You’re facing a strategic decision about how to test a new high-ticket offer ($1,000) alongside your existing offer ($50 with 15% conversion rate). The core question is whether to test the new offer by inserting it in the upsell flow (conservative approach) or by replacing the current main offer (higher risk but faster testing).
Key Findings:
Risk vs. velocity tradeoff is the central consideration
Current offer generates reliable revenue ($7.50 average value per visitor)
New offer has potential for 20× revenue per conversion but unknown conversion rate
Testing in the upsell position may provide insufficient data
Direct replacement risks significant revenue disruption
Top Recommendations:
Implement a phased testing approach with controlled traffic allocation to balance risk and insights
Start with a 90/10 split favoring your existing offer
Establish clear conversion thresholds for expanding the test
2. Decision Matrix
Criteria
Weight
Option 1: Test in Upsell
Option 2: Replace Main
Option 3: Phased Testing
Risk Level
30%
9 (Low risk)
3 (High risk)
7 (Moderate risk)
Test Velocity
25%
3 (Very slow)
9 (Very fast)
7 (Moderate speed)
Data Quality
25%
4 (Limited sample)
8 (Full traffic)
8 (Controlled sample)
Revenue Protection
20%
8 (Minimal disruption)
2 (High disruption)
7 (Managed disruption)
Weighted Score
100%
6.05
5.5
7.25
Risk-Adjusted Outcomes:
Scenario
Test in Upsell
Replace Main
Phased Testing
Best Case
$50 offer maintains + small $1,000 upsell revenue
$1,000 offer converts at 1%+ ($10+ per visitor)
Identifies optimal offer mix with minimal revenue loss
Expected Case
Inconclusive data after weeks of testing
Either clear success or failure within days
Clear direction within 2 weeks with <10% revenue impact
Worst Case
Months of testing with no actionable data
Complete revenue collapse if $1,000 offer fails
10-15% temporary revenue reduction during testing
3. Action Plan
Immediate Next Steps:
Create the sales page for the $1,000 high-ticket offer
Set up split testing infrastructure to control traffic allocation
Establish baseline metrics for current conversion rates and revenue
Define clear success criteria for the high-ticket offer (minimum acceptable conversion rate)
Resource Allocation:
Marketing: Create compelling value proposition for high-ticket offer
Tech: Implement split testing functionality and tracking
Analytics: Set up dashboard to monitor real-time conversion data
Sales: Prepare to handle higher-touch inquiries for premium offer
Timeline:
Week 1: Preparation and infrastructure setup
Week 2: Initial testing with 90/10 split (90% to current offer)
Week 3: First data review and potential allocation adjustment
Week 4: Comprehensive analysis and expansion decision
Week 6: Full implementation of winning strategy
Success Metrics:
Primary: Total revenue per visitor (current: $7.50)
Secondary: Conversion rate of high-ticket offer (target: ≥0.75%)
Tertiary: Customer satisfaction and retention across offers
Monitoring Framework:
Daily: Conversion rates, revenue, and technical performance
Weekly: Statistical significance of test data
Bi-weekly: Decision points for traffic allocation adjustments
4. Contingency Planning
Key Risk Triggers:
High-ticket conversion falls below 0.5% after sufficient traffic
Overall revenue drops by more than 15% during testing period
Technical issues impact the customer journey
Sample size remains too small after 2 weeks
Alternative Paths:
1. If high-ticket performs exceptionally well (>1% conversion):
Accelerate traffic allocation to 50/50 split
Prepare for full replacement ahead of schedule
2. If high-ticket underperforms (<0.5% conversion):
