# KPI Evaluation

![](/files/-MZHrHn--8kxoZabNgc2)

Once your product is deployed and users start interacting with it, their insights will guide you through the next optimizations steps. A good way to evaluate the success of these is by defining **KPI (Key Performance Indicator)** that are aligned with your [Business Objectives](/productize-ml/productize-it/ml-lifecycle.md#business-objectives) as described before.

Based on the customer interactions with the product, you will be able to customize the product experience for each customer segment. During this iterative process, there must be constant communication between all stakeholders involved, in order for Product Managers to guide the Data Science team toward the most impactful features to include for the business.

## Business Impact Analysis

**Business Impact Analysis** (BIA) tries to predict the consequences of disruptions to a business by collecting data and measuring metrics to develop alternative strategies if our proposed solution fails. For AI solutions, BIA is highly recommended since the risk of integration and customer adoption is usually pretty high.

## Readings

{% embed url="<https://www.mckinsey.com/featured-insights/artificial-intelligence/notes-from-the-ai-frontier-modeling-the-impact-of-ai-on-the-world-economy>" %}


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