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How can a large company detect the root cause for a sudden increase in churn, requested credits or charge backs?

Here are two ideas:
  • Survey your customers, including those who left or who are causing problems.
  • Automatically analyze / summarize what users post on Twitter and other social networks about your company. Look for shifts in what users are posting now, vs. 3 months ago.This can be performed with very inexpensive / open source tools. We do it with Perl scripts, web crawling and advanced data mining.
The problem could be external (related to what your competitors are doing), and using internal data only to find the root cause, might not work.

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We don't do much in the way of survey's, however we've leveraged SAS to pull in various rss data to detect differences. Additionally, we predefine segmentation and monitor changes in segments or migration between segments. Sudden changes in a given segment can tell you where to focus your search for answers.
AS suggested what difference do you foresee between the segments.Can you elaborate on this more?
This solution is by doing some modeling on the internal data. Carry out logistic modeling on the data belonging to the period in which the increase in churn is seen. The logistic coeffcients will show which variable is the most contributing factor and can be indicative of the root cause of the churn.
Agree with Tom. Once a problem has been identified, and you are looking for a root cause, you need to get out there and brainstorm within/ and external to company to ascertain what is going on. There comes a time when further data modeling will not help. You can certainly use things like fishbone diagrams, but the important factor is causation, and not necessarily association, so there needs to be a lot of why's . Of course you do need good stated facts as evidence.

-Ralph Winters
Also known as Ishikawa diagram. The first one answers the question of why it's called that
How many tweets /month do you need to implement idea 2? Any rule of thumb?
/ Tomas
Not just tweets, but any RSS or blog data. How many... depends on how striking the difference between today tweets and tweets that are 2-month old. If people have shifted their mind 180 degrees, you should be able to draw conclusions from 20 tweets or less.

If the tweet volume is really high, you should break it down into segments, and check what people say within each segment.
A related CRM question: what types of modeling techniques are commonly used to *predict* churn as opposed to explaining root causes post facto? Does anyone have benchmarks about such cases? Thanks.
Hi Tom,

Appreciate the response. Thanks.

Are you (or anyone) aware of the performance of such models? We have used several models/techniques, but unfortunately, the speed of business limits us from really quantifying the ROI and helping us identify the best fits or performers. I am interested in learning if there are documented ROI results.
BR, No there shouldn't be. If there are, I would also be interested in seeing them. These are all different statistical techniques that exist only in the purest sense. They are not tied to specific applications. Often the choice is up to the modeler, and what you have expertise in doing. It is also dependent upon the number of degrees of freedom in each model, and the model assumptions you are willing to make. Typically for a churn problem I would use a logistic model, and support it with decision tree analysis. But a lot of people nowadays are doing churn using survival analysis.

-Ralph Winters
For involuntary I would say analyze the billing data and for voluntary churn make sure you include callcenter data. Performing text mining on call center data will definately give you a lot of insight, but unfortunately this data is not always captured with a high level of accuracy by the call center agents. You should also take note of new promotions by competitors and analyze the influence of these promotions on your revenue and churn.
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