Dynamic Pricing
Pricing experiment to balance supply and demand of service partners and users booking a service
Project owner - Research, Userflows, UI design, UAT
Background
What is Sendhelper?
A Singapore-based home service platform, connecting users with experts for tasks like home cleaning and AC maintenance services. Users book a service, select a date and time, and get matched with an expert for the service.
Booking traffic based on time slots
Few time slots are always booked more than the others. For example, Monday & Tuesday mornings have drastically lower bookings than Saturday & Sunday afternoons.
Challenge
How might we balance supply and demand without increasing manual workforce?
Problem
Research
Initial research
I conducted initial research to understand why the number of bookings is higher in certain time slots than the others. There were some reasons to this:
Service partner demand surges over weekends
Weekdays are unlikely days for users to book a cleaning service
The marketing team also ran campaigns to test discounts on specific days of the week and it proved to be highly successful.
Dynamic pricing on flight booking
Users have a clear view on the lowest ticket prices available on the specific date
Competitive analysis
I also studied about how dynamic pricing strategy worked as a business concept to get a bigger understanding.
As part of research, I looked at some direct competitors to understand their pricing models. Since their base price itself was much low, there was no sign of dynamic pricing tactic used in most of the competitors.
Solution
Using discounts to balance supply and demand
Providing exclusive and limited discounts to users on low demand slots, we incentivise users to rethink their service booking slots.
Seamless integration
Following a similar pattern as in the existing booking flow, we help users to not get distracted by any elements sticking out too much. This also negates friction in the booking flow overall.
Highlight best usecase
Helping users identify the discounted days and time slots provides clarity. This also helps users to easily compare and identify discounted slots, aiding in decision making.
Price transparency
As users are price sensitive, by being transparent with the price breakdown it builds trust and reassurance. Also a sense of satisfaction by knowing that they secured a great deal.
Stabilising booking traffic
If the low-demand time slots are filled up to an optimal count, the discount is removed and the standard pricing is displayed. This ensures that users don't try to overbook a standard slot again.
Adjusting the hourly rate
Once limited slots in low demand slots are filled, the price is reset to standard S$30/hr to avoid overbooking.
Design
Designing a seamless and frictionless experience
I designed the main flow with first-time user in mind, ensuring that they can easily understand, scan and select the cost-effective time slot, while maintaining a friction-less flow to complete the service booking.
How can I emphasise the discount on final price?
The bottom sticky navigation bar contains the final service price along with a CTA to proceed with the booking. I decided to use this component to show the discounted price.
In search of the suitable icon
I explored different options for the price change. Ultimately, the trending-down icon was chosen because it effectively conveys the concept of fluctuating prices.
tHE TEAM
Iterating with the PMs and Engineers
I worked closely with the PM and engineering team throughout the process to ensure smooth communication and alignment with the updated business interests.
Although the final designs were approved, we faced technical limitations due to new priorities. I adjusted the designs to commit to updated, shorter engineering timelines.
An alternate design was finalised and handed over to the team for development.
Date selection
Updated calendar view explaining on which days user can avail maximum discounted slots
Launch
Putting everything together
After development, I conducted design QA and user acceptance testing (UAT) to ensure that everything worked as expected. Any bugs found were logged in Jira and fixed based on their priority.
Once the staging environment reached a satisfactory state, the dynamic pricing feature was launched!
Results
And the results were a success!
65%
boost in service experts efficiency, in 2 months!
