Finding the ideal match, but who is the ideal match?
An analysis of relationships and how to translate emotional and biological triggers into the digital world.
In this article, the experiences of users on Tinder are going to be analyzed to understand what motivates them to look for one type of relationship or another and what processes, both emotional and biological, give rise when the spark of attraction between two people occurs.
The work is part of the UX/UI training given by Ironhack, in which a new feature has to be proposed, taking into account the business, users, technology, and design aspects.
Context
Tinder is the leading application in the match environment, with a 31% share of the world market (above Bumble or Badoo). Its value proposition claims to be “connecting users with strangers whom they wouldn’t normally meet, rather than connecting with people a user already knows.” It achieves this with a range of features deployed in a freemium business model, where users can find people they may be interested in based on their profile and can try to connect with them.
In this context, an investigation into the app was proposed, in which we aimed to discover ways to add a feature that would help users find “the ideal match”.
Research
Goals
The objectives of the research are two:
- Identify what users define as an “ideal match”.
- Identify possible solutions to show “the ideal match” to the user within Tinder.
Questions
The research seeks to answer three key questions:
- What generates the ideal match?
- How is it generated?
- When is it generated?
Desk findings
A desk research plan was developed to search for possible answers to these questions, in which we saw that we could talk, at the level of mechanisms, about what an ideal match is in the mind of each one, from both a biological and psychological point of view. But to analyze the problem, it was determined to find the fundamental piece that triggers the process at both levels: dopamine (known as the hormone involved in the reward system of the brain).
Analyzing what processes release this neurotransmitter (food, sex, addictive behaviors, among others), the question arose: what resources do companies use to provoke this response in their clients and/or users?
In the first instance, and to try to answer this question in a more general way, digital resources were searched in different applications and gamification systems to benchmark the different experiences and analyze the hotspots where the user experiences a stimulus related to the game (pleasure). In a second step, a benchmarking of applications to find a partner (Bumble, Badoo, Adopte, OkCupid) was done.
User findings
In the user interviews, several insights of value for research were found, and that could potentially mark the direction of the next steps:
- There is a cognitive bias associated with the perceived value of Tinder (“Tinder is only for the casual seeker”).
- There is a gender bias in terms of the success rate and the value proposition (“women have an easier time looking for and finding a partner than men”).
OK, now. Who is the ideal match?
Once all the information was available, we proceeded to elaborate on a definition that would allow us to ground concepts:
The ideal match can be a person of the sex to whom you feel attracted and who satisfies your emotional and/or sexual needs in the short or long term, without a loss of identity and based on a transaction of trust that is established based on previous experiences and future expectations.
Confluence
Seeking to turn the last statement into something to work on, the next question was asked: How can this experience be maximized by simplifying the interaction in a way that is actionable within Tinder?
As a starting point, we found that Tinder has a method to “profile” users: personal topic tags. In this way, we can investigate how to generate value thanks to the expectations of the users that they have with the compatibility of the possible matches. It was then decided to define more parameters in order to define the use cases required for the feature’s MVP.
One MVP to rule them all
Actionable parameters
Bearing in mind that we wanted to efficiently and effectively verify the perceived value of the concept of the ideal match within the application, parameters were sought with which to work within the functionalities themselves, without adding any additional ones. The parameters defined as working were the following:
- As mentioned, the personal preferences tags
- The distance between users
- User activity (user logged in/user logged out).
- Notifications (both external and internal).
The ideal match. How do we validate them?
We start from the assumption that, at an undetermined distance, there may be a person with high compatibility for a user and that person can enter or leave a certain distance perimeter (from now on, let’s say that this person is the ideal match). The tool to trigger the user in this situation is a notification (either inside or outside the app).
Based on this scenario, several use cases were defined, of which four were worked on:
- An inactive user moves out of the defined range zone of another inactive user who is 100% compliant, and the former receives a notification.
- An active user leaves the defined range zone of another inactive user and is 100% compatible the second he receives a notification.
- An inactive user leaves the defined range zone of an active user and is 100% compliant before receiving a notification.
- An active user leaves the defined range zone of an inactive user and is 100% compliant the second he receives a notification.
Concept testing: storyboard
To represent the problem, a storyboard was created based on a use case to teach and collect findings on how the two parties involved would deal with (or think they would deal with) the situation.
In the storyboard, we explain the story of Dertin and Tinderella. Both share 100% of the preferences that they have indicated in the tags (they are “the ideal match” with each other). When Dertin leaves Tinderella’s designated range, she gets a notification that her ideal match is drifting away from her. At this point, users are asked: “What would you do?”
Concept testing findings
As a main finding, most users assume that, once the notification arrives (and they see it immediately), both in first and third person, they will tap on it.
All of the users interviewed said they would enter the application if they saw the notification, no questions asked.
Hi-fi prototype
Based on the findings of the storyboard, a second prototype was tested, which was linked to the presented case, seeking for users to enter more into the context of the situation (“Do you think Tinderella would do this, but would you?! Let’s see it!”).
2nd round: product testing
During product testing, the user can swipe left or right to determine whether they like the person they are looking at. When a person appears who has 5 matches in terms of likes, it is considered an “ideal match,” so a notification is shown. Also, the heart of the like button starts throbbing, as it would happen in a romantic scenario. You can watch the animation here.
Mind the business
In order to take into account the business, we also wanted to test user acceptance if we showed a step with a pricing table, in which the user is shown that he has to pay to access the functionality.
Product testing findings
As a main insight, we have found that most users, when they find the ideal match within the application, never consider swiping to the left (NOPE).
Considering the importance of this and in contrast to this reaction, users feel a lot of frustration when they have been shown the pricing table blocking the match. Most agree that they would first like to try the feature at least once before paying for it (following the business model of the other features of the application, such as Superlike or Boost).
Wrapping up
Recovering the goals presented at the beginning, we can say that:
Have we identified what users define as an ideal match?
Yes, but with nuances. We know that app users have different perceptions of the “ideal match,” and we need to iterate on the design and business strategy to add value to the user journey while maximizing business benefits.
Have we identified possible solutions to show “the ideal match” to the user within Tinder?
Yes, but we have to analyze more deeply the impact of the feature on business in order to try to achieve the right balance between the value proposal and the value perception of the new feature.
Has the outcome of the MVP been satisfactory?
Yes, but:
- User profiling could have been further explored.
- Onboarding of the feature in the profiling process has yet to be investigated in order to define the best entrypoint to the feature and present the pricing options.
Thanks for reading this far; it means a lot. If you want further details about this or other case studies, do not hesitate to contact me on LinkedIn.
