How I optimized the onboarding flow of Haqdarshak retaining more users

Haqdarshak is an app that helps individuals to find suitable government schemes. The app features eligibility for various schemes, as well as a detailed list of required documents and assistance with the application process.

When it appeared in Shark Tank India, Haqdarshak was talked about by media like business insiders and influencers like beer biceps💥. It’s indeed the need of the hour and claims to have created a significant impact on people in India. Excited I went to try the app myself and found :

60% uninstalled the app several times before could finally get through. 😲

Play Store feedback

This was a curious problem and a great cause, let’s try to make it accessible using UX.

So I reached out to the founder ☎️

I wrote to the founder to be able to be equipped with more data and make some real impact

Aniket told me he was looking for the same and

📈Identifying bottlenecks through data

We went through Google Analytics and found out, that even though a significant amount of traffic arrived on the app, only 20% of users would complete sign up and around 5% would apply for a scheme.

The numbers are fictional but provide insights into the underlying trends and dynamics.

🔍When we met the users (Contextual Enquiry)

Here’s the previous onboarding flow with screens

Digging deeper into the problem, I introduced 6 people to the app and observed them use it. Here are the key findings:

Users were not able to make sense of the flow and hence hesitated to give personal information this early and dropped off after a point. (4 in 6 users)

Users faced problems receiving OTP. (Source: Google Play Store review)

The unconventional flow perplexed users so they got anxious about the duration it was going to take to finish signing us. Why collect pin, state and district names separately?

Some secondary trends that emerged

1. Someone with business in various states will be interested in various states. but a user here can select only one state.
2. App feedback to users’ actions was faulty and misleading.
3. The homepage was difficult to navigate and the user lost interest and willingness to explore by the time they reached there. of the users uninstalled as they were already so confused by login flow and homepage navigation looked like another challenge with nothing of interest in front. At this point, we lost 70% of the users.
4. A bias was discovered where a scheme that was for EWS students was recommended to only rural student persona while it was meant for everyone who belongs to the EWS category.
5. Most of the users thought it was a government app which was not the case. (6 in 6 users)

Here are a few goals I set for this quest now:

“If you don’t ask the right questions, you don’t get the right answers. A question asked in the right way often points to its own answer.” — Edward Hodnett

Simple — Unbiased — Entice — Establish Trust — Inclusive

• Simple - How can we make the onboarding process as simple as possible, while still gathering the information we need to make accurate recommendations?
• Unbiased - Are there any potential biases or assumptions that we need to be aware of when recommending government schemes to users?
• Enticing - How can we communicate the benefits and potential cost savings of government schemes to users in a way that is clear and compelling?
• Establish Trust- How can we create a sense of trust and credibility with users, given that the app is providing recommendations for potentially sensitive and important financial decisions?
• Inclusive — Are there any specific user segments or personas that have unique needs or preferences when it comes to government schemes, and how can we tailor the onboarding experience to meet their needs?

Final Concept

(Skipping the messy details of ideation)

The new concept uses the concept of chunking, in 3 steps while establishing its value to the user:

1. The user selects the state and is landed to feed
2. Now when they are convinced that this app is useful and want to apply to a scheme the app prompts sign up, still in sign up we take basic details
3. they fill in advanced information either by applying or through a filter quiz.

Few possible behavioural biases that we kept in mind

1. Reciprocity Bias: Reciprocity is the tendency to respond to a positive action with another positive action. By providing value upfront through the app’s feed, users may feel more inclined to reciprocate by completing the sign-up process.
2. Choice Architecture — Chunking: Chunking involves breaking down information into smaller, manageable parts. By not asking for all information at once during sign-up, the redesign employs chunking to make the process feel less overwhelming and more user-friendly.
3. Loss Aversion: Loss aversion refers to the tendency to prefer avoiding losses over acquiring equivalent gains. The initial exposure to the app’s feed creates a positive experience, and users may be reluctant to “lose” the benefits by abandoning the sign-up process.
4. Social proof: Social proof is the tendency to follow the actions of others. If users see that others are engaging with the app and finding value, they may be more inclined to sign up.
5. Progressive Disclosure: Progressive disclosure involves revealing information gradually rather than all at once. This approach aligns with the psychological principle of not overwhelming users with too much information upfront.

Prototype

Few metrics to study to improve the flow further with time

• Net promoter score(NPS)
• Customer satisfaction (CSAT)
• 52% of users faced problems creating accounts in the app. (Source: Google Play)
• The number of users who complete the onboarding process
• DAU/MAU
• Average session duration