Continuous Engine Improvement

The improvement cycle runs quarterly. Step one: aggregate all outcomes from the last quarter. Step two: analyze which recommendations produced the expected outcomes and which did not. Step three: identify the patterns in failed recommendations — which contexts led to wrong recommendations? Step four: update the logic layer — new rules, retrained models, adjusted ranking weights. Step five: validate the updated engine against a holdout set of recent decisions. Step six: deploy the improved engine. The cycle is non-negotiable. An engine that ran well in Q1 may recommend poorly in Q3 because the market, the customers, and the competitive landscape have changed. The quarterly cycle keeps the engine aligned with current reality.

1. Quarterly Outcome Analysis What percentage of recommendations were accepted? Of those accepted, what percentage produced the expected outcome? Of those overridden, what were the outcomes? This analysis reveals both engine accuracy and calibration.
2. Pattern Analysis on Failures When the engine recommended wrong, what was different about the context? New customer segment, unusual deal size, competitive change? The failure patterns reveal the edges of the engine's competence.
3. Logic Update and Validation Update rules or retrain models based on the analysis. Validate on the most recent quarter's decisions. If the updated engine would have produced better recommendations, deploy it. If not, investigate further before changing.