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Tutorial 4D: Building a time series model for NPS with H2O Driverless AI

Introduction​

Overview​

Customer satisfaction is a cornerstone of success in the banking sector, and the Net Promoter Score (NPS) serves as a critical metric to measure customer loyalty and satisfaction. NPS is determined by asking customers a simple question, such as, How likely are you to recommend our bank to a friend or colleague? Individual customers give a score from 0 - 10 based on how likely they are to recommend the brand to their friends and family. Customers who score 9 and 10 are classified as promoters, 0 to 6 detractors and 7 and 8 are passives. The overall NPS is calculated as the difference between the percentage of Promoters and Detractors

Objectives​

Understanding and predicting NPS over time allows banks to take proactive steps to address customer concerns, improve services, and boost overall satisfaction. This tutorial demonstrates how to build a time series model using H2O Driverless AI to forecast NPS, enabling data-driven decision-making in the banking sector.

By the end of this tutorial, you will,

  • Understand the importance of forecasting NPS in the banking sector.
  • Learn the steps to build a time series model using H2O Driverless AI.
  • Gain insights into training, and interpreting time series models for actionable outcomes.

Prerequisites​

  • Access to the Lab 23 in Aquarium containing H2O Driverless AI v1.10.7 (LTS).
  • Familiarity with H2O Driverless AI is essential. Alternatively, completion of the following tutorial is also acceptable: Tutorial 1A: Introduction to H2O Driverless AI.
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