The “Conversion Rate Optimization Prompt” provides a structured template for using Polar MCP (with data from tools like Google Analytics) to systematically analyze an ecommerce site’s conversion performance. It specifies the data inputs needed (traffic sources, device type, user segments, funnel steps, product/pricing breakdowns, etc.), the analyses to run (site-wide conversion rate, funnel drop-offs, segment comparisons, product/pricing effects), and the output format — including prioritized recommendations (quick wins, tests, larger initiatives) along with a projected revenue impact
Ensure you have the Polar MCP installed (see Using Polar MCP).
Create a new Project in Claude.
In the Instructions box paste the instructions from below.
Update the
USER INPUTSto specify details.To generate a report, start a new chat in the project with something like: “run the report”.
You can then:
ask follow-up questions to go deeper
tell Claude you want to amend the report
customise the instructions yourself for future use
Instructions
Put these in a Claude Project “Instructions” fields.
USER INPUTS:
- Analysis Period: {time_period} [default: last 90 days]
- Email Revenue Target: {revenue_target}% of total [or specific $]
- Include SMS: {include_sms} [Y/N - if using Attentive]
PROMPT:
Analyze email {include_sms ? "and SMS" : ""} marketing performance for {time_period} using Klaviyo {include_sms ? "and Attentive" : ""} data.
**Overall Email Performance:**
- Email revenue contribution: (percentage_email_revenue)
- Current: X% of total revenue
- Target: {revenue_target}%
- Gap to target: $X
**Flow vs Campaign Analysis:**
- Flow Performance:
- Revenue (klaviyo_sales_main.raw.flow_revenue)
- Orders (flow_orders)
- Revenue per recipient (flow_revenue_per_subscriber)
- Conversion rate (flow_placed_order_rate)
- Campaign Performance:
- Revenue (campaign_revenue)
- Orders (campaign_orders)
- Revenue per recipient (campaign_revenue_per_subscriber)
- Conversion rate (campaign_placed_order_rate)
**Top 5 Revenue-Generating Flows:**
1. [Flow name]: $X revenue, X% conversion rate
2. [Flow name]: $X revenue, X% conversion rate
[Identify optimization opportunities for each]
**Bottom 5 Flows by Conversion:**
[List with specific fixes needed]
**Campaign Performance Patterns:**
- Best performing day of week
- Optimal send time
- Subject line patterns that work (if identifiable)
- Ideal frequency (correlate sends with unsubscribe rate)
**Engagement Metrics:**
- Open rate (campaign_unique_open_rate) vs industry benchmark
- Click rate (campaign_unique_click_rate) vs benchmark
- Unsubscribe rate (campaign_unsubscribe_rate) - flag if >0.5%
- Email list growth rate
**Segmentation Performance:**
- New vs returning customer emails
- By purchase frequency (identify segments if named)
- By engagement level
- Revenue per segment
{include_sms ?
"**SMS Performance:**
- Total SMS sent (attentive_events.raw.total_sms_sent)
- Campaign vs Journey breakdown
- Subscription vs unsubscription rate
- SMS ROI calculation" : ""}
**5 Implementable Recommendations to Reach {revenue_target}:**
1. **Flow Optimizations** (implement this week):
- Add [specific flow] based on behavior gap
- Optimize [flow name] timing/content
- Expected impact: +$X monthly
2. **Segmentation Strategy** (next 2 weeks):
- Create VIP segment for top 20% customers
- Win-back campaign for 90-day lapsed
- Expected impact: +$X monthly
3. **Campaign Improvements**:
- Shift sends to [optimal day/time]
- Increase frequency to [X] per week for engaged segments
- Expected impact: +$X monthly
4. **Automation Additions**:
- Price drop alerts
- Back in stock notifications
- Expected impact: +$X monthly
5. **Testing Roadmap**:
- Subject line A/B tests
- Design template tests
- Send time optimization
**Total Expected Revenue Increase:**
$X monthly (+X% of total revenue)
Timeline to reach {revenue_target}: X weeks