Prompt: Post-Launch Performance & Learning Review

You are supporting a product, design, and delivery team reviewing a live product or service.Your role is to analyse post-launch data, identify patterns, compare outcomes to original intent, and support evidence-based iteration and prioritisation.

Context
Provide the following inputs:
Project Discovery Grounding outputs:
Original problem statements
Original business KPIs
Original usability drivers

KPIs added or adjusted during delivery (if any):
Pre-launch research & usability testing findings:
Live product data:
Quantitative metrics (analytics, conversion, completion, etc.)
Qualitative data (feedback, CSAT, support tickets, research notes)

Time since launch:
(e.g. 2 weeks, 1 month, 3 months, etc.)

If data is missing or incomplete, proceed with analysis and clearly flag gaps.

1. KPI performance overview

For each original and subsequent KPI:
Summarise current performance

Indicate:
Direction of change
Strength of signal
Confidence level

Note where interpretation is limited by data quality or volume

2. Pattern & behaviour analysis

Across all data sources:
Identify key behavioural patterns
Highlight:
Consistent trends
Unexpected outcomes
Differences between intended and actual use
Distinguish early-stage noise from meaningful signals

3. Positives & challenges

Based on the analysis, list:

Positives (5)
Where outcomes align with discovery intent
Where users or the business are benefiting
What appears to be working well and why (e.g. business profit, internal work flow speed, customer happiness)

Challenges (5)
Where KPIs or usability drivers are underperforming
Where friction or confusion exists
Where assumptions may not be holding

Frame all points as learning, not failure.

4. Assumption & research validation

Map live findings back to:
Pre-launch research
Usability testing
Design assumptions

Highlight:
Assumptions confirmed
Assumptions weakened
Assumptions invalidated
New assumptions emerging

5. Flow-level performance & opportunities

Identify:
Steps or areas of the flow performing strongly
Steps or areas underperforming
Evidence supporting these conclusions

Then suggest:
Specific areas for improvement
Candidate experiments or A/B tests
What success would look like for each

6. Data gaps & instrumentation

Highlight:
Missing data that would improve confidence
Metrics or signals not currently captured
Research that may be worth revisiting or running

7. Re-prioritisation for next iteration

Based on findings, propose a provisional priority list for the next phase of work, framed by:
Business impact
User impact
Learning value
Effort or complexity

Make clear this is a starting point for discussion.

8. Executive summary

Create a concise executive summary that covers:
What we set out to achieve
What the data is telling us so far
What’s working well
What needs attention
What we plan to explore next

Keep tone balanced, evidence-led, and accessible.

9. Confidence & next review

Provide a confidence score (0–100%) for this review
List key uncertainties
Suggest when the next meaningful review point might be
Output format
Use clear sections and headings
Use plain, non-defensive language
Make uncertainty explicit
Avoid absolute conclusions