Predicting and Preventing Churn using Customer Analytics


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Last week I was at the TSIA’s 2012 Technology Services World event in Las Vegas, where I had the pleasure of speaking during John Ragsdale’s Power Hour session: Big Data – Three Inspiring Stories of Service Analytics.

Walker used our time to play a new version of Face Value (the customer card game) specifically developed to make a few key points about how, with the convergence of cloud, consumption economics, renewals-based revenue, and big data, it is more critical than ever that companies have an alerting system founded on customer analytics that will trigger attention towards customers at real risk of not renewing their business.

Participants agreed there were two critical takeaways from the session:

  1. Having the right data matters.
    There are hundreds of potential pieces of information to consider about any one customer, but not all of them will have the same influence on their likelihood to continue to do business with you. Customer analytics identifies what matters.
  2. The more data you have, the harder it gets to manage.
    Even if you have all the ‘right’ data about customers, the more of it there is, the more challenging it becomes to consistently assess renewal risks in a timely way. An automated customer analytics-based tool makes all the difference.

By harnessing the power of big customer data sets, companies can establish a ‘radar‘ founded on proven customer predictive analytics that considers all the relevant information and flags customers in need of proactive intervention before it is too late.

Republished with author's permission from original post.

Jennifer Batley
Batley Advisory
Jennifer Batley is a strategic leader with over 25 years’ global experience advising B2B executives and companies looking to recover, transform, scale, or sustain business advantage. Expert in advancing customer experience strategy from vision to execution, she puts customers at the heart of business to generate high impact results through metrics-driven operational change.


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