FitReserve & Classpass - Web Scraping

Posted on Dec 10, 2019


Fitness aggregators are changing the way people workout, giving more flexibility for the users to try different modalities under a single membership.

In the US, Classpass (“CP”) is the largest fitness aggregator. However, new entrants – like FitReserve (“FR”) – might still have its share in this evolving market.

Because one of the key drivers for success of a fitness aggregator is the quality and extension of its network, this project aims to analyze the best strategy for FitReserve’s supply growth by investigating Classpass’s network in some of US major cities.



FitReserve is currently available in 5 major metropolitan regions, with a total of 611 studios. Currently, FitReserve's network is approximately 15% the size of Classpass' (in the main cities).

For the purpose of this project, I have only considered the cities that anchored each metropolitan area.



Classpass is present in more than 40 cities in the US, but for this project we will focus on the 13 cities where FitReserve is also present - representing over 5k studios. 

NYC and LA are the top two markets for CP, followed by Chicago, and Boston, indicating that based on the supply size FR is focused on the correct cities.

  • The studios with more reviews are in NYC, LA, and Washington DC, followed by Boston, Chicago and Dallas. FT is present in all those cities but Dallas.

Grouping the ratings per city, we see that there is some consistency in the quality of the studios, with all the cities with an avg. rating above 4.5 – one potential gap, to be further explored, would be San Jose, which has the lowest average rating and New York City.

Classpass (in these major cities) has a high concentration of Yoga and Strength Training type of classes, followed by Pilates, Dance and Barre

Using the number of reviews per class type as a proxy for demand, we noticed that CP has a high volume of users going to Running, Cycling, Pilates, and Barre. Therefore, FR when focusing its expansion could aim to bring these types of studio to its network.

The average rating per class type indicates that besides Running classes, Prenatal and Acupuncture, despite the lower number of reviews per Studio, could also be included in the FR prioritization. Class types with high ratings could make the User more satisfied and engaged to FR’s product.


How FR should prioritize its growth expansion?

Based on CP data, FR should:

  • Continue to grow in its current cities where it seems to have the largest potential for network growth and demand.
  • If it needs to open new cities, San Diego and Dallas should be next.
  • Look for running and cycling studios, which seems to have a higher demand, and also Yoga and Strength Training, which have the highest number of studios.
  • Approach the top-rated studios first.


FR should start targeting the studios with high rating and high number of reviews (studios in the top-right corner of the graph below)

Individual studios prioritization criteria (selected studios):

  • Popular Activities (ST, Running, Cycle, Yoga, Pilates)
  • FR’s Current Cities (NYC, LA, Chicago, Boston, DC)
  • Ranked by Avg. Rating


Conclusion and Next Steps

  • With over 50 thousand gyms/studios in the USA, the success of FR’s network growth is highly dependent on how well it defines its lead scoring/prioritization strategy.
  • Looking at CP current network (main competitor) and targeting its key studios can be a smart strategic move for FR, which allows it to have a better return in its marketing/personnel investment.
  • As further investigation in the analysis, I would remove from the analysis the overlapping FR/CP studios to have a clearer view of the networks.
  • Finally, I would also get other sources of data (i.e. from YELP or other aggregators) to avoid being restrictive in targeting studios that are part of the CP network only.

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

About Author

Luana Stamato Caldeira

Luana S. Caldeira is a data science fellow at NYC Data Science Academy. She holds a bachelor's degree in Economics and has several years of experience in finance and asset management.
View all posts by Luana Stamato Caldeira >

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