Assessing Incentive Models and their Role in Ride Sharing Platforms
Ride sharing systems are a major part of the service industry today. The number of people who depend on these platforms to reach their destination are ever increasing. It has been an observed trend that these platforms use a monetary incentive based model to motivate their drivers to improve their performance by increasing the number of rides they take in a day. This system has both positive and negative effects on the stakeholders of the system. The goal of this project was to understand what were the pain points related to the changing incentive models in the ride sharing platforms, and to try and identify possible points of interventions.
The entire project was carried out following a modified framework foresight model which would be appropriate for the scale and duration of the project.
Each step of the process was framed and defined keeping one specific purpose in mind. The process was a key part of keeping our focus on the problem at hand and not getting lost in the sheer volume of the problem.
The entire project is best explained keeping in mind the order of activities and how each activity influenced the other in the final inferences made.
Scanning
By using the term scanning we defined a process of combining both primary and secondary forms of information gathering. Upon gathering this information it is carefully analysed to understand the trends and changed occurring in the current system, and zoning into a particular issue that seems to be prevalent.
Our primary research included taking interviews of drivers on major ride sharing platforms like Ola and Uber, and understanding how they perceive the the system to be, and getting their point of view. These interviews were semi structured, meaning that only the questions that were pre planned were as conversation starters and questions through the interview were based on what the drivers expressed during the interview itself.
The interviews were an interesting insight into the lives and minds of this community. While there were some pain points described by them that were seemingly obvious, there were certain changes occurring in the system which they believed did not bode well for them
By creating affinity maps and qualitatively analysing these interview, we identified certain key pain points that these drivers spoke of repeatedly across various interviews. Some of the key points we discovered were :
1. There is no clarity in how incentive objectives are set
2. These incentives have become harder to achieve as compared to when these ride sharing platforms had just launched
3. Pick ups in high traffic zones are strenuous.
4. Drivers who take their cars on EMI from the platforms have to pay a certain fee on a regular basis irrespective of whether they have earned that much in the stipulated time duration
Due to the time duration for this project being rather short, rather than conduction a broad academic analysis and literature review, we restricted ourselves to focusing on our insights from our primary research and gaining a better understanding of those inferences. Our primary focus was towards understanding why monetary incentives were such a pain point for drivers.
Our secondary research steered us towards some interesting theories such as the Motivation Crowding Theory. This particular concept gave us our first insight on how we could change the current system and maybe change how drivers look at these incentives.
It was through a combination of these factors that we were able to understand the direction we hoped to move forward in.
Framing
This part of process involved two major steps. Firstly, insight derivation and secondly, defining the problem statement with which we hope to go forward with.
The objective of framing is to correlate the inferences and observations from our research and identifying areas that can be possible points of value addition into the system. We identified 3 major ways of framing:
1. Persona Building
2. Calibrating data
3.Problem Statement Generation
For persona building we made use of generalised demographic references and creating a target persona for whom we would create our intervention, or rather would keep in mind when designing our intervention.
Apart from this we used the Godfather as a stand in reference for our story board so that were had a base narrative to derive our inspiration from.
Our next step was calibrating data and identifying common and unique points between different sources of information. This was a crucial step in identifying and framing our problem statement.
After identifying these points, we framed our problem statement to define the direction in which we want to have potential problem statements. The final problem statement was defined as :
“Ride sharing as a system is one that greatly favours riders while often ignoring the needs of the drivers involved. These drivers are usually observed to have low levels of motivation and are often shepherded into taking more rides. These incentives are measured and displayed in a way that makes them extremely difficult to achieve on a regular basis. The arbitrary trips that are caused by the incentive module leads to exploitation of drivers, which affects them economically, socially, mentally and also physically. On the other hand these cab companies are luring these drivers through these incentive scheme traps and gaining profit while ignoring their adverse effects. This brings the issue in the limelight on how can such things be resolved, while maintaining company profits. Can this monetary incentive-based model be designed in a way that it benefits the drivers of these ride sharing platforms? “
Visioning
The third step in our design process. This is where we identified possible interventions and ideated on whether they are valid interventions or not. From this step onwards, the process isn’t a linear one, but more of a cyclic process which depended on a iteration based approach between, Visioning, Designing and Futuring.
Design Intervention Building
Idea 1
This is a method that will ease the drivers from picking up customers from high traffic zones. A driver will accept the invite of the customer and can forward it to the driver who is already in that traffic zone, through an app or a feature in their app. That driver will take the customer to the origin driver who had initially accepted the request and then he can take the customer to his desired location. This will save the time for the driver and also for the customer.
Idea 2
These cab companies can use their cars as the switch model by allocating 2 or 3 drivers on a shift basis. This means every driver has to serve limited amount of driving time on the particular car and once his shift (hours) is complete the next one will reach to his destination and will continue the rides further. By this method companies can generate more profit by making their cars available for more hours and drivers can get more number of days to complete their targets.
Idea 3
Companies like to generate profits by putting their cars on roads for longer period, ignoring the fact that drivers suffer physically, mentally, socially. This creates lack of motivativation and aspiration for drivers and leads bad expericence for the user and the company. The target completion is their urge but to meet that they pay a heavy price.
Hence to counter that we have created a system which will give them the control to choose their own driving plan which will cause them less stress and this will lead to profit for the company and drivers motivated.
The system will allow them to choose their driving plan through selecting the number of days and rides. This option will decide the incentives they will receive.
AFFINITY MAPPING
This method is to cluster the data generated through ideation and deducing insights from it. These clusters led to the further development of ideas, which lead us to a conclusion on the basis of which our system design will be introduced. The insights generated has been categorised in 3 -
1. Ownership
2. Incentive
3. Shifts
These 3 are the conclusion of our affinity mapping that which shares the idea of how a system should be interpreted in the existing model of uber.
Our goal is now to focus on these points and generate a system which will solve the incentive biased system and will give some amount of control to drivers to complete their target without any hassle.
Ownership
A trend noticed after qualitative analysis of the transcript revealed that ownership of the vehicles and the accountability that it entails may be a pain point for a driver in the system of ride sharing
Incentive
By defintion incentives are monetry bonuses given to drivers in order to motivate them to stay on the road longer and service more rides. While this may have been created to be a positive reinforcement, it is no longer so in it’s current form it is turning out to be detrimental to the system
Shifts
A possible intervention that we came up with during the course of this project was a shift based driver ecosystem, where drivers would have greater control over their working hours than they currently have.
One of the exercises we undertook to gain a better clarity of our problem and it’s potential interventions was the value framework model and value flow model. While they may not have been used in the traditional sense, they were used to understand where our intervention can add value and whether it is an innovation worth pursuing based on the values it may add.
The value models were particularly useful in times when there was too much information to look at and not enough insight and the intervention was rather complicated to analyse linearly.
The top half indicates the 4 levels of stakeholders, who they are and at what level they exist and are affected by the system, while the bottom half indicates the perspectives that the innovation affects.
The Value Flow Model is essentially a tool created to depict the non linear flow of how elements and parts of a system interact with each other and bring value to it. It visualises and represents various parts of a network, and helps understand the value creating chain in the system. It helps gain a dynamic view of how financial and non financial aspects of a network/system can help bring value to it.
Designing
The screens were created to be low fidelity representations of the idea we had honed in on. Since the intervention considered had further areas to be worked on before it could be tested or incorporated into the system.
Futuring
The system design generated creates the opportunity for the future scope and also the challenges with it. Futuring lets us to see through the system and helps us to acknowledge the loopholes in it and provides us the chances to improve it.
Also it opens a path toward any future development of it. This gives a system a scope of improvement in many levels.
DESIGN OPPORTUNITIES
1. The shift and days will be worked upon to make it a smooth transition between screens
2.The idea for weekly streaks count will have to be improved or have to be consolidated for incentives
3.Trips focused rides and incentive plans can be introduced.
FUTURE SCOPE
1.On the basis of their performances and rating they will receive certain perks from the company
2.The system design can give opportunity to driver to choose their cars as per their rating
3.Drivers can switch cars in between shift.
Work Flow
The work flow basically talks about our journey on the project. It shows how we reached to the conclusion of our project by doing various research and trying our hands on different methodologies to derive our data for our system design.
Conclusion
While the intervention generated does not add much value to the existing system in its current form, I strongly believe that it can help solve certain issues with existing ride sharing platforms if worked and further researched upon.
