Effective Campaign Management

Third Eye
Third Eye Data
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This is a success story about how we 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 over 2 continents and 4 countries and continues to grow rapidly. The company’s aim is to expand its customer base and retain existing customers with optimizing company’s profit.

Business Goals

The company approached ThirdEye with 2 business problems.

  1. 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.
  2. At the same time, the company wanted to optimize the template that they use 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 the right customer. This process is called Template Optimization.

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

The process steps were as follows:

  • Extract Data from source and load the data into the NoSQL (Couchbase) database.
  • Setup campaign.
  • Create an initial segment of customers and set the number of templates for the campaign.
  • Run template optimization.
  • Send emails to users.
  • Capture the user’s response.
  • Once the campaign is completed, run decision tree algorithm to find the 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
  • Spring 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 its’ customer base with better engagement towards the campaign. ML-based campaign management with automated message sending option significantly reduces communication gap, spending, manual effort etc which are trade-offs from an operational perspective.