The competition in the fitness industry is fierce (!): Studios need to keep customers coming in order to remain profitable and use their resources (staff, space…) efficiently. For customers, gym memberships are an investment, yet, it seems there is always something wrong: either contracts are too long/inflexible, the classes are not interesting… the list is endless! But maybe there is a solution…

Let’s check out Lymber!

Lymber is a San Diego-based startup launched in 2016 with the goal to make people exercise more often by providing them with “as they go” fitness offers (Lymber Fitness Inc, 2016). The Lymber app aggregates open class spots from currently about 180 (local) partnering gyms and offers them to interested customers. Lymber’s dynamic algorithm calculates the rates under consideration of factors such as instructor popularity and time/day of week; whether a rate will “surge” or drop as the class moves closer is a matter of supply and demand. Sounds familiar right? it is the same principle followed by hotels or transport providers such as Uber!

How does it work in practice?

Efficiency Criteria: Weighing Cost And Benefits

As you can see from figure 1, time and effort to use Lymber is very low for all participants. What about cost? Lymber does not charge a subscription fee from customers. It only makes money if its partnering gyms make money using their system (~25% commission fee), therefore it encourages repeat visits to your gym (Lymber Fitness Inc, 2016).

Customers pay for what they “use” only. In this way, they can explore multiple sports (yoga to spearfishing, now that is variety!) and different studios without extended commitment. Whether you are a bargain hunter or are looking to score a spot in the most popular class around town: this app opens up a whole new world of choice and flexibility! Through booking, customers co-create value by implicitly providing participating studios with demand and service valuation information they can use revise their service and pricing strategy in the future (Bertini & Koenigsberg, 2014) (Saarijärvi et al. 2013).

Studios maximize revenue without having to insanely discount their services and use their capacities efficiently by filling up empty spaces. Drawing on historical and real-time data, Lymber’s algorithm will propose the best prices to studios without taking away their control (remember the price floor and ceiling). At the same time, studios can attract new customers; potentially even some who end up joining long-term. Finally, don’t forget the importance of impressions on crowd dynamics: having more people in the gym implies popularity which can entice others to give it a try as well.

A critical forecast: What does the future hold for Lymber?

Naturally, the founders’ main goals are to attract more partners and more customers. But will it work? I believe Lymber is a very interesting approach for studios and customers alike. Yet, one also has to be critical: workout-aholics looking for a regular gym visit are better off with a sports-pass (San Diego Union Tribune, 2016). Next, surge prices and relative prices for membership and “Lymber lessons” have to remain reasonable. For a lot of people, fitness does not compare to hotels and transport after all. Finally, competition is already on the rise, similar business models are on the rise elsewhere (see Dibs, NYC). Should the system turn out to be successful, studios could also decide offer as-you-go offers in their own apps, cutting out Lymber as the intermediary. Whether Lymber will surge or drop, we will see!

References

Bertini, M. and Koenigsberg, O. (2014). When customers help set prices. MIT Sloan Management Review, 55(4), p.57.

Lymber Fitness Inc. (2016). Online: https://www.getlymber.com/ last accessed 27.02.2016

Saarijärvi, H., Kannan, P. K., & Kuusela, H. (2013). Value co-creation: theoretical approaches and practical implications. European Business Review, 25(1), 6-19.

San Diego Union-Tribune (2017). “The latest craze in fitness? Dynamic pricing”. Last accessed 27.02.2017, online: http://www.sandiegouniontribune.com/business/technology/sd-fi-lymber-app-20161220-story.html

San Diego Union-Tribune (2017). “Lymber wins local startup competition”. Last accessed 27.02.2017, online: http://www.sandiegouniontribune.com/business/technology/sd-fi-lymber-app-20161220-story.html

Movie theaters regardless of size are facing declining attendance (Business Insider, 2017).  Customers are unwilling to pay a lot of money when they can simply stay home and stream movies. Also, a lot of people’s tastes in movies is just not as “mainstream” but the movies they crave are not shown at most theatres (Business Insider, 2017).

Still, let’s face it, staying home watching a movie is simply not the same as on the big screen. So, what if you let customers demand a movie?

Demand.film is an Australian firm which takes a crowdfunding approach to cinema-on-demand (also available in UK, Ireland, NZ) by enabling users (age 18+) to request a movie event at local partnering theatres. The majority of movies offered are studio classics, indies, artsy- and foreign films. While the concept is not entirely new (see Graphr and Tugg), Demand.film is the first larger on-demand cinema platform serving markets beyond the US. Moreover, it uses blockchain technology to support producers, distributors and exhibitors with transparent and reliable sales data (Forbes, 2017).

How does it work?

 

Efficiency Criteria: Weighing Cost And Benefits

For a user, the time and effort needed to place or support a request is really low and tickets only have to be paid by the attendees if the screening is confirmed to happen (leapfrogfilms, 2017). Partnering theatres of course will have to check their schedule and also determine required attendance thresholds.

How does this crowdfunding approach create value for customers, theatres and platform?

Customers co-create by driving the entire process from requesting (incl. time, date and location) to “spreading the word” about the screening of their wishes. If successful, a whole crowd of friends and strangers can finally enjoy “their” movie on a big screen, in a social atmosphere outside the own house.

Theatres help making it happen by approving the request. Further, hence they use capacities more wisely and can generate additional revenue without uncertainty: if the event happens, this money is “safe”. They meet customers’ demand and have the opportunity to welcome (potentially) new customers at their venue. The insights can be a trigger for theatres to think about future events and screenings by illustrating potential demand (Saarijärvi et al., 2013).

Even the filmmakers gain increased exposure and reach for their films; especially those which are not regular movie material.

Finally, Demand.Film only wins if everyone else wins by charging a ticket fee (~ GBP 1.65 per ticket).

The contractual obligations are addressed by Demand.films “terms of use” which reflect standard contract law, all under one condition: if the attendance threshold is not met, the screening will not happen. Naturally, films are legally obtained and of appropriate content. While users can spread the word about a proposed event by any means, all payments and organization are performed through Demand.film as the intermediary.

What does the future hold for Demand.film?

Naturally, the main goal is expanding the partner and user network as well as geographical reach. Can it be a success? Certainly, never underestimate the power of niche markets and don’t forget about the reach of social media! Will it be a success? The on-demand economy is still on the rise in offline settings and competition never sleeps, so it will remain interesting. All I know is that “I would be in”, would you?

 

Resources:

Business insider (2017). “Movie theater attendance is declining as cord cutting becomes more popular”. http://www.businessinsider.com/movie-theater-attendance-is-declining-as-cord-cutting-becomes-more-popular-2016-9?international=true&r=US&IR=T last accessed 28.02.2017

Forbes (2017). “Tugg And Gathr Face Competition From A New Cinema-On-Demand Platform“ Online: https://www.forbes.com/sites/dongroves/2016/09/12/tugg-and-gathr-face-competition-from-a-new-international-platform/#60a94aaa4eff last accessed 28.02.2017

Leapfrogfilms (2017). Demand.film https://demand.film/

Leapfrogfilms (2017). Demand.film example https://tickets.demand.film/event/1115

Saarijärvi, H., Kannan, P.K. K, Kuusela, H. (2013). European Business Review 25 (1), pp. 6-19

COVER: Leapfrogfilms (2017). Demand.film https://demand.film/

 

 

 

We all know the situation: We are looking at a website to find the perfect product. A recommender system helps us sift through all the different options but we also take a look at customer reviews to make the best pick.

Recommender system have been found to be more useful (Senecal and Nantel, 2004) and to increase choice quality and confidence for a customer (Aljukhadar et al., 2012). At the same time, reviews from other users have been perceived as more trustworthy (Senecal and Nantel, 2004). So, what do you think happens when both, a recommendation generated by a system and previous customer’s review are available?

This is what the researchers Daniela Baum and Martin Spann (2014) tested during an experiment with 1332 participants of different age groups and gender from a large European country. Divided into 15 groups, the (informed) participants were asked to pick a gift for a friend on a hypothetical webpage. They picked one low-involvement product

(clock radio / package of coffee) and one high-involvement product (digital camera/ hotel stay). Depending on the group, participants received different information when making their pick, here some examples (also see cover picture): some participants did not receive any recommendation or review; for some, the provided recommendation and the review contradicted each other, while in other cases they supported each other. Now, let us take a look at the most interesting results of the study:

1.   Unsurprisingly, if the recommender system’s suggestion and the customer review support each other, more customers follow the recommendation. This scenario is particularly effective for low-priced, low involvement products, because it simplifies decision-making and increases confidence in our choice (Aljukhadar et al. 2012; Baum & Spann, 2014)
2.    A review which contradicts the recommendation or praises an alternate product (figure 1), significantly decreases the probability of following the recommender system’s suggestion on average by 30% for both, low and high-involvement products.
3.   Negative reviews have the largest impact for experience goods. For example, when choosing a hotel, only 17% of participants still followed the system’s recommendation in the presence of a negative review compared to 68% in case of a recommendation and a positive review. The authors attribute this finding to the category “experience product” where previous customer’s experience is perceived to be more informative also considering the financial implications (Baum & Spann, 2014). While similar results were found for all the other products, only in this case results were significant.

What do these findings imply and why are they important? The results of this study show that by disclosing information wisely, firms can greatly influence decision-making influence customers. They decide about displaying reviews, how many will be shown and also which (type of) review will be made available. Still, firms are well-advised to keep showing negative reviews as well and must carefully consider them to maintain and improve customer satisfaction.

Would the findings of this study be the same in real life? It is likely, but this study is limited by the fact it lacks a lot of information which customer encounter in real-life: brands, different prices, different product looks, more information. The authors should have opted for an online experiment on a real platform; despite its drawbacks the results would simply be more realistic. Only further experiments and data analyses will be able to answer this questions fully.

Sources:

Aljukhadar, M., Senecal, S. and Daoust, C.E. (2012). Using recommendation agents to cope with information overload. International Journal of Electronic Commerce, 17(2), pp.41-70.

Senecal, S., Nantel, J. (2004). The influence of online product recommendations on consumers’ online choices. Journal of Retailing 80, pp.159–169

Baum, D., Spann, M. (2014).  The Interplay Between Online Consumer Reviews and Recommender Systems: An Experimental Analysis. International Journal of Electronic Commerce  19(1). pp. 129–161

 Cover picture:

Baum, D., Spann, M. (2014). Appendix 1 The Interplay Between Online Consumer Reviews and Recommender Systems: An Experimental Analysis. International Journal of Electronic Commerce  19(1). pp. 129–161