How Machine Learning Can Help Cut Customer Churn

How often do you focus your efforts solely on retaining your existing customers rather than winning new ones?

Marketing Metrics revealed that the probability of selling to an existing customer is 60-70%, whereas winning business with a new customer is significantly more difficult - occurring between 5-20% of the time. 

It’s clear then that maintaining a healthy relationship between you and your returning customers whilst minimising customer turnover is absolutely essential. Besides providing you with the potential for a sizeable amount of new business, they are much more likely to continue to opt for your products and services over your competitors. If they lose interest in what you’re selling or you make them unhappy in some way, your competitors will be there waiting to scoop them up.

So how can you combat this? Here are a few ways Machine Learning and automation can help you reduce customer churn and maximise customer retention.

Immediate Changes

Customer retention is a front-of-mind issue for many businesses, yet most don’t have solid strategies in place to mitigate customer churn. For instance, thousands of companies reach out to existing customers with surveys - only to get poor return rates back. They then send a blanket email to the entire customer base or groups based on historical data. Such “scattergun” approaches are likely to have little to no positive effect on their retention numbers.

If they were to implement a Machine Learning model, they could quickly identify the biggest flight risks and give their team an idea of the key customers they should be contacting. Going further, ML on response rates to different forms of outreach would allow them to determine which approach is most likely to garner a positive outcome.

The Way Forward

As well as changes to day-to-day operations, Machine Learning models also provide the team with insights into why some customers become flight risks more than others. Using these insights, your team now has a chance to target at-risk customers with approaches that speak to their pain points - improving the likelihood of retention, as well as identifying some key insight to help guide long-term strategy changes to the goods and services you offer.

More traditional approaches, due to the efficiency and rapid development of ML-driven solutions, are quickly being phased out by many companies. The Harvard Business Review found that 48% of the organisations they surveyed now use automated processes in their outreach and sales approaches.

Interested in learning how Machine Learning can help your business? Read our complimentary E-Book!