alternative is surfaced in the majority of over-spec-prone sessions | Right-size / alternative-surfaced rate | Recommendation output + recommendation-quality loop (#352) |
| H3 | Right-size, don't up-sell | Recommended tier trends down toward sufficiency vs. historical selection tier | Over-specification delta (recommended vs. historical tier) | Recommendation output vs. baseline |
| H4 | Educate through every step | Teaching the trade-off raises confidence and reduces churn at each question | Time-on-question, back-navigation / answer churn, "engaged" signal | Behavioral event stream |
| H5 | Educate / guide | Questions that lose people are identifiable and fixable | Per-factor drop-off (#350) | Event stream + snapshot |
| H6 | Product-market fit of the catalogue | What buyers actually need is visible as demand | Demand intelligence — answer distributions (#353) | Genuine-human answer rollup |
| H7 | Guided path effectiveness | Some tracks/persona paths convert better than others | Track / persona-path effectiveness (#354) | Snapshot by track |
| H8 | Selection precedes commitment | Selection feeds real pipeline — sessions become known leads and, ultimately, bids | Conversion rate → downstream bid outcome | Eloqua association (#351) + bid data (later) |
A bet is refuted as readily as confirmed — H3 trending the wrong way, or H1 showing guided sessions abandoning as often as filters, is a signal to change the design, not to hide the number.
The end-to-end measurement path
The chain from an anonymous click to a bid outcome is deliberately staged, so each link is measurable on its own and the whole is auditable.
anonymous session known lead downstream
──────────────────── ────────────── ──────────────
guided-selection flow ──► lead capture / sync ──► Eloqua contact ──► bid / sale
│ │ │ outcome
▼ ▼ ▼
session snapshot + eloqua_session_contacts lead intelligence
behavioral event stream (durable association) (#351)
(funnel, per-factor, = the "converted" scored/segmented
demand, engagement) funnel stage for sales follow-up- Session behaviour — the anonymous session snapshot is the source of truth for funnel counts; the behavioral event stream adds inside-a-step signals (time-on-question, answer churn,
/compare, media views, CTA click, engagement). Both are PII-free by construction and gated by consent. - Lead intelligence — when a visitor is captured, a durable Eloqua association (
eloqua_session_contacts) is written; this is the converted funnel stage, deliberately the stable lead fact that survives the transient sync queue and PII purge. Lead intelligence (#351) scores and segments those leads for follow-up. See Eloqua integration. - Downstream sale lifecycle — the association is the join key that will let session behaviour be tied to the eventual bid / sale outcome (won, value-engineered out, lost to a competitor). That last link depends on bid data landing (via the Eloqua integration, #350/#351) — the plan is built so the join exists before the outcome data does.
Data integrity — why the numbers are trustworthy
The measurement plan is only as good as the data under it. Three properties make it defensible:
- Genuine-human filter. Metrics are computed over a trustworthy base that excludes bots and non-human traffic — see the trustworthy base.
- PII-free by construction. Event payloads carry only knowledge-base ids and bounded numeric measures; free text is dropped at the write layer, so no email, name, or note can reach the analytics stores. Conversion is tracked by a durable association, not by storing personal data.
- Consent-gated. Behavioural capture respects the 2-tier consent model; a session without measurement consent is not tracked.
What's live vs. planned
- Live now: the session snapshot funnel, the behavioral event stream, and the analytics read paths (funnel, per-factor drop-off, demand intelligence, track effectiveness, recommendation quality) — visible in the admin Analytics screen.
- Planned: the downstream join to bid outcomes, which lands with the Eloqua lead-capture and lead-intelligence work (#350/#351). This page documents the plan so the collection is in place before the outcome data is.
For admins
The metrics behind H1–H7 are already visible in the admin Analytics screen. A contextual link from that screen back to this rationale is delivered by the admin↔docs cross-referencing work (#568).