Case Study – Machine Learning Based Campaign Management 2019-05-22T11:12:06+00:00

Case Study: Machine Learning based Effective Campaign Management

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


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


Company approached ThirdEye with 2 business problems.

The company wanted to promote their product based on the customers’ behaviour. The behavior includes its usage, spending and 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 they use to send messages through several channels in order to increase customers’ engagement.


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 right template for right customer. This process is called Template Optimization.

The second solution was Decision Tree machine learning approach to select 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


The customer got better than expected ROI (Return on Investment) with a huge opportunity to increase its’ 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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