Stuck with 2Y’s: Latch (Pitch)

Ask: $ 200,000 ($ 100,000 – initial cloud data storage, 60,000 – team member salaries, 40,000 – marketing & advertisement and other)


According to Pew Research poll, 40% of Americans use online dating(1) and 59% “think online dating can be a good way to meet people”(2). UK country manager of a dating app, eHarmony, Romain Bertrand mentioned that by 2040, 70% of couples will get to meet online(3). Thus, the online dating scene is a huge and ever growing market. Nevertheless, as of 2015, 50% of the US population consisted of single adults, only 20% of current committed relationships have started online, and only 5% when it comes to marriages(1). There is a clear opportunity to improve the success rate of dating apps and improve the dating scene in the US (for a start).

As per Eli Finkel from Northwestern University (2012) (3), likelihood of a successful long-term relationship depends on the following three components: individual characteristics (such as hobbies, tastes, interests etc.), quality of interaction during first encounters, and finally, all other surrounding circumstances (such as ethnicity, social status etc.). As we cannot affect the latter, dating apps have been historically focusing on the first, and have recently started working with the second factor, by suggesting perfect location for the first date etc.

For individual characteristics, majority of dating apps and websites focus on user-generated information (through behavioral surveys) as well as user’s social network information (likes, interests etc.) in order to provide dating matches.  Some websites, such as Tinder, eHarmony and OkCupid go as far as to analyze people’s behavior, based on their performance on the website and try to match the users to people with similar or matching behavior.

Nevertheless, current dating algorithms do not take into account vital pieces of information that are captured neither by our behavior on social media, nor by our survey answers.


Our solution is an application called “Latch” that would add the data collected through wearable technology (activity trackers such as Fitbit), online/offline calendars, Netflix/HBO watching history (and goodreads reviews), and user’s shopping patterns via bank accounts to the data currently used in apps (user-generated and social media) in order to significantly improve offered matches.

According to John M. Grohol, Psy.D. from PsychCentral, the following are the six individual characteristics that play a key role in compatibility of people for a smooth long-term relationship (4):

  • Timeliness & Punctuality (observable via calendars)
  • Cleanliness & Orderliness (partially observable – e-mails/calendars)
  • Money & Spending (observable via bank accounts)
  • Sex & Intimacy
  • Life Priorities & Tempo (observable via calendars and wearables)
  • Spirituality & Religion (partially observable via calendar, social media, Netflix/HBO patterns, and e-mail)

Out of the six factors mentioned above, 5 are fully or partially observable and analyzable through the data already available online or offline via the sensors mentioned earlier. As all the information we would request digs deeper into privacy circle of a target user, we would be careful to request only information that adds value to our matching algorithm and will use third-parties to analyze such sensitive info as spending patterns.

Commercial Viability – Data Collection

As a new company entering the market Latch would have a clear advantage over the current incumbents, as it would not have to use old and commonly used interface of dating process. As per Mike Maxim, Chief Technology Officer at OkCupid, “The users have an expectation of how the site is going to work, so you can’t make big changes all the time.”

Prior to the launch, we would have to collect initial information. In order to analyze only the relevant data we would have to analyze the behavioral patterns of current couples before they started dating. Thus, we would aggregate data available on their historical purchase decisions and time allocation in order to launch a pilot.


The pilot version will be launched for early adopters based on human- and machine-analyzed historical data of existing couples. The early adopters would use automated sensors (Fitbit, gmail etc.) to aggregate data on their spending and behavioral patterns, which will be compared by Latch to algorithms developed on previous experience of existing couples, and will generate matches. Further, the future success rate and compatibility of the matched early adopters will be fed back into the data, and used for further pattern recognition and improved matching algorithms with next users. Future expansion opportunities exist by integrating DNA ancestry analysis (such as provided by MyHeritage DNA), digging deeper in geolocation data (suggest which coffee shops both matches visited), matching games/apps usage history on smartphones, and other.



Alexander Aksakov

Roman Cherepakha

Nargiz Sadigzade

Yegor Samusenko

Manuk Shirinyan

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