Case Study – Machine Learning Based Campaign Management 2019-07-25T07:47:04+00:00
SUCCESS STORY

Case Study: Machine Learning based Effective Campaign Management

Company spends lots of money to promote their products. This is a success story about how ThirdEye managed their campaign effectively to optimize ROI (Return on Investment).

THE CUSTOMER

The customer is a leading budget telecom provider headquartered in California, US. It has business spread across 2 continents and 4 countries and continues to grow rapidly. The company’s aim is to expand its customer base and retain existing customers by optimizing company’s profit.

BUSINESS GOALS

Company approached ThirdEye with two business problems.

The company wanted to promote their products based on the customers’ behavior. The behavior includes usage, spending and is also based on certain customers’ attributes like region, subscription etc. The company wanted to increase ROI for each campaign and optimize revenue.

At the same time the company wanted to optimize their template that was used to send messages through several channels in order to increase customers’ engagement.

THE SOLUTION

ThirdEye proposed Machine Learning based solutions to accomplish customer needs. The machine learning solutions were supervised so that it can be tweaked as per needs.

The first solution was a Multi Arm Bandit algorithm base machine learning process to choose the right template for right customer. This process is called Template Optimization.

The second solution was Decision Tree machine learning approach to select the right subset of customer to campaign a product to ensure higher conversion rate.

The process steps were as follows:

  • Extract Data from source and load the data into NoSQL (Couchbase) database.
  • Setup campaign.
  • Create initial segment of customers and set number of templates for the campaign.
  • Run template optimization.
  • Send emails to users.
  • Capture user’s response.
  • Once campaign is completed, run decision tree algorithm to find right set of customers to promote a product.

Technologies Incorporated:

  • Couchbase Server – NoSQL Database
  • Elastic Search – To offload indexing from Couchbase Server
  • Hadoop Framework – For processing data
  • Python – Machine Learning and other data process
  • Sring Boot – To build the APIs on top of Elastic Search and Couchbase Server

VALUE CREATED

The customer got better than expected ROI (Return on Investment) with a huge opportunity to increase the customer base with better engagement towards campaign. ML based campaign management with automated message sending option significantly reduces communication gap, spending, manual effort, etc. which are trade off from operation prospective.

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