Have you ever thought about the game plan of the big transportation companies conquering the world? Ride hailing companies like Uber or Lyft, bike and e-scooter sharing companies like Bird or Lime and more. All these have a unique Game Economics that determine their strategy. So let’s understand some of the rules that govern these companies. As usual, this is a very technical post, written for those that really need to understand the game.
The Economy of the Network Effect
The economy of ride-hailing companies such as Uber is a special case of a Network Effect. The best example for this economic model is the telephone, and I’ll present it here: When the telephone network was established, no one had any reason to join, after all, who could you call when no one else had a phone? As more users join, the benefit each user gets is higher and thus more users are likely to sign in. In this type of network there is a point of “Critical Mass”, once the number of users is over that initial run and the network is gaining momentum and growing almost by itself.
Ride-hailing companies are based on a Network Economy but it is a Dual network!!!
The best example for understanding a dual network is a matchmaking app… (for straights that is). The viability for men to join the app increases the more women it has, and the viability for women joining increases as there are more men. In other words, if it’s raining men (hallelujah) it is very worthwhile for women to join but in fact … less worthwhile for men. Once there are actually plenty of men competing for each woman, new male users are less likely to join the app. In a dual network, the service improves for you as it has more participants of the opposite gender but deteriorates as it has more participants of your own gender.
The transportation companies’ services are dependent on both drivers and passengers. They are known as Supply and Demand and at their core they behave like the matchmaking game. For a driver, the viability of participating in a service rises with every additional passenger that joins but drops with each new driver. For passengers, the service improves as it has more drivers and lessens with each new passenger. If we draw it as graphs we will see the following picture:
In fact this is also the economy of AirBnB, WeWork and many others.
So that’s the general theoretical basics, but now it is time to understand in depth the special problems encountered by transport companies. Most of all…
How do you take over the world?
As I wrote, in economic activity with a network effect we aim to reach the critical mass, the tipping point in term of the number of users. Once we get through it the network will grow by itself.
But if you are a global company, how can you reach a critical mass at all? Is it really necessary to get 10% of the world’s population?
Well, no. In the transportation world, activity is very local. The fact that Uber controls the market in Manhattan doesn’t make any impact in Philadelphia. All transportation services are needed on a regional basis. Therefore Uber and it’s like are seeking to get control by reaching a Local Critical Mass.
When we think about the world, we usually divide it to countries and nationalities. Global transport companies see the world in a completely different way. For them, the world is built from cultures and cities. When a company like Uber wants to grow, it operates in an urban framework, city by city. Each time it explores where to go next it will prefer cities with similar culture. Melbourne, for example, is closer to Uber than Moscow. This is the reason why local companies are emerging in the world and pose a real tough competition for Uber in their local market. In fact, Uber or Lift have no advantage when they enter a new city. They need to begin the entire process of re-enlistment with all that entails.
I do not want to elaborate on it in this post, but there are number of tactics to get that local critical mass. Let’s move on to another challenge …
Why are there no normal men and why are there no taxis at rush hour?
Here is the main problem when facing the matchmaking game and the transportation services, the dual networks are not symmetrical. Let’s try to explain it plainly, to see what it means.
Let’s take as an example a world of matchmaking using a horribly simple theoretical example: We are on an island, there are 100 men and 100 women, evenly distributed of all ages between 1 and 100. That is, there is one boy and one girl that are one year old, one boy and one girl two years old, and so on up to… one old man and one elderly woman that are 100 years old. On the face of it, the situation is completely balanced and we can find a bride for every groom… However, in our theoretical island any woman over the age of 20 wants to get married while men want to marry only if they are over the age of 30?
Houston, we have a problem. At any given time we have 80 women looking for partners but only 70 men. We can never find a perfect solution for all.
This is the meaning of a “Asymmetrical Dual Networks”.
In the world of transportation, the problem is that most passengers want to travel during the same 3 hours of the day while drivers need at least 8 hours of rides in order to have a valid income. Simply putting it – the network will be optimal for passengers if the number of drivers is adjusted to peak demand, but it will be optimal for drivers if adjusted to low demand.
This problem has no perfect solution. The optimum requires that passengers will be disappointed as they get poor service at peak hours, and drivers will be dissatisfied that there are not enough rides to make a living.
Again – it is a problem that can not be solved and is built in the market.
The way Uber and similar companies try to deal with it is to “balance” the networks. By raising prices during peak hours, they try to cause two effects. First, to move some passengers’ demand to off-pick cheaper hours, and second, to encourage as many drivers as possible to travel during peak hours.
Data gathered during past years shows that this solution is not good enough. For most passengers the option to travel in different hours is inconvenient and they feel they are being screwed by those high prices, while drivers still can not earn a living wage from working during peak hours alone… especially in the congested cities where the number of rides a driver can actually make in an hour is low during rush hours.
Can one company conquer the transportation world?
I think that the economic model of the market, the fact that it is very easy to enter the market as a business and compete in it, the fact that customers do not have loyalty or real differentiation between those services, are all hints that the market will behave in the future like the aviation market. At any given moment there will be several competing companies on the same lines and passengers and this leads us to the last question we will with deal today …
The one key indicator that holds it all
How do we know which company will flourish and which will tumble? We are today at a stage where all those companies are losing money but in 10 or 20 years, when Uber type service will be as simple and common as flight services, who will win?
The way to answer this question is by examining THE one economic parameter that will be the key for that entire market.
If in airlines we measure the efficiency by looking at Revenue per seat miles (the number of miles a plane flies multiplied by the number of passenger seats available per flight) in companies such as Lift, Uber or Didi, the most appropriate parameter would be Revenue per Seat Hour. Since passenger seats are not really owned by those companies, the key is to count all available seats from all cars that were available for service at that specific hour. A company that will have better Revenue per Seat Hour will be able to attract more drivers, give better passenger discounts and push the competitors out of that city.
Just as we look at airline reports for that Revenue per seat mile as the comparative parameter predicting growth, we will compare transportation companies by this Revenue per Seat Hour in the future. Focusing on this one parameter immediately defines ways to improve. Increasing Revenue per Seat Hour can be achieved for example, by:
- Raising prices or cutting costs (as usual)
- Traveling faster (more passengers per hour)
- Boarding passengers faster, matching better cars and passengers
- Traveling several passengers together (less empty seats in the car)
- Stopping activity all together when there are not enough passengers
- … and more
I’ll stop Here. In the following articles I will address some of the specific problems in the Ride-Healing market and I’ll also discuss the fundamental difference between dock-less and docked bike-sharing services and which one is more likely to lead.
I would love to hear your comments and questions about the subject, please ask me for clarification if you think some issues are unclear.
…and be careful out there, real roads are more dangerous than the theoretical ones.