Onboarding project | Gensol Engineering Pvt. Ltd
Define your ICPs
ICP Table ⤵️
Criteria | User 1 | User 2 | User 3 |
Name | Mr. Ankit Dwivedi | Mrs. Shalini Choudhary | Mr. Devansh Agrawal |
Age | 40 years | 25 years | 28 years |
Demographics | Gender: Male; Level of education: Graduation; Employment: Self employed; Marital Status: Married | Gender: Male; Level of education: Post-Graduation; Employment: Self employed; Marital Status: Unmarried | Gender: Male; Level of education: Post-Graduation; Employment: Working in an MNC; Marital Status: Unmarried |
Need | Transport goods from one point to another in the following way: | Transport goods from one point to another in the following way: | Transport goods from one point to another in the following way: |
Company Size |
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| NA |
Industry Domain and nature of business |
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| NA |
Pain Point | * Variable and high pricing, which creates issues in his costing estimation | * Lack of trusted delivery platforms - had a bad experience with Porter as the final product needs to reach the final customer in a specified manner | * Wants to shift his household goods from one point to another - but not able to find trustworthy vendors |
Solution | For Point-to-point deliveries, he uses On-Demand platforms like Porter For customisable deliveries, he still uses the Naka | For Point-to-point deliveries, she uses On-Demand platforms like Porter Uses cab services (Ola/Uber) to send her stock sometimes | Uses Porter for smaller loads within the city For larger goods/intercity movement, uses packers and movers services like APML |
Decision Maker |
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Marketing Pitch | Transport your goods anywhere in Delhi NCR in the cheapest, safe and hassle-free manner | Transport your goods anywhere in Delhi NCR in the safest possible manner! | House shifting made easy - Use our services to move your home in a safe and homely manner! |
Frequency of use case |
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| Rarely requires it - 1-2 deliveries every 6 months |
Average Spend on the product | Rs. 3000-3500/day | Rs. 1000-1200/day | Rs. 2000/year |
Frequently used apps |
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ICP Prioritization:
Criteria | Adoption Rate | Appetite to Pay | Frequency of Use Case | Distribution Potential | TAM ( users/currency) |
ICP 1 | High | High | High | Moderate | Large |
ICP 2 | High | High | Moderate | High | Low |
ICP 3 | High | Moderate | Low | High | Low |
Using the above framework, ICP 1 and ICP 2 are the ones we will be focussing on, given the High frequency of usage, higher appetite to pay, and given the penetration in the market
A table is shared below for your reference to put down your user goals, respective ICPs, JTBDs and validate your goals.
Goal Priority | Goal Type | ICP | JTBD | Validation approach | Validation |
Primary | Functional | ICP 1 | Jobs the app is completing for the user: | User interviews / User Calls | " I want a one stop solution for all my deliveries" " I want to transport my goods in a safe and timely manner" |
Secondary | Financial | ICP 1 | Jobs the app is completing for the user: | User interviews / User Calls | "I want a reasonable price for my deliveries as logistics cost contributes 5-7% of my total costs" |
Primary | Functional | ICP 2 | Jobs the app is completing for the user: | User interviews/ User Calls | "I am the one the major brain of the company, so freeing up time from my logistics help me focus on other things, like cloth designing, getting more clients etc." |
How to do an onboarding teardown?
Take screenshots of each page of the interface, note each interaction and user touchpoint, and assess based on user empathy:
- What is working well on the screen and why?
- What is not working and why?
- What changes/improvements do you suggest can be made? Why do you think that would be better?
- Where does the “aha” moment occur?
- Evaluate your onboarding on the cognitive biases.
Please find the above points covered in this document:
Parameters to track your activation metrics:
- D1, D7, and D30 retention
- DAU / MAU
- Subscription rate vs retention
- Average TAT
- User Cohorts
- Acquisition source
- Product reviews
- Add more based on specific context
Reminder: This is not the only format to follow, feel free to edit it as you wish!
To understand the activation metrics, the following steps were followed:
Step-1: Speaking to Users
- Speaking to Retained users: I spoke to retained users. These were those customers who were placing orders with us MoM
- Power users (>=5 orders in a month): I spoke to users from this cohort. This was because these customers had a repeat use case of our application
- M1 Retention for active users: Due to the cyclic nature of demand, most of the orders take place in the last and first week of the month. Hence, we tracked the M1 retention of cohort of the users from the inception of the app
- Median M1 Retention (>=1 order in the first month): 26%; Median M1 Retention (>=3 orders in the first month of activation): 57%
Step-2: Observe User behaviour:
- We looked at the retained and power user behaviour across the following touch points in the application and drew the journey backwards:
- First app signup date
- First price review page landing
- First order placed date (irrespective of completed or not)
- First completed order
Step-3: Map your customer journey
- All these events were placed plotted on a graph: X-axis: % of users || Y-axis: Time from the first signup date (app install)
We compared this graph for three types of cohorts:
- Cohort-1: >=1 orders
- Cohort-2: >=3 orders
- Cohort-3: >=5 orders
We got a metric type: 3 completed orders with 30 days
Step-4: Validate your activation metric
We validated the activation metric on the following parameters:
- Does it impact the retention curve?
- Yes, it did: The greater the completed orders for the customers within 30 days (>=5 cohort had a higher completed orders in the given time frame; and this kept increasing for power users) - Does it improve referral or WOM?
- Yes, it did, we spoke to customers fromt
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