Revolutionizing Customer Conversations with AI: Hyper Personalization

Revolutionizing Customer Conversations with AI: Hyper Personalization

Table of Contents:

  1. Introduction
  2. Who We Are: Commonwealth Bank of Australia
  3. The Journey of Hyper Personalizing Next Best Actions
  4. The Origin of the Program
  5. The Three Pillars of the Program
  6. People: Building a Modeling Community
  7. Capability: Hyper-Personalizing Customer Conversations
  8. Customer: Identifying the Right Conversations
  9. Use Case: Benefits Finder
  10. Use Case: Bill Sense NBC
  11. Use Case: Savings Habits
  12. Scaling Up and Future Models
  13. Summary
  14. FAQ

The Journey of Hyper Personalizing Next Best Actions

In this article, we will take a closer look at the journey undertaken by the Commonwealth Bank of Australia (CBA) to deeply integrate h2o's AI capability with their decisioning ecosystem. This strategic program, known as hyper personalizing next best actions (NBCS), aims to revolutionize the way CBA interacts with its customers through the use of AI.

Introduction

The Commonwealth Bank of Australia (CBA), one of the largest organizations in Australia, is dedicated to providing a full range of financial services to help all Australians build and manage their finances. As part of their commitment to customer satisfaction, CBA embarked on a journey to hyper personalize their next best actions or conversations with customers. This involved leveraging h2o's AI capabilities to deeply understand each customer individually and deliver tailored experiences.

Who We Are: Commonwealth Bank of Australia

CBA, with millions of customers, shareholders, and employees, has established itself as a leader in the banking industry. They prioritize customer engagement and continually Seek ways to enhance their services. CBA aims to be the go-to financial institution for Australians, and their focus on hyper personalization through AI reflects their commitment to meeting customer needs.

The Origin of the Program

The hyper personalization program, known as NBCS, emerged from the need to future-proof and expand CBA's customer engagement engine (Cee). Cee is a decisioning app that interacts with millions of customers across various channels, making millions of decisions every day. However, CBA recognized the potential for more experimentation and personalization in their customer conversations.

The Three Pillars of the Program

To address the challenges of hyper personalization, CBA identified three key pillars: people, capability, and customer. These pillars formed the foundation of the program and guided CBA's approach towards achieving their goals.

People: Building a Modeling Community

Recognizing the limited availability of data scientists and decision scientists, CBA focused on upskilling their analysts in h2o's driverless AI module. By establishing modeling labs and an internal academy, CBA aimed to democratize the art of modeling and foster collaboration between analysts and business teams.

Capability: Hyper-Personalizing Customer Conversations

CBA aimed to go beyond traditional segmentation and cohorts by leveraging campaign response data and machine learning capabilities to deeply understand each customer interaction. Through the use of h2o models, CBA sought to create more personalized and Relevant conversations with their customers.

Customer: Identifying the Right Conversations

Identifying the customers who would benefit the most from these conversations was crucial. CBA implemented techniques such as propensity modeling and control groups to measure the effectiveness of their conversations. By focusing on the customer's needs and interests, CBA aimed to increase the likelihood of a positive response and overall customer satisfaction.

Use Case: Benefits Finder

One of the early use cases of the hyper personalization program was the Benefits Finder, a tool designed to help customers connect with government rebates and benefits. By analyzing customer data and predicting positive responses to conversations about government programs, CBA witnessed a significant uplift in customer awareness and engagement. This tool has helped customers access over one billion dollars in grants, rebates, and concessions since its launch.

Use Case: Bill Sense NBC

Another use case, Bill Sense NBC, aimed to predict a customer's future bills and help them manage regular payments and bills. By offering a personal finance tool that consolidates bill management, CBA witnessed a 168% uplift in the number of customers signing up for this feature.

Use Case: Savings Habits

To encourage savings habits, CBA introduced a simple initiative for transaction account customers with idle funds. By opening new savings accounts for customers who had never considered it before, CBA saw a 102% uplift in new savings accounts being opened.

Scaling Up and Future Models

CBA is actively working with their business partners to Scale up their successful NBCS initiatives. They are modeling a wide range of H2O-driven AI models, incorporating different lines of business in the bank. The goal is to maximize customer satisfaction and deliver tangible business outcomes. The speed and efficiency of h2o's automated ML tool, coupled with the power of their advanced feature engineering, have enabled CBA to deliver results within a fraction of the time it would traditionally take.

Summary

The Commonwealth Bank of Australia's hyper personalization journey has been a Game changer in their efforts to provide exceptional customer experiences. By integrating h2o's AI capabilities, CBA has revolutionized their decisioning ecosystem, enabling them to deliver highly tailored conversations and experiences to their customers. Through successful use cases such as Benefits Finder, Bill Sense NBC, and Savings Habits, CBA has achieved significant uplifts in customer engagement and satisfaction. As they continue to scale their NBCS initiatives and explore new possibilities, CBA reaffirms its commitment to leveraging AI for customer-centric innovations.

FAQ

Q: How does hyper personalization reduce customer churn?

A: The primary aim of hyper personalization is to deliver exceptional customer experiences and meet customers' needs in moments that matter. While reducing churn may be an indirect outcome of hyper personalization, the primary focus is on driving positive customer outcomes and building lifelong customer relationships.

Q: How does h2o.ai compare to other modeling tools in terms of uplift?

A: When comparing h2o.ai with previous modeling technologies, two key aspects stand out: the richness of data and the speed to market. h2o.ai allows for analyzing a greater volume and variety of data, resulting in more accurate models. Additionally, the turnaround time from model development to campaign optimization is significantly faster, enabling Timely implementation of personalized conversations.

Q: How can the results of an uplift model be interpreted, and what are common pitfalls to avoid?

A: To interpret the results of an uplift model, it is essential to analyze the key drivers behind the predictions. Surrogate models and explainable AI techniques can help identify the impact of variables on customer behavior. However, it is crucial to avoid overcomplicating interpretations and ensure clear communication with business stakeholders who are interested in understanding the why behind the predictions.

Q: Can driverless AI solve computer vision problems?

A: While driverless AI offers some image classification capabilities, for more complex computer vision tasks such as segmentation, object detection, and natural language processing, hydrogen torch is the more suitable tool. Hydrogen torch is a no-code deep learning platform designed specifically for these advanced use cases.

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