Understanding Customer Journeys
See the complete path from first click to purchase. Understand which touchpoints actually drive conversions.
A customer sees your Facebook ad. They don't buy. A week later, they Google your brand. They browse, but don't buy. Then they get your email. Finally, they purchase.
Who gets credit for that sale?
If you're using last-click attribution, the email gets 100%. But was it really the email that convinced them? Or did the Facebook ad plant the seed?
Customer journeys reveal the truth.
Why Journeys Matter
The Multi-Touch Reality
Here's what typical purchase paths actually look like:
| Industry | Avg. Touchpoints | Avg. Days to Convert |
|---|---|---|
| Fashion E-commerce | 4-6 | 3-7 days |
| High-Value Products | 8-12 | 14-30 days |
| B2B SaaS | 10-20 | 30-90 days |
| Impulse Purchases | 1-2 | Same day |
The insight: If it takes 8 touchpoints to convert, and you're only optimizing for the last one, you're ignoring 87.5% of the journey.
What You Miss Without Journey Data
| Scenario | What You Think | What's Actually Happening |
|---|---|---|
| Facebook ROAS is 1.5x | Underperforming | Facebook starts 60% of journeys that convert via Google |
| Email ROAS is 12x | Amazing channel | Email only converts warm leads from paid ads |
| Direct traffic is huge | Great brand awareness | Actually retargeting cookies from last week |
Anatomy of a Journey
Every journey we track includes:
The Journey Object
****
āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā ā CUSTOMER JOURNEY ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā¤ ā ā ā ID: journey_abc123 ā ā Customer: customer_xyz789 ā ā Status: converted ā ā ā ā Timeline: ā ā āāā Started: Jan 15, 2024 @ 3:22 PM ā ā āāā Converted: Jan 22, 2024 @ 8:45 PM ā ā āāā Duration: 7 days, 5 hours ā ā ā ā Touchpoints: 6 ā ā āāā [1] Facebook Ad Click (Jan 15, 3:22 PM) ā ā āāā [2] Organic Search (Jan 17, 10:15 AM) ā ā āāā [3] Direct Visit (Jan 19, 6:30 PM) ā ā āāā [4] Email Click (Jan 21, 9:00 AM) ā ā āāā [5] Product View (Jan 22, 8:30 PM) ā ā āāā [6] Purchase (Jan 22, 8:45 PM) - $127 ā ā ā ā Attribution (Position-Based): ā ā āāā Facebook Ad: $50.80 (40%) ā ā āāā Organic Search: $12.70 (10%) ā ā āāā Direct: $12.70 (10%) ā ā āāā Email: $50.80 (40%) ā ā ā āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā ****Journey Statuses
| Status | Meaning | What Happens |
|---|---|---|
| in_progress | Customer is active, no purchase yet | Still collecting touchpoints |
| converted | Purchase completed | Attribution calculated |
| abandoned | 30+ days, no purchase | Journey closed, marked abandoned |
| expired | Exceeded attribution window | Rare, usually very long journeys |
Touchpoint Types
We categorize every interaction into touchpoint types:
Paid Channels
These are your ad dollars at work:
| Touchpoint | What It Captures | Common Sources |
|---|---|---|
| paid_search | Search ads | Google Ads, Bing Ads |
| paid_social | Social ads | Meta, TikTok, LinkedIn |
| display | Banner/programmatic | GDN, The Trade Desk |
| video | Video ads | YouTube, CTV |
| native | Native ads | Taboola, Outbrain |
| affiliate | Affiliate links | Rakuten, ShareASale |
| influencer | Influencer posts | Tracked via UTMs |
Organic Channels
Free traffic (sort of):
| Touchpoint | What It Captures | How It's Identified |
|---|---|---|
| organic_search | SEO traffic | Referrer from Google/Bing without gclid |
| organic_social | Non-paid social | Referrer from social domains |
| direct | Direct/bookmark | No referrer |
| referral | Other sites | Referrer from non-search, non-social |
Owned Channels
Channels you control:
| Touchpoint | What It Captures |
|---|---|
| Email campaigns | |
| sms | Text messages |
| push | Push notifications |
| in_app | In-app messages |
Offline Channels
Beyond digital:
| Touchpoint | How to Track |
|---|---|
| store | POS integration |
| phone | Call tracking |
| event | Event check-in |
Attribution Models Compared
The big question: How do you divide credit among touchpoints?
Model Comparison
| Model | How Credit is Divided | Best For |
|---|---|---|
| Last Click | 100% to final touchpoint | Simple analysis, bottom-funnel focus |
| First Click | 100% to first touchpoint | Awareness channel evaluation |
| Linear | Equal split across all | Fair, unbiased view |
| Time Decay | More credit to recent touchpoints | Short purchase cycles |
| Position Based | 40% first, 40% last, 20% middle | Balanced emphasis |
| AI-Powered | ML model based on your data | Most accurate for your business |
Real Example: $100 Purchase
A customer's journey: Facebook Ad ā Organic Search ā Email ā Direct ā Purchase
| Model | Organic | Direct | ||
|---|---|---|---|---|
| Last Click | $0 | $0 | $0 | $100 |
| First Click | $100 | $0 | $0 | $0 |
| Linear | $25 | $25 | $25 | $25 |
| Time Decay | $10 | $15 | $25 | $50 |
| Position Based | $40 | $10 | $10 | $40 |
| AI-Powered | $35 | $20 | $30 | $15 |
The insight: Last click says Facebook did nothing. First click says email did nothing. Both are wrong. AI-powered attribution weights based on what actually drives conversions in your specific business.
Which Model Should You Use?
****` Are you optimizing awareness campaigns? āāā Yes ā First Click or Position Based āāā No ā
Is your purchase cycle < 7 days? āāā Yes ā Time Decay or Last Click āāā No ā
Do you have 100+ conversions/month? āāā Yes ā AI-Powered (best accuracy) āāā No ā Linear or Position Based ****`
Customer Lifecycle Stages
As customers interact with your brand, they progress through stages:
The Lifecycle
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| Stage | Criteria | Marketing Action |
|---|---|---|
| Anonymous | Unknown identity | Retargeting, lead magnets |
| Identified | Email/phone collected | Nurture sequences |
| Lead | Showed purchase intent | Sales outreach, offers |
| Customer | First purchase | Onboarding, cross-sell |
| Repeat Customer | 2+ purchases | Loyalty program |
| VIP | Top 5% by value | VIP treatment, exclusives |
| Churned | 90+ days inactive | Win-back campaign |
| Reactivated | Returned after churn | Re-engagement focus |
Stage Transitions
The AI tracks transitions and identifies patterns:
| Transition | Typical Trigger | Insight |
|---|---|---|
| Anonymous ā Identified | Newsletter signup, checkout | Collect email early in funnel |
| Customer ā Repeat | Re-purchase within 60 days | Send reminder email at day 45 |
| Repeat ā VIP | 5+ purchases or high LTV | Invite to VIP program at 4th purchase |
| VIP ā Churned | 90+ days no purchase | Intervene before churn (day 60 alert) |
Using Journey Data
In the Dashboard
Go to Analytics ā Journeys to see:
| View | What It Shows |
|---|---|
| Journey Explorer | Individual journey timelines |
| Path Analysis | Most common conversion paths |
| Attribution Report | Revenue by channel and model |
| Funnel Analysis | Drop-off between stages |
Via API
****`bash GET /api/v1/tracking/journeys?customerId=xyz789
Response: { "journeys": [...], "summary": { "totalJourneys": 3, "convertedJourneys": 2, "avgTouchpoints": 5.3, "avgDaysToConvert": 8.2 } } ****`
For AI Optimization
Journey data powers AI recommendations:
- "Customers who see 3+ product pages convert at 4x higher rate"
- "Email touchpoints within 24 hours of cart abandonment have 22% conversion rate"
- "Facebook + Google together drive 3x more conversions than either alone"
Recap
Here's what you learned:
- Last-click is lying to you - Most customers need multiple touchpoints
- Journeys show the complete picture - Every interaction from first touch to purchase
- Attribution models vary widely - Choose based on your business
- Lifecycle stages help targeting - Right message at right stage
- Journey data powers AI - The more complete, the smarter recommendations
Understanding journeys transforms how you think about marketing. It's not "which channel performs best?" but "how do channels work together?"
Next step: Configure attribution settings to choose your default model.
Key Takeaways
- 1Average customer needs 6-8 touchpoints before purchasing
- 26 attribution models to choose from (including AI-powered)
- 3Track journey across devices when users are identified
- 4Predict customer lifetime value based on journey patterns
Frequently Asked Questions
How do you track users across devices?
What happens to abandoned journeys?
Can I customize the attribution window?
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