Background
In today's digital world, people are spending more time than ever on their mobile phones, and remote work has made face-to-face interactions increasingly scarce. As a result, the need for meaningful connections and relationships has never been more important.
This is where SoulMate comes in. SoulMate is a dating app that uses specialized psychometric questionnaires to match users based on their preferences and create genuine matches, unlike the superficially driven algorithms of the current cache of dating apps. By taking a more scientific approach to matching, SoulMate aims to create more fulfilling and long-lasting relationships for its users. SoulMate is poised to make a meaningful impact on the way people find love and connection in the digital age.
Role & Duration
Role: UX Researcher | SHL
Team size: 2
(UX: 2)
Duration: 3 months
(Feb 2023 - Apr 2023)
Introduction
Responsibilities
Conducting user research (Quantitative + Qualitative) to understand every facet of the user and their problem
Conducting competitor and market research to understand the global industry
Scoping features and creating solutions to distinguish product
Creating user personas, journeys, wireframes and seeing through the entire product life cycle
Mentoring a UX designer to help them develop their skills and knowledge
Initial problem statement
The struggle to find meaningful connections and fulfilling relationships is a global problem affecting people from all walks of life and traditional dating methods have become less effective, with many people struggling to find compatible partners due to differences in values, lifestyle, and personality. The rise of superficial dating apps that prioritise superficial factors over real compatibility have not only failed to meet the needs of the users but are actually contributing to the problem by treating people like objects.
Who is the user
SoulMate's target users are individuals who are looking for meaningful connections and are open to or already using apps to find compatible partners. They value compatibility are interested in taking a more thoughtful approach to dating. They may have struggled to find compatible partners using traditional methods or via the current online dating apps, and are looking for an alternative way to meet like-minded individuals.
The Process
We based our approach on the industry standard double diamond process.
Discover
Methodology
Initial survey
We wanted to create a service or platform to alleviate this problem in society. The first goal is to understand the landscape and pinpoint the actual problem and current solutions. We did this using the 4 methods below:
Initial Survey
Ethnographic research
Literature review
Competitor research
We conducted surveys to validate our problem statement and get a stab at what people think of online dating. Here are the results. We recruited extended friends, family, colleagues and hosted our survey on services such as SurveySwap.
The survey was conducted among 107 participants. Here are some summary demographics:
We had a young audience with 96.3% of participants falling within 18-34 years.
They were fairly global with people from 13 different countries.
51.4% were female, 42.1% were male, 3.7% were non-binary and 2.8% preferred not to disclose their gender.
We also asked them to rate their appearance and the results follow a bell curve which gave us some ball park indication of the significance of our sample size
Majority of the participants used either Tinder, Bumble, Hinge or Badoo. This was used to determine focus for competitor research. Scroll down to later sections.
Research goals
We started listing the hypothesis down as we wanted to keep a running count. The first 3 are shown below. These 2 were based on early anticipation of the possible concepts to come.
Hypothesis
The main course starts now…
We got a pretty clear understanding of their goal, we then investigated the frequency of matches. To do this, we asked them 2 questions; the first using a Likert scale and the second using actual values.
On the whole people got less matches than they thought they did
69.1%
Of participants were on dating apps to find a relationship.
Male vs Female difference in frequency of matches
We then investigated if there was a discrepancy between the male and female groups. Both groups were roughly equivalent in terms of sample size (M: 45, F:55) hence were suitable for statistical analysis. Unfortunately the non-binary group had an insufficient sample size (4).
The graphs below show the distribution in terms of histograms and a combined plot of the Kernel Density Function. You can visually see a difference in terms of distribution of no of matches. The Mean and Standard Deviations are also shown below the graphs.
Since the variance (S.D^2) was different and the data was not normally distributed (quite skewed), I couldn’t use a parametric test such as a T-test or Anova. Alas I resorted to the Mann-Whitney U test.
I created a python script to execute a one tailed U test and used the following hypothesis:
Null: There is no difference between the 2 groups
Alternative: Females get more matches than Males
A p value of 0.00037 means that we can reject the null hypothesis which means:
Outcome: Females get statistically more matches than males
I also checked the Cohen’s d value. This will tell us how meaningful the difference in matches is, in practical application.
Cohen’s d = 0.724 : This means a large practical impact
Chatting and meeting
This section deals with chatting and meeting the people that participants matched with.
We also noticed that participants were interested in reading people’s bios as well. So it wasn’t entirely mindless swiping
We then moved on to ask questions about their satisfaction and willingness to try different apps
59.8% of people chat with 10% of the people they match with
84.8% of people meet around 10% of the people they chat with
Overall most participants are only meeting around 1% of their matches
Overwhelming majority of participants are not satisfied with their current dating apps and would be willing to try out a new dating app.
I also put in a contextual inquiry to validate if users would be willing to complete a relatively long quiz while setting up their profile. The answers are shown in the graph below and were very positive. At this point we have validated all 3 of our initial hypothesis.
We also checked if participants would be willing to pay to use our dating app. The results are shown below and were promising.
Not only were participants willing to try out a new app that required them to complete a 15 min quiz but they were also willing to pay for it, more so than they were for current dating apps.
Summary of survey
Majority of participants spend time reading bios of profiles
Participants rarely chat or meet with matches
Most people find the quality of matches unsatisfactory
Overwhelming majority willing to spend 15 minutes on survey to improve match compatibility
After the survey we still had unresolved questions. To tackle these, we used a combination of Ethnographic research and literature review.
Keep reading…
Ethnographic research & In-depth interviews
Margaret Mead, the world-renowned anthropologist, famously said:
"What people say, what people do, and what people say they do are entirely different things."
We had attitudinal data from the surveys and we conducted Ethnographic research to collect some behavioural data.
But often, people are guarded and will hold back their perspectives in a research setting. This phenomenon is known as the Hawthorne effect: people are likely to change their behaviour if they know they are being observed.
One way that we, counteracted this challenge—and got people to open up—is by diverging from traditional Q&A-style interviews and instead installed and used various dating apps ourselves, to really put ourselves in the user’s shoes and this allowed us to converse and discuss the app with other participants as kindred spirits without them even knowing they are part of our research. This really helped and got them to open up. We involved 10 people in our study, recruited them via personal connections. They are all between 25-35 and were all single except 2.
We spent 1 month using 4 dating apps and swiped on 100 profiles every day, the results are shown below. After chatting for about 1 hour, we revealed our motives as we didn’t want to mislead anyone or hurt their feelings.
We created a qualitative map of users thoughts, observations, & survey result data in order to define our UX persona, user journey and problem statement. We split the data up in multiple themes.
Summary of ethnographic research
Most participants weren’t compatible with the people they matched with for a long term relationship.
User profiles lacked sufficient information to accurately judge a person's character / personality / interests.
People falsely portrayed their attributes on their profile which were discovered during chatting for example, very few people in Tinder’s gamer category were actually gamers. People also falsely portrayed their intentions which lead to wasted time and effort.
People who got a lot of matches didn't put in much effort in chatting and considered it a chore.
We noticed that endless swiping caused a tendency to nitpick and disqualify people based on superficial features such as clothing style or hair style in a way we never would have had we met the same person in a social setting.
Participants with experience in long-term relationships believed that great relationships were built on compromise and that dating apps promoted the exact opposite by lulling users into thinking someone new will always come along who will match their every criteria. Some exact quotes were ‘grass is greener’ and ‘shopping’
Some participant were afraid to be filtered out by personality-based matching, as they thought this would only reduce their chances. This was quite important and we took it into great consideration when designing our filters/ algorithm.
Despite the terribly low odds of finding a compatible partner, most participants remained hopeful and were willing to go the extra mile to find someone.
Literature review
At this point, we had data regarding what people think they do via surveys (attitudinal). We observed what they actually do via ethnographic research (behavioural). But we still couldn’t explain why they act this way. That is why we conducted a literature review.
According to Statista, the online dating audience will grow to 53.3 million by 2025, compared to 44.2 million users in 2020. A 2019 study from Stanford found that online dating is officially the most common way for U.S. couples to meet, accounting for nearly 40% of couples having first met online. These statistics coincide with our survey results, which show that the most common motivation for using online dating is still to find romantic partners.
Differences in the online dating experience for men vs women
Some of the differences in experience are outlined below and coincide with our observations.
Men and women in the top 10% (meaning, the 10% of users who receive the most matches) typically see a minimum of 5 matches per day, and a select few can see dozens per day.
On the other hand, while some people see a large number of matches, others see very few. Men in the bottom 10% see just 1 match per week at most. However, women in the bottom 10% see at least 7 matches per week.
However, a match is far from a real date. On average, it requires 57 matches for one meet-up and over five times that for either a relationship or sexual encounter to occur. As a result, it would take an average man almost 6,000 swipes over two months (100 a day) to score a date. It is unfortunate but the numbers coincide with our initial survey results.
The graphs below show the match rates with respect to swipe rates, explained underneath.
Men match with around 1.8% of the people they swipe right on which is around 1% of their total swipes as men swipe right about half the time.
Women match with 36.0% of the people they swipe right on, but interestingly this is only 1.8% of their total swipes, as that women swipe right only 5% of the time.
Reasons for this include but are not limited to:
Gender imbalance: There are often more men than women on dating apps, which can result in more competition among men. For example, Tinder has a gender ratio of 21.9% women and 78.1% men.
Selectiveness: Women may be more selective in their swiping, leading to fewer matches but potentially more meaningful connections.
Algorithms and bias: Dating app algorithms may be biased towards showing women more matches to increase their engagement on the app, potentially leading to biases against certain groups of men.
Ergo…
Number of matches is not the problem
Despite having many matches. Most sources say women who are looking for a relationship are still equally unsatisfied as men. This proves that simply getting more matches will NOT solve the problem.
So what’s real reason for the dissatisfaction? Keep reading.
The real reason is too much choice
People who are presented with more potential partners report experiencing choice overload.
In marketing and consumer behaviour, the psychologists Sheena Iyengar and Mark Lepper published a now well-cited study about jam and choice. They did a study using samples of jam and proved customers feel more satisfied with their choice when they have fewer options.
Problems with “relation-shopping”
"Relation-shopping" is a term used to describe the commodification of people in the online dating world, where potential partners are presented as items on a shelf with a personalised ad and a few pictures. This approach has several drawbacks as users:
Are more willing to change their mind. When people are just one profile in a sea of millions, they are viewed as replaceable. Common consequences were negative behaviours like ghosting which have a negative impact on self esteem and was also observed in our ethnographic research.
Are less satisfied with their choices. People tend to idealise potential partners when they meet online. This can create a desire to find the "perfect person" even though such a person does not exist, leading to disappointment.
Sift through more profiles but spend less time looking at each one, in a way that wouldn’t be done in a social setting where people would spend some time to get to know each other. Once again, de-personalising the dating process by treating people as just another option instead of individuals with unique qualities.
Negative consequences on self esteem
Research indicates that not getting enough matches on dating apps can have detrimental effects on an individual's self-worth and self-esteem. For instance, a study conducted by Toma et al. (2018) and published in the journal Cyberpsychology, Behaviour, and Social Networking, revealed that receiving fewer matches on dating apps was associated with less favourable perceptions of one's own profile, resulting in lower levels of confidence and self-esteem. Additionally, studies have suggested that the constant swiping and comparing oneself to others on dating apps can also negatively impact self-esteem. In a study by Strübel and Petrie (2017), it was found that individuals who were dissatisfied with their appearance were more likely to experience lower self-esteem after using dating apps.
Personality over appearance
Profiles of physically attractive people may be treated more favourably and appear to have better-adjusted personas. However, in reality, personality becomes more important than superficial traits in relationships, and even less physically attractive people become more attractive to us when we perceive them to have a good personality.
This may be a problem for dating apps, as they have a limited capacity to convey personality and can easily be manipulated, as revealed by our ethnographic study. According to Leslie Zebrowitz, people tend to pair off with individuals with similar levels of attractiveness and IQ, rather than constantly aspiring to date more attractive partners or smarter. Research by Eli Finkel and Eastwick suggests that people also use different models when evaluating potential partners online and in real life.
Online, people often use a “joint evaluation mode” that considers multiple factors, including attractiveness, income, and other traits.
In real life, when deciding whether to pursue a relationship, most people adopt a “separate evaluation mode” that involves evaluating the individual and whether they are a good fit, rather than comparing them with other people.
Finkel's paper, published in Psychological Science, suggests that this “joint evaluation model” can lead users to focus on certain qualities they ‘think’ are important in a potential partner, while overlooking qualities that actually matter.
Much like the distinction between attitudinal information and behavioural observation, the characteristics that people say they want in a partner do not correspond to those that they genuinely value and desire. Current dating apps only exacerbate this problem by promoting the former while ignoring the latter.
In summary, physical attraction primarily plays a role in initial attraction, but personality eventually overcomes the significance of appearance in long-term relationships.
Importance of Sexual Compatibility
Choice overload effect
The same phenomenon appears to be happening in online dating. Leslie Zebrowitz, a psychology professor at Brandeis University, notes that too many options can be overwhelming: "It seems counter intuitive, but too many choices may leave people less satisfied. We may feel that having more options means that we are eventually bound to find the 'perfect' person."
During our ethnographic research, we came across an intriguing comment that sparked further investigation. A screenshot of the exact quote is displayed on the right.
Upon further research, we discovered that we had underestimated the significance of sexual compatibility and kinks. We also realised that many individuals feel uncomfortable sharing their sexual preferences with strangers, whether it's through text or by displaying it on their dating profile. None of the current dating apps have incorporated these preferences into their matching algorithms. See the competitor research below for more details on each app's features and propositions.
According to a study published in the Journal of Sex Research, sexual compatibility is a vital component of intimacy. The study found that couples who share similar sexual preferences and interests tend to report higher levels of intimacy and relationship satisfaction (Baumeister et al., 2017). These findings suggest that understanding and embracing one's own kinks and sexual preferences, as well as finding a partner who shares similar interests, can play an essential role in developing a satisfying and fulfilling sexual and romantic relationship.
Competitor research
The graph on the right shows user base of most common dating apps. We focused on the top 4 apps to do a high level competitor analysis as these were also noted as the most prevalent ones in our survey. We aren’t going to dig too deep in the details but instead at this point will analyse the value proposition.
Tinder
Bumble
Badoo
Hinge is a dating app that differentiates itself from other apps by focusing on users looking for long-term relationships. The app uses the Gale-Shapley algorithm to match users with potential partners and has the ability to sort matches based on different criteria, such as compatibility and distance. However, these sorting options are only available to users who subscribe to the app's premium service.
Hinge also emphasises the importance of complete profiles, encouraging users to add photos and fill out prompts that provide insight into their personality and interests.
One unique feature is that Hinge does not have an overall "like person" or "swipe right" option. Instead, it forces users to send a like by liking a specific prompt or picture, and gives the ability to send a note with each like which basically serves as a conversation starter and helps break the ice with potential users even before they become matches.
Another good feature of Hinge is its "We Met" feedback system, which allows users to provide feedback on their dates with a particular match. This information is used to refine the app's algorithm and improve future matches
.
While Hinge does have its benefits, one major flaw is that it doesn't allow you to add your interests like Badoo does. This means that even if the algorithm is good, it can only be so accurate based on the superficial data that Hinge asks for, such as height and ethnicity. While 20% of Hinge users reported finding a long-term relationship, which is greater than Tinder's 13%, there is still a lot of opportunity for growth, with 80% of users yet to find a long-term relationship. This is where we come in to help bridge the gap.
A little something about filters in all the apps
Tinder is one of the most widely recognised dating apps, boasting a large user base as its primary selling point. With only a basic filtering system, users can swipe through profiles based on minimal criteria. Tinder has an interesting groups feature which allows users to join groups based on their interests and swipe through profiles of others in the same group. However, statistics suggest that there is no way to verify users' interests, for instance, many users who join the gamers' group are not actual gamers.
Moreover, Tinder also allows users to display up to 5 interests as easily visible tags/badges on their profile, but it does not offer the ability to filter matches based on these interests nor does it prioritise profiles with similar tags. This limitation may result in users missing out on potential matches with similar interests. Tinder has also received criticism for promoting hookup culture and superficiality in dating. It also has a relatively low success rate, with only about 13% of users reporting finding a long-term relationship on the app.
Recent reports from whistle blowers at Tinder suggest that the app utilises an invisible score based on no of matches received to influence visibility and place on queue. Users are not aware of the existence of this score and these tactics have raised concerns about Tinder’s ethical practices and their impact on user well-being.
Bumble was founded by Whitney Wolfe Herd, who co-founded Tinder before leaving the company and suing it for sexual harassment. Bumble's "women message first" policy was intended to give women more control over their dating experience and to reduce the number of unwanted messages they receive. On paid subscription, Bumble offers quite a few more filters than Tinder, but does not include major personality or interest-based filters.
It is also to be noted that despite having fewer users than Tinder, Bumble has a better gender ratio and gives users a 24-hour window to reply, which time-boxes matches and encourages them to make a move sooner.
The app offers a variety of features that set it apart from other dating apps in the market. The first is a wide variety of filters. Users can filter their searches by religion, orientation, goal, interests, and more. The filter for interests is especially useful and practical, as it allows users to find people with similar interests. However, the filter doesn’t have the ability to match people with combinations of interests or related interests, as it uses an exact match algorithm. For example, someone who has an interest in movies may not be matched with someone who has an interest in films.
Badoo also has a rudimentary personality filter that asks users to select whether they are an introvert or extrovert and does not provide the most value as it isn’t specific enough.
Badoo Live is another unique feature that allows users to broadcast themselves in real-time to other users on the app. This feature can be used for a variety of purposes, such as hosting a Q&A, playing games, or simply socialising.
Hinge
Despite their differences, all dating apps face a common challenge when it comes to user filtering preferences. Research shows that women filter out up to 70% of potential matches, while men filter out up to 55%. See graph on the right.
Filtering criteria often revolve around superficial attributes like height or age, which reinforces the "relation-shopping" mindset and reduces the chances of finding a truly compatible match.
As such, dating apps need to find new ways to help users filter based on more meaningful attributes, such as shared interests or values, in order to improve the user experience and increase the likelihood of successful matches. We have done an analysis on the filters available in each of the apps shown below.
Define
At this point, we were ready to start collating all our research. We created a revised problem statement broken down into 5 bit sized chunks.
We started by identifying some fundamental archetypes of users on online dating apps. By drawing on common patterns and motivations, these archetypes served as a preliminary documentation of the different types of individuals who use dating apps. From there, we were able to delve deeper into the nuances of each archetype and craft detailed personas and stories that reflect the diverse range of users on these platforms.
Inadequate personality/interest-based matching/filtering
Most dating apps rely solely on superficial characteristics such as looks, age, and location to match users, often resulting in low compatibility. Users also misuse filters by basing them on superficial characteristics, which only reduces their likelihood of finding a compatible match.Lack of information in profiles to judge key character traits
Manually analysing profiles is not very helpful as profiles typically lack information about personality traits. For example, it may be difficult to gauge someone's sexual preferences and kinks solely based on their pictures. This lack of information makes it quite easy for users to either accidentally or intentionally misrepresent themselves via their profiles, compounding the frustration and disillusionment.Choice overload effect
The excessive amount of choice offered by dating apps can lead to "relation-shopping," where users constantly evaluate potential partners against each other, leading to a constant search for someone "better." This can create a mindset of never being satisfied, leading to the constant swiping and unmatching that is often seen on dating apps.Disproportionate matching
Dating apps perpetuate a cycle of disproportionate matchmaking, in which some users receive a lot of matches while others struggle to get even one. This creates a sense of dissatisfaction and disappointment for both parties involved, leading to a range of negative effects. Some users may even experience lower self-esteem and feelings of rejection, while others may feel overwhelmed by the choice overload.Lack of transparency
Finally, there is a lack of transparency around the algorithms used by dating apps, leading to uncertainty and mistrust among users. Without a clear understanding of how matches are made, users are left to speculate and question the validity of the matches they receive.
Archetypes
Revised problem statement
We used the 4 archetypes to create a storyboard that captures most of the problems plaguing current dating apps.
Lights! Camera! Action!
Storyboard
Personas
We focused on the 2 archetypes who would be our target users and crafted very detailed personas for each of them, we used results of our previous research but also peppered some extra semi-structured interviews to validate these personas. The iterated and simplified personas are shown below:
Journey map
We crafted a simple journey map to visualise our target user's end-to-end experience with existing dating apps. By exploring the various stages and touch-points of her journey, we were able to gain invaluable insights and a deeper understanding of her needs and pain points. We centred the map around Emily (see persona above) and used Tinder as her app of choice.
Design
Solution modelling
We considered all the opportunities in the Journey map in the previous section and had some workshops to put together possible solutions to the problem. The high level solutions are shown below. We will soon begin a deep dive into every facet of each solution as we start the iteration. Yes, you may think we have already entered deep waters but trust me, we are merely swimming in the shallows.
1. Limited matches
Limit the number of matches or chats/likes available at any given time to users. This will encourage users to be more selective and not endlessly swipe or like without the intention of chatting with their matches.
2. Personality quiz (AI enabled)
Introduce a personality quiz during the user profile setup process. The quiz will contain key personality metrics that govern compatibility, as well as a brief introduction on how users spend their time and their interests. AI language models can be used to analyse and categorise answers. I.e., interest in movies should be in the same category as interest in films. We will also include questions about users' value systems, intentions, likes, dislikes, and romantic/sexual preferences. With this information, we can create a more holistic picture of each user compared to current dating apps.
This provides an ideal opportunity to apply Artificial Intelligence to combine and match the parameters. I dabbled in Python/TensorFlow to run a small experiment using Google’s Universal Sentence Encoder to check semantic textual similarity of different pairs of interests that our personas Emily and Eric have. I have plotted the similarities in a heat-map below.
This exercise was me applying some of my AI background to merely proof the concept. The actual algorithm that we will employ is going to be far more complex and will require some experimentation to get it to speed. We are intending to build a feedback mechanism into the app to allow users to rate their matches in terms of compatibility. This data will be used to refine the algorithm further.
Research shows that people trust and value results more when they obtain them after a carefully crafted delay, even if the delay isn't real.
Also, if you are wondering whether people will be willing to invest effort into filling a 10 min quiz, we have already proven that hypothesis using the initial survey (Hypothesis 1). The main hypothesis for this solution is that factors such as the interests mentioned are an indicator of compatibility. (Hypothesis 5 shown on the right)
To investigate the quiz further, we interviewed a psychometrician from the university of Oxford. We aren’t going to bore you with the details of the whole 2 hour long interview but suffice it say, we were thorough and walked through every facet of the problem and our proposed solution.
“...these things are generally difficult to predict based on a single factor alone but we do know that shared interests and some personality traits can play a major role when it comes to compatibility...”
“...and yes, in our day to day lives, we usually employ a criteria based selection, i.e., is she attractive whereas online, we tend to employ a norm based selection, i.e., is she more attractive than others in the group...”
“...I believe your solution has potential to alleviate some of these issues and am excited to see what the results are...”
Interview with expert
4. Advanced filtering
Provide advanced filtering capabilities based on metadata collected from the personality quiz. This can help users quickly find potential matches, but the app will remove superficial filters such as height and weight. We noted that Tinder doesn’t keep a height filter while all the other apps do.
We will include filters based on specific scenarios identified through usability tests and research conducted in real-time in an agile way. Experimentation via the app itself can help determine which filters are the most effective. The idea is to provide filters that are useful and not unnecessarily restrictive or superficial.
5. Public metrics
Display all metrics to users about themselves to maintain transparency and show specific metrics on other people’s profiles so that users are always aware of relevant statistics for example, a single proprietary metric can be constructed based on the quiz that tells that user the compatibility which can be displayed on user profiles as well as key ratios such as ‘reply ratio’ to allow users to judge better on how they want to dedicate their limited likes/time.
For example, if Luke matches with a lot of people but consistently doesn’t reply to them, his ‘reply ratio’ will start to go down and since it’s displayed on his profile, people will be a bit more hesitant to spend their likes on Luke, which means the number of likes that Luke has will reduce. And as Luke gets fewer matches, he will be encouraged to converse more. It will also work in reverse, for example if Eric gets very few matches but converses with all of them, his ‘reply ratio’ will be high which will encourage more people to match with him.
Let’s contrast our solution vs Tinder’s algorithm which assigns users an invisible score based on no of matches received. Think of the user as a ball on the surface. With Tinder’s algorithm, if you are someone with low matches, then the algorithm will show your profile less frequently to other users which will in turn cause a negative feedback loop. Since your frequency is reduced, the probability of getting a match is lower which means you will get more reduction in matches, which will reduce visibility and so on feeding a vicious cycle.
Our algorithm on the other hand will do the opposite as described in the diagram above. If you get less matches, you will have a greater probability of increasing your chat ratio which will increase the number of matches you get, automatically correcting the error like a stable autopilot system.
Provide a predefined recommended list of profiles with personality information displayed on their profiles. This will effectively reduce the pool of available candidates from an infinite set to a predefined set, making it easier for users to choose who to engage with. While the ‘joint evaluation mode’ cannot be completely eradicated, the degree to which it negatively impacts users will be greatly reduced.
3. Recommended lists
Task flows
We are still working on this section and will update once done
Thank you for reading my case study :)
Please contact me for any enquiries
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