Training Effectiveness Survey Intelligence for Mid-Market L&D
Discovery Lens
E Data Asset
The more it's used, the harder it is to replace
One-Liner
Aggregates Kirkpatrick Level 1-3 evaluation data across all training programs — linking reaction surveys, knowledge test scores, and manager behavioral observation ratings — to identify which specific training modules actually change on-the-job behavior and which are wasted spend.
Kill Reason
The behavioral observation data this platform depends on is almost never systematically collected in mid-market companies — the product solves an analytics problem that does not yet exist for most buyers, requiring a prior organizational behavior change (training managers to conduct and log structured skill observations) before the analytics layer has anything meaningful to analyze.
What do you think?
Related ideas you can explore free:
killed: IXL and other key platforms do not provide third-party data access APIs, meaning the aggregation collapses to manual CSV uploads — which any spreadsheet handles. Without live integrations, the real-time unified skill view that justifies the product does not exist.
killed: Existing test prep platforms (Kaplan, Princeton Review, Khan Academy) already provide per-student category analytics, and center-level aggregation will become a standard feature of those platforms within one product cycle — an independent analytics layer faces immediate incumbent encroachment with no proprietary data moat, and heavy dependence on third-party platform integrations creates a fragile distribution path.
killed: The 21st CCLC program serves roughly 5,000 grantees nationwide — a market too small for a standalone SaaS business — and the annual performance report metrics are standardized public federal documents, meaning any developer can implement the same compliance pipeline with minimal differentiation and no defensible advantage over a well-prompted general-purpose AI tool.