Feature ROI Framework
A structured method for estimating the return on investment of product features in B2B SaaS environments where revenue attribution is complex.
Context
In B2B SaaS, feature ROI is rarely obvious. Features do not generate direct revenue in isolation. They influence retention, expansion, win rate, and enterprise deal size — all of which involve attribution complexity. This framework provides a systematic approach to estimating feature value.
Model Explanation
Feature ROI is composed of four impact vectors:
Retention impact Does this feature reduce churn risk in the existing customer base? Estimate: (affected ARR) × (churn reduction %) × (confidence %).
Expansion impact Does this feature unlock upsell or cross-sell revenue? Estimate: (expansion pipeline influenced) × (conversion uplift %) × (confidence %).
Acquisition impact Does this feature improve win rate in competitive deals or open new segments? Estimate: (affected deal pipeline) × (win rate uplift %) × (confidence %).
Cost impact Does this feature reduce support burden, implementation complexity, or operational cost? Estimate: (cost category) × (reduction %).
Sum these vectors against the fully-loaded cost of building, shipping, and maintaining the feature.
Application
Use Feature ROI when:
- Prioritizing between features that serve different commercial outcomes
- Justifying engineering investment to a CFO or board
- Deciding whether to build a feature for a single enterprise customer vs. the broader market
Decision Impact
Feature ROI converts subjective prioritization debates into quantitative estimates with explicit confidence levels. It also forces product and commercial teams to align on the assumptions behind each estimate — which is often more valuable than the number itself.