Customer Qualification Scorecard
Assess if your prospect is ready for your AI solution
How This Works
Answer the questions below about your solution and your prospect. The scorecard will analyze the fit and provide a Go/No-Go recommendation with specific gaps to address.
💡 Tip: Be honest about your prospect's capabilities. False positives lead to failed projects.
Pro Tips for Maximum Value
🎯 When to Use This Tool
Use this after discovery questions but before investing in a detailed proposal. The scorecard helps you avoid wasting time on prospects who aren't ready. A "No-Go" saves you months of effort on a deal that would fail anyway.
✅ Best Practices
- •Be brutally honest - Overestimating prospect capabilities leads to failed implementations that damage your reputation
- •Share results internally - Use scorecard output to align sales, engineering, and exec teams on deal feasibility
- •For "Caution" scores - Present gaps to the prospect transparently and negotiate gap-filling into the SOW
- •Track over time - Save scorecard results to measure if a prospect becomes more qualified 6-12 months later
🚨 Red Flags That Override Score
- ✗Prospect says "we'll figure out data quality later" (they won't - walk away)
- ✗No executive sponsor or champion (project will die in committee)
- ✗Timeline expectation is 50%+ shorter than typical for the tier (unrealistic)
🔗 Combine with Other Tools
If "Go" → Use Pricing Framework to build a proposal → Use ROI Calculator to justify investment
If "Caution" → Use Technical Readiness Assessment to detail specific gaps to address
If "No-Go" → Politely decline or recommend data readiness services before re-engaging
Assessment Questions
Unsure? View tier definitions
Learn more: Data Readiness Guide
Continue Your Workflow
For qualified prospects, these tools help you build the proposal: