Case Studies
We empowered a leading food delivery player to improve customer experience with impact analysis and SOP change


Resolve frequent wrong order deliveries and customer complaints by conducting an impact analysis and reforming the order SOP
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The client is a leading food delivery and Q-Commerce platform in India, with over 100 million customers and a revenue of $900 million. They are revolutionizing the industry with their technology and commitment to customer satisfaction.
The client faced a challenge in ensuring the accuracy of food orders delivered to customers through its platform. Instances of incorrect orders being handed over to delivery executives by restaurants have led to a surge in customer complaints and a decline in overall order ratings.
The absence of a centralized tracking system further exacerbated the issue, resulting in low visibility and hindering the client's ability to effectively monitor and address these discrepancies. Thus, there was a need to conduct an impact analysis of reforming the Order Handover Standard Operating Procedure (SOP) to mitigate the frequency of wrong order deliveries and enhance customer satisfaction.
In addressing the challenge, we adopted a structured approach involving Pre-Post Analysis to comprehensively assess the impact of the proposed SOP reform. Our solution entailed the introduction of QR scanning during order handover from restaurants to delivery executives. To evaluate the effectiveness of this reform, we conducted a 1-month transaction period pilot with selected restaurants.
During this pilot phase, restaurants were categorized into different cohorts based on their adherence to the reform and the type of Kitchen Order Ticket (KOT) system used (handwritten/POS). We meticulously assessed both check and success metrics across these cohorts to gauge the reform's impact.
To facilitate ongoing monitoring and analysis post-scale-up, we developed a robust dashboard. This dashboard provides a clear and concise view of various metrics related to the reform's impact. It offers insights at multiple levels, including city, brand, restaurant, and KOT type, enabling stakeholders to track performance effectively and make informed decisions.
We observed a drop in wrong order % by one point



M1 needed to rethink how customers pre-ordered the Samsung Galaxy S25. The traditional experience of long queues, complex forms, and multiple customer service calls wasn't delivering the seamless journey their customers expected from a leading Singapore telco.
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The client is a leading food delivery and Q-Commerce platform in India, with over 100 million customers and a revenue of $900 million. They are revolutionizing the industry with their technology and commitment to customer satisfaction.
The client faced a challenge in ensuring the accuracy of food orders delivered to customers through its platform. Instances of incorrect orders being handed over to delivery executives by restaurants have led to a surge in customer complaints and a decline in overall order ratings.
The absence of a centralized tracking system further exacerbated the issue, resulting in low visibility and hindering the client's ability to effectively monitor and address these discrepancies. Thus, there was a need to conduct an impact analysis of reforming the Order Handover Standard Operating Procedure (SOP) to mitigate the frequency of wrong order deliveries and enhance customer satisfaction.
In addressing the challenge, we adopted a structured approach involving Pre-Post Analysis to comprehensively assess the impact of the proposed SOP reform. Our solution entailed the introduction of QR scanning during order handover from restaurants to delivery executives. To evaluate the effectiveness of this reform, we conducted a 1-month transaction period pilot with selected restaurants.
During this pilot phase, restaurants were categorized into different cohorts based on their adherence to the reform and the type of Kitchen Order Ticket (KOT) system used (handwritten/POS). We meticulously assessed both check and success metrics across these cohorts to gauge the reform's impact.
To facilitate ongoing monitoring and analysis post-scale-up, we developed a robust dashboard. This dashboard provides a clear and concise view of various metrics related to the reform's impact. It offers insights at multiple levels, including city, brand, restaurant, and KOT type, enabling stakeholders to track performance effectively and make informed decisions.
We observed a drop in wrong order % by one point
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