A Telecom major was grappling with high customer attrition. The firm was one of the largest Telecom companies in the world and a market leader in Asia.
The marketing team’s heuristics-driven approach to customer retention was dated and ineffective. Reviewing the business performance in a weekly huddle, the CEO knew they had to do something different.
The firm turned to data science to solve this challenge. Machine learning (ML) algorithms were trained to predict customer churn. Simple algorithms such as decision trees used attributes such as “bill amount” and “outgoing call pattern” to improve customer retention by 39%.
While the marketing team was thrilled with these results, the data science team turned to advanced black-box algorithms such as Neural Networks that pushed accuracy even higher. Pilot tests run on high-value customers turned out to be a resounding success – Artificial Intelligence (AI) delivered 66% higher customer retention than the traditional approach.
The solution was ready for rollout, or so it seemed. Then, things turned south.
The marketing product managers flatly refused to use the solution. They found it hard to trust an algorithm that spat out a set of customer names with little explanation. Many of these recommendations were counter-intuitive and the entire process felt wrong.
Despite the data-backed results, they gave the data science solution the cold shoulder. The graveyard of AI projects is filled with such advanced, accurate, and well-meaning yet unused solutions.
Continue reading: https://www.forbes.com/sites/ganeskesari/2022/09/29/want-to-get-your-team-to-adopt-ai-follow-these-8-critical-steps-for-success/
The marketing team’s heuristics-driven approach to customer retention was dated and ineffective. Reviewing the business performance in a weekly huddle, the CEO knew they had to do something different.
The firm turned to data science to solve this challenge. Machine learning (ML) algorithms were trained to predict customer churn. Simple algorithms such as decision trees used attributes such as “bill amount” and “outgoing call pattern” to improve customer retention by 39%.
While the marketing team was thrilled with these results, the data science team turned to advanced black-box algorithms such as Neural Networks that pushed accuracy even higher. Pilot tests run on high-value customers turned out to be a resounding success – Artificial Intelligence (AI) delivered 66% higher customer retention than the traditional approach.
The solution was ready for rollout, or so it seemed. Then, things turned south.
The marketing product managers flatly refused to use the solution. They found it hard to trust an algorithm that spat out a set of customer names with little explanation. Many of these recommendations were counter-intuitive and the entire process felt wrong.
Despite the data-backed results, they gave the data science solution the cold shoulder. The graveyard of AI projects is filled with such advanced, accurate, and well-meaning yet unused solutions.
Continue reading: https://www.forbes.com/sites/ganeskesari/2022/09/29/want-to-get-your-team-to-adopt-ai-follow-these-8-critical-steps-for-success/
