Recommendations#
Vector's core value proposition: turning raw analytics data into prioritized, actionable recommendations.
How It Works#
- Data Collection: Vector pulls metrics from your connected integrations
- Pattern Analysis: AI identifies anomalies, trends, and opportunities
- Impact Scoring: Each insight is scored on impact, confidence, and ease
- Ranking: Recommendations are sorted by overall priority score
- Specification: Generate LLM-ready specs for engineering handoff
Impact Scoring#
Every recommendation includes three scores on a 1-10 scale:
Impact Score#
What it measures: Potential business value if implemented
Factors:
- Revenue impact (direct or indirect)
- User experience improvement
- Conversion rate potential
- Traffic/engagement lift
Example: A checkout optimization affecting 45% of users with $12k/mo potential lift = Impact 8.5
Confidence Score#
What it measures: How certain Vector is about this recommendation
Factors:
- Data volume (more data = higher confidence)
- Statistical significance
- Pattern consistency over time
- Multiple corroborating signals
Example: Clear drop-off at a specific step with 10,000+ data points = Confidence 9
Ease Score#
What it measures: Implementation complexity (higher = easier)
Factors:
- Technical complexity
- Design changes required
- Dependencies and risks
- Estimated effort
Example: Adding social proof (copy + testimonials) = Ease 9 Example: Full checkout redesign = Ease 5
Priority Score#
The overall priority score combines all three:
Priority = (Impact × 0.4) + (Confidence × 0.3) + (Ease × 0.3)
This formula prioritizes high-impact, high-confidence, easy wins—the low-hanging fruit that delivers ROI fastest.
Recommendation Categories#
Recommendations are tagged by category:
| Tag | Focus Area |
|-----|------------|
| conversion | Funnel optimization, checkout, signups |
| engagement | User retention, feature adoption |
| ux | Navigation, usability, accessibility |
| performance | Speed, load times, technical debt |
| growth | Acquisition, virality, referrals |
Recommendation Lifecycle#
Pending#
New recommendations awaiting review. These appear in your main feed.
Actions:
- Accept: Move to your implementation queue
- Dismiss: Remove from active view (can be recovered)
- Generate Spec: Create engineering handoff document
Accepted#
Recommendations you've decided to implement.
Track these through implementation and measure results.
Dismissed#
Recommendations you've decided not to pursue.
Reasons might include:
- Already planned or in progress
- Not aligned with current priorities
- Technical constraints
- Business reasons
Dismissed items can be reviewed and reconsidered later.
Generating New Recommendations#
Click Generate New to trigger a fresh analysis. This will:
- Pull latest data from all connected integrations
- Run AI analysis across all metrics
- Generate new insights based on recent patterns
- Add new recommendations to your feed
Note: Generation uses AI credits. We recommend running this weekly or after significant product changes.
Best Practices#
- Review weekly: Set a recurring time to review new recommendations
- Accept strategically: Don't accept everything—prioritize what aligns with current goals
- Track outcomes: After implementing, note the actual impact
- Provide feedback: Dismissing with reasons helps improve future recommendations