The Stardom Culture Among Apps | Drop in Rating RCA

Aamir Ahamed
7 min readNov 10, 2021


Hypothetical Problem Statement: You are the PM of a leading taxi aggregator platform and the rating of the app has gone down from 4.5 to 3.8 in the last 2 Weeks.

Rating of an app is a north star metric used by companies to measure their success and also plays a pivotal role in the search algorithm in App Stores. Therefore a decline in the rating is a red alarm indicating customer dissatisfaction and leads to less app discovery by the users.

Framework to approach the problem.

1. Assumption:

Due to lack of access to accurate data and metrics, i will be making the following assumption:

  • The drop in rating is due to users from Indian market only.
  • A driver and user interview was conducted in the last 2 weeks.

2. Internal and External Factors

Internal Factors:

These are the resulting factors due to the decisions taken internally by the company or its departments.

In order to understand the possible internal factors, an interview would be necessary with each department involved with the product. As a Product Manager, i will be focusing on the following questions while interacting with each department.

External Factors:

A business cannot control external factors. All it can do is react to them and make decisions to help it remain successful. It is often dependent on the social and economic condition of the market, competition strategies and offers etc.

To understand the external factors, a primary research on the market followed by an interview with the marketing team would be necessary.

The marketing team can further help in understanding if there was any bad PR around our company recently. Few aspects to focus:

  • Was there any driver strikes recently which led to unavailability of drivers?
  • Did any user have a bad experience with our company which was highlighted in social media and became viral.
  • Any controversies which led to defamation of the company indirectly.

In 2017, SnapDEAL received lakhs of bad reviews due to a contreversial remark made by SnapCHAT Founder.

3. Hypothesis and Validation Using Data Points

From the interviews and research conducted in the last exercise, i am going to assume the following hypothesis:

Hypothesis 1

Drivers are cancelling the rides after getting to know the destination from the user. This is leading to dissatisfaction among user due to longer waiting time. The frustration can force a user to post bad reviews.

Data Points:

  • We observed a 30% increase in the number of cancellation in the past 30 days.
  • Number of negative reviews accusing driver of cancellation due to drop location has increased by 40%

Hypothesis 2

The competition has reduced prices which makes our platform expensive. Loyal customers are feeling cheated due to major different in pricing.

Data Points:

  • Competition analysis and comparison shows average rate per ride to be almost equal.
  • No offers were released by competition in the past 30 days.

Hypothesis 3

Drivers are not accepting online payments. They either cancel the ride if the user wants to make online payment or demands cash once they are at the drop location.

Data Points:

  • Number of successful online payments has been on decline since 2 months.
  • Cash to Online Payment ratio is 1:5

Hypothesis 4

There was a protest by the drivers due to insufficient fare share from rides. This has been majorly triggered due to recent hike in fuel prices. Social media has been showing support to the drivers by giving low rating for the App.

Data Points:

  • The number of reviews in the app store showing support to the protest was only 1% of the total
  • Our company had recently made policy changes to accommodate the demands of the drivers.

4. Selection & Neglecting Hypothesis

From the available data points, following hypothesis qualifies as major issues with possible effects on the rating.

Problem 1: Drivers are cancelling the rides after getting to know the destination from the user. This is leading to dissatisfaction among user due to longer waiting time. The frustration can force a user to post bad reviews.

Cause of Problems:

In order to find the solution to this problem, let us first understand the different reasons why a Driver would choose to cancel a ride after hearing the drop location. I will be assuming that a driver interview was conducted to understand the issues.

  1. Drivers are getting less pickups from the region of drop
  2. Drivers do not prefer going to remote areas due to increase in fuel prices recently.
  3. The drop is too far from the city for the fare amount displayed
  4. Traffic in that region is very high
  5. The road to the drop location is very bad
  • The recent festive season which resulted in the surge of cab booking also produced low reviews as more users faced cancellation.
  • From the problems faced by the drivers it is very evident that the drivers are cancelling rides to specific region due to lack of fare and time.
  • It was also understood from the drivers that they do not get to know the drop location before accepting the ride. This is why drivers call the user and confirms the drop location before picking them up.


  • Incentivise drivers for rides to certain locations

The first step would be to collect real time data regarding the drop location with maximum cancellations. Whenever a ride is booked to these locations, the app should show a popup showing incentivised fare alert to the driver before they can accept. Eg: If the fare share for the driver is supposed to be X, the incentivised fare share will be 1.5X. The app can also show the approximate distance to the destination before a driver can accept the ride. The user can also be charged a premium for this ride if the chosen destination is remote and less common.

Another incentive for the driver can be Cash points for these rides. If a driver choses to accept rides to these destinations, the driver will earn certain points for every KM. This can be later reimbursed as cash.

From the business perspective, we can also consider increasing the hiring of drivers from these regions so that the drivers would not mind taking rides to places near their houses

Problem 2: Drivers are not accepting online payments. They either cancel the ride if the user wants to make online payment or demands cash once they are at the drop location.

Cause of problem:

In financial year 2021, over 40 billion digital transactions worth more than a quadrillion Indian rupees were recorded across the country. These numbers alone show the importance of digital payments for India’s financial services sector. However, there is still a lack of trust and difficulties faced by certain population when it comes to online payments. From the driver interview, following reasons were found:

  1. Drivers rely on daily wage to take care of their household expenses. Our platforms online payout to the drivers are only once a week. Therefore the drivers find it very hard to make their ends meet.
  2. The drivers do not feel motivated when there is no money in their hand by end of the day.
  3. Drivers are not trained on online payments or does not understand how it works. Therefore there is a lack of trust in the system.


  • Scratch cards for drivers accepting online payment: This is a proven strategy which was used by Google Pay to increase their customer base in India. However, for our app i would propose giving scratch cards to drivers for accepting online payments. For every payment they accept online, the app can give them a scratch card rewarding them with small amounts such as INR 30, 50 etc depending on the ticket size. We can also introduce Cash points here to incentivise the driver to accept online payments.
  • 24 Hour settlement to drivers based on the amount earned that day: We can work on a model where a driver will be eligible for daily payout if he/she has earned a minimum amount. Eg: If the driver earned INR 2000, the payout will be processed within 24 hours.
  • In app training module and support system to help drivers get accustomed to online payments. This can also include a leaderboard of drivers who has made maximum incentives from online payments.

Disclaimer: The views, opinions and data presented in this article is personal and not accurate. The primary purpose of this article is to practice and understand the problem solving exercise.



Aamir Ahamed

Product Enthusiast | Program Manager @MyGate