# Cx Onboarding Friction > Use to find the activation blockers visible in support contacts from new customers, before they show up in retention numbers months later. Trigger for "why aren't new customers activating", "what do new customers contact us about", onboarding drop-off, time-to-value analysis, first-90-days support experience, or a retention problem that traces back to activation. - Skill: `rulebase-co/cx-onboarding-friction` (Agent Skill) - Install (CLI): `npx skillmds add rulebase-co/cx-onboarding-friction` - Raw SKILL.md: https://api.skillmd.com/api/skills/rulebase-co/cx-onboarding-friction/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: rulebase-co (https://skillmd.com/u/rulebase-co) - Updated: 2026-08-19 - Page: https://skillmd.com/skills/rulebase-co/cx-onboarding-friction --- # Onboarding friction in support contacts New customers contact support about the things that are stopping them getting started. That makes the first weeks of a customer's support history the most direct evidence available of what blocks activation — and it arrives months before the retention curve shows the consequence. Two things make this analysis different from ordinary contact-driver work, and both change the method. ## New-customer contacts are not the same population **A contact from a new customer is usually a blocker; a contact from an established one is usually an exception.** The same category means different things at different tenures — "how do I invite a user" is onboarding friction in week one and an edge case in year two. So: - **Segment by tenure, in short buckets** early on: days 0–7, 8–30, 31–90. A single "new customer" bucket of 90 days blurs the week-one cliff, which is where most of the signal is. - **Never compare new-customer contact rates to the org average.** New customers contact more; that is expected and not a problem in itself. - **Compare cohorts to each other** — this month's new customers against last quarter's — because the interesting question is whether onboarding is getting better. ## Contacting support is a sign of *effort*, and silence is ambiguous The customer who contacts support is trying. The one who hit the same blocker and silently stopped is the more expensive case, and they are invisible here. That means support data over-represents the persistent and under-represents exactly the customers who churn fastest. Two consequences: - **Pair support contacts with product activation data** wherever you can. A blocker that generates few contacts but sits at a high drop-off step is more important than a chatty one that everyone gets past. - **State the coverage** — what share of non-activating customers ever contacted support. If it is low, this analysis names *some* blockers and cannot claim to be the ranked list of all of them. ## The classification that makes this actionable Ordinary contact-driver taxonomy names topics. For onboarding, classify by **what kind of failure it is**, because each has a different owner: - **Missing capability** — they need something the product does not do. - **Discoverability** — the product does it and they could not find it. The most common, the cheapest to fix, and the one most often misread as a training problem. - **Setup complexity** — technical steps beyond what the buyer can self-serve (integrations, DNS, SSO, data import). Look for where these stall rather than fail. - **Data migration** — getting their existing data in. Reliably underestimated at purchase and a frequent silent killer. - **Permissions and access** — someone internal has to approve or provision something. Not your product's fault and entirely your problem, because it stalls the clock. - **Expectation mismatch** — they bought something that does not do what they thought. This is a sales or marketing finding, and it should be routed there rather than absorbed by support. - **Wrong-fit** — they should not have bought. Worth counting honestly; a rising rate here is a targeting problem. Report the mix. An onboarding problem that is 60% discoverability is a very different programme from one that is 60% expectation mismatch. ## Measure time, not just volume Volume tells you what people hit. Time tells you what it costs. - **Time-to-first-value**, and the gap between cohorts who contacted support and those who did not. If contacting support *shortens* time to value, support is a working part of onboarding and should be resourced as such rather than minimised. - **Stall duration** — how long between a blocker being raised and the customer progressing. A blocker that adds three days is different from one that adds three weeks even at the same volume. - **Contact latency** — how long a customer struggled before asking. Long latency on a common blocker means many others hit it and never asked. - **The unresolved tail** — new customers whose blocker was never resolved and who then went quiet. Cross-reference activation status. This small list is the most valuable output of the analysis and the easiest to act on. ## Traps - **Counting onboarding contacts as a support cost to reduce.** Suppressing them by making support harder to reach makes activation worse. If the goal is fewer onboarding contacts, the mechanism has to be removing the blocker, not the channel — and the guardrail is activation rate, not contact volume. - **Attributing everything to the customer's setup.** "They configured it wrong" is usually a discoverability finding wearing a blame hat. - **Ignoring the assisted cohort.** Customers with a dedicated onboarding manager generate few tickets and hit the same blockers. Their friction is in call notes and internal threads, not the queue, and excluding them biases the ranking toward self-serve segments. - **Seasonal cohorts.** Customers who signed up during a promotion, an event, or a competitor's outage are not comparable to steady-state cohorts. - **Reading a mid-funnel improvement as success** when the earlier step got harder and fewer people reached it. ## Guardrails - **This names blockers; it does not size them.** Support-visible frequency is not population frequency. Say which claim you are making. - **Route findings to the owner** — product, docs, sales, provisioning — rather than filing them all as support improvements. Most onboarding friction is not support's to fix. - **Cite ids and counts; do not paste transcripts.** New-customer conversations often contain implementation detail and credentials-adjacent material. ## Present results to the user 1. **Coverage** — what share of non-activating customers contacted support, and therefore what this analysis can and cannot rank. 2. **Contacts by tenure bucket**, cohort over cohort, so the direction is visible. 3. **The failure-type mix**, not just the topic list, with an owner per type. 4. **Time costs** — time-to-value with and without support contact, stall duration, contact latency. 5. **The unresolved-and-went-quiet list**, by id, cross-referenced against activation. The most actionable output. 6. **Expectation-mismatch and wrong-fit volumes**, routed to sales and marketing rather than buried in a support report. 7. **What product data would settle** that support data cannot.