Case Study:
Mathematical Optimization & Machine Learning
The Problem
As part of the ongoing effort to bring on new members, the Alternative Marketing team at AARP had a lengthy, complex, and arduous monthly campaign selection process due to the use of multiple channels with geographic, supplier, audience, and budget requirements and targeting options.
The program director sought a more rigorous analytic solution to improve the efficiency of the program and optimize acquisition.
In addition, speed-to-market and improved decision making necessitated the automation of this complex process.
The Solution
A variety of detailed data was setup for automated ingestion, cleaning and analytic preparation into LityxIQ. Machine learning models were built on segments comprised of channel, geography and audience to predict expected response to receiving an AARP membership offer.
An automated scoring process was then put into production. Finally mathematical optimization was utilized to make the best decision of who to market and through what channel under requirements of organizational diversity objectives, budget allocation across sub-channels, and newspaper minimum insert, among others.
This was also automated and put into a production process.
“Machine learning models were built on segments comprised of channel, geography and audience to predict expected response to receiving an AARP membership offer.”
The Outcome
Reduced a 2wk highly complex manual process to an automated one that completes in a couple of days.
Over 90% improvement in acquisition rates at a lower cost-per-acquisition.
Why LityxIQ
Lityx is the first to make available autoML with Mathematical Optimization in our AI platform. With no coding, required, data scientists and non-data scientists have access to Accelerated, Actionable Business Solutions that are Transparent and can be Trusted.
Additional Solutions Available:
Customer Retention and Journey Analysis
Sales Forecasting
Insights Dashboard Automation
Offer Optimization
Get Started
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