DS-301d · Module 2

Data-Driven Target Setting

3 min read

Targets that are too easy produce complacency. Targets that are impossible produce disengagement. The optimal target is 10-20% above the current baseline — achievable with improved performance, but not without effort. AI-assisted target setting uses historical performance trends, market conditions, and investment changes to propose targets that are calibrated to the specific context. A team that improved conversion rate by 3% per quarter for four quarters should target 3-5% improvement, not 15%. A team launching a new product might target 20% above baseline because the investment warrants it. Context determines the target. The target determines the behavior.

Do This

  • Set targets based on historical trend data plus a stretch factor appropriate to the investment
  • Differentiate targets by segment — mature products and new products have different improvement curves
  • Review and adjust targets quarterly based on actual performance and changing conditions

Avoid This

  • Set targets by adding 20% to last year's number regardless of context — aspiration is not methodology
  • Set the same improvement target for a mature product and a new one — the growth curves are different
  • Punish target misses without investigating whether the target was calibrated correctly