A major problem in the health club industry is customer retention - it may well be the industry's single largest issue. Hence the constant aggressive push to get customers signed up and in the front door, at a rate faster than they are exiting out the back door. I have seen figures showing that as many as 40% of customers churn in the average health club, regardless of the exact numbers, it is a known fact in the industry that it is a higher number than any health club manager wants it to be; and obviously if it can be reduced, it adds directly to the club's bottom line.
Equally plenty of members renew their memberships year in, year out. Accordingly, any customer retention strategy should involve two key components: 1) identifying those customers at risk of leaving and 2) targeting those at risk with appropriate interventions.
It is beyond the scope of this blog (or my expertise!) to go into intervention methods. However, I would like to discuss briefly on identification of at risk customers - which is where predictive analytics comes in.
Like all businesses health clubs have limited resources, and it is absolutely pointless for them to to invest resources to try and retain each and every customer, when a good deal of them are not at risk in the first place. If a customer is identified as 'at risk' there is a strong business case to be built around investing resources in trying to retain that specific customer (theoretically you could afford to invest up to $1 less than the cost of acquiring a new customer, and still be ahead of the game), conversely if they are not 'at risk' and are going to re-sign anyway, you may just as well burn the money as hand it over to that specific customer in the form of an incentive or time invested in chasing them.
The other consideration is, it is far easier to actively try to retain 2,000 customers than 4,000 customers, so by segmenting, and making the size of the job more manageable, it makes it more likely that a health club will do something - and if we know nothing else, we know that doing something is usually better than doing nothing.
So we have a clear business case for identifying which customers are most at risk of churning. Our next mission then, would be to take our database of current members and identify which ones are 'at risk' and which ones are 'loyal'. Ideally we would take it one step further than this, and be able to rank our whole customer database in rank order from those statistically 'most at risk' to those 'least at risk'. The benefit of doing this, is that it provides our sales/retention staff with a sequenced work list, which they would start at the top of and work their way down. This simple act in itself would give us comfort that our resources are being focused on those that most require them. This can even be taken one step further, and we can - again using statistical methods - determine the statistically optimal place in the list to stop.
Though we have a business case, and a reasonably clear vision of what would be useful, the problem is that for most health clubs, the scenario I have outlined above is closer to science fiction, than something they perceive they can practically deploy within their club. So the status quo prevails: 1) do nothing, 2) treat all customers as equally at risk, or 3) do some random haphazard interventions with no real science behind who is targeted and who is not.
In conclusion let's discuss how we can take this utopian vision and turn it into an actionable reality. Ironically for many health clubs this vision can be actualized faster than it took me to write this blog - literally.
Most health clubs have a reasonable amount of data about their members. Let's imagine that we have all the data about every member of our club for the last five years, lined up in an Excel spreadsheet. Every row is a unique member, every column is information we know about that member. We call these input columns, and they would be things like: her age, her marital status, # of visits in January 2010, number of visits in January 2009, etc. payment method, # of address changes, average time she spends in health club, etc, etc it would be no problem to have 100 or even 500 columns, and in the very last column (our target column) we add a label 'loyal' or 'at risk'. Anybody that left our club previously is labeled as 'at risk' and 'anybody' who re-signed is labeled as 'loyal'. We would eliminate from the spreadsheet anyone who had not had been with us a year yet, as we don't know what they are likely to do.
Now I will skip over the math here, which nobody would want to try at home, but you can take it on good authority that there are patterns within all the input columns that can help to predict the customers propensity to churn (as you would well expect). A human cannot detect these patterns, but there are software applications that can, then once patterns are defined, the software can look at the patterns in this year's (or this month's) customers and output the exact previously mentioned ranked list, complete with the optimal point in the list to stop making interventions.
I would encourage anybody that is interested to visit www.11AntsAnalytics.com and take a look at the QuickStart tutorial video about 11Ants Model Builder on the home page, which will better show the process (the data is different, but it won't require much imagination for it all to make perfect sense). Feel free to email me if you have questions about this - doing this sort of thing is ten times easier than most people imagine.
Although this post has been focused on customer retention in health clubs, identical principals apply to any subscription based or long term service business (telcos, cable television companies, banks, insurance companies, etc).
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