For a conceptual overview of Goal Analytics, Journey Analytics, and uplift measurement, see Analytics in Product Concepts.
6.1 Goal Analytics
Goal Analytics shows performance toward your business goals.Accessing Goal Analytics
- Click Analytics > Goal Analytics
- Select an goal from the dropdown
What is an Goal?
An goal is a measurable business goal:- Increase purchases
- Boost app engagement
- Reduce churn
- Drive signups
Key Metrics
Understanding Uplift
Uplift measures the incremental impact of your treatments.- Treatment conversion rate: 5.0%
- Control conversion rate: 4.0%
- Uplift = (5.0 - 4.0) / 4.0 = 25%
Goal Analytics Dashboard
The dashboard shows:Date Range Selection
Filter analytics by time period:- Today
- Yesterday
- Last 7 Days
- Last 30 Days
- Last 90 Days
- Custom range
Interpreting Results
Positive Uplift (Good):- Treatments are driving incremental conversions
- Your personalization strategy is working
- Treatments may not be effective
- Check content, targeting, or timing
- Consider A/B testing alternatives
6.2 Journey Analytics
Journey Analytics shows journey-level performance.Accessing Journey Analytics
- Click Analytics > Journey Analytics
- Select a journey from the dropdown
Journey-Level Metrics
Journey Analytics Dashboard
The dashboard includes:Treatment Comparison
Compare treatments within a journey:
Use this to identify top performers and optimize.
Surface Analysis
See how different surfaces perform:Filtering Options
Filter journey analytics by:- Date range
- Treatment type
- Surface
- Status
6.3 Key Metrics Explained
Engagement Metrics
Impressions- Count of times treatments were shown to users
- Each view = one impression
- Same user can have multiple impressions
- Count of user interactions with treatments
- Clicking CTA button
- Engaging with treatment content
Conversion Metrics
Conversions- Users who completed the goal action
- E.g., made a purchase, signed up, completed profile
Impact Metrics
Uplift- The incremental impact of treatments
- Compares treated users vs control group
- Positive uplift = treatments are working
- Whether uplift is reliable or due to chance
- Look for confidence indicators
- Generally need 95%+ confidence
6.4 Control Groups
What is a Control Group?
A control group is a subset of users who don’t receive treatments, used to measure true impact.How Control Groups Work
Why Control Groups Matter
Without a control group, you can’t know if conversions would have happened anyway. Example:- 1,000 users converted after seeing treatment
- But how many would have converted without treatment?
- Control group answers this question
Control Group Size
Typical control group sizes:- 5-10% of eligible users
- Must be large enough for statistical significance
- Balance: larger = better measurement, but fewer treated users
6.5 Reading Analytics Reports
Time Series Charts
What to look for:- Overall trends (up, down, flat)
- Sudden changes (investigate causes)
- Day-of-week patterns
- Seasonal effects
Comparison Tables
Sorting:- Click column headers to sort
- Identify top/bottom performers
- Use filters to focus analysis
- Compare similar treatments fairly
Export Options
Export data for further analysis:- CSV download
- Date range selection
- Metric selection
6.6 Analytics Best Practices
Regular Review Cadence
What to Monitor Daily
- Sudden drops in impressions (delivery issues?)
- Unusual CTR changes (content problems?)
- Conversion rate shifts (external factors?)
What to Analyze Weekly
- Treatment comparison within journeys
- Surface performance patterns
- Uplift trends
Optimization Process
- Measure - Establish baseline metrics
- Analyze - Identify improvement opportunities
- Hypothesize - What change might improve results?
- Test - Create variant treatments
- Learn - Analyze results, apply learnings
- Repeat - Continuous improvement
Common Analysis Mistakes
6.7 Attribution
What is Attribution?
Attribution determines which treatments get credit for conversions.Attribution Window
The time period during which a conversion is attributed to a treatment. Example: If attribution window is 7 days:- User sees treatment on Day 1
- User converts on Day 5
- Conversion is attributed to the treatment
Multi-Touch Attribution
When users see multiple treatments:- First touch: Credit to first treatment
- Last touch: Credit to last treatment
- Multi-touch: Credit distributed
Note: Attribution settings are configured by your admin. Contact them for details on your attribution model.
6.8 Analytics Troubleshooting
No Data Showing
Possible Causes:- Date range doesn’t include activity
- Journey/treatments are new
- Filters are too restrictive
- Data processing delay
- Adjust date range
- Remove filters
- Wait for data processing (up to 24 hours)
Metrics Look Wrong
Check:- Correct goal/journey selected
- Date range is appropriate
- No filters hiding data
- Compare to raw treatment counts
Uplift is Negative
Investigate:- Treatment content issues
- Wrong audience targeting
- Timing problems
- External factors affecting control differently
CTR is Zero
Check:- Treatments are actually delivering (impressions > 0)
- CTA is visible and clickable
- Tracking is configured correctly
6.9 Advanced Analytics Features
Cohort Analysis
Analyze performance by user cohorts:- Sign-up date cohorts
- Behavioral cohorts
- Segment-based analysis
A/B Testing Analysis
Compare treatment variants:- Statistical significance indicators
- Winner determination
- Confidence intervals
Custom Metrics
Some organizations configure custom metrics. Check with your admin for available custom metrics.6.10 Analytics Permissions
Who Can Access Analytics
Requesting Access
If you can’t access analytics:- Note the specific section you need
- Contact your admin
- Request appropriate permissions