What metrics would you use to evaluate Anaplan's Predictive Insights feature?

Introduction

Evaluating Anaplan's Predictive Insights feature requires a comprehensive approach to product success metrics. To address this challenge effectively, I'll follow a structured framework that covers core metrics, supporting indicators, and risk factors while considering all key stakeholders.

Step 1

Product Context

Anaplan's Predictive Insights feature is an AI-powered tool integrated into their Connected Planning platform. It leverages machine learning algorithms to analyze historical data and generate forecasts, helping businesses make more informed decisions.

Key stakeholders include:

1. Financial planners and analysts (primary users)
2. C-suite executives (decision-makers)
3. IT departments (implementation and maintenance)
4. Anaplan's product team (development and improvement)

User flow:

1. Data input: Users upload or connect relevant historical data.
2. Model selection: The system suggests appropriate predictive models.
3. Forecast generation: AI algorithms process data and generate predictions.
4. Analysis and refinement: Users interpret results and adjust parameters as needed.

This feature aligns with Anaplan's strategy of enhancing their platform's predictive capabilities, differentiating them in the competitive enterprise planning software market. Compared to competitors like Oracle and SAP, Anaplan's Predictive Insights aims to offer a more user-friendly, flexible approach to AI-driven forecasting.

Product Lifecycle Stage: Early growth. The feature has been released but is still evolving and gaining adoption among Anaplan's customer base.