Persona Builder

Build behavioral personas from real session data — identify who your users actually are based on what they do, not what you assume.

When to Use

• "What types of users do we have?"
• "Build personas for the product team"
• "Who are our power users vs casual users?"
• "What's the difference between users who convert and users who don't?"
• "Define our ideal customer profile from behavioral data"

Mental Model

Traditional personas are built from interviews and assumptions. Behavioral personas are built from what users actually do. The difference matters:

• "Sarah the Marketing Manager, 34, Chicago" — traditional persona (demographic)
• "The Power Browser: visits 3+ times/week, compares products extensively, reads reviews, converts on 4th visit" — behavioral persona (what matters for product decisions)

Workflow

Step 1: Discover behavioral clusters

Run broad metrics to find natural grouping dimensions:

fullstory:build_metric(query="sessions per user in last 30 days", output_type="top_n")
fullstory:build_metric(query="pages per session", output_type="top_n")
fullstory:build_metric(query="time on site per session", output_type="top_n")
fullstory:build_metric(query="features used per user", output_type="top_n")

Look for natural breakpoints:

• Session count: 1 (one-and-done), 2-5 (occasional), 6-20 (regular), 20+ (power)
• Pages per session: 1-2 (bouncers), 3-5 (browsers), 6+ (deep explorers)
• Features: 1-2 (single-purpose), 3-5 (multi-feature), 6+ (power user)

Step 2: Build segments for each cluster

fullstory:build_segment("users with 20+ sessions in last 30 days") → power_users
fullstory:build_segment("users with 1 session in last 30 days") → one_timers
fullstory:build_segment("users with 2-5 sessions, 3-5 pages per session") → casual_browsers

Step 3: Profile each persona

For each segment, pull sessions and analyze:

fullstory:get_sessions(segment_id, limit=10)
→ session-context: "What is this user's typical flow? What features do they use? What frustrates them? What do they achieve?"

Build a profile for each persona:

• Name: Memorable label ("The Power Browser", "The Task Completer")
• Behavior: What do they do? (typical session flow)
• Frequency: How often? (sessions per week/month)
• Goals: What are they trying to accomplish?
• Friction: What frustrates them? What blockers do they hit?
• Conversion: Do they convert? What triggers conversion?
• Size: How many users are in this persona? (% of total)

Step 4: Compare personas

Present side by side:

| Persona | Users | Sessions/Mo | Conv Rate | Top Frustration |
|---------|-------|-------------|-----------|-----------------|
| Power Browser | 1,200 (8%) | 12 | 32% | Slow search results |
| Task Completer | 4,500 (30%) | 3 | 45% | Checkout form on mobile |
| Casual Browser | 6,200 (41%) | 2 | 8% | Can't find pricing |
| One-Timer | 3,100 (21%) | 1 | 2% | Site loads slowly |

Step 5: Generate persona cards

## Persona: The Power Browser
**1,200 users (8%)** | 12 sessions/month | 32% conversion

### Typical Session
Lands on homepage → searches for product → browses 5-8 product pages → reads 2-3 reviews → adds to cart → checks shipping → may or may not complete purchase

### What They Want
- Fast, accurate search (their top frustration is slow search)
- Detailed product specs and comparison
- Trust signals (reviews, ratings, shipping info)

### How to Serve Them
- Improve search performance (their #1 complaint)
- Add product comparison feature
- Surface reviews and ratings prominently

### What Converts Them
- Free shipping threshold met
- Product review rating >4.5 stars
- Competitor price comparison visible

Step 6: Distribute

Format the personas for different audiences:

• Product team: Behavioral profiles with feature implications
• Marketing: Messaging angles per persona, channel preferences
• Engineering: Technical friction points per persona, performance priorities
• Exec: Persona mix trends, revenue per persona

Persona Discovery Dimensions

Guidelines

• Behavior trumps demographics. "34-year-old woman in Chicago" tells you nothing about what to build. "Compares 5+ products before purchasing" tells you everything.
• 3-5 personas max. More than that and they blur together. If you find 8 clusters, merge the smaller ones into the larger ones.
• Personas should be discoverable in the data, not invented. If you can't build a segment for a persona, it's not a persona — it's a guess.
• Update personas quarterly. User behavior changes as the product evolves.
• The "One-Timer" persona (1 session, never returned) is often the largest group. That's normal. Focus product decisions on the personas that generate value (Power Browsers, Task Completers).