Peter van der Leun

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Tinder averages 1.8 billion swipes per day and consequently generates 26 million matches per day [1]. The prospect of social discovery and online dating in a comprehensive, user-friendly mobile design has proved to be incredibly lucrative. Match Inc., its parent company, is hence obtaining a surging amount of data through all of its users, but how is your data being used for optimizing the online dating environment? Essentially, Tinder capitalizes on the excessive amount of data in two ways:

For its matching algorithm. “With the updated algorithm, machine learning technology assesses and interprets the signals sent by our millions of users” stated Tinder CTO Ryan Ogle in 2015. The algorithm now, for example, makes use of A/B testing, swapping the first photo seen by other users when they visit your profile, and reordering the photos by analysing the responses (swiped left or swiped right) [2].
For its behavioural analytics platform called Interana. As data volumes grew, performance dropped off and so Tinder turned their eyes to a new data analytics partner. Interana makes use of market segmentation, splitting the users into various cohorts based on the demographics and helps Tinder in terms of conversion, retention & engagement [3]. Tinder acknowledged that their user data could be skewed in the form of deceiving answers by their customers. Humans lie, to put it succinctly. By integrating the Interana platform, Tinder constantly analyses user actions against their compatriots to identify differences in the behaviour and therefore tries to “match” Tinder to the user.

All in all, Tinder copes with the challenge of keeping up with their ever-growing data and implementing it to their profit. Online dating is often labelled successful because of its anonymity. This anonymity causes a so called “low investment, low stakes attitude” that many young adults adopt on online dating [4]. However, apparently (related to the vast amount of user data Tinder obtains) people want to share their data in an effort to find a suitable dating partner.

What is the trade-off between sharing personal information in a dating profile on a low-end application like Tinder and losing the perception of anonymity? And how could Tinder improve their fake profile/data problem?

[1] https://insidebigdata.com/2015/10/05/tinder-and-interana-find-a-match/

[2] https://www.analyticsindiamag.com/ai-dating-apps-machine-learning-comes-rescue-dating-apps/

[3] https://digit.hbs.org/submission/tinder-romance-algorithm-just-keep-swiping/

[4] https://sophia.stkate.edu/cgi/viewcontent.cgi?article=1580&context=msw_papers

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Everyone is probably aware of the ‘Moneyball’ concept; the popularized story of American baseball club Oakland Athletics, whose General Manager Billy Beane threw out traditional scouting methods, based merely on intuition, and implemented an evidence-based statistical approach aimed at bringing in undervalued baseball players. Thriving in the 2002 season and eventually clinching the American League West title, Moneyball has become the go-to story for information goods-related approaches in the sports industry.

The worldwide sports market is a $90 billion dollar industry [1], however it has been predominantly based on intuition and gut-feeling until the early 2000s. Only then Big Data Analytics started to emerge as a tool for helping sports teams in their decision-making. The use of information within the sports industry knows three primary ways of change:

It can help improve player and team performance; in this case it’s mostly a way of translating the data into comprehensible visualizations for the players themselves. Players will to tend to accept instructions sooner when they know there’s an underlying truth in the data.
It has a considerable predictive value; by analysing and tracking player behaviour and by comparing that data against others, teams can develop an efficient recruitment policy. Moreover, clubs can perform predictive analyses on opponents. What will there style of play be? What will there next move be? Questions that data analytics can frequently offer an answer to. [2]
It can help sports teams (which are essentially firms) in enabling a highly-efficient business- and information strategy. An example of this is Danish football club FC Midtjylland who went from near-bankruptcy to the Champions League in the span of one season, employing a data-driven strategy and reaping its financial benefits.[3]

A takeaway to bear in mind. More information does not necessarily mean more success. Clubs additionally need to focus on employee-recruitment, hiring skilled data analysts.

Since the global sports market is so substantial, why do you think it took relatively long for evidence-based approaches to emerge?
How can sports teams try to differentiate in handling their information strategies to gain competitive advantage?

[1] Statista (2017), https://www.statista.com/statistics/370560/worldwide-sports-market-revenue/

[2] Principa (2015), https://insights.principa.co.za/4-cool-ways-data-analytics-is-changing-the-world-of-sports

[3] The Guardian (2015), https://www.theguardian.com/football/2015/jul/27/how-fc-midtjylland-analytical-route-champions-league-brentford-matthew-benham

Beyond Moneyball: how Big Data is revolutionizing the sports industry

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