Databricks Case Studies Leveraging IoT and Machine Learning to Enhance Customer Experience: A Case Study on AIR MILES Reward Program

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	Leveraging IoT and Machine Learning to Enhance Customer Experience: A Case Study on AIR MILES Reward Program Databricks

Leveraging IoT and Machine Learning to Enhance Customer Experience: A Case Study on AIR MILES Reward Program

Databricks

Technology Category	Analytics & Modeling - Machine Learning Sensors - Environmental Sensors
Applicable Industries	Consumer Goods Retail
Applicable Functions	Product Research & Development Sales & Marketing
Use Cases	Retail Store Automation Theft Detection
Services	Data Science Services
Challenge	Since 1992, LoyaltyOne has operated the AIR MILES Reward Program, generating $70 billion in revenue across their network. Despite having billions of data records from collectors and retail partners, AIR MILES struggled to foster stronger touch points with its customers due to a lagging legacy infrastructure. This outdated system prevented the company from gaining a holistic view of their customers, thereby hindering their ability to improve retention and customer lifetime value. AIR MILES wanted to leverage the petabytes of customer data generated by 558 million transactions per year to enhance the collector experience and enable their partners to better engage and drive revenue. However, their existing technology was unable to support this vision. The company's data scientists couldn't access data older than 5 years, and complicated queries limited what they could do with the data they did have access to. Pipeline development alone took 3 to 4 months, stifling their ability to deliver innovative solutions to market in a timely manner. Read More
About Customer	LoyaltyOne, the owner and operator of the AIR MILES Reward Program, is a coalition rewards program that offers an open and flexible model providing collectors with more ways to earn and redeem Reward Miles through partnerships with other businesses. With nearly 10 million active collector accounts earning rewards through everyday purchases at more than 300 retailers, AIR MILES generates $70 billion in revenue across their network. The company captures petabytes of customer data generated by 558 million transactions per year, which they aim to leverage to enhance the collector experience and enable their partners to better engage and drive revenue. Read More
Solution	AIR MILES migrated workloads to the Databricks Lakehouse Platform, a fully managed cloud-based platform that provided speed, efficiency, and openness. All data, including operational data such as website and mobile logs, email communications, sales and marketing campaigns, and transactional data from partners, was made readily available to all departments across the company. Business intelligence (BI) dashboards via Looker and Tableau enabled analysts and non-technical users to make data-driven decisions. The data science team was able to quickly build, test, and deploy machine learning (ML) models to deliver various use cases serving a wide variety of internal teams and partners. The marketing team used ML to better understand web traffic performance across their mobile and web platforms and optimize the user experience. The finance team was able to more accurately forecast revenue, and the customer care team improved customer support through ML-powered dashboards. The fraud department could detect patterns and outlier behavior to ensure promotional offers were redeemed as intended. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Sensors - Environmental Sensors

Applicable Industries

Consumer Goods

Retail

Applicable Functions

Product Research & Development

Sales & Marketing

Use Cases

Retail Store Automation

Theft Detection

Services

Data Science Services

Challenge

Since 1992, LoyaltyOne has operated the AIR MILES Reward Program, generating $70 billion in revenue across their network. Despite having billions of data records from collectors and retail partners, AIR MILES struggled to foster stronger touch points with its customers due to a lagging legacy infrastructure. This outdated system prevented the company from gaining a holistic view of their customers, thereby hindering their ability to improve retention and customer lifetime value. AIR MILES wanted to leverage the petabytes of customer data generated by 558 million transactions per year to enhance the collector experience and enable their partners to better engage and drive revenue. However, their existing technology was unable to support this vision. The company's data scientists couldn't access data older than 5 years, and complicated queries limited what they could do with the data they did have access to. Pipeline development alone took 3 to 4 months, stifling their ability to deliver innovative solutions to market in a timely manner.

About Customer

LoyaltyOne, the owner and operator of the AIR MILES Reward Program, is a coalition rewards program that offers an open and flexible model providing collectors with more ways to earn and redeem Reward Miles through partnerships with other businesses. With nearly 10 million active collector accounts earning rewards through everyday purchases at more than 300 retailers, AIR MILES generates $70 billion in revenue across their network. The company captures petabytes of customer data generated by 558 million transactions per year, which they aim to leverage to enhance the collector experience and enable their partners to better engage and drive revenue.

Solution

AIR MILES migrated workloads to the Databricks Lakehouse Platform, a fully managed cloud-based platform that provided speed, efficiency, and openness. All data, including operational data such as website and mobile logs, email communications, sales and marketing campaigns, and transactional data from partners, was made readily available to all departments across the company. Business intelligence (BI) dashboards via Looker and Tableau enabled analysts and non-technical users to make data-driven decisions. The data science team was able to quickly build, test, and deploy machine learning (ML) models to deliver various use cases serving a wide variety of internal teams and partners. The marketing team used ML to better understand web traffic performance across their mobile and web platforms and optimize the user experience. The finance team was able to more accurately forecast revenue, and the customer care team improved customer support through ML-powered dashboards. The fraud department could detect patterns and outlier behavior to ensure promotional offers were redeemed as intended.

Impact #1	With the Databricks Lakehouse Platform, AIR MILES surpassed the initial goal of obtaining real-time data insights and fully integrated a data-first approach throughout the company. The platform's performance resulted in faster insights to the end users. They implemented a metrics catalog framework that allowed them to curate and manage all their business rules in one centralized location. This higher throughput accelerated the time-to-market of new insights and ML-powered solutions. Over 100 internal AIR MILES associates across departments and teams, plus additional users and data vendors, are now able to access data insights fed through BI tools. With over 23,000 collector attributes at their fingertips, their data science teams have developed over 250 ML models to hone personalization for business growth.

Impact #1

With the Databricks Lakehouse Platform, AIR MILES surpassed the initial goal of obtaining real-time data insights and fully integrated a data-first approach throughout the company. The platform's performance resulted in faster insights to the end users. They implemented a metrics catalog framework that allowed them to curate and manage all their business rules in one centralized location. This higher throughput accelerated the time-to-market of new insights and ML-powered solutions. Over 100 internal AIR MILES associates across departments and teams, plus additional users and data vendors, are now able to access data insights fed through BI tools. With over 23,000 collector attributes at their fingertips, their data science teams have developed over 250 ML models to hone personalization for business growth.

Benefit #1	Data pipeline development time reduced from 3-4 months to just 2 days
Benefit #2	Access to over 30 years of historical data
Benefit #3	Over 100 data pipelines delivering faster time-to-insights

Benefit #1

Data pipeline development time reduced from 3-4 months to just 2 days

Benefit #2

Access to over 30 years of historical data

Benefit #3

Over 100 data pipelines delivering faster time-to-insights

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Overview

Leveraging IoT and Machine Learning to Enhance Customer Experience: A Case Study on AIR MILES Reward Program

Operational Impact

Quantitative Benefit