This is what marketing machine learning looks like.
Customers succeed with Skafos.
Metis Machine’s team of data scientists and machine learning experts partners with companies who know ML gives them a competitive advantage.
Goal: Reduce customer cancellations
Data + Analytics + Expertise = Results
Finding new customers is likely to cost you 5x as much as retaining existing ones. This reality drives nearly all loyalty programs and marketing toward existing customers. Starbucks spends millions annually to make their loyalty program work and toward making smarter in-app recommendations.
What if you could predict which of your customers will leave?
Stop Guessing and Start Improving
Machine learning can help you identify which of your existing customers are most likely to churn and when. A predictive model can determine the critical features associated with cancellations by incorporating multiple sources of data. From customer interactions, customer attributes, and external factors like competition and seasonality, machine learning can provide insights about your customers, why they might be unhappy, and how you can take action. By leveraging a churn model tailored to use your data, you can make better, more cost effective decisions on targeted retention efforts.
How ML can defeat churn
- Isolate key characteristics of customers that are and aren’t historically loyal
- Identify what characteristics and efforts resulted in loyal customers
- Alert trends and risks for churn BEFORE customers leave
- Improve alerts based on current marketing efforts
Our ML Jump Start services are designed to help you gain quick insights and value from machine learning so your first effort has a lasting impact where it counts–your business. Leverage our expertise and give your team a head start so that cycles are not spent re-learning the mistakes others have already made.
Keep Your Customers
Utilize your historical customer data with smart technology to decrease churn and increase revenue. Contact us to set up a 15 minute consultation today.