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July 18, 2024
Check out how Zomato incorporates Data Analysis to Enhance its Operational Efficiency in this Python Use Case ! Read along to see the detailed Project !
Zomato is an Indian multinational restaurant aggregator and food delivery company, co-founded by Deepinder Goyal and Pankaj Chaddah in 2008. Originally launched as FoodieBay, the company rebranded to Zomato in 2010 to avoid naming conflicts and to broaden its scope beyond food. Zomato offers information, menus, user reviews of restaurants, and food delivery services from partner restaurants across more than 1,000 cities and towns in India. Competing primarily with Swiggy, Zomato has expanded its operations internationally to various countries including the UAE, UK, and Brazil, though it now focuses mainly on India and the UAE.
Zomato's journey has included significant acquisitions, such as Seattle-based Urbanspoon in 2015, which facilitated its entry into the U.S. and Australian markets, and quick-commerce company Blinkit in 2022 for $568 million.
As of 2024, the company reported a revenue of ₹12,114 crore ($1.5 billion), with an operating income of ₹291 crore ($35 million) and a net income of ₹351 crore ($42 million). Zomato's assets total ₹23,356 crore ($2.8 billion), and it employs over 6,000 people. Major stakeholders include Info Edge, Antfin Singapore, and co-founder Deepinder Goyal, who serves as the CEO and MD. Zomato continues to expand rapidly, particularly in its quick-commerce sector, aiming to reach 1,000 stores by March 2025.
Now, let's dive into some insights (just for learning purposes!) on how Zomato might analyze their data to improve their Operational Efficiency.
Firstly, let’s inspect our dataset. This dataset contains 45593 rows and 11 columns, providing information about delivery person, ratings, restaurant geographic information, delivery information.
To start with the analysis, it is necessary to follow the 4 factors of Data Analysis and that are :
1. Identify the users or stakeholders for the analysis.
2. Design Empathy Map to define Users' Goals and Challenges or pain points.
3. Identify Metrics or KPIs Matter the Most.
4. Understand the Objectives and Goals.
5. Ask Business Questions
I know it looks a bit overwhelming, that's why in this article, we'll lay the foundation for a top-notch analysis.
Define the User or stakeholder who will use the analysis’s data story. In our case, We’ll use a Data Analyst. Recognizing their diverse needs, challenges, and priorities becomes the cornerstone for tailoring an effective data story.
Since, we have defined the User Persona & have mapped the needs, challenges and responsibilities. Our Next step will be to design an Empathy Map which will map the pain points of the user.
To truly connect with the experiences and expectations of a Data Analyst, the creation of an empathy map is invaluable. This visual tool allows for a deeper understanding of the emotions, aspirations, and pain points of users. By empathizing with their perspectives, we can design a data story that not only meets functional requirements but also resonates with the human elements of their roles.
The heartbeat of any analysis lies in KPI’s and their Metrics. It's crucial to identify the KPIs that matter most to achieving the defined objectives and by focusing on the most relevant metrics, organizations can gain actionable insights into their analysis.
Based on the insights gathered from the empathy map and key performance indicators (KPIs), it is crucial to define the goals and objectives of the Data Analyst. This alignment ensures that the analytical efforts contribute effectively to decision-making processes. Here are the key objectives and goals:
The objective of the Data Analyst within a corporate context is to utilize data analysis techniques to drive informed decision-making and enhance the company’s operational efficiency and strategic planning. By interpreting complex datasets, the analyst aims to identify opportunities for improvement, optimize processes, and support data-driven business strategies.
Support Strategic Decision-Making: Provide actionable insights to support strategic decisions by analyzing market trends, customer behavior, and operational performance.
Improve Operational Efficiency: Identify and resolve process bottlenecks to streamline workflows and optimize resource allocation.
Ensure Data Quality: Maintain high data quality and integrity by establishing robust data management practices and ensuring data consistency.
Enhance Data Visualization: Develop clear and effective dashboards and reports to communicate insights to stakeholders.
By aligning with these objectives and goals, the Data Analyst ensures that their analytical efforts contribute to the overall operational success and strategic growth of the company.
After identifying the user persona and relevance KPIs, we must analyze different business questions to derive the data driven solutions.This involves asking strategic questions that directly align with overarching business objectives. In case of Food Delivery Analysis, let’s analyze the following questions:
After analyzing this dataset, we can observe how the delivery workforce is distributed in terms of their age, performances and ratings. Also, the above analysis was able to identify that overall ratings and age of the riders are important factors for delivery time. We can observe from our analysis that, though our delivery time is less than half an hour, there are still some orders that are taking longer duration for delivery i.e. an hour, which needs to be looked at. In terms of delivery time by riders, we need to investigate the riders with 2 and 3 rating if there is any issue for them as they are taking longer time for delivery. Though there are variations in age of riders, delivery times are not affected that much, with only slight changes by age.
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