Contextual cross and up-sells to drive $7M in impact (2023)

Lean experiments launched and tweaked monthly

Role: Product design manager (with IC responsibilities)

In order to boost GMV (gross merchandise value) and improve user adoption of GrabFood and GrabMart, the team laid out an ambitious plan to nudge consumers from other verticals like GrabTransport or GrabFin to try GrabFood
I designed experiences focusing on some of the top consumer use cases - consumers on their way home from work could order dinner on the way (Transport to Food) or users who ordered from a restaurant might want to add-on items from a nearby convenience store (Food to Mart)
By working in an empowered team that strategised twice weekly with the heads of product, design and engineering, we were able to keep our execution extremely agile - meeting early in the week to discuss the parameters and then on Thursday to critique the design
Being conscious of the tight delivery timelines, I reused many components from the library, mixing and matching across user flows - it guaranteed shorter consumer learning curves and fast go-to-market launches
The estimated impact for the 6 ideas that we were able to explore to execution was to be around $7M by the end of December 2023

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Left: When driver is otw to consumer’s pickup, design plants the idea of ordering food on the way |
Right: Once the user is sat in the car, design allows user to browse their favourite or most ordered from restaurants to place an order

Left: When driver is otw to consumer’s pickup, design plants the idea of ordering food on the way | Right: Once the user is sat in the car, design allows user to browse their favourite or most ordered from restaurants to place an order

Optimising order fulfilment to reduce drive wait time at restaurants (2022)

Fine-tuning allocation algorithms by designing better input mechanisms

Role: Product design manager (with IC responsibilities)

Each item on a merchant’s menu has a cooking time associated with it. It is prudent for the Grab system to know the accurate cooking time in order to find drivers that will arrive just in time for pickup. For this, we needed a signal from the merchant that the order is either ready or delayed.
This was needed for two reasons. Firstly, so Grab can fine-tune its algorithm and not need an input from the merchant eventually. Secondly, to triage similar triggers from other parts of the order fulfilment flow for on-time pickup and drop.
My team designed the early versions of the experiment that was due for an upgrade. User feedback was telling us that merchants already knew at the time of receiving an order if it might be delayed.
To give merchants more control over the estimated cooking time, we wanted to allow them to increase the prep time when kitchens were busy or delayed
I helped realise the final design of this experiment by rethinking the information architecture of the order card and introducing an option to extend cooking time in a tap
- Test group 1: Merchants could either mark an order Ready or extend prep time by 15 min based on the items in the order and status of the kitchen
- Test group 2: Merchants could extend prep time in increments of 5 (uptil 15 min) thereby exercising more control
Success metrics included improved food prep time prediction accuracy and savings in driver wait time
Along with other features, we were able to save 2.3k man hours

Left: When driver is otw to consumer’s pickup, design plants the idea of ordering food on the way | Right: Once the user is sat in the car, design allows user to browse their favourite and most ordered from restaurants to place an orde

Piloting Grab Group Buy in Indonesia (2021)

Owning end-to-end user experience

Role: Senior product designer

In January 2021 we decided to take a bet on the new grocery model sweeping across China - community buying
A team of 8 - 3 PMs, 2 engineering leads, 1 analytics, 1 product marketing and me - were tasked with launching Grab Group Buy (GGB) in 4 months
I started by designing an ideal user experience, including order management for community leaders, working alongside PMs and engineers to understand timelines, feasibility and constraints
GGB launched in August of 2021 in Bogor, Indonesia with 50 community leaders and we continued to iterate on our MVP over the next 4 months, introducing new features like Leader Discovery
At its best, Group Buy averaged ~500 shipments a day and had 1300+ active group leaders per month
Group Buy was sunset in December ’21 with the new understanding that this model was going to be an operations-intensive service until we built the technology to support supply chain requirements

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Top left to right: Community leader’s app home page; Community leader’s order fulfilment | Bottom left to right: Consumer’s community leader discovery; Consumer’s buying experience

Renovating GrabFood Restaurant Menu (2020)

Designing around ambiguity