Strategic Analysis of Swiggy's Restaurant Dataset

swiggy's logo from 1000logos.net. Strategic Analysis of Swiggy’s Restaurant Dataset.

[Audio] Swiggy is a consumer-centric organization that offers an easy-to-use convenience platform accessible through a unified app. Our mission is to elevate the quality of life of the urban consumer by providing unparalleled convenience. Convenience is what drives us, making us get out of bed and say, "Let's do this." As pioneers in the hyperlocal commerce industry, we have successfully launched Food Delivery in 2014 and Quick Commerce in 2020, earning recognition as leaders in innovation and a brand synonymous with our presence in various categories..

SWIGGY’S JOURNEY.

[Audio] Founded in 2014 by Sriharsha Majety, Nandan Reddy, and Rahul Jamini, Swiggy began as a simple idea to solve the massive problem of helping people get food from their favorite restaurants without having to leave their homes. As engineers from Birla Institute of Technology and Science, Pilani, Sriharsha and Nandan noticed a gap in the online food ordering market in India. Despite numerous food tech startups entering the market, no one was able to crack the food delivery code due to the complex and costly logistics and infrastructure required. In 2013, they launched a different company called Bandal, which focused on courier service logistics. However, they soon realized the high demand potential for food delivery and pivoted Bandal into what we now know as Swiggy. But Swiggy’s journey was not without its challenges. Most restaurants in India did not have their own delivery services, and even if they did, it was difficult to ensure timely deliveries. Swiggy decided to build its own logistics network and hired a fleet of delivery partners. This approach was groundbreaking for its time, as it allowed Swiggy to control the entire delivery process, ensuring reliability and efficiency. Though it required a substantial upfront investment, this proved to be Swiggy’s differentiating factor and helped them stand out from their competitors. With their logistics model in place, the founders focused on creating a seamless app experience for users. Swiggy’s app was designed to make it easy for customers to browse menus, place orders, and track their delivery in real-time. This feature alone became a game-changer, as customers could see exactly when their food would arrive..

[Audio] The problem statement is clear: we need to analyze the factors that influence customer ratings on Swiggy. We're looking at various variables such as cuisine type, location, price, and vegetarian classification to see how they affect overall restaurant performance. Our ultimate goal is to identify patterns that lead to higher customer satisfaction and better ratings. By doing so, we hope to provide valuable insights that can help restaurant owners and food businesses improve their services and offerings to attract more customers.

[Audio] The dataset examined today originates from Kaggle, a well-known platform for data enthusiasts and professionals. The data source is a repository created by rrkcoder, featuring a comprehensive collection of information about Swiggy's restaurants. This dataset offers valuable insights into various aspects of Swiggy's operations, including performance metrics, customer preferences, and platform optimization strategies. By analyzing this data, we can gain a deeper understanding of how Swiggy functions and identify areas for improvement.

Data Dictionary. A table with text and images Description automatically generated.

[Audio] The Swiggy platform presents various dining options to customers, featuring both vegetarian and non-vegetarian choices. The menu organizes dishes by type, such as North Indian, Chinese, and Italian, making it simple for customers to browse and locate their desired meal. Furthermore, the menu includes filters like price range, cuisine, and rating, enabling users to narrow down their search results. This intuitive design allows customers to rapidly discover the ideal dish to suit their tastes..

[Audio] The image shows a cellphone's features and functionalities. Different icons, buttons, and menus are displayed on the screen. The description suggests it is part of a dataset analyzing user behavior and preferences..

[Audio] The limitations of our analysis are quite evident. We have been restricted by the lack of customer review text, which has hindered our ability to conduct sentiment analysis and gain deeper qualitative insights. Furthermore, we are missing crucial variables such as delivery time, food quality, and staff behavior, making it challenging to grasp what truly drives customer satisfaction. Additionally, the dataset is static, failing to capture any changes over time, thereby precluding us from conducting trend analysis. Moreover, there is a possibility of sampling bias if the data is concentrated in specific regions or types of restaurants. Lastly, while our analysis has revealed correlations, we cannot confirm causation without employing more advanced modeling techniques or experimental data..

[Audio] The systematic approach to understanding the data began by ensuring its quality, removing duplicates and handling missing values. This led to creating a comprehensive data dictionary, defining each column and its meaning. Descriptive statistics were then applied to summarize key variables, examining cuisine types, locations, pricing, and ratings. Visualizations such as histograms, bar charts, and pie charts were used to identify patterns and relationships within the data, revealing factors impacting restaurant ratings on Swiggy. Exploratory insights were drawn, uncovering the underlying dynamics driving customer preferences and influencing restaurant performance..

High Level Concept. A diagram of a network AI-generated content may be incorrect..

[Audio] The analysis relies on several assumptions to ensure its validity. It is assumed that the data provided is accurate and consistently collected over time, meaning that any errors or inconsistencies in the data would impact the reliability of our findings. Average pricing is taken to represent the typical cost for two people dining, rather than being specific to individual customers, allowing us to generalize our results to a broader population. Ratings are considered to be on a uniform 5-point scale, reflecting genuine customer feedback, enabling us to compare ratings across different restaurants and cuisines. It is assumed that offer details are current and relevant to customer choices, allowing us to analyze how these offers impact customer behavior. Finally, cuisine types, location data, and the “Pure Veg” indicator are presumed to be reliable and correctly categorized, enabling us to draw meaningful conclusions about their relationship to restaurant performance. By acknowledging these assumptions, we can better understand the limitations of our analysis and refine our methods to improve accuracy..

[Audio] Our analysis has revealed a highly competitive food delivery market where restaurants prioritize variety, affordability, and user satisfaction. Most restaurants maintain decent ratings by offering promotions to attract and retain customers. Furthermore, the prevalence of budget-friendly pricing and vegetarian options suggests that restaurants are responsive to local preferences. However, inconsistencies in data formatting and missing values may limit our ability to draw more detailed conclusions. To improve our understanding and inform better decision-making, it is recommended that data collection be standardized, cuisine tags be enhanced, and the effectiveness of promotional offers be monitored. This will enable us to develop targeted marketing strategies and optimize the Swiggy platform for improved performance..

[Audio] Here we see a step-by-step description of how to analyze data using apps and AI tools. This process involves several stages, starting with data collection and cleaning, followed by data transformation and visualization. Various apps such as Excel, Tableau, Power BI, and AI tools like machine learning algorithms are used to perform these tasks. The goal is to gain insights into performance, preferences, and platform optimization. By analyzing this data, we can identify trends, patterns, and correlations that can inform business decisions and drive growth..

The Dashboard.

[Audio] The analysis reveals that Baskin-Robbins Ice Cream and Kwali Walls Frozen are the most popular restaurants, indicating a strong demand for frozen desserts. Moreover, the cuisine analysis indicates that various cuisines, including Afghani, Cafe Coffee Day, La Pino'Z Pizza, Chai Sutta Bar, Pizza Hut, Domino'S Pizza, Subway, KFC, The Beijing Waffle Company, and others, are well-liked by users. This data underscores the significance of providing diverse options to accommodate different tastes and preferences..

[Audio] Certain cuisines have gained immense popularity, with notable spikes evident in the data. This suggests that consumers have developed a strong affinity for these types of food. Furthermore, the diverse range of cuisines listed on the right-hand side indicates that there is a wide variety of options available to customers. This diversity can cater to different tastes and preferences, ultimately enhancing the overall dining experience..

[Audio] These top restaurants have been consistently performing well on Swiggy, indicating their popularity among customers. They include Baskin Robbins Café, Coffee Day, Chai Sutta Bar, Domino’s Pizza, KFC, Kwality, Walls Frozen Desserts, La Pino’z Pizza, Pizza Hut, and Subway, along with The Belgian Waffle Co. This suggests that they are among the most frequently ordered from on Swiggy..

[Audio] Swiggy's data shows that some restaurants excel more than others, with KFC and Domino's Pizza having the highest order counts. Notably, Baskin Robbins and Kwality Walls lead in the dessert category, indicating a strong demand for desserts. These findings can guide strategic choices for Swiggy and its partner restaurants..

[Audio] Swiggy's data shows that it caters to various tastes by offering a diverse range of cuisines and restaurant types. Well-known chains like KFC, Domino's, and Subway are popular choices, while ice cream and dessert brands like Baskin Robbins and Kwality Walls also have a significant presence. These findings highlight consumer preferences and inform strategies for improving the platform's performance..

[Audio] To optimize their performance, we recommend that Swiggy focus its marketing efforts on popular cuisines and high-performing restaurants. This will allow them to capitalize on existing demand and build upon successful strategies. Additionally, encouraging smaller or less popular cuisines/restaurants to offer promotions or discounts can help them gain traction and attract more customers. On the other hand, restaurants should analyze customer preferences to tailor their menus accordingly, ensuring that they cater to the tastes and needs of their target audience. Furthermore, leveraging Swiggy's platform to promote special deals or new menu items can help them stay competitive and attract more customers. By implementing these recommendations, both Swiggy and the restaurants can improve their performance and achieve greater success..

[Audio] The dataset presents a dynamic and customer-centric food delivery landscape, where restaurants strive to provide diverse offerings, affordable prices, and exceptional user experiences. Most eateries maintain respectable ratings by offering promotions to attract and retain loyal customers. The prominence of budget-friendly pricing and vegetarian options underscores the industry's adaptability to regional tastes. However, the presence of inconsistent data formatting and missing values may hinder more profound discoveries. To mitigate this, it is advisable to standardize data collection procedures, refine cuisine categorization, and closely track the efficacy of promotional offers to facilitate informed decision-making and targeted marketing strategies..