PRODUCT CASE STUDY · CHOWDECK
From 25 to 60 Minutes: Why Chowdeck Users Can’t Tell Who Is Late
What one user interview taught me about order tracking.
By ADEOJE uthman Bolarinwa
1 · THE PRODUCT AND THE PROBLEM
Chowdeck and the waiting game
Chowdeck has quickly emerged as a leading food delivery platform in Nigeria, celebrated for its memorable marketing, fast delivery riders and broad restaurant network. It serves busy people who want food at their door without cooking or queuing.
But high-volume operations bring friction. From user feedback and my own observation, one problem keeps coming up: inaccurate estimated times (ETAs) and an opaque order status after the order is placed, especially at peak meal hours.
To understand it better, I spoke to Maryam, a banker in Ibadan who orders lunch on Chowdeck.
REAL USER INSIGHT · INTERVIEW
“I ordered lunch at 1:30 PM with an expected delivery time of 25 minutes because I ordered from ITEM 7 because their food doesn’t take much time. At 20 minutes the status was still stuck on ‘vendor accepted order’ because normally after vendor accept order then rider will accept order so it means at that particular time they have not assigned my order to any rider so suddenly the ETA changed to 60minutes without explanation. I couldn’t tell if the restaurant was slow or if it was a chowdeck that did not assign my order to any rider.”
- Maryam, Banker
- What I found vs. what I am assuming
FOUND IN THE INTERVIEW
She expected 25 minutes.
At 20 minutes the status still said “vendor accepted order”.
The ETA then jumped to 60 minutes with no explanation.
She could not tell who was at fault.
ASSUMING, NOT YET VERIFIED
No rider had been assigned yet. This is Maryam’s reading, not something I confirmed.
The kitchen was the real bottleneck.
Other users feel the same, especially at peak hours.
2 · UNDERSTANDING THE USER
Meet Maryam
This persona comes from one interview and my own observation. It is a starting point, not a final picture.
Name: Maryam Tijani | Age: 32 | Occupation: Banker | Gender: Female | Location: Ibadan, Nigeria | Income Level: Slightly above minimum wage
GOALS
Get fresh food delivered reliably and quickly to the doorstep.
Enjoy seamless transactions from ordering to doorstep delivery.
FRUSTRATIONS
Faces late deliveries, wrong items and high fees.
Deals with unhelpful customer support when issues arise.
BEHAVIOURS
Orders meals quickly during peak work hours using mobile apps.
Frequently checks ratings and reviews before picking a restaurant.
MOTIVATIONS
I want to save time and energy without cooking.
Desires convenience and freedom from kitchen chores
DEFINITION OF SUCCESS
Receiving a meal within a predictable, transparent window without needing to check support or refresh the app constantly.
3 · MAPPING THE USER JOURNEY
Maryam’s journey, from hunger to meal
This journey mapping describes Maryam’s experience from hunger trigger to meal consumption, reveals critical drop-offs during the order status phase.
Stage
User Action
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Emotions
Pain Points
1
Browsing
Opens app, searches for local rice dishes.
Hungry
Overwhelmed by too many vendor options.
2
Checkout
Selects payment option, reviews ETA (25 minutes)
Satisfied
Minor fee addition at summary screen.
3
Order Wait
Waits for delivery while working; check app status.
Anxious.
ETA jumps from 25 minutes to 60 minutes without notification.
4
Delivery
Meets rider at the reception, receives package.
Relieved
The food is warm but the work break is already over.
4 · THE BIGGEST PAIN POINT
Stage 3: Order Wait
The critical pain point is stage 3. My working theory is that it stems from a lack of split status update between vendor preparation and rider dispatch. When a restaurant is delayed, the system defaults to extending rider arrival estimates. That gives the user an impression of rider unreliability when the bottleneck may actually be kitchen queue latency.
Why it matters
Waiting is where trust is won or lost. An anxious, uninformed user contacts support, cancels, or loses confidence in the app.
What evidence would help me find the root cause
Order timestamps: how long between “vendor accepted” and “rider assigned” at lunchtime.
How often an ETA changes after checkout, and by how much.
Support tickets that mention ETAs or delays.
More interviews: do users blame the rider or the restaurant?
5 · RECOMMENDING AN IMPROVEMENT
Split-stage progress tracking
Separate the tracking UI into two explicit, independent progress meters: Kitchen Preparation Time and Rider Transit Time. If kitchen preparation exceeds the normal time, send a notification (e.g., “Kitchen is experiencing high volume (+8mins)”). Likewise for the rider: if there is any delay, a notification should show the additional time.
My reasoning
Today one bar and one ETA hide two different problems. Two meters tell Maryam who is slow, and a short message turns a surprise into an update she can plan around.
How I would measure it
Support tickets about order status: per 100 orders. I expect them to fall.
Cancellation rate: for orders where the ETA changed. I expect it to fall.
6 · LESSONS AND CONCLUSION
What I learned
Providing transparent communication and real-time clarity builds user trust, even when the underlying service experiences delays.
Solving operational friction in on-demand delivery requires more than logistical speed; it demands clear, proactive communication. With split-stage tracking and automated notifications, platforms like Chowdeck can turn anxious, opaque waiting into a predictable experience, reducing support tickets, lowering cancellations and preserving customer trust.
