Ed Trainor emerges from New York Sports Club in Manhattan after a rigorous morning workout. He peers down at his heart rate monitor to check how many calories he’s burned so far: a little over 1,000. He’s nearly halfway to his daily goal and he’s determined to reach it.
His methods? Well, they’re a bit unorthodox.
“I commute through Penn Station each day,” Trainor says. “If I’m behind on my calories I’ll walk around the block to go up another staircase.” After his commute on the train he reaches the parking lot. He zigzags through cars, taking the most indirect route possible to his vehicle. Every step counts!
“Here I am, changing my behavior because I’m wearing this device,” he says.
The root of his obsession is the perfect marriage he’s found between human behavior, fitness and technology. He even helped start a group of like-minded people from the fitness and technology industries to vet his interests. It’s called the Fitness Industry Technology Council (FITC).
The members of the FITC include Trainor, Kevin Steele, Ph.D., principal of Communication Consultants, Inc., Dave Flynt, principal interaction designer of Precor, Don Moore, embedded fitness market segment manager of Intel, Jon Zerden, chief technology officer (CTO) of Athletes Performance and Arlen Nipper, president and CTO of Eurotech, Lloyd Gainsboro, of Dedham Health and Athletic Complex, Joe Cirulli, of Gainesville Athletic Club and Mike Motta, of Plus One Health Club Management.
The goal of the group is to create a platform that allows for innovation, or, as Jon Zerden likes to say, to create the “plumbing” behind fitness equipment.
In order to create the plumbing, the FITC has split into three different groups designed to solve three different problems: the data standards group has set out to define a precise way to measure fitness data, like how many calories are burned during a workout; the cost-optimization team exists to ensure that any new technology is affordable, to facilitate the scale needed to achieve ubiquity; and, the communications specifications group aims to make fitness data accessible for web developers.
In the software world there is a term for what the FITC aims to accomplish; it’s called an “Applications Protocol Interface” (API). An API is simply a guide to creating a piece of software that can work with another piece of software. Apple, for instance, publishes an API for their App Store, which allows developers to create pieces of software for iPhone users. The set of standards the FITC is setting out to create will serve as the API for fitness equipment manufacturers to create a more seamless user experience for gym members.
The group has launched headfirst into the quixotic search for principal technology standards in the fitness industry. If they succeed, they’d have accomplished a feat that could expand the entire industry. “We could really capture more than the 15 percent of consumers we do currently,” explains Trainor.
Of the companies represented in the FITC, Intel is by far the largest. They have an obvious interest in the group: if equipment manufacturers produce smarter machines they’ll need computer chips to do so. Still, the number of pieces of fitness equipment produced each year pales in comparison to the amount of computers, laptops and net books sold to consumers. When asked why Intel had taken an interest in the fitness industry, Edward Hill, director of marketing for Intel’s embedded computing division, replies, “The volume is still interesting enough for us to participate.”
“I think we’re looking at an opportunity for the fitness industry to move into a new frontier,” says Trainor. “The health care industry is about to break and prevention is the way to go.” Exercise is, of course, what Trainor really means by prevention. Hill thinks when fitness data is available, health care insurers will be less interested in visits to the gym than, say, heart rate, which provides evidence not only that a person visited the gym but did more than sit in the whirlpool for 30 minutes.
“Does that mean every time they’re active they have to walk through the front door of our gym?” Trainor asks the question rhetorically. “Of course not.”
When he’s not working out at New York Sports Club, you can find Ed Trainor surfing off of New York’s coast. “I’m usually in the water for an hour and a half… Never have I gotten out of the water without burning at least 1,200 calories.”
The challenge for the FITC is to take two different activities, like lifting weights and surfing, and capture the essence of each workout in a set of data that says something meaningful.
How, exactly, the FITC plans to accomplish that is largely theory at this point. Questions are in greater supply than answers, but Trainor promises that’s about to change. At this week’s IHRSA convention and trade show the FITC will deliver a position statement that, according to Trainor, “will be the tipping point for us moving forward.”
Ed Trainor emerges from New York Sports Club in Manhattan after a rigorous morning workout. He peers down at his heart rate monitor to check how many calories he’s burned so far: a little over 1,000. He’s nearly halfway to his daily goal and he’s determined to reach it.
His methods? Well, they’re a bit unorthodox.
“I commute through Penn Station each day,” Trainor says. “If I’m behind on my calories I’ll walk around the block to go up another staircase.” After his commute on the train he reaches the parking lot. He zigzags through cars, taking the most indirect route possible to his vehicle. Every step counts!
“Here I am, changing my behavior because I’m wearing this device,” he says.
The root of his obsession is the perfect marriage he’s found between human behavior, fitness and technology. He even helped start a group of like-minded people from the fitness and technology industries to vet his interests. It’s called the Fitness Industry Technology Council (FITC).
The members of the FITC include Trainor, Kevin Steele, Ph.D., principal of Communication Consultants, Inc., Dave Flynt, principal interaction designer of Precor, Don Moore, embedded fitness market segment manager of Intel, Jon Zerden, chief technology officer (CTO) of Athletes Performance and Arlen Nipper, president and CTO of Eurotech, Lloyd Gainsboro, of Dedham Health and Athletic Complex, Joe Cirulli, of Gainesville Athletic Club and Mike Motta, of Plus One Health Club Management.
The goal of the group is to create a platform that allows for innovation, or, as Jon Zerden likes to say, to create the “plumbing” behind fitness equipment.
In order to create the plumbing, the FITC has split into three different groups designed to solve three different problems: the data standards group has set out to define a precise way to measure fitness data, like how many calories are burned during a workout; the cost-optimization team exists to ensure that any new technology is affordable, to facilitate the scale needed to achieve ubiquity; and, the communications specifications group aims to make fitness data accessible for web developers.
In the software world there is a term for what the FITC aims to accomplish; it’s called an “Applications Protocol Interface” (API). An API is simply a guide to creating a piece of software that can work with another piece of software. Apple, for instance, publishes an API for their App Store, which allows developers to create pieces of software for iPhone users. The set of standards the FITC is setting out to create will serve as the API for fitness equipment manufacturers to create a more seamless user experience for gym members.
The group has launched headfirst into the quixotic search for principal technology standards in the fitness industry. If they succeed, they’d have accomplished a feat that could expand the entire industry. “We could really capture more than the 15 percent of consumers we do currently,” explains Trainor.
Of the companies represented in the FITC, Intel is by far the largest. They have an obvious interest in the group: if equipment manufacturers produce smarter machines they’ll need computer chips to do so. Still, the number of pieces of fitness equipment produced each year pales in comparison to the amount of computers, laptops and net books sold to consumers. When asked why Intel had taken an interest in the fitness industry, Edward Hill, director of marketing for Intel’s embedded computing division, replies, “The volume is still interesting enough for us to participate.”
“I think we’re looking at an opportunity for the fitness industry to move into a new frontier,” says Trainor. “The health care industry is about to break and prevention is the way to go.” Exercise is, of course, what Trainor really means by prevention. Hill thinks when fitness data is available, health care insurers will be less interested in visits to the gym than, say, heart rate, which provides evidence not only that a person visited the gym but did more than sit in the whirlpool for 30 minutes.
“Does that mean every time they’re active they have to walk through the front door of our gym?” Trainor asks the question rhetorically. “Of course not.”
When he’s not working out at New York Sports Club, you can find Ed Trainor surfing off of New York’s coast. “I’m usually in the water for an hour and a half… Never have I gotten out of the water without burning at least 1,200 calories.”
The challenge for the FITC is to take two different activities, like lifting weights and surfing, and capture the essence of each workout in a set of data that says something meaningful.
How, exactly, the FITC plans to accomplish that is largely theory at this point. Questions are in greater supply than answers, but Trainor promises that’s about to change. At this week’s IHRSA convention and trade show the FITC will deliver a position statement that, according to Trainor, “will be the tipping point for us moving forward.”
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 members 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 members 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 any reduction adds directly to the club’s bottom line.
Equally plenty of members renew their memberships year in, year out. Accordingly, any member retention strategy should involve two key components: 1) identifying those members at risk of leaving and 2) targeting those at risk with appropriate interventions.
It is beyond the scope of this article to go into intervention methods. However, I will address the identification of members at risk of terminating their memberships (‘at risk’ members) – and how predictive analytics can be applied to help with this.
Like all businesses health clubs have limited resources, and it is absolutely pointless for a club to invest resources to try and retain each and every member, when a good deal of them are not at risk in the first place. If a member is identified as ‘at risk’ there is a strong business case to be built around investing resources in trying to retain that specific member (theoretically you could afford to invest up to $1 less than the cost of acquiring a new member, 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 individual in the form of an incentive or time invested.
The other consideration is, it is far easier to pro-actively try to retain 2,000 members than 4,000 member, so by segmenting, and making the size of the task more manageable, it increases the likelihood 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 members are most at risk of churning. Our next mission then, would be to take our database of current members and identify which ones specifically 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 sequentially. This simple act in itself would give us comfort that our resources are being focused on those that most require them – a form of retention triage if you will. 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 the managers of most health clubs, the scenario 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) perform some random haphazard interventions with no real science behind who is targeted and who is not.
So to get to the point of execution, and movement from theory to reality, let’s discuss how we would 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 article – literally.
Most health clubs have a reasonable amount of data on 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 the information we know about that member. The columns we call input columns as they are the inputs that help us make our prediction about that persons future behaviour, these would contain things such as: her age, her marital status, change of 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 terminated their membership previously is labeled ‘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 have any conclusive information about their behaviours.
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. This is as you would well expect, for example prior to terminating a membership, a member may start coming in less frequently, and if this data is recorded this would show up, or a change in marital status may impact an individuals propensity to re-sign, and most likely it is an aggregation of many factors. Typically a human cannot detect these patterns, but there are software applications that can, and once the patterns are defined, the software can look at the patterns in an unseen group of members and make a prediction as to each individuals propensity to churn, and then output these members in a sequenced list as described previously, complete with the optimal point in the list to stop making interventions.
To explain it a slightly different way, we are: 1) consolidating historical data about behaviours that we think may be correlated to an individual churning from historical members 2) we are letting software examine that data for patterns and how they relate to how a member churned or did not 3) that relationship is frozen in a ‘predictive model’, and finally 4) the model is applied to unseen members to statistically predict their behaviour (vis a vis churning or not).
I would encourage anybody interested to visit www.11AntsAnalytics.com and watch the 11Ants Model Builder QuickStart tutorial video, 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.
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