BACKGROUND, GOALS & BRIEF
AirAsia Ride entered the ride-hailing market in 2021, inheriting AirAsia's low-cost model DNA. Although its competitive rates attract riders during peak periods, drivers are less motivated leading to low job take-up rate. As a result, riders turn to competitor apps to book their rides, leading to rider churn and low conversion.
To overcome this, AirAsia plans to introduce “Self-Surge Pricing”, a price-bidding concept within the app. The price-bidding concept allows riders to propose a higher price to secure the ride, which in turn encourages drivers to take up these jobs.
Our mission was to validate this new Self-Surge Pricing concept, and differentiate between “Bid Pricing” (another term they use for Self-Surge Pricing) which happens before the ride commences, and “Tipping” which comes at the end of the ride.
HIGH LEVEL TIMELINE
Total - 1 month,
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4 weeks research work and concept validation
TOPICS COVERED
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Discovery Research
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Usability Testing
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Heuristics Evaluation
PROJECT SCENARIO
I am the Team Lead leading a team of 5 UX/UI designers, liaising with our AirAsia client
Our Research Objectives:
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Examine and evaluate the effectiveness of Self-Surge Pricing from the end-user’s perspective, specifically:
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Users’ perception towards self-surge pricing
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Understand application for use-cases for Self-Surge Pricing
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Level of acceptance & willingness to adopt the feature
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Expected price thresholds
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Identify usability issues of the Self-Surge Pricing prototype
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Explore the concept and expectations about Tipping for ride-hailing
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Provide recommendations and/or solutions that address the concept of Self-Surge Pricing and Tipping
How Might We make peak-hour surge pricing rates and tipping work for passengers and drivers, so that passengers will still enjoy competitive and affordable prices while drivers would be keen to take jobs on AirAsia’s RIDE platform.
INITIAL SURVEYS CONDUCTED BY CLIENT
Unmoderated Test and Guerilla Interviews were conducted to test the implementation and feasibility of Self-Surge Pricing (the way users key in the Self-Surge), but no interviews/testing were done on the concept’s user perception and level of acceptance.
Key learnings gleaned from the initial research:
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Over half of the riders will use another app if unable to get a driver after 5 mins. Only 1 in 3 riders would choose to wait for a driver to pick up a job.
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Only a minority (11%) are willing to pay extra to attract drivers, to avoid waiting. Riders are less willing to pay an increment given the availability of alternative apps and when they are less time-sensitive.
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Riders are less willing to tip if they pay extra to prioritize their ride - thinking that tipping is equivalent to the extra pay upfront.
Further research is needed to explore the potential and opportunity of the Self-Surge concept.
WHAT ARE OTHER RIDE-HAILING PLATFORMS DOING TO HELP RIDERS WITH RIDE PRIORITIZATION?
COMPETITIVE ANALYSIS
To understand this, our team delved into the mobile app screens of competitor apps, Grab & Gojek to learn more about how they implement Surge Pricing and Tipping.
We learnt that Surge Pricing kicks in only when it is Peak Hours, Bad Weather and Fewer Drivers than Riders.
We also learnt that Grab & Gojek would recommend alternative types of rides to help riders secure rides.
WHAT ARE RIDERS' CURRENT RIDE-HAILING HABITS & USER JOURNEY? WHAT IS THEIR EVALUATION OF THE SELF-SURGE PRICING CONCEPT?
RESEARCH ACTIVITIES
We ran 6 User Interview sessions to understand users' pain points when it comes to Surge Pricing and their habits, preferences, motivations and user journey when using ride-hailing services.
We asked the interviewees lots of questions about their current ride-hailing solutions and their past experience with Surge Pricing and tipping:
Recall the last time you used a ride-hailing app where there was high demand for drivers / when the price for the ride was relatively high...but you booked it anyway.
What happened after the app displayed the fare? What did you do?
What are your thoughts and feelings about the price bidding feature?
(Probe: confirm the fare, compare price with other apps, wait it out, select different ride)
FINDINGS
We found out that:
1) The majority of users would compare prices between ride-hailing platforms when they need a ride to get the cheapest option available, even during Surge timings.
The most common ride-hailing platforms used are usually between Grab, Gojek and TADA.
2) Their main reasons for using ride-hailing apps are to travel for family purposes, ride to the airport, late night-out or for work.
3) For tipping, there are conditions that must be met before users consider tipping the driver, and this is usually when the driver has provided exceptional service during the ride. If not, the default mode is that users won't tip at all since there's no tipping culture in Singapore and they reason that they are already paying for the service.
If they pay extra already for the "Bid Your Fare" portion before the ride, they will not tip and expect not to see the tip option again at the end of the ride.
“I already give additional money at the beginning, so I don't have intention to give a tip at the end. Whether I tip or not depends on whether the driver give me better service.”
4) When users participate in the Price Bidding, they expect the feature to guarantee them drivers as fast as possible.
"If it doesn't come faster, than I think this bidding system is a failure. When I bid higher, the whole motivation is that I can get a driver straight away."
5) All users think that when they "Bid their Fare" they are competing with other riders for a limited number of available drivers to take their rides.
Hence, they want more information displayed specifically on how many rival bidders there are and how many available drivers within their vicinity.
They also want historical information on how much others are bidding and a suggested bid amount by the app, so they can gauge how much is a fair amount to bid for, because they might not know the amount to get from point A to B.
6) Users level of acceptance of the "Bidding" feature is lukewarm as it's uncertain on how bidding can guarantee a driver.
And they don't know how much higher they need to pay in order to secure a driver, so they don't have motivation to use the Bidding feature again.
7) Users view the "Bidding" feature as something beneficial for the drivers more than the users, as they reason the drivers could decide not to accept their ride if there is no additional bid.
Thus, it's not clear to users what is the benefit of the bidding feature.
"To me, it's just something that benefits the drivers by getting extra money for the same destination. But how does it benefit the customer?"
8) Some users also say "Bidding" is an additional step to key-in which could be stressful and unnecessary when their situation is that they are already rushing for time.
"It just complicates the whole ride-hailing experience. It's a whole extra step that includes a lot of stress... In the end, I'm losing money and I'm late already."
9) Users envision themselves using the "Bidding" feature during situations when they are in a rush and urgently need to get to another destination (e.g. Airport transport, important meetings, late for work), as they understand that by bidding they would have a higher chance of securing a ride.
10) The price threshold for their Bid fare that users are willing to go up to is approximately 10%-100% of the total original fare, depending on the urgency of the ride - less urgent would be 10%, urgent cases would be 50%-100% more.
Recommendations
Tipping
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Place the tipping option at the end of the ride, together with default rating comments about the ride experience.
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Leave the tipping option up to the rider to decide if it's worth it to tip the driver extra.
Guarantee Drivers
Instead of leaving the matching up to drivers to accept or not, make it such that riders who bid the suggested amount by the app are guaranteed an automatic match with a driver by the system.
Ride Transparency
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Display information on how many other rival bidders there are and how many available drivers within the rider's vicinity.
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Also display a suggested default average bid amount for riders to fall back on in cases where they don't know what to bid.
Imperfect Information
To tackle imperfect information that riders have, set up an AI feature where it gives guidelines and prompts when riders under-bid or over-bid, to direct riders on what is a fair amount to bid.
When to Introduce Self-Surge?
Introduce and highlight the Self-Surge/Bid feature during peak hours, bad weather, or popular locations.
Price Threshold
Input the maximum Bid amount as no more than 100% of the total original fare.
Reactions to the Prototype (Usability Testing Findings)
Heuristics Evaluation & Impact
We highlight the good UI/UX features of competitors and other industry apps:
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Grab
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Bolt
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InDrive
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Ryde
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Gojek
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eBay
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Carousell
, as a form of recommendation for Airasia RIDE to consider implementing for its Self-Surge pricing and tipping concept.
Insight: Current implementation of the prototype screens needs further improvement and refinement before it is acceptable to most Users.
REFLECTION
Getting users to embrace a new concept and way of doing things is a difficult task. I learnt that to do so, not only must the solution be frictionless and communication of the concept be compelling and clear, all parts of the user's journey, including their perceptions and motivations, have to be considered as well.
Thanks to proper user research, we discovered that Users were lukewarm towards the idea of Self-Surge Pricing, and that current implementation of the prototype screens needs further improvement and refinement before it is acceptable to most Riders. Thus, this research helped out AirAsia RIDE in the way they plan to implement the Self-Surge Pricing concept in future.
This was also my first time managing so many people (a total of 4 other UI/UX designers not including myself), balancing their needs and assigning job scopes fairly. What I did was have them pair up in teams of 2 for the User Interviews/Usability Testing, with one person being the facilitator and the other the note-taker. This helped to make sure that interviewing was not an overwhelming task on each individual and they have someone else around to provide support to them.