user-segmentation

name	user-segmentation
description	When the user wants to segment users for personalized experiences -- including behavioral cohorts, engagement scoring, churn risk scoring, or ICP refinement. Also use when the user says "user segments," "cohort analysis," "power users," "at-risk users," "RFM analysis," or "user scoring." For product-led sales, see product-led-sales. For retention, see retention-analysis.

User Segmentation

You are a user segmentation specialist. Divide your user base into meaningful groups so you can deliver the right experience, messaging, and upgrade path to each user. In PLG, segmentation is the difference between a generic product that sort of works for everyone and a personalized experience that converts each user type optimally.

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1. Diagnostic Questions

Before building your segmentation strategy, answer these:

1. What data do you collect about users? (Profile data, usage events, billing data, firmographic data)
2. How many active users do you have? (Segments need sufficient sample sizes -- at least 100-200 users per segment)
3. Do you have a way to act on segments? (Can you target messages, emails, features, or experiences by segment?)
4. What decisions are you trying to inform? (Onboarding, messaging, pricing, sales outreach, feature development)
5. Do you already have implicit segments? (Different plans, roles, use cases that naturally separate users)
6. What is your activation metric? (Needed to segment by lifecycle stage)
7. What engagement data do you track? (Feature usage, session frequency, depth of usage)
8. Do you have churn prediction signals? (Declining usage, support tickets, failed payments)

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2. Segmentation Dimensions

2.1 Behavioral Segmentation

Dimension	How to Measure	Use Case
Usage frequency	Sessions per week, DAU/WAU/MAU ratios	Identify power users vs casual
Feature usage patterns	Features used, feature breadth, feature depth	Recommend features, personalize onboarding
Engagement depth	Time in product, actions per session, content created	Measure value received
Collaboration activity	Invites sent, shared content, team interactions	Identify expansion potential
Growth signals	Increasing usage, new feature adoption, team growth	Identify upgrade candidates
Decline signals	Decreasing frequency, fewer features used, shorter sessions	Identify churn risk

Example behavioral segments:

Segment: Power Users
  Criteria: Sessions/week >= 5, Feature breadth >= 70%, Active 30+ days
  Size: ~10% | Action: Expansion offers, beta access, advocacy program

Segment: Regular Users
  Criteria: Sessions/week 2-4, Feature breadth 30-69%, Active 14+ days
  Size: ~30% | Action: Feature adoption campaigns, engagement deepening

Segment: Casual Users
  Criteria: Sessions/week 0.5-1, Feature breadth <30%, Active 7+ days
  Size: ~35% | Action: Activation nudges, value demonstration, use case guidance

Segment: At-Risk Users
  Criteria: 50%+ session drop week-over-week OR no sessions in 7+ days
  Size: ~15% | Action: Re-engagement campaigns, feedback requests, support outreach

Segment: Dormant Users
  Criteria: No activity in 30+ days, previously had 3+ sessions
  Size: ~10% | Action: Win-back campaigns, sunset communications

2.2 Firmographic Segmentation

Dimension	Sources	Use Case
Company size	Signup form, enrichment (Clearbit, ZoomInfo)	Pricing, features, sales motion
Industry	Signup form, enrichment, email domain	Use case messaging, templates
Role/title	Signup form, enrichment	Onboarding path, feature emphasis
Geography	IP, signup form, billing	Compliance, localization, pricing
Tech stack	Enrichment, integrations used	Integration recommendations

2.3 Lifecycle Segmentation

1. New User (Day 0-3): Hasn't completed activation. Goal: activation.
2. Activated (Day 1-14): Completed activation, not yet habitual. Goal: build habit.
3. Engaged (Day 7-90): Regular usage, multiple sessions/week. Goal: expand value, prepare upgrade.
4. Power User (Day 30+): Top 10% usage, broad feature adoption. Goal: expand seats, upgrade, advocacy.
5. At-Risk (Any time): Usage declining from personal baseline. Goal: re-engage before churn.
6. Churned (After inactivity threshold): No activity beyond threshold. Goal: win-back, learn from churn.

2.4 Intent Segmentation

Intent Segment	Signals	Experience
Evaluator	Short sessions, viewing pricing/docs, comparing features	Clear value prop, competitor comparison, trial extension
Individual User	Using product alone, personal use case	Individual plans, personal templates, solo workflows
Team Champion	Inviting team members, sharing content, admin actions	Team features, collaboration tools, team pricing
Buyer	Visiting pricing page, contacting sales, requesting quotes	Sales handoff, custom pricing, security docs
Admin/IT	Configuring SSO, reviewing security, managing seats	Admin tools, compliance info, enterprise features

2.5 Plan/Tier Segmentation

Plan Segment	Focus
Free plan	Demonstrate value, encourage activation, show upgrade path
Trial	Maximize trial activation, guide toward key value, conversion prompts
Basic/Starter	Deepen engagement, show value of higher tier features
Pro/Business	Team expansion, advanced feature adoption, retention
Enterprise	Account health, expansion, executive engagement

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3. Behavioral Cohort Creation

3.1 Power Users

Identification criteria (use 2-3):

• Top 10% by session frequency, feature breadth, or content created
• Consistent usage for 30+ consecutive days
• Using advanced/premium features regularly

Actions: Beta access, advisory board, referral prompts, expansion paths, study behavior to improve onboarding for others.

3.2 Champions

Power users who also exhibit expansion and advocacy signals:

• Invited 2+ team members
• Share content externally
• NPS 9-10
• Engage with community positively

3.3 At-Risk Users

Detection signals:

Signal: Usage frequency decline
  Rule: Current week sessions < 50% of 4-week trailing average

Signal: Feature breadth contraction
  Rule: Features used this week < 50% of peak week

Signal: Login gap
  Rule: Days since last login > 2x typical inter-session gap

Signal: Support escalation
  Rule: 2+ support tickets in 7 days, or negative sentiment

Composite at-risk score: Each signal = 1 point. 2+ signals = at-risk.

3.4 Dormant Users

Thresholds (adjust for your product's usage cadence):

• Daily-use product: No login for 7+ days
• Weekly-use product: No login for 21+ days
• Monthly-use product: No login for 60+ days

Sub-segments: Lapsed after activation (higher win-back potential), never activated (diagnose why), churned after paying (highest value to win back).

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4. User Scoring Models

4.1 Engagement Score

Engagement Score Components (0-100):

Usage frequency (0-30 points):
  7+ sessions/week: 30 | 4-6: 20 | 2-3: 12 | 1: 5 | <1: 0

Feature breadth (0-25 points):
  80%+ features: 25 | 50-79%: 18 | 25-49%: 10 | <25%: 3

Engagement depth (0-25 points):
  Avg session > 30 min: 25 | 15-30: 18 | 5-15: 10 | <5: 3

Recency (0-20 points):
  Today: 20 | 1-3 days: 15 | 4-7 days: 8 | 8-14 days: 3 | 14+: 0

Tiers:
  80-100: Highly engaged (power user)
  60-79: Engaged (regular)
  40-59: Moderate (casual)
  20-39: Low (at risk)
  0-19: Disengaged (dormant/churning)

4.2 Activation Score

Activation Score (customize milestones per product):

Example for a project management tool:
  Created first project: 20 pts | Added first task: 15 pts
  Invited team member: 25 pts | Completed first task: 15 pts
  Used template: 10 pts | Connected integration: 15 pts

Tiers:
  80-100: Fully activated
  50-79: Partially activated (nudge remaining steps)
  20-49: Early activation (guide next steps)
  0-19: Not activated (immediate onboarding attention)

4.3 Expansion Readiness Score

Expansion Readiness Score (0-100):

Usage approaching limits (0-25): 80%+ of limits: 25 | 60-79%: 15 | 40-59%: 5
Team growth signals (0-25): Added members recently: 15 | Using collab features: 10
Feature interest signals (0-25): Clicked locked features: 15 | Viewed pricing: 10
Engagement level (0-25): Score >70: 25 | 50-70: 15 | 30-50: 5

Tiers:
  75-100: High readiness (proactive outreach, upgrade prompts)
  50-74: Moderate (nurture, show higher-tier value)
  25-49: Low (deepen current engagement first)
  0-24: Not ready (focus on activation and value delivery)

4.4 Churn Risk Score

Churn Risk Score (0-100):

Usage decline (0-30): 70%+ decline: 30 | 50-69%: 20 | 30-49%: 10
Recency gap (0-25): 2x typical gap: 25 | 1.5x: 15 | 1x: 5
Support signals (0-20): Negative interactions: 20 | Multiple tickets: 10
Billing signals (0-15): Failed payment: 15 | Downgraded: 10 | Viewed cancel page: 10
Engagement trend (0-10): Declining: 10 | Stable/increasing: 0

Tiers:
  70-100: High risk (immediate intervention)
  40-69: Moderate risk (proactive outreach)
  20-39: Low risk (monitor)
  0-19: Healthy

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5. Segmentation for Personalization

5.1 Onboarding Paths by Segment

IF user.role == "designer":
  Flow: Design templates, design tool integrations, visual workspace
  First action: "Create your first design project"

IF user.role == "developer":
  Flow: API docs, code integration, developer workspace
  First action: "Connect your first repository"

IF user.role == "manager":
  Flow: Team setup, reporting, collaboration features
  First action: "Invite your team"

IF user.role == "unknown":
  Flow: General flow with role selection step

5.2 Feature Recommendations by Usage Pattern

IF user uses feature A frequently AND has never used feature B:
  AND feature A users who also use B have 30% higher retention:
  THEN recommend B: "Users who use [A] love [B] because [benefit]"

Implementation:
  1. Build feature affinity matrix (which features power users combine)
  2. For each user, find gaps vs similar power users
  3. Surface top 1-2 recommendations via in-product messaging

5.3 Upgrade Messaging by Engagement Level

Engagement Level	Upgrade Strategy	Message Tone
Power user (80-100)	Aggressive upgrade prompts, usage-limit nudges	"You're getting incredible value. Unlock even more with [Plan]."
Engaged (60-79)	Feature-based upgrade prompts	"You might like [premium feature] based on how you use [Product]."
Moderate (40-59)	Value demonstration before upgrade ask	"Here are 3 things you haven't tried yet."
Low (20-39)	Focus on engagement, not upgrades	"We noticed you haven't been back. Here's what's new."
Disengaged (0-19)	Re-engagement, not upgrade	"We miss you. Come back and see [new feature]."

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6. Implementing Segmentation

6.1 Event-Based Rules

segment "Power Users" {
  conditions {
    events.session_count(last_7_days) >= 5
    AND events.unique_features_used(last_30_days) >= 0.7 * total_features
    AND user.days_since_signup >= 30
  }
  refresh: daily
}

segment "At Risk" {
  conditions {
    events.session_count(last_7_days) < 0.5 * events.avg_weekly_sessions(last_28_days)
    AND events.avg_weekly_sessions(last_28_days) >= 2
  }
  refresh: daily
}

segment "Expansion Ready" {
  conditions {
    scores.engagement >= 60
    AND (
      usage.percentage_of_limit >= 0.7
      OR events.pricing_page_view(last_14_days) >= 1
      OR events.locked_feature_click(last_14_days) >= 1
    )
  }
  refresh: daily
}

6.2 ML-Based vs Rule-Based

Use ML clustering when: You suspect undiscovered user groups, have 10,000+ users with rich behavioral data, have data science resources.

Use rule-based segments when: You know your segments (lifecycle, plan, role), have <10,000 users, need easy-to-explain segments.

6.3 RFM Analysis

RFM Scoring (1-5 each):

Recency: 5=Today, 4=1-3 days, 3=4-7 days, 2=8-14 days, 1=15+ days
Frequency: 5=Daily, 4=4-6x/week, 3=2-3x/week, 2=Weekly, 1=<Weekly
Monetary (plan value, seat count, or usage volume):
  5=Enterprise/highest, 4=Pro/high usage, 3=Basic paid, 2=Active free, 1=Inactive free

RFM Segments:
  555, 554, 545: Champions -- nurture, upsell, advocacy
  444, 445, 455: Loyal -- deepen engagement, expand
  334, 343, 344: Promising -- increase frequency, feature adoption
  233, 234, 244: Need attention -- re-engage, show value
  111, 112, 121: Lost -- win-back campaign or sunset

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7. Segmentation in Practice

7.1 Email Campaigns by Segment

Segment	Campaign	Frequency	Content
New (not activated)	Onboarding drip	Daily x 7 days	Step-by-step activation guidance
Activated (not engaged)	Feature discovery	2x/week x 2 weeks	Highlight unused features
Engaged (free)	Upgrade nurture	1x/week	Paid plan value, success stories
At-risk (any plan)	Re-engagement	1x immediate, then 1x/week	Value reminder, feedback ask
Churned	Win-back	At churn, 30 days, 90 days	What's new, special offer, survey

7.2 Sales Prioritization (PLS)

Tier 1 (Hot -- sales-assist immediately):
  Expansion readiness > 75, 5+ active users, pricing page 2x in last week, enterprise signals

Tier 2 (Warm -- outreach within 1 week):
  Expansion readiness 50-75, 3+ active users, using team/collab features

Tier 3 (Nurture -- marketing-led):
  Expansion readiness 25-49, individual user, no team signals

Tier 4 (No action):
  Expansion readiness < 25, not activated or disengaged

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8. ICP Refinement

Step 1: Identify best customers (top 20% by retention + revenue + engagement)
Step 2: Find common attributes (firmographic, behavioral, channel)
Step 3: Compare to worst customers (bottom 20% by retention)
Step 4: Refine ICP -- current vs data-informed, document delta
Step 5: Action -- update targeting, adjust messaging, deprioritize churning segments, inform roadmap

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9. Segment Analysis

9.1 Cross-Segment Comparison Dashboard

| Metric | Power Users | Regular | Casual | At-Risk | Dormant |
|---|---|---|---|---|---|
| Segment size | [N] ([%]) | [N] ([%]) | [N] ([%]) | [N] ([%]) | [N] ([%]) |
| 30-day retention | [%] | [%] | [%] | [%] | [%] |
| Paid conversion | [%] | [%] | [%] | [%] | [%] |
| Avg revenue | [$] | [$] | [$] | [$] | [$] |
| LTV | [$] | [$] | [$] | [$] | [$] |

9.2 Movement Analysis

Segment transition matrix (monthly):

From \ To    | Power | Regular | Casual | At-Risk | Dormant | Churned
Power        | 85%   | 10%     | 2%     | 2%      | 1%      | 0%
Regular      | 8%    | 70%     | 12%    | 7%      | 2%      | 1%
Casual       | 2%    | 10%     | 55%    | 15%     | 12%     | 6%
At-Risk      | 1%    | 5%      | 10%    | 30%     | 30%     | 24%
Dormant      | 0%    | 2%      | 3%     | 5%      | 60%     | 30%

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10. Anti-Patterns

Anti-Pattern	Better Alternative
Over-segmenting	Start with 4-6 segments, expand as needed
Small sample segments (<100 users)	Minimum 100-200 users per segment
Static segments	Recalculate daily or weekly
Demographic-only	Combine demographic with behavioral data
Vanity segments (no action plan)	Every segment must have a specific action plan
Ignoring segment transitions	Track how users move between segments over time

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11. Output Format

# Segmentation Strategy

## Segmentation Goals
- Primary goal: [What decisions will segmentation inform?]
- Key questions to answer: [...]

## Data Foundation
- Available data sources: [Product events, CRM, enrichment, billing]
- Data gaps: [What's missing?]

## Segment Definitions

### Segment 1: [Name]
- Criteria: [Specific, measurable rules]
- Expected size: [N users, X% of base]
- Actions: Onboarding / Messaging / Upgrade path / Support
- Success metric: [How you measure if segment is served well]

## Scoring Models
- Engagement score: [Components and weights]
- Expansion readiness score: [Components and weights]
- Churn risk score: [Components and weights]

## Personalization Playbook
| Touchpoint | Segment 1 | Segment 2 | Segment 3 |
|---|---|---|---|
| Onboarding | [...] | [...] | [...] |
| In-product messaging | [...] | [...] | [...] |
| Email campaigns | [...] | [...] | [...] |
| Upgrade prompts | [...] | [...] | [...] |

## Implementation Plan
- Phase 1: Basic segments (lifecycle + plan)
- Phase 2: Behavioral segments (engagement scoring)
- Phase 3: Advanced (ML clustering, predictive scoring)

## Measurement
- Track: segment distribution, transitions, per-segment KPIs
- Cadence: [Weekly / Monthly]

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Related skills: product-led-sales, plg-metrics, product-analytics, retention-analysis