Can ‘Active Users’ determine the success of a dating app? | #facebookdating | #tinder | #pof

In today’s world, digitisation is a fact of life. Encompassing the personal and the professional, the last decade has witnessed a steady shift online. More than 4.5 billion people were active internet users in 2020, comprising 59 percent of the global population. Social media has proved almost as popular, with approximately 3.8 billion users in January 2020 – an increase of more than 9 percent over the same time last year.

As impressive as these figures are, what they don’t tell us is the critical role mobile internet time – and mobile app usage – have played in driving this growth. According to a study conducted by market research company eMarketer, adult smartphone users spend 4 hours per day using the mobile internet, with 88 percent of that time allocated to mobile apps and only 12 percent to the mobile browser.


The Numbers Game

With apps forming such a crucial part of our daily lives and routines, it becomes important for the companies behind them to be able to track and measure their impact and success. For the longest time, the gold standard of tracking metrics has been dictated by Weekly Active Users (WAU) and Monthly Active Users (MAU). The logic behind this was simple – if these figures rose, you knew that your app is growing, and that was all that mattered.

But this purely quantitative approach is an oversimplification of the enormously diverse app ecosystem that exists and neglects to account for other KPIs that offer a more balanced view of an app’s performance. Two such metrics are the Average Revenue per User (ARPU) and customer Life Time Value (LTV). While the ARPU is the amount of revenue each active user contributes over a specific period of time, LTV is the projected revenue that a customer will generate over their lifetime.

In the case of subscription- and community building -centric apps, these metrics are far more relevant indicators of performance than the MAU. This distinction can best be highlighted by the following case study, in which metrics such as the MAU, ARPU, and Cost per Install (CPI) of two distinct dating apps operating in India are compared.


A Study in Contrasts

App A assumes that their measure of success is in increasing MAU. This means that they have generous offerings like free ‘Likes’, with the app relying on engagement rather than results to generate user interest. It aims to derive value by creating a network that’s as large as possible, with its users serving as the product. Facebook and Instagram are two of the most prominent examples of this model. However, this strategy works best if the app were to monetize itself through in-app advertising.

App B, meanwhile, is focused on community building. It aims to acquire high-intent users who are willing to pay for results. Under this model, there is an in-built scarcity of digital goods such as ‘Likes’, encouraging users to use them sparingly and thereby increasing the overall ‘seriousness’ of the community while sacrificing engagement. As a result, it’s positioned as a platform – the app, for all intents and purposes, works directly for the customer. This is the route freemium subscription services such as Spotify have taken.

These fundamental differences are immediately made apparent through the numbers on display. As a free app, the goal of dating app A is to maximise its MAU. This manifests through a vastly wider target audience, targeting users the length and the breadth of the country across a wider age range. Dating app B, in comparison, is more focused in terms of the users it hopes to acquire, targeting a slightly older demographic and limiting themselves to the nation’s metropolises.

The next table charts the CPI for these competing apps. Another metric that is typically overlooked in favour of the brute force approach of MAU, CPI is the price of acquiring new customers for a mobile app. Looking at the table, two things are immediately obvious – that the CPI per female user is substantially higher than that of male users in both apps, and that the CPI across sexes for app B is much higher than that of app A.

The first of these data divergences is easily explainable, as men are substantially more likely to register for a dating app. The second comes down to the targeted nature of app B’s audience, which is laser-focused on building a community of mature, urban users for its platform. Both of these factors combine to drive up the CPI, as people with a cosmopolitan mindset tend to be more discerning in the apps and services they install and use.

This brings us on to the monthly marketing budget, where we saw another sharp differentiation. Although the budget deployed by both apps is the same, their gender wise breakup is substantially different, with app A splitting it equally between men and women and app B choosing to prioritise women over men in a 75:25 ratio.

Now that we’ve arrived at the MAU figures for each app, the results of their different approaches are apparent. App A’s broad criteria for its clientele has resulted in significantly more users, 120,000, than app B’s selective approach, which has gained 15,000 users – only 13 percent of its competitor’s figures.

But it’s only once we come to the ARPU that the true efficiency of these respective strategies becomes apparent. Despite app A boasting an MAU figure that is eight times that of its competitor, its ARPU figures for male and female users clock in at just 2 and 0.5 respectively. Compare that to app B, with an ARPU of 30 across genders. The last only compounds these figures, showing us that app A’s total revenue of INR 210,000 is less than half that of app B’s INR 450,000.


Immediate Gratification versus Long-Term Success

As these figures demonstrate, correlating MAU with the overall success of a dating app is a risky proposition, especially in a market like India. India’s cultural division, and the stark differences between the expectations of users from across different regions and socio-economic backgrounds, has an enormous impact on whether an app ultimately succeeds or fails. Despite app A’s impressive MAU figures and low CPI, its failure to target any specific segment and lack of a community is likely to result in low user satisfaction, stagnant organic growth, a high rate of churn amongst its users, and an inevitable downward spiral.

Where app B truly differentiated itself was in its commitment to building a community and prioritising the safety, accessibility, and satisfaction of its female user base. By recognising that only 10 percent of urban women work in India, and structuring their app to appeal to this niche demographic, app B succeeded in creating a space and a user base that appealed to independent women looking for serious relationships. In the process, they facilitated more offline dates, added more value, and gained more word-of-mouth traction. This ultimately resulted in a higher ROI (despite higher CPI and a lower MAU) and set the foundation for a brand with organic growth and a runway that extends well into the future.

Although there are many different metrics that are used to showcase an app’s performance, revenue is ultimately the truest measure of success. And in the dating game, nothing embodies that better than ARPU.


By Able Joseph, Founder, Aisle

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