Swiggy Data Analysis in Excel

Swiggy was founded in July 2014, an Indian online food ordering and delivery platform. It is based in Bangalore and operates in 500 cities. Besides food delivery, Swiggy also provides on-demand grocery deliveries under the name Instamart, and a same-day?package delivery service?called Swiggy Genie.

Indian market is dynamic and there are other substitutes of Swiggy like Zomato, uber eats, Doordash, Deliveroo etc. Data Analysis is very crucial to get ahead of the competition, finding trends in the market, customer preferences and making marketing strategies.

Here we have a Swiggy dataset which we are going to analyze. Before diving deep into the analysis let look at the architecture of the project.

Architecture

1. Data description- We have 4,25,118 rows and 18 columns. The dataset is in the form of .xlsx format.
2. Define the use cases - At this stage we shall define several use cases to perform the analysis and this will help get the key insights from the data based on which business decisions to be taken.

• Avg Rating per area
• Unserviceable area
• Veg & Non-Veg
• Overall avg rating
• Most served cuisine

Data Import

The data is imported from Kaggle in .xlsx format.

Data Pre-processing and Data cleaning

It is a step in the data analysis where a number of techniques are used to better understand being used.

• Identifying null/missing values
• Finding white spaces/blank rows
• Modifying data type from general to text, number or date
• Merging and splitting columns
• Removing duplicates
• Changing the case of text

1. Null values in Columns Avg rating and cost for two replaced with 0.

Select the columns >> press 'find & select' >> select ' go to special' >> select blank >> enter 0 >> press ctrl+shift+enter.

2. Extracting cost-for-two from column cost-for-two-strings. We used mid() to extract. You can also use text-to-column.

Select the cell J2 >> mid(J2,4,4) >> now the numbers are still in text >> Select the cell H2 >> value(J2) >> It will convert the text into numbers.

3. Deleting blank columns. Here last columns name city is blank so we shall delete the columns.

Select the column >> right click and select delete.

4. Deleting blank rows. There can be several blank rows, to find them we will use 'Find & Select' option in edition tab.

Select the option 'find&Select' on option tab >> click ' go to special' >> select 'blank' >> all the blanks cells will be highlighted >> now press delete option in cells tab >> select 'delete sheet rows'

Analyze the data

Once the pre-processing the data, we are good to go with our actual analysis where we make pivot table use slicers and chart.

Visualize & share meaningful insights

Finally, it's time to turn our data into some sort of visual representation. In short, data visualization is the process of translating large datasets and metrics into chart graphs and other visuals such as Bar chart, columns chart, heatmap, pie chart, combo chart, scatter plot, stacked bar and column charts and many more. The resulting visual representation of data makes it easier to identify and share insights about the information represented in the data.

1. Avg rating per area.

In this section we analyzed the data and tried to analyze following:

• Avg rating in all area. Here you can see Navijivan, Koramangalam, Jamalla, Ayanavaram are the areas where customer usually give high rating. We can conclude that Swiggy's service in these areas are better than low rating areas and customer are more than satisfied.

2. Unserviceable areas

In this chart it is clearly visible unserviceable areas in Ahmedabad, Bangalore and Chennai. Bangalore has high serviceable areas despite of company's headquarter is there. Reasons of unserviceable areas can be many. Some of them are following:

1. Customer location is far from the restaurant's delivery radius.
2. Restaurants currently not accepting orders.
3. The area is blocked due to under construction road, bridge and the roads are not proper.
4. Limited capacity of delivery persons in peak hours and festive seasons.
5. High surge prices could be charged to customers due to stress on the delivery fleet.

3. Veg & non Veg

This pie chart shows the percentage of veg and non veg items on the list. The cuisines provided by restaurants are as per the demand in the market and we can clearly conclude that more customer demand Non-Veg. Among all the cuisine ordered 65% are non-veg.

4. Overall Avg Rating

The overall rating given by customers to restaurant accumulated near by 3.9 to 4.5 which actually shows the satisfaction level of the customers. The mean value is between 4.2 to 4.3. A highest near 250 restaurants got 4.4 rating then above 200 restaurants got 4.3.

5. Most served Cuisine

Here we tried to find the top 10 most served Cuisines. The Indian is on the top 90+ restaurants offering then came South Indian with 70 restaurants having on their menu. The 10th one is ice cream desserts with 20+ restaurants offering. We can conclude that Indian is very popular.

Dashboard

This is our final dashboard. We have used slicers to easily toggle through veg & non-veg, type and areas.

Conclusion

Swiggy as a online food delivery platform is growing rapidly with 1,40,000 restaurants, present in 500 towns and active delivery partners to the tune of 2.1 lakh and counting.