Aided by machine learning, customer success teams can finally scale effectively and be proactive to potential churn situations.
As businesses shift to subscriptions and recurring revenue models, loyal customers remain a top priority. To identify customers with cross-sell and up-sell potential while also detecting at-risk accounts, leading customer success functions turn to machine learning for greater insights.
Watch the video below to see how machine learning can help you leverage data to target customers and keep potential deserters from leaving.
As companies transition to recurring revenue models, one of the biggest threats to sustainable revenue growth is customer churn… While sales teams are bringing clients in the front door, customer success teams work to make sure they are not sneaking out the back.
Cross-functional teams are focused on driving customer retention.
Customer success teams need to track customer health quickly and accurately.
In most cases, identifying at-risk customers happens too late in the cycle – leaving companies without the ability to prevent churn.
They need more lead time to implement effective strategies to change outcomes with their customers.
Enter machine learning.
Customer success teams create and manage massive amounts of data. By combining this with other external data sets, machine learning allows them to uncover hidden signals in the data that humans miss.
Models can be developed to predict likely churners and down-graders well in advance. This gives customer success managers more time to change the outcome… directing more resources and efforts to the right accounts.
This means more efficiency in the customer function, happier clients, and most importantly, improved profitability.
Data-driven companies are already seeing the real impact that machine learning can have on customer success… Better lead time, and more revenue.
It is time to turbo-charge your growth through customer excellence
Let KPMG show you how.