Intent Journeys

Visualize how users progress from curiosity to conversion.

What Are Intent Journeys?

An intent journey is the complete conversational path a user takes from their first query to booking or conversion. Unlike traditional analytics that only show "User clicked → User converted", intent journeys reveal the entire story.

Traditional Analytics View

User clicked your link → User booked

What you see: 1 click, 1 conversion What you miss: Everything that happened before the click

Intent Journey View

Turn 1: "Coffee shops in Portland" (Low intent)
Turn 2: "Coffee shops with outdoor seating" (Medium intent)
Turn 3: "Artisan Coffee Roasters hours" (High intent)
Turn 4: "Book table at Artisan Coffee" (Conversion)

What you see: Complete journey with intent progression What you gain: Understanding of how users make decisions

Why This Matters

The AI Conversation Difference

Microsoft research shows AI search sessions have 22% more chat turns than traditional search. Users don't just search once and click - they have conversations that evolve.

Key findings:

• Average AI search: 4.2 turns before conversion

• Average traditional search: 1.3 clicks before conversion

• AI users arrive 3x more qualified

• Journey length predicts conversion likelihood

Real Business Impact

Businesses using intent journey analytics see:

• 40% better lead qualification (focus on high-intent users)

• 25% shorter sales cycles (understand where users are in journey)

• 2x better conversion rates (optimize for journey stages)

• Predictive insights (know who's likely to convert)

Understanding Intent Progression

Intent Strength Levels

Low Intent (0-39 points)

• Early exploration phase

• General questions

• No urgency signals

• Example: "What coffee shops are in Portland?"

Medium Intent (40-69 points)

• Active research phase

• Comparing options

• Asking specific questions

• Example: "Compare Artisan Coffee and Stumptown"

High Intent (70-100 points)

• Ready to take action

• Asking about availability, pricing, booking

• Urgency signals present

• Example: "Book table at Artisan Coffee tonight"

How Intent is Scored

Our AI analyzes multiple signals to calculate intent scores:

High-Intent Keywords (25 points each):

• Action words: book, reserve, schedule, buy, order

• Urgency: now, today, tonight, urgent, need

• Contact: call, visit, hire, contact

Medium-Intent Keywords (15 points each):

• Research: price, cost, how much, available

• Comparison: compare, best, recommend, versus

• Validation: review, rating, testimonial

Contextual Factors:

• Previous queries in session: +5 points each

• Session duration > 5 minutes: +10 points

• Citations received: +8 points each

• Comparisons appeared in: +12 points each

• Location indicators ("near me"): +15 points

Example Calculation:

Query: "book table at italian restaurant tonight"

Keywords:
- "book" = 25 points (high-intent action)
- "tonight" = 25 points (urgency)

Context:
- 2 previous queries = 10 points
- 1 citation = 8 points
- Session duration 7 min = 10 points

Total: 78 points = HIGH INTENT

Journey Visualization

Timeline View

Your dashboard shows every turn in the conversation:

Journey #12345 - Session started Dec 7, 2025 at 2:15 PM

Turn 1 (2:15 PM) - Query
"Coffee shops in Portland"
Intent: LOW (25 points)
→ Your business: Impression

Turn 2 (2:17 PM) - Refinement
"Coffee shops with outdoor seating and wifi"
Intent: MEDIUM (45 points)
→ Your business: Citation (secondary)

Turn 3 (2:19 PM) - Refinement
"Artisan Coffee Roasters menu and prices"
Intent: MEDIUM (62 points)
→ Your business: Citation (primary)

Turn 4 (2:21 PM) - Refinement
"Book table at Artisan Coffee for 4 people today"
Intent: HIGH (88 points)
→ Your business: CONVERSION ✓

Journey Metrics:
- Total turns: 4
- Duration: 6 minutes
- Intent evolution: Increasing
- Peak intent: 88
- Converted: Yes

Intent Progression Chart

Visual representation of how intent evolved:

Pattern: Steadily Increasing (High conversion probability: 80%)

Journey Patterns

Pattern Types

1. Steadily Increasing (Best for conversion)

• Intent grows with each turn

• User getting more specific

• Conversion probability: 80%

2. Spike Pattern (Sudden decision)

• Low intent, then sudden jump

• User found what they needed

• Conversion probability: 60%

3. Volatile Pattern (Exploring)

• Intent goes up and down

• User comparing many options

• Conversion probability: 30%

4. Decreasing Pattern (Lost interest)

• Intent drops over time

• User not finding what they need

• Conversion probability: 10%

Query Refinement Analysis

Refinement Types

Specification - Adding more details

• "restaurants" → "italian restaurants"

• "coffee" → "coffee with outdoor seating"

• Indicates: User knows what they want

Clarification - Seeking understanding

• "Artisan Coffee" → "Artisan Coffee hours"

• "menu" → "vegetarian menu options"

• Indicates: User gathering information

Comparison - Evaluating options

• "Artisan Coffee" → "Artisan Coffee vs Stumptown"

• "best coffee" → "compare coffee shops"

• Indicates: User in decision phase

Location - Adding location context

• "coffee" → "coffee near me"

• "restaurants" → "restaurants in downtown Portland"

• Indicates: User ready to visit

Timing - Adding time constraints

• "restaurants" → "restaurants open now"

• "book table" → "book table tonight"

• Indicates: High urgency, ready to act

Refinement Patterns

Narrowing (60% conversion rate)

• User getting more specific with each query

• Adding constraints and details

• Strong buying signal

Example:

"restaurants"
→ "italian restaurants"
→ "italian restaurants with outdoor seating"
→ "italian restaurants open now"

Deciding (80% conversion rate)

• Moving from research to action

• Intent score jumps significantly

• Strongest conversion signal

Example:

"coffee shops"
→ "Artisan Coffee reviews"
→ "book table at Artisan Coffee"

Broadening (20% conversion rate)

• User expanding search

• May not find what they need

• Lower conversion probability

Example:

"Artisan Coffee"
→ "coffee shops in Portland"
→ "cafes and restaurants"

Exploring (30% conversion rate)

• General exploration

• No clear direction

• Moderate conversion probability

Example:

"coffee"
→ "best coffee"
→ "coffee shops with wifi"
→ "coffee or tea"

Your Dashboard

Overview Tab

Key Metrics:

Total Journeys:        1,247
Converted Journeys:    456 (36.6%)
Avg Intent Score:      52.3
Avg Journey Length:    4.2 turns
Avg Duration:          6.8 minutes

Intent Evolution:
- Increasing:  67% (835 journeys)
- Stable:      18% (224 journeys)
- Volatile:    10% (125 journeys)
- Decreasing:   5% (63 journeys)

Intent Distribution:
- High Intent:    234 journeys (18.8%)
- Medium Intent:  678 journeys (54.4%)
- Low Intent:     335 journeys (26.8%)

Individual Journey View

Click any journey to see:

• Complete timeline with all turns

• Intent score at each turn

• Your touchpoints (impressions, citations)

• Refinement pattern analysis

• Conversion outcome

• Journey metrics

Example:

Journey #12345
Started: Dec 7, 2025 at 2:15 PM
Duration: 6 minutes
Turns: 4
Pattern: Steadily Increasing
Converted: Yes ✓

Timeline:
[Turn 1] Query: "coffee shops" (Intent: 25)
  → Impression in summary

[Turn 2] Refinement: "coffee with outdoor seating" (Intent: 45)
  → Citation (secondary placement)

[Turn 3] Refinement: "Artisan Coffee menu" (Intent: 62)
  → Citation (primary placement)

[Turn 4] Refinement: "book table at Artisan Coffee" (Intent: 88)
  → CONVERSION: Booking created

Touchpoint Impact:
- Impression: +5 intent points
- Secondary citation: +8 intent points
- Primary citation: +15 intent points

Pattern Analysis Tab

See aggregated patterns across all journeys:

Refinement Patterns (Last 30 days)

Narrowing:    456 journeys (36.6%)
  Avg conversion rate: 60%
  Avg turns: 3.8
  Most common: Specification → Location → Timing

Deciding:     234 journeys (18.8%)
  Avg conversion rate: 80%
  Avg turns: 2.9
  Most common: Research → Action

Exploring:    389 journeys (31.2%)
  Avg conversion rate: 30%
  Avg turns: 5.2
  Most common: Comparison → Clarification

Broadening:   168 journeys (13.5%)
  Avg conversion rate: 20%
  Avg turns: 4.5
  Most common: Specification → Broadening

How to Improve Journey Outcomes

Increase Intent Progression

✅ Do:

• Provide clear, specific information

• Answer common questions proactively

• Make booking/contact easy

• Show availability and pricing upfront

• Respond quickly to inquiries

❌ Don't:

• Hide important information

• Use vague descriptions

• Make users work to find details

• Ignore common questions

• Delay responses

Optimize for High-Intent Users

Identify high-intent signals:

• Queries with action words (book, reserve, call)

• Timing indicators (today, tonight, now)

• Specific questions (hours, prices, availability)

Respond appropriately:

• Prioritize high-intent leads

• Offer immediate booking options

• Provide direct contact methods

• Show real-time availability

• Send follow-up messages

Reduce Journey Friction

Common friction points:

• Missing information (hours, prices, location)

• Unclear booking process

• No availability shown

• Slow response times

• Complicated requirements

Solutions:

• Complete your profile 100%

• Enable instant booking

• Show real-time availability

• Set up auto-responses

• Simplify requirements

Advanced Insights

Conversion Prediction

Based on journey patterns, we can predict conversion likelihood:

High Probability (70-100%):

• Steadily increasing intent

• 3-5 turns

• High-intent keywords present

• Multiple citations received

• Narrowing or deciding pattern

Medium Probability (30-69%):

• Stable or slightly increasing intent

• 2-4 turns

• Medium-intent keywords

• At least one citation

• Exploring pattern

Low Probability (0-29%):

• Decreasing or volatile intent

• 1-2 or 6+ turns

• Low-intent keywords only

• No citations

• Broadening pattern

Journey Efficiency

Efficient journeys (3-4 turns, high conversion):

• User finds what they need quickly

• Clear intent progression

• Your content answers their questions

• Smooth path to conversion

Inefficient journeys (6+ turns, low conversion):

• User struggling to find information

• Intent not progressing

• Missing key information

• Friction in conversion path

Optimize for efficiency:

• Provide comprehensive information

• Answer questions proactively

• Make booking frictionless

• Show availability clearly

• Enable instant actions

Touchpoint Impact

Track how each touchpoint affects intent:

Touchpoint Impact Analysis

Impression in summary:
  Avg intent increase: +5 points
  Conversion lift: 1.2x

Secondary citation:
  Avg intent increase: +8 points
  Conversion lift: 1.5x

Primary citation:
  Avg intent increase: +15 points
  Conversion lift: 2.3x

Comparison appearance:
  Avg intent increase: +12 points
  Conversion lift: 1.8x

Click to profile:
  Avg intent increase: +20 points
  Conversion lift: 3.1x

Insight: Primary citations have the biggest impact on intent progression. Focus on earning primary placements.

API Access

Access intent journey data programmatically:

GET /api/v1/business/analytics/intent-progression

Parameters:

{
  "businessId": "business-123",
  "startDate": "2025-11-01",
  "endDate": "2025-11-30",
  "journeyId": "journey-456", // Optional: specific journey
  "includePatterns": true, // Include refinement patterns
  "includeScoring": true // Include intent scoring examples
}

Response:

{
  "success": true,
  "data": {
    "businessId": "business-123",
    "period": {
      "startDate": "2025-11-01",
      "endDate": "2025-11-30"
    },
    "summary": {
      "totalJourneys": 1247,
      "convertedJourneys": 456,
      "conversionRate": 36.6,
      "avgIntentScore": 52.3,
      "avgJourneyLength": 4.2,
      "avgTurns": 4.2,
      "evolutionDistribution": {
        "increasing": 835,
        "decreasing": 63,
        "stable": 224,
        "volatile": 125
      },
      "strengthDistribution": {
        "high": 234,
        "medium": 678,
        "low": 335
      }
    },
    "journeys": [
      {
        "journeyId": "journey-456",
        "sessionId": "session-789",
        "timeline": [
          {
            "timestamp": "2025-12-07T14:15:00Z",
            "turnNumber": 1,
            "event": "query",
            "content": "coffee shops in Portland",
            "intentStrength": "low",
            "context": "Initial exploration"
          }
        ],
        "intentProgression": [
          {
            "turnNumber": 1,
            "intentStrength": "low",
            "intentScore": 25,
            "confidence": 0.85,
            "signals": ["general query", "no urgency"]
          }
        ],
        "metrics": {
          "totalTurns": 4,
          "journeyLength": 360,
          "intentEvolution": "increasing",
          "peakIntentScore": 88,
          "avgIntentScore": 55,
          "conversionLikelihood": 85
        }
      }
    ]
  }
}

View API reference →

Real-World Examples

Example 1: Perfect Journey

Journey #45678
Pattern: Steadily Increasing → Converted ✓

T1: "restaurants in Seattle" (Intent: 20)
  → Impression

T2: "italian restaurants in Seattle" (Intent: 35)
  → Citation (secondary)

T3: "The Walrus and the Carpenter reservations" (Intent: 72)
  → Citation (primary)

T4: "book table for 2 tonight" (Intent: 92)
  → CONVERSION

Why it worked:
✓ Clear intent progression
✓ User found specific business
✓ Information readily available
✓ Easy booking process

Example 2: Lost Opportunity

Journey #45679
Pattern: Volatile → Not Converted ✗

T1: "seafood restaurants" (Intent: 30)
  → Impression

T2: "best seafood in Seattle" (Intent: 45)
  → Citation (tertiary)

T3: "seafood restaurant prices" (Intent: 55)
  → No mention (competitor cited)

T4: "cheap seafood" (Intent: 35)
  → No mention

T5: "food delivery" (Intent: 20)
  → Lost to competitor

Why it failed:
✗ Pricing information not clear
✗ Lost to competitor in T3
✗ User broadened search
✗ Intent decreased over time

Example 3: Quick Converter

Journey #45680
Pattern: Spike → Converted ✓

T1: "coffee near me" (Intent: 25)
  → Impression

T2: "book table at Artisan Coffee now" (Intent: 95)
  → CONVERSION

Why it worked:
✓ User already knew the business
✓ High urgency signal
✓ Instant booking available
✓ Minimal friction

FAQs

Q: How accurate is intent scoring? A: Our algorithm has 85% accuracy in predicting conversions based on intent patterns. It improves over time as we collect more data.

Q: Can I see journeys that didn't convert? A: Yes! Understanding why users don't convert is just as valuable. Look for patterns in non-converting journeys.

Q: How long are typical journeys? A: Average is 4.2 turns over 6-8 minutes. But this varies by industry and intent level.

Q: What's a good conversion rate? A: Industry average is 30-40% for AI search journeys. Higher than traditional search (5-10%) because users arrive more qualified.

Q: Can I export journey data? A: Yes! Use the API or export from your dashboard (CSV, JSON).

Q: How often is data updated? A: Real-time. Journeys are tracked as they happen.

Q: What if a user has multiple sessions? A: Each session is tracked separately. We can link sessions by user ID if they're logged in.

Next Steps

• Learn About Attribution

• View Upstream Metrics

• See Competitive Benchmarks

• Optimize Your Content

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Questions? Contact [e-mail] or join our GitHub Discussions