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The Crazy possibilities of 10 minutes Cabs: Part-1

I have written essays on 10 minutes of Groceries and 10 minutes of food. Since I was wrong in my assumption regarding 10 minutes of food. A few tweets demanding 10 minutes of Cabs, seeded the possibility of 10 minutes Cab in my mind. When I started writing this essay, I wanted to apply ZILA’s method - we used a similar method to decide: How many Community Partners/pick-up points we need in a City/District/Block etc. and eliminated the last-mile delivery from the system - to propose a 10 minutes cab system. Yesterday, I realised it is already 5000+ words, and therefore I decided to make this a two-part essay. I have worked on a similar exercise in past to solve problems. But this essay boosted my leadership confidence. I realised arithmetic formulas (I have not used formulas in this essay) are the best way to measure and control the input metrics, and hence performance of an organization.

Historically, boundaries pushed by one human motivated humanity. The first person on the moon, Neil Armstrong, motivated the entire humanity to dream of multi-planetary. The Wright brothers' first successful flight motivated humanity to fly like birds. Elon’s SpaceX, the first reusable rocket, motivated humanity to travel to other planets. The impact can be at a family, Village, Block, Subdivision, district, state, or country level. For example: In my family, I am the first Graduate. And it took four generations - almost 40+ years. But the 2nd Graduate from my family took only five years: My brother recently cleared his CA exam. The bottom line is when we see someone pushing ceilings - we believe we can.

Something similar is happening after the introduction of 10 minutes grocery delivery. The Metro consumers are expecting everything in 10 minutes (Jeff Bezos predicted this long back). Whether they need groceries or food in 10 minutes or not - is debatable. But the one service that they want in under 10 minutes, any guess, of course, everyone wants an ambulance in 10 minutes, but I am talking about Cabs. I think metro’s consumers would love to have Cabs in under 10 minutes.

I couldn’t stop thinking about the possibility of 10 minutes Cabs after coming across multiple tweets.

We all hate cities' traffic - Mumbai, Delhi, Bangalore etc. But the high frequency of cancellations from Drivers is a new addition to the existing problem. In any business model, the satisfaction of all the stakeholders is important. And in today's scenario: Drivers are not happy due to high fuel prices, a high commission by cab operators, and significant first-mile costs (uncounted): We shall look at uncounted costs in part-2 of the essay.

I think the proposed idea of 10 minutes cab can

  1. Reduce cities traffic

  2. Improve Driver’s income

  3. Enhance the experience and Save customers time

  4. Reduce cities’ pollution etc.

The billion-dollar question is - if I have such a brilliant idea, why am I not working? And therefore, before going into details few pointers:

  • It is not about any idea. History can verify this: most large businesses are built by founders because they had this obsession with bringing changes that they wanted to see in the world. Most of these founders also had some form of, personal connection with the problem - the power of the subconscious mind. After building products/services for different Target Groups (TG), I am at squire root 1 - I am referring to my dad’s Kirana Store in Village. I remember helping him at the age of 12, and I am obsessed with improving this part of India. My heart functions to see the changes that I envision every day. So, it is not about any idea.

  • The solution that I have proposed in this essay - is a part of the method we have applied in ZILA. We used a similar approach to decide: How many Community Partners/pick-up points do we need in a City/District/Block etc. We eliminated the last-mile delivery from the system and validated whether today’s investment will give us the Right to Win in the long term or not. i.e I have not spent time specifically solving this problem. :)

  • This model does require some flexibility from the customers - max 5 minutes walk [ Good for health]

Now that you have some context about the essay's origin. We should deep dive into the problem and solution statement.

Problem Statement: Guaranteed 10 minutes Cab

I am a fan of Amazon’s Working Backward framework, and we are going to use the same - we shall start with stakeholders: Driver, Riders, and Company [I am a third person].

Before deep-diving into the solution, we should look at the pre-Online era of the Cabs booking process. I have learnt over time, that the best way to understand a subject is to uncover curtains from History. This shall also allow us to think about this problem from the first principles.

Ola launched its service in 2010 and TaxiForSure in 2011 in selected cities. In 2010, I was in my 11th in a small District of Bihar called Darbhanga. So, I don’t have the personal experience, and hence I talk to a few senior folks to understand this. The input from senior folks allowed me to gather the necessary information.

To have a clear picture, let’s assume I am travelling from my Apartment to my Office in 2009 (I was 15 in 2009 ;)). I have removed the meter from the equation because a. Drivers used to charge: meeter fare + some extra or b. Without meter. Based on my discussion: I have listed three methods.

Method:1 I need to call the offline Cab operator, Share Info (Destination, Pickup Point, Pickup timing), negotiate the price, the driver will come to my pickup location, and I will get on board, I will pay the driver once I will reach my destination.

Method:2 Walk out of my Home, stand on one side of the road, signal empty cabs, if the driver stops, tell him the destination, negotiate the price (If the driver is ready to go to the destination), get on the cab, pay cash to the driver at the destination.

Method:3 Come out of my house, walk to the nearest Cab station, talk to a few drivers, negotiate the price, get in the cab, and pay cash at the destination.

If I had access to DALL.E 2, I could have generated images for all three methods. Meanwhile, I loved the DALL.E 2. I think there are some extraordinary use cases in Retail, Agritech, Media & Entertainment. You should check if not, yet.

The economics of Drivers, Customers, and Company all evolves around individual activities. And activities can be marked in terms of Time and Money (We all know both are interchangeable for metro consumers, unlike non-metro consumers where time ≠ money)


Time (Customer): Call Cab Driver, Share Info, Negotiate Price (Time that you spend to agree for the fare*), Get in the Cab, Actual travel duration, Pay Cash

Time (Driver): Time it took for the driver to come from his/her location to the customers’ address (First-Mile), Actual travel duration, Collect Cash

Money: Price save due to negotiation and the actual fare


Time (Customer): Walk from my house to the road, wait time for the Cab (Waiting for an empty cab, Driver rejection: Cab driver not agreeing to go to my location), Share Info, Negotiate Price, Board on the Cab, Actual Travel Duration, Pay Cash

Time (Driver): Info Collection, Price Negotiation, Actual Travel Duration, Collect Cash

Money: Price save due to negotiation and the actual fare


Time (Customers): Time duration from my house/office to my nearest Cab station, Talk to a few drivers, Share info, Negotiate Price, Actual travel Duration, Pay Cash

Time (Drivers): Info collection, Price Negotiation, Actual Travel Duration, Collect Cash

Money: Price save due to negotiation and the actual fare


Method-1 would be more expensive than the other two for consumers.

Method-1 can be best suitable for travelling with luggage

Method-2 Can be best suitable for Office and short-distance travelling.

Method-3 Can be best suitable at Bus Stand, Airport, Locations around Cab stands etc.

Among the three methods, the customers’ satisfaction had been uncertain: Especially regarding the price and the behaviours of the drivers. But Drivers seemed to be satisfied even though they were doing a lesser number of rides per day.

If we combine Time and Money and separate that for customers and drivers - we will have comparable data before and after the Online Cab era.

First, we need to take the union of time from all three methods because money is common in all three methods. I have created a separate worksheet. You can access it by clicking on this link.

Here is a summary of the worksheet:

N = Actual Travel Duration (Let’s keep this constant).

Key observations-2:

  • X>Y and Z (Logically, the extra cost and time of Driver's First-mile)

  • The price negotiation was common in all three methods - the part customer always wanted to avoid.

  • For customers, Method-1 is the most efficient - 8 minutes.

  • Method-3 seems to be efficient for both Riders and Drivers - Low cost for riders, and zero First-Mile travel for drivers.

  • In general, 10 to 12 minutes happened to be enough to get in a Cab in the pre-Online Cab era.

  • For any of these methods, customer satisfaction was inversely proportional to time and money. A customer wanted to get on a cab as soon as possible at the lowest price.

  • For any of these methods, driver satisfaction was directly proportional to the money and inversely proportional to the time.

Online Cab Era:

A glance at the chart, and you can tell the possibilities of massive disruption with the help of technology.

We should start with the online cab process to validate our assumptions.

There is no Cab sharing feature right now, and hence we will consider only one method. [I am assuming: she is a 2nd-time rider in an ideal situation - there can be multiple edge cases]

Rider Book a ride through a mobile phone. The online cab operator will assign a Driver (The distance can be 200 meters to 3KM. If it is more than 3KM the driver is going to cancel anyway). This will allow riders to see the assigned driver's details: Name, Photo, rating, location, live location, price, ETA etc. At the same time, the driver can also see customers' details (Name, Location etc.). All of these activities are performed by a machine - an efficient system saving time for both Riders and Drivers. The driver is en route toward the rider's pickup point. The rider waits, till the time the driver reaches the pick-up location, gets in the cab, and drives toward the destination. We need to plot this in terms of time and money.

You can refer the sheet-2 of this worksheet:

WooW! The online Cab companies disrupted the experience, and everything boils down to just a. Wait Time (T), and b. Actual Travel Duration (N). Massive time optimization for riders and drivers both - that’s why we all love technology.

At the beginning of the Cab operations - there were fewer drivers on the road - the wait time was a little longer. But there were no complaints from the rider's side. (If you look at the efficiency: it is massive) and the driver's wait time was compensated by the Online Cab Operators. The crazy efficiency from the customer’s side increases the LTV. And that was the validation for the model. If online cab operators could optimize the wait time: the companies were ready to disrupt the world.

The conviction got strengthened based on intelligent business design and formed a strong flywheel.

More Drivers, Shorter wait times, better experience, and more riders. Over time the flywheel will bring the right to win.

Online Cab Operators’ Flywheel

Unlike Amazon's flywheel, online Cab's flywheel depends on external factors. For example, at the beginning of the operation, Amazon could only control Click to Ship which allows them to display: place this order before 6:00 PM, and it will get shipped by today. But they also wanted to control Ship to Delivery and hence remove their dependency on the 3P delivery. Amazon controls both or built capabilities that allow them to control both. Online Cabs' inputs are still not in control - the supply side.

The online Cab operators started compensating heavily on both sides - a lower price for riders and onboarding bonuses, fixed daily compensation, Lower commission etc. for drivers. These online Cab operators also collaborated with the financial institutions to help anyone who would like to become a driver and earn a respectable monthly income. And boom, the flywheel was in full motion. The flywheel worked well in the developed market, after a time riders were ready to pay an extra to have a better experience. That allows companies to maintain their unit economics. But India is not a great market for convenience: Most of the services are still limited to 5 to 10 million consumers. For example, India has 140 million online shoppers and 5 to 6 million shoppers drive almost 60% of the eCommerce GMV.

To repeat the same flywheel, the Indian Online Cab operators added more drivers in congested and packed cities. Daily income, Daily bonuses on the number of riders per day, Monthly bonuses on achieving a certain number of riders per month, lower commission etc. on the Driver's side and lower price to consumers fueled flywheel. And therefore if you can recall your interaction with the drivers, all of them were happy: 50K to 70K ho jata he mahine ka (I earn 50K to 70K per month). But the honeymoon can’t be for long if it is not a real marriage. As companies started focusing on economics, Indian consumers restrict themselves from using it, and the highly enthusiastic drivers' happiness converted into Okies - Ghar Chal jata he bas (It is enough to run households’ needs). Things were still under control thanks to the introduction of the pool and sharing features. These sharing features allowed consumers to go from point A to B at a relatively lower price. At least, the flywheel was rotating. And then companies hit the biggest roadblock - the Pandemic.

If you are with me, great! In the 2nd part of the essay, I will talk about

  • the Negative Flywheel

  • Uncounted Cost

  • Impact of Uncounted cost and the Negative Flywheel on the drivers

  • Why the cost component can’t be removed from the system

  • From sharing economy to ownership economy (Google Trends are highlighting the same)

  • The proposed solution

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