Just a year ago, we reported that Peloton ($PTON) was having a bit of a hard time. Reviews were slumping as unhappy customers complained of delayed deliveries and poorly constructed bikes and, while the weather improved, more people were taking to the roads with real cycles and questioning the point of a $2,500 at-home spin machine. But the company fixed its problems and trudged its way through a lukewarm IPO that reflected market on what many considered to be a luxury, niche product.
And then we were all stuck at home. Sure, some skilled people who have the bike and equipment can still go for a bike ride, but the notion of an at-home spin class while being completely socially distanced has suddenly become a great idea.
And Peloton's review activity and scores are showing this change in consumer attitude.
Beginning in January — perhaps as part of a cold-weather alternative — Peloton review count and cumulative average began to increase. But it wasn't until February and March, when the reality of quarantined sunk in, that the review count at peloton.com absolutely took off.
The accelerating reviews began in February, then cooled off, but unlike this time last year, are rising again despite the warmer weather.
In March, review count grew by 11%. In March of 2019, growth was at a slow 1.6%. Meanwhile, average review score on the site is now at a near-perfect 97.7%.
The rise in review counts and scores coincide with a massive swell in people talking about Peloton on Facebook ($FB), as we measured two weeks ago when the brand's "Talking About" count on the world's largest social network soared by nearly 65%.
Sure, a Peloton Cycle may be an expensive way to get some exercise when stuck indoors, but as people shuffle off gym and yoga memberships, the monthly cost of a Peloton has suddenly become a bit less of a luxury and perhaps even more of a necessity.
About the Data:
Thinknum tracks companies using the information they post online - jobs, social and web traffic, product sales and app ratings - and creates data sets that measure factors like hiring, revenue and foot traffic. Data sets may not be fully comprehensive (they only account for what is available on the web), but they can be used to gauge performance factors like staffing and sales.
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