Data Drivers

Customer Rewards, With A Bit Of Muscle

The best way to encourage good behavior is to reward it. And what could be better behavior than going to the gym? In the latest Data Drivers, Perkville CEO and Founder Sunil Saha discussed the healthy trends one fitness center enjoyed upon adopting rewards programs.

No doubt you are already slacking on your New Year’s resolutions. Fess up — how many times have you been to the gym?

Thought so.

Maybe if there were some type of reward for committing to breaking a sweat…

In the latest installment of Data Drivers, PYMNTS’ Karen Webster and Sunil Saha, CEO and founder of Perkville, delved into the fitness niche via a case study focused on a fitness center and loyalty programs offered there. The data offers a microcosm that shows how rewards can be a treadmill to the treadmill, keeping members interested and, most importantly, dedicated to repeat visits.

First Data Point: 33 Percent

This is the percentage by which users of the fitness center increased their monthly personal training sessions, according to Saha. Saha said that his firm’s case study centered on causation rather than just correlation, with focus on consumer behavior before joining the rewards program compared to that behavior after joining the program. As for retail rewards at this center, he said, offerings ran the gamut from t-shirts to, at the top end, an aspirational goal — a free year’s membership. In addition, the fitness center partnered with local merchants to provide goods and services that they knew their members would value, such as massages.

Not only did the health club manage to boost usage of training sessions, in tandem with rewards, but it also was able to reduce its cancellation rates (or churn) by about 16 percent, translating into what Saha called “huge dollars for them.”

As for keeping consumers motivated, Saha stated that there is a fine line to walk for merchants, as “the rewards can’t be too rich to bankrupt the business, and at the same time, they can’t be too little because they need to garner that consumer interest.” Impact came, and comes, from discounts offered for the services provided by the business, he said. In the case of the health club, offering a free month’s pass for a friend costs the business nothing but offers up a store of value for the member.

As for time parameters, Saha noted that consumers — as high as 70 percent of them, in this case study — ideally wanted to redeem their rewards within the first year. “If it takes longer than that,” he said, “you’re probably going to lose interest.” That rule of thumb extends across verticals, said Saha, including salons, beauty and other industries served by Perkville. Rewards on referrals, he continued, should happen instantaneously, as the lifetime value of a new customer to the fitness center can run into the thousands of dollars.

Data Point Number Two: 17 percent

This is the percentage by which those members of the health club in question increased monthly referrals, acknowledged Webster, bringing new clients into the gym. This stat comes despite the fact that, as the duo agreed, referrals are a hard business, mandating as they do that the person being referred will have the same lifestyle choices, dedication and interest in, say, joining a health club in the first place.

Saha noted that Perkville took some cues from LinkedIn, with the idea of inviting new members into the network, so to speak. For the health club’s referral program, Saha said, one advantage came with an easy-to-use interface that alerts peers and friends (AKA would-be clients) to that offer. And, he said, once that person comes in and, for example, buys a membership or a yoga pass, “that’s when the referral is rewarded.”

Data Point Number Three: 3.5 percent

This is the total cancellation and reduction rate that the firm saw, compared to a whopping tally of four times that amount the year before the rewards programs were implemented. Upon joining the program, said Saha, members were coming onto the premises 13 percent more than before, on average, “and they saw the biggest increases from the people who had [previously] been coming in the least frequently,” he said. That means that the biggest impact centered on the members who had been least engaged and had been most likely to cancel.

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