Computer says yes

By Nicholine Hayward on Monday, 4 September 2017

What FinTech can learn from the online dating industry

In the early days of digital, if you wanted to see the cutting edge of innovation, you had to look at adult content whether you liked it or not.

With big money to be made and an enterprising cohort of digital sharks happy to extract maximum returns from their customers, adult publishers were the early pioneers of the business models, marketing strategies, technologies and techniques that we take for granted online today. Secure transaction processing, search engine optimization, locationbased targeting and the freemium model all had their origins in the adult industry. They were the masters at monetizing user-generated content long before the digital marketing mainstream.

Where adult has led, its more socially acceptable younger brother, online dating, has followed. And as the ultimate person-to-person business model,it offers some intriguing learnings for the wider business community.

In a world in which consumers have more choice and power than ever before, and a brand has a few seconds to elicit an action, it takes a compelling combination of clarity, brevity and desirability to cut through. Just like in dating.

Sarah Willersdorft of the Boston Consulting Group recently presented a fascinating TED talk into why brands need to think of themselves as online dating profiles.

The correlation with online dating particularly applies to FinTech, a market keen to embed itself in the hearts, minds and social networks of financial consumers in the same way that online dating has gone from stigmatized to normalized in just a few years. It’s also a category that, like finance, has always been keen to attract more women. While online dating is a smaller market than FinTech, with an estimated 91 million users worldwide compared to 1.2 billion for mobile banking, there are a number of useful lessons that FinTech can learn from the category.

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Customer satisfaction vs customer retention

Tinder is considered a success (despite it being a loss leader for its owner IAC) because its users have a long term relationship with the site, if not with each other. The reason why dating startups struggle to attract investment is that the quicker and more effective they are at matching people to find their happy ever after, the lower the revenue potential for the site. As a satisfied customer is effectively a lost customer, churn is the curse of dating sites. Tinder is different because its users aren’t there to find ‘the one’ and then delete the app.

FinTechs need to create a business model that simultaneously gives customers the outcome they need while giving them a reason to keep coming back for more. Rather than enable that ‘once in a lifetime’ event, however effectively, FinTechs need to think small – to the ‘little and often’ interactions that are a part of everyday life. So an online mortgage broker, rather than thinking in terms of a 3 to 5 year repeat business cycle, should be looking to the additional services they can sell in the meantime, potentially through a network of partners, such as regular valuations and surveys, property maintenance services, alerts on the local property market or an incentivized referral scheme. These don’t have to be paid-for services, they could simply be ways of staying in touch and keeping the relationship going in between the big events.

The freemium model

With regard to revenue, the freemium model that the online dating companies use is a classic example of a way to build a sustainable income stream. They have learned the lessons of the music industry, which paid the price of being too open with their content and as a result is struggling to break even.

Micropayments to make the big small

Coffee meets Bagel has a ‘beans’ microcurrency system in which customers can purchase add-on services and privileges. Likewise, Tinder offers Tinder Plus, with unlimited ‘superlikes’, no advertising and a rewind function to reverse regretted decisions. Others offer the opportunity to buy a time-limited taster offer for a discount fee, or the ability to view, but not contact, matches for free. Rather than charge bigger fees for a bigger portfolio of added value services, FinTech might consider breaking down those options into relatively painless micropayments that customers can pick and mix as they choose.

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Experience as brand

With dating sites, the experience is the brand, and as important to the user as the outcome. Tinder has its iconic ‘swipe’ ritual of left for no, right for yes, which exemplifies the simplicity of the experience in a way that can easily be described and demonstrated to friends in a crowded pub. Arguably, the Tinder swipe is as much a part of its brand as its logo. To gain similar traction, FinTechs need to have a repeatable, physical action to further cement the connection between the user and the brand to become an embodiment of the brand promise itself.

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Creating new solutions, not solving old problems

One reason the early dating sites were viewed with suspicion is that they tended to position themselves as the answer for people who had been unlucky in love – not the best strategy for attracting an early adopter audience. Today, the opposite is true and most online daters aren’t there to solve the problem of being single, but to enjoy its opportunities.

Tinder is not a challenger brand to traditional courtship but by contrast, many FinTechs like to position themselves as the ‘good guys’ relative to the mainstream financial industry. FinTechs should be focusing on what they are, and the benefits they bring rather than what they are not and the problems they solve.

The platform principle

Like Uber and Airbnb, online dating is a platform connecting people. The service is the connection. Peer-topeer lending and borrowing works on this principle, but where else might this concept lead? Are we likely to see emerging markets in social insurance, or financial advice?

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The brand of me – and us

One thing that the dating sites do very well is give people lots of chances to talk about themselves, to tell stories through their profile information, their photos and links to their social profiles. Tinder now allows you to link your Instagram and Spotify accounts to your profile. We tend to think of personal finance as just that – personal – but given our love of sharing andour changing attitudes to privacy – we might see the emergence of the ‘brand of me’ culture extending to our finances and letting where we bank, how we save and invest, contribute to that story of ourselves.

By the same token, some dating sites are now offering a choice of group social events to which members are invited based on location, age, gender and interests, in an attempt to connect them with like-minded individuals and create a sense of a shared purpose or focus. FinTechs might start catering to people who feel daunted by going it alone by offering investment or retirement planning clubs and communities. Or enterprising independent financial advisers might offer customers the chance to club together and receive group-based advice at a much lower cost and greater convenience than having a personal adviser on a one to one basis.

Alternatively, just as the next step in online dating sites could be sites that enable people to teach each other things, we may see financial knowledge marketplaces, where people can learn peer-to-peer in a more structured fashion than the social financial communities such as Moneysaving Expert, but without the regulatory constraints and liabilities of formal advice.

Matching algorithms

</br> A key selling point of many dating sites, unsurprisingly, is the quality and uniqueness of their matching algorithms in being able to source compatible partners. However complex the end result, the concept of a matching algorithm is simple – it’s about finding correlations between two datasets and pairing them up based on their shared attributes. </br></br> There is an obvious application for a similar principle in FinTech. As long as the right data is captured about the customer, algorithms could be used to segment audiences based on financial personality and values, to position and personalize products to meet emotional needs and aspirations, ensure chatbots echo the language and tone of the customer, and more accurately match partners in Peer to Peer transactions. Communities of FinTechs, incubators and investors could also use matching algorithms amongst themselves, to source potential partners and optimise their networks and ecosystems. </br></br> Interestingly, one thing we haven’t yet seen from online dating is the use of big data to tell its customers more about the experiences of people like them. They’re using it to refine their matching algorithms, but not to tell us how many ‘people like us’ have recently joined the site, or the kind of people that people like us went on dates with or where they went. There’s no, Amazon-style “people who bought this also bought” functionality. Given that finance is just as personal as dating, there’s an obvious opportunity for FinTech companies to make better use of big data to tell their customers how people like them save, invest or plan their retirement.

Profiling through gamification


After years of trying to elicit more and more information from their members, forward-looking dating sites are now taking the opposite approach.

With the increasing sophistication of some dating sites’ matching algorithms, this has led to an unsustainable number of questions new members are required to answer. eHarmony, for example, asks 155 questions, each requiring careful consideration. Rather than get people to fill out endless questionnaires or write long profile pieces, Tinder allows users to link to their Instagram and Spotify accounts, where their pictures and music can tell a thousand words, eliminating the need to write a thousand words.

Others are using gamification. For example, OKCupid uses quizzes, while on DatePlay members play a series of mini-games, which the site then uses to analyse people’s personalities and preferences in order to suggest compatible matches.

FinTechs are already using gamification in the form of modelling tools and calculators and infographics to make complex topics such as compound interest, easier to understand or dull ones, such as budgeting, more interesting. In other words, gamification is used to convey information but not capture it. In future, they might use it for data and insight, to gain a deeper understanding of their customer’s personality, attitudes and behaviours, their propensity to investment risk, their loyalty, values or even honesty and how they would react to certain events and scenarios.

The idea of using technology to capture data withouthaving to ask questions also extends into fraud prevention. Insurance companies have long been using voice analysis software to detect potentially fraudulent claims over the phone and the use of in-car telematics has the potential for a much fairer and more open relationship between drivers and their insurers.

More recently, data sc ientists have developed a digital lie detector – a text analytic algorithm for written communications online. This analyses word use, sentence structure and language to identify signs of deception, such as minimal use of referencing and selfdeprecation, over-structured arguments, superfluous descriptions and mimicking the linguistic style of the recipient. There is an obvious application for this in online dating, to identify potential romantic fraudsters and scammers, but where it gets interesting in FinTech is that it can be used by consumers to detect whether their provider is being honest with them, as well as by providers looking to check a consumer’s authenticity.

With the advent of DNA dating which uses people’s genetic profiles to find compatible matches, there are further applications for BioTech in online dating that can be equally applied to FinTech. For example, it’s only a matter of time before smart, connected technology such as bio-sensors and lenticular implants, which measure our physical and emotional responses, will be harnessed to help people check out potential dates and measure whether their interest is reciprocated and the attraction is mutual.

It makes sense then, that FinTech companies can harness the same technology to know when someone is stressed, excited or interested to understand their customers’ emotional needstates and respond accordingly. Combined with Artificial Intelligence and machine-learning, whereby the more data a system captures the more personalized its response becomes, this creates a fascinating scenario in which a robot has a relationship not with us, but with our bodies’ sub-conscious reactions.

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So in summary, the key lessons that FinTechs can learn from online dating, arguably an industry also founded on the premise of helping people achieve their life goals or simply make the everyday easier and more enjoyable, would be: </br></br>
  • Provide little flings to fill the gaps in between big relationships
  • Make it physical with iconic brand actions and experiences
  • Don’t ask too many questions, find other ways to capture data and insights
  • Provide connections to and insights into ‘people like me’
  • Create partnerships based on mutual compatibility

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