---

name: outreach-userflow-analyzer
description: Use when the legal AI product team needs to analyse user flows within the product to identify drop-off points, conversion blockers, or opportunities for product-led growth. Maps the journey from first visit through onboarding to retained usage, with specific attention to the conversion steps critical for a legal professional audience. Triggers on requests to improve user activation, reduce churn, or understand user behaviour.
license: MIT
metadata:
id: outreach.userflow-analyzer
category: outreach
intent: ["outreach", "userflow", "product analytics", "conversion", "onboarding"]
related:
- outreach-growth-agent-runner
- outreach-inbox-scan
- outreach-haqq-ai-viz
priority: P3
source: Louis — HAQQ Legal AI (github.com/sboghossian/mini-claude-for-legal)
version: "1.0"

Userflow Analyzer

Understanding where legal professionals drop off in the product journey is essential for growth: a legal AI product that loses users at the onboarding step is a product that will not grow through word-of-mouth referrals. This skill maps and analyses the critical user flows, identifies blockers specific to a legal professional audience, and recommends interventions.

Purpose

Analyse the user journey for a legal AI product to:

1. Identify the highest-drop-off stages in the acquisition → activation → retention funnel
2. Understand the specific friction points for legal professionals (trust, accuracy concerns, workflow integration)
3. Produce a prioritised list of product and messaging interventions
4. Design A/B test hypotheses for the highest-impact improvements

Inputs

Input	Description	Required
Analytics data	Funnel data from PostHog, Mixpanel, GA4, or equivalent	Yes
User feedback	Support tickets, interviews, survey responses	Yes
Product flows	Screen recordings or user session replays	Helpful
Conversion targets	What is the primary activation event? (first legal query answered, first document drafted, first team member invited)	Yes

The legal professional user journey

Legal professionals have specific trust and adoption barriers that differ from general SaaS users:

Stage	What happens	Legal-specific friction
Acquisition	User discovers product via PR, SEO, referral, or conference	Trust: "Is this built for my jurisdiction?"
Landing page	User reads about product	Credibility: no logos/testimonials from known law firms = high bounce
Sign-up	Email registration or SSO	Privacy: "Where does my data go?" (client confidentiality concern)
Onboarding	First interaction with the product	Competence anxiety: "If AI gives wrong advice, I'm liable"
First activation	User asks first legal question or uploads first document	Accuracy check: first output quality determines retention
Retained usage	User returns and integrates into workflow	Workflow fit: does it reduce effort or add a step?
Advocacy	User refers colleagues or leaves a review	Professional risk: lawyers are cautious about recommending tools

Analysis framework

Step 1 — Map the funnel

Build a quantified funnel:

Visitors → Signups → Onboarding completed → First legal query → Return visit → Weekly active → Monthly active
[N]       [N] (X%)  [N] (X%)               [N] (X%)           [N] (X%)      [N] (X%)        [N] (X%)

Identify the step with the largest proportional drop-off. This is the highest-priority fix.

Step 2 — Segment by user type

Legal users are not homogeneous:

Segment	Typical conversion pattern	Key blocker
Solo practitioner	High motivation, low tech confidence	Complexity of setup
In-house GC team	Approval required before team adoption	Security/data governance concern
Law firm associate	Can try independently; needs firm approval for full use	Billability of AI-assisted work
Legal operations	Power user; evaluates rigorously	Integration with existing workflows (DocuSign, iManage, etc.)

Segment conversion metrics separately — aggregate metrics hide the pattern.

Step 3 — Identify friction by stage

Acquisition: are the right users landing? Bounce rate by source, time on page for key pages.

Sign-up: form abandonment? Email-only sign-up converts better for legal professionals than social login (Google/LinkedIn can feel non-anonymous for sensitive work).

Onboarding: time to first activation event. If > 10 minutes, the onboarding is too long. Legal professionals have no patience for feature tours when they have a document to review.

First activation: was the first output accurate? This is the make-or-break moment. Monitor first-query topics and check output quality for the most common first questions.

Retention: do users return? Daily/weekly/monthly return rates by cohort. "Sticky" features for legal professionals: multi-jurisdiction comparison, clause library, template generation.

Step 4 — Prioritise interventions

Score each identified friction point by:

• Impact (number of users affected × severity of drop-off)
• Effort (engineering time to fix)
• Confidence (how certain are we this is the cause?)

Top 3 interventions for most legal AI products at early stage:

1. Trust signals on landing page: add firm logos, lawyer testimonials, data privacy statement
2. Shorter onboarding: reduce time to first legal answer to < 3 minutes
3. First-query quality: ensure the most common first queries (non-compete, NDA, employment) produce excellent output — these are the product's auditions

Output format

Produce a one-page funnel analysis:

## Funnel Summary
[Quantified funnel table]

## Highest Drop-Off: [Stage]
Cause hypothesis: [1–2 sentences]
Evidence: [data + user quotes]
Recommended fix: [concrete product or messaging change]
Expected impact: [% improvement estimate]

## Top 3 Interventions (ranked)
1. [Intervention] — Impact: H/M/L, Effort: H/M/L
2. [Intervention] — Impact: H/M/L, Effort: H/M/L
3. [Intervention] — Impact: H/M/L, Effort: H/M/L

## A/B Test Hypotheses
[2–3 specific tests to run next]

Related skills

• [[outreach-growth-agent-runner]]
• [[outreach-inbox-scan]]
• [[outreach-haqq-ai-viz]]