⏮️ Context
What is the context and status quo of the opportunity
🎯 Problem Statement
Lack of Automation in Drive-thru Mobile Orders
The drive-thru experience for Mobile Ordering customers is hindered by a lack of automation in the order-to-kitchen workflow in BookinAll. The QR code scanned by customers at the drive-thru does not include their Loyalty ID, necessitating manual intervention by cashiers to send the order to the kitchen for preparation. This lack of automation not only consumes valuable time but also increases the likelihood of human error, ultimately resulting in longer wait times and a degraded customer experience. Streamlining this process is essential to improve service speed and enhance customer satisfaction.
Current Experience
Step 1: In the sales screen, click on the PIE symbol. | |
Step 2: Click on "WinRest Booking - Pending Orders" | |
Step 3: If there are orders ready to be handled, you will need to give your approval to proceed with processing them. Once approved the order is sent to the kitchen. | |
If no orders are waiting to be processed, a notification will inform you of this. |
❔ Expected Outcome
To address the identified opportunity, the proposed solution is to enhance the existing BookingAll system by integrating the customer Loyalty ID into the drive-thru order workflow. This involves updating the getOrders
endpoint to include the customer's Loyalty ID. Once the endpoint is updated, the BookingAll system will be configured to automatically send the order to the kitchen without requiring manual intervention from the cashier.
Key Features:
Endpoint Modification: Update the
getOrders
endpoint in the RBI Partners API to include the Loyalty ID of the customer.Workflow Automation: Adjust the BookingAll system to recognize the Loyalty ID and automatically send the order details to the kitchen, bypassing the need for manual processing.
Error Handling: Implement robust error-handling mechanisms to ensure orders are correctly processed even if there are issues retrieving the Loyalty ID.
Performance Monitoring: Introduce monitoring tools to track the performance of the new automated workflow and identify any potential bottlenecks or issues.
Business Impact:
Reduced Wait Times: Automation will significantly cut down the time required to process drive-thru orders, leading to faster service times and improved customer satisfaction.
Enhanced Accuracy: Minimizing human intervention will reduce the likelihood of errors, ensuring that orders are accurately prepared.
Increased Efficiency: Streamlining the order-to-kitchen workflow will lead to better resource utilization and operational efficiencies.
Customer Retention: Improved service quality and reduced wait times will contribute to higher customer retention rates and increased loyalty program engagement.
Alignment with Strategic Goals: This solution aligns with RBI's strategic goals of enhancing customer experience and leveraging technology to improve operational efficiencies.
❓ Open questions
How is this integration working now? What is the trigger point?
The Customer scans a QR Code containing an ID of the Order
The BookingAll solution gets information from the
getOrders
endpoint
Is BookingAll an RBI or Partner tool?
It’s a partner tool
Is there a dependency on BookingAll? (new endpoint?)
They will need to adjust the order-to-kitchen workflow to read the new LoyaltyId data from the
getOrders
endpoint and automatically send the Order to the kitchen.BookingAll to confirm the development timeline
Need to organize integrated tests and UAT with BookingAll
Does this impact other countries? Is BookingAll used by all of Iberia?
Should this be sent to any other partner platform? (autoking?)
By adding the information to the Partners API any other platform that might make use of it can already benefit from this solution.
Is there documentation on BookinAll integration?
Do we have data on how many Mobile Ordering orders are picked up in the drive?
No, the data is not specific enough at the time.
📈 Success Metrics
Metric Title | How to Measure: | Success Criteria: |
---|---|---|
Insights
1️⃣ Stakeholder Interviews
[Document here the main insights from interview if applicable]
2️⃣ Analytics
[Document here the main insights from analytics if applicable]
3️⃣ User Research
[Document here the main insights from research if applicable]
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