Leveraging IoT and Machine Learning to Enhance Customer Experience: A Case Study on AIR MILES Reward Program
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
About The 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.
The 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 This outdated system prevented the company from gaining a holistic view of their customers, thereby hindering their ability to improve retention and customer lifetime value.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.
The 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.
Operational Impact
Quantitative Benefit
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