Alternative Pricing Strategy in Ride-hailing systems
An effort to bridge the pricing disparity.
When we were first given the brief of Ride-sharing systems at scale, we could see ourselves pursuing many different directions.The primary interviews gave us many insights into the inner workings of the ride-hailing platforms and the pain-points as well. But the the most apparent issue that came through to us in our primary interviews was the dependency of drivers and riders both on these platforms, and how pricing and economics affected them. We as consumers are always looking to pay less for a service whereas the drivers whose livelihoods depend on this service are looking to earn more. An equilibrium definitely needs to be established to fight this disparity.
“We are totally dependent on the platform due to car loans.”
“We don’t get paid to travel to the pick-up point, we pay out of our pockets.”
We began searching for any pricing models used by any of the platforms and unsurprisingly did not find much information. We then shifted our focus to surge pricing and price discriminations, when we stumbled on a double-bidding system used in China by DiDi Dache to book cabs. This was an incredibly interesting model but was scrapped eventually by the Chinese government, so we thought we could pursue this and see where it leads.
Looking at current aspects
Before we could jump into “making”, we had to look at the aspects of how pricing works at the moment.
We also looked into previously proposed theories of double-bidding auctions and single bidding auction systems. But since time is an important factor while booking cabs, we chose to stick with the single bid system where only the rider can quote a price.
The research considers that the passengers are sensitive to immediate pricing and the drivers are sensitive to long-term pricing. Hence, a look at the surge price can immediately make a passenger log out from the app.
Insights generated from our research
· Bringing an equilibrium between the profits of company and the driver, simultaneously taking care of the expenditure from the passenger becomes a major point of concern
· Surge is introduced for a reason but company would gain enough profit if the surge is compensated with the increased service rate.
· Increase in service rate implies increase in the number of driver partners for the company
· The transport network companies make profit without owning a cab. They produce profit by networking. Hence it is important to find an alternative model that would increase the match creation of drivers and riders.
Principles of the proposed mechanics
When a surge is introduced, the rider gets an option along with it to “go for a different price”.
We believe that riders who have some time to spare and not in a hurry will also be willing to save a few bucks and select this option. If not, the rider will go along with regular steps as per surge pricing.
After the rider selects the option, he will see a screen where he can add to the base fare before he places his ‘bid’ with the cap being the surge price cost.
The rider enters an amount as he sees fit, there is ‘trending price’ graph to guide them with that. It gets added to the ‘base fare’and a final amount is generated.
‘Base fare’ is the estimate of the same ride with regular pricing.
150 (Base Fare) + 60 (riders addition) = 210 (final amount)
The rider confirms the amount and the request to drivers is sent as our per allocation model.
The extra amount that a rider adds on top of the base in this example ₹60, it will go the driver entirely and the platform gets their share from the original fare (₹150). Hence the driver ends up getting a bigger share.
Once a request is sent to a driver he gets 10 secs to accept the ‘bid’, he also receives the pick-up location he is also informed of the price during regular hours vs what a rider is offering at the moment. But he is not shown the destination, he is informed once he accepts the trip.
The driver can accept the trip, where he considers that the pick-up point is not too far away or the extra money offered is worth his time and fuel.
It is upto him to weight the pros and cons of the particular offered fare then accept or decline.
We are proposing this feature as a part of any existing platform. Screens we created are low fidelity examples of what it could be. Following are the rider’s screens.
We thought of including a draw on map feature where the rider can draw on an interactive map to guide the driver-partner’s gps in cases where verbally guiding is not an option or difficult.
Following are low fidelity examples of the driver screen.
The color fading in the ‘Accept Trip’ button acts as 10sec timer.
We also realise that if this model gets implemented it might result in some ramifications in the future like taking a toll on the traffic and environment. We have explored a few of them in the Futures Wheel below.
The working of this system completely depends on the efficiency of the algorithm. We have assumed a high level of efficiency for our simplicity sake with only proximity as a factor. But in the real world setting a number of variables like customer rating, driver rating, live traffic situation, time of day etc., which will be accounted for by the algorithm.
We also anticipate that this model if implemented for long could result in increased number of vehicles on road causing obvious problems. But we hope that it will atleast be a temporary solution to bridge the pricing disparity that exists.
Additional App Features
We had thought of a few more features to include in the platform but decide against it for the time-being since they did not connect with our concept of pricing strategy and to avoid any confusion.
· A feature for special needs of the rider. For example: Pets on ride, patient along, senior citizen on the ride, rider could be hearing or speech impaired etc. Driver would be awarded with tips for accepting such rides.But a consequence of this could be the tips or reward becoming a norm.
· Sound notification on driver app when bid price is offered by any rider.
· Sound notification on rider’s app when the driver has arrived to the pick-up location.
· An AR based feature to easily recognise/locate the cab through photo capture.
· Feature to inform new drivers about high demand areas. An in app assistant to inform the driver about the working of the model to achieve transparency in pricing mechanics.
Human Centered Design — Harinie A, Meghana R.U, Radhika B.
Design Led Innovation — Pritam S., Saili G.