rulebase-co Self-service funnel analysis
Teams report article views and ticket volume as if one explains the other. They do not. A customer can read three articles and still open a ticket — and that sequence is usually a self-service failure, not success. The funnel you need is:
search (or land) → article view → [optional: in-article action] → contact (or exit)
Without instrumented steps and a contact-after-view flag, you are optimising page traffic while contacts stay flat.
Define the funnel steps
| Step | Event | Minimum fields |
|---|---|---|
| Enter | Search submit or direct article land | session_id, timestamp, query or article_id |
| View | Article body rendered | article_id, session_id |
| Engage | Scroll depth, time on page, clicked CTA | optional but useful |
| Resolve signal | "Helpful" vote, flow completion, no contact in window | session_id, customer_id |
| Contact | Ticket/chat/call opened | customer_id, timestamp, channel |
Contact-after-view (CAV) — contact within a defined window (often 24–48h) after
an article view in the same session or for the same customer_id:
CAV rate = contacts after view / article views (not "deflection")
High CAV on an article means the article is associated with failure — wrong content, stale steps, missing prerequisite, or it describes a problem you cannot self-resolve. That is the prioritised fix list, not "low views."
Instrumentation requirements
If any of these are missing, state what you cannot conclude:
| Gap | Consequence |
|---|---|
| No search logging | Cannot diagnose findability vs content |
| Anonymous views only | Cannot link to tickets |
| Ticket opens not tied to identity | CAV is understated |
| Bot and KB on different sessions | Double-count or miss paths |
| No article version on view event | Stale fixes look ineffective |
Join keys: customer_id when logged in; session_id with careful TTL for
anonymous; never rely on email subject matching.
Diagnose drop-off by stage
| Drop-off pattern | Likely cause | Action |
|---|---|---|
| Search, no click | Findability, titles, synonyms | Query clustering, title rewrites |
| Click, immediate bounce | Wrong result ranked first | Snippet quality, intent routing |
| Long read, then CAV | Content wrong or incomplete | Rewrite against resolved tickets |
| High views, low CAV, high contacts elsewhere | Article fine; process blocks self-serve | Fix product or policy path |
| No search, direct land, CAV | Links from product/errors | In-app copy vs KB parity |
| Views falling, contacts flat | Traffic shifted to bot or phone | Cross-channel funnel |
Match KB to contact drivers
Export top contact drivers (taxonomy or tags) and map each to:
- Primary article — exists? accurate? last updated?
- CAV rate and view volume
- Contained bot sessions on same intent, if applicable
| Driver tier | KB priority |
|---|---|
| High volume + high CAV | Rewrite immediately |
| High volume + no article | Write net-new |
| Low volume + high CAV | Fix or delete misleading article |
| High views + low CAV | Candidate for promotion / in-app link |
| Stale + high views | Harmful — update before new content |
An article that ranks well in search but drives CAV suppresses deflection — customers waste time before contacting anyway. That is worse than no article.
Success metrics that are not vanity
| Metric | Use | Misuse |
|---|---|---|
| CAV rate by article | Prioritise fixes | Sole deflection KPI |
| Search success rate | Findability | Ignoring content quality |
| Contacts per 1k MAU | Holistic self-service | Blaming support for product bugs |
| Issue-level self-serve rate | Requires identity + driver | Article views alone |
True deflection requires knowing whether the contact would have happened without self-service — historical funnels only describe association. Holdouts and matched pre/post at driver level are stronger when you need a causal claim.
Traps
"Helpful" votes. Satisfied readers vote; angry readers contact. Votes skew positive; CAV catches the rest.
Publishing more articles. Coverage without CAV analysis increases findability of wrong answers.
SEO traffic. External hits inflate views; segment logged-in and in-app paths.
Contact button on every page. Makes CAV the rational choice; measure button placement experiments.
Present results to the user
- Funnel counts — search → view → contact with conversion rates at each step; define the attribution window used.
- Top CAV articles — volume, driver mapping, staleness, suggested fix type (rewrite, merge, deprecate, in-app only).
- Search gap list — queries with no click or high click-then-CAV.
- Driver coverage table — top N drivers vs KB asset and CAV rate.
- Instrumentation gaps — what biases the funnel (identity, session, channel).
- Prioritised actions — ordered by expected contact reduction, not page views.