Onboarding project | Adani Digital Labs

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Onboarding project | Adani Digital Labs

Project Aim :

This project aim to focus on the all sort of onboarding aspects of my adaniOne product.

Here I have channelised all the inputs and incorporated customer feedbacks which needs to worked upon to improve the product.

Lets start to first Listing the Ideal customer profiles for AdaniOne transport services which is trains and buses.

The two ICPs listed are the ideal profile of the customers who are the target audience and are highly intended people to uses these services basis their routine and necessities.

ICPs -

Approach

Thinking of the person for whom this product will have perfect market fit. They are majorly belonging to tier 2, 3 cities and have now migrated to tier 1 cities in order to have better jobs and lifestyles.

These people prefer convenience and easy to understand ux ui and have high frequency of train bookings.

While the other person is still routed to the origin but often travels to meet their relatives, they are not the regular user of the app but have hight intent of being loyal customer considering the pricing structure and competitiveness.

Ideal Customer profile	ICP 1	ICP 2
ICP Name	Tarun	Alisha
Age	25-40 years	20-30 years
Travel Goals	Get access to wide variety of travel options at best prices	Maximise travel within given budget
Income Levels	20k- 60k per month	5k-15k per month
Gender	Male	Female
Location	Tier 1,2,3 cities in India like Delhi, Kanpur, Etawa, Lucknow, Amritsar	Tier 3,4 cities in India like Vijaywada, Bihar, Jhansi, Mauranipur
Companies	Own business, pvt jobs, government jobs, prep for govt. services	Housewife, interns, travelling for job, early jobers
Marital Status	Both married and unmarried	Both married and unmarried
How do they spend their weekdays	1. Working 2. Socialising with friends	1. At college/work 2. Going out with friends/ colleagues 3. Household chores
How do they spend their weekends	1. Spend time with family at home 2. Watching Tv/OTT 3. Calling friends home for party	1. Hanging out with friends/chilling 2. At home watching OTT/social media 3. Shopping with friends
Apps they spends most of their time on	1. Instagram, YT, 2. Messaging apps- whatsapp	1. Social Media- IG, YT 2. Messaging/dating apps- Tinder, whatsapp 3. OTT apps
What do they spend most on	Petrol, Rent, savings	Shopping, Grocery, Household expenses
Frequency of feature usage	Open the app- once every 3 days Wishlist- once a week Add to cart/buy- once a month	Open the app- once a week Wishlist- once a fortnight Add to cart/buy- once in 2 months
Willingness to pay	High value seeking cohort. They would be ready to pay for the convenience and service if they like would book again	Moderate Value seeking cohort because of limited budget. They would be searching extensively for options from both offline and online and then finally buy whichever is a better fit and lower price
Problem Statement	Too many apps in the ecosystem would trust the direct app but one loyal then sticks to the platform also create WOM	Always seeking better price
How technically sophisticated are they?	Moderate	Low
What role do they play in decision making process	High	Low
What technologies are they using?	Laptop, mobile, smartwatch, tablet	Mobile
Are they more driven by a desire to be innovative, or reduce risk	Ready to explore with the created noise in market	Ready to explore only after checking the offers and pricing
Time Vs features	Time is important	Features is important

Looking at the demographics and the kind of traffic we are receiving on the platform ; it is observed that maximum bookings are being done by male user that female. It seems that the male person is booking services from their account.

There are more male travellers than female travellers hence it is an opportunity to grab the female driven booking by proving more security features or communication which targets the female audience and encourage the travel.

The maximum booking is of AC services than the non-Ac ones , around 72% of bookings comprises of AC bookings and only 28% is the non-AC bookings blended for both Trains and buses.

On further deduction and analysis the traffic bases on city wise bookings, here is the pie chart for the same. It is seen that 8.1% bookings are done for bengaluru route followed by Ahmedabad and Delhi.

Maximum searches have been contributed by Maharatra state followed by Uttar Pradesh, Here is the detailed searches data in descending order :

Region	Overall Bus Search
Maharashtra	714
Uttar Pradesh	548
Delhi	390
Gujarat	357
Karnataka	352
Rajasthan	298
Madhya Pradesh	272
Tamil Nadu	250
Telangana	245
West Bengal	191
Haryana	106
Punjab	87
Andhra Pradesh	68
Kerala	64
Odisha	63
Bihar	60
Chandigarh	31
Himachal Pradesh	22
Uttarakhand	20
Assam	17
Jammu and Kashmir	13
Chhattisgarh	9
Jharkhand	9
(not set)	7
Mizoram	6
Dubai	4
England	2
Florida	2
Goa	2
Nagaland	2
Tripura	2
Andaman and Nicobar Islands	1
Eastern Province	1
Federal Territory of Kuala Lumpur	1
Illinois	1
Manipur	1
Massachusetts	1
Meghalaya	1
Ontario	1
Samara Oblast	1
Texas	1

JOBS TO BE DONE (JTBD)

Functional (primary) : Providing integrated service for all travel needs

Social (secondary) : Self booking app ecosystem, no thirds party support required

Financial (brand) : To generate growth by maximising the traffic and user onboarding

USER RESEARCH :

Case Category	Case Sub Category	Total Count	%Age
Buses		51
	Operator / Bus Issue	20	39%
	Bus Delayed	9	18%
	Quick resolution	8	16%
	Great app experience	8	16%
	Contact Details of operator/Driver	4	8%
	Boarding Location	2	4%
Train Related		220
	Cancelled PNR refund	67	30%
	Refund not received	59	27%
	Booking status	58	26%
	Train status /Info	12	5%
	Booking query	6	3%
	Refund Breakup	4	2%
	Others	14	6%
Grand Total		271

Onboarding Teardown

Google Slide Link:

https://docs.google.com/presentation/d/1NA6P6NNRIrv-4CQwJ-TXP3iifrDxe-qlgdfSrYdUKHM/edit?usp=sharing

ACTIVATION METRICS

Metrics to track

D1 : the focus is on the first-time user experience (FTUE)

For tracking the first time user experience we first called up our this week first 10 users and took their honest feedback

Questions covered -

Questions	Customer 1	Customer 2	Customer 3	Customer 4	Customer 5	Customer 6	Customer 7	Customer 8	Customer 9	Customer 10
Any issues with the app download	No	No	No	No	No	No	No	No	No	No
Did you face any issues with the signup	No	No	No	No	No	No	No	No	No	No
Look and feel of the app was good?	Yes	Yes	No, find redbus better	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Were they able to identify various services of AdaniOne	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Train / bus booking ecosystem was easy to understand	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Was customer able to make seemless booking	No	No	Yes	Yes	Yes	Yes	Yes	Yes	No	Yes
Issues logging in to IRCTC account	yes some lag	No	No	No	Yes	No	Yes	No	No	No
Any issues on the payment flow or page	No	No	No	No	No	No	No	No	No	No
Overall Rating	5/5	4/5	3/5	5/5	5/5	5/5	5/5	5/5	5/5	5/5

D7 : Users who have progressed further into the core

For Trains - Monthly 62% of the people are actually converting their searches to landing to the "Select Seat" page

Below is the date to back uo the analysis

For Bus - Monthly 44% of the people are actually converting their searches to landing to the "Select Seat" page

Below is the date to back up the analysis

D30 retentions : Users may have reached end page and may become bored or dissatisfied. This is when adanione needs to examine the deeper aspects of the customer retention.

As maximum drop has been detected in the last step and people are actually ending or dropping out of the app.

Recommendation to start showing some sort of offer or gratify this customer as their are high chances that user has found better prices else where.

Secondly the we have detected the payment failure rate to another cause of the drop, hence need to improve the success rate of the payments or just reflect upi as the payment method as it contributes more than 75% of the success transactions.

DAU & MAU

For Trains daily active users are 5059 and monthly active 216165 users

For Buses daily active users are 1978 and monthly active 48622 users

Total onboarded train users : 7157

Engagement Rate : 78.22%

Bus Pages	Users
Home Page	3560
Listing Page	2052
Review	736
Payment Page	325

Hypothesis 1 : Time taken to create an event

For testing this hypothesis, I recorded the journey from opening app till booking calculating the overall time taken to search and book.

Test Condition 1 : When user knows exactly which train / bus it want to book

In this situation the average train booking time was 1 minute and for bus it took 40seconds .

The payment page took another 20 seconds in both the journey with UPI/secured card as payment method.

Test Condition 2 : When user wanted to test and explore pricing and offers available on the platform

Here the it me aprox 2min in trains to fully explore the functionality checking all cancellation policies offers till booking.

Hypothesis 2 : % users who made the reservation

More folks are completing their bus bookings (10.97%) than train bookings (7.36%). I'm wondering if that's because of differences in ticket prices, seat availability, or just personal travel style.

Hypothesis 3 : Highest drop step in onboarding

We're losing a lot of potential passengers after they search for their ride, especially on the bus side. It seems like something right after that search step isn't clicking with them

As overall bus searched in a month is 2379 vs completed bookings in only 261

Where as for trains overall searched in a month is 2527 vs completed bookings is only 186 .

Hypothesis 4 : Reserved vs actual attendees

Method : For both the businesses we checked the number of people who have made the payment successfully vs who have completed the journey

Train : 267 people made the payments, out of them only 186 passengers have actually availed the services contributing to 69% drop

Bus : 233 people made the payments, out of them only 181 passengers have actually availed the services contributing to 77% converted

USER ACQUIRING SOURCES

TRAINS & BUSES –

We use various acquiring sources all of them listed as below :

· Direct

· Paid Search (cpc) – cost per click

· Cross network

· Organic Search

· Affiliates

· Paid social

· Referral

· Push notifications on phone

· SMS

· Email

· Banner displays

· Paid video

In a month

Train month searches = 2527

Users who have selected order completed their searches = 1064

Search to book conversion - 7.36%

Funnel view of AdaniOne Trains

Session	Channel
Direct,(none),23418
Paid	Search,cpc,14096
Cross-network,cpc,7810
Unassigned,(not	set),7497
Organic	Search,organic,1719
Affiliates,affiliate,537
Paid	Social,cpc,400
Referral,referral,375
Mobile	Push, Notifications,whatsapp_push,94
SMS,sms,77
Unassigned,(none),52
Email,email,31
Mobile	Notifications,banner_push,30
Display,banner,26
Display,cpc,26
Unassigned,chatbot,23
Organic	Social,social,21
Unassigned,affiliate_171_,18
Paid	Other,cpc,15
Organic	Search,referral,13
Organic	Social,referral,13
Unassigned,digitalpromotion,13
Organic	Social,skyscanner,11
Display,display,6
Organic	Social,google_flight,5
Paid	Video,cpc,4
Organic	Social,push,3
Organic	Search,search,2
Organic	Social,sms,2
Organic	Social,whatsapp_push,1

Funnel view of AdaniOne Buses

PRODUCT REVIEWS

Top 5 best product reviews on app store >>

CONCLUSION

The user onboaring and teardown will help building the product and strong positioning in the market.

The customer feedbacks are most critical part hence needs to be worked upon and problems related to user experience should be solve in immediate priority.

Also since the drop from search to booking is higher hence its recommended to focus more on the marketing channels and communications as currently the app is low with the market discovery.

Overall product experience is so far good but constant improvement is necessary here.