A Detailed Analysis of Food Delivery Aggregator Data (1).pdf

How to Effectively Use Competitor Price Trackers:
Operational Guidance and Benefits
Introduction
In the dynamic landscape of eCommerce, pricing strategy stands paramount, especially for digital storefronts.
Modern consumers are discerning, often prioritizing price comparison as a pivotal step before purchasing. The
allure of securing the best deals and most competitive prices drives this behavior. Consequently, a significant
portion of online shoppers actively engage in price monitoring across various platforms. Recognizing this trend,
online retailers must adeptly employ competitor price trackers to stay ahead.
By leveraging retail data scraping techniques, businesses can collect comprehensive retail data, offering insights
into the broader market's pricing landscape. Such price intelligence empowers retailers to craft a robust pricing
strategy, bolstering sales, enhancing profit margins, and fostering customer loyalty. Moreover, a data-driven
eCommerce pricing approach ensures competitiveness, with dynamic pricing becoming increasingly prevalent.
In essence, utilizing a competent competitor price tracker facilitates effective pricing optimization and solidifies a
retailer's position in the fiercely competitive eCommerce arena.
How to Use Zomato API to Scrape Restaurant Data – A
Detailed Guide
In the dynamic world of digital gastronomy, where culinary delights and dining experiences
are increasingly explored and celebrated online, the demand for up-to-date restaurant data
is insatiable. As food enthusiasts and businesses seek the latest insights into menus, reviews,
and more, the Zomato API emerges as a powerful tool for scraping this invaluable
information.
Introduction
Our comprehensive guide delves into the intricate art of utilizing the Zomato API to scrape
restaurant data. Whether you're a restaurateur striving to understand your competitors, a
food blogger searching for fresh content, or a data enthusiast eager to explore culinary
trends, this guide will serve as your compass in the world of data extraction.
We will navigate through the intricacies of Zomato Scraper, uncovering its capabilities to
provide you with rich, real-time restaurant data. From scraping restaurant details to
accessing customer reviews, our detailed guide ensures that you harness the full potential of
this resource.
What are Datasets? - A Comprehensive Guide
This blog will provide a comprehensive overview of datasets, including their definition, different
types of datasets, and strategies for maximizing the value of data.
What is a Dataset?
Extracting Information from TripAdvisor: A Guide to
Scraping Data from Hotels and Restaurants
Introduction
Leveraging the appropriate API makes scraping TripAdvisor on a large scale a straightforward task.
You don't need to possess advanced computer skills to achieve this. Our comprehensive, step-by-
step guide is designed to walk you through extracting data from TripAdvisor using a user-friendly
web scraping tool.
TripAdvisor stands out as a powerhouse with an extensive database comprising over 8 million
locations, 1 billion reviews, and support for 29 languages. As of 2022, when the cumulative reviews
surpassed the one billion mark, it became evident that TripAdvisor's meticulous gaze would soon
cover every restaurant, hotel, vacation rental, or attraction listing.
Amidst TripAdvisor's vast sea of information lies a treasure trove awaiting extraction, analysis, and
innovative presentation. For those in the tourism, hospitality, or travel sectors, harnessing
TripAdvisor data proves invaluable for monitoring competitors and supporting strategic business
decisions. The TripAdvisor Scraper facilitates seamless and rapid web scraping, offering the most
straightforward route to consistently obtaining data at scale. This article delves into the myriad
benefits of scraping TripAdvisor, shedding light on how this process can be initiated, including
exploring the initial steps involving API utilization. Dive into the realm of data-driven insights with
TripAdvisor scraping.
Unlocking the Potential: The Advantages of
Scraping TripAdvisor Data
A Detailed Analysis of Food Delivery Aggregator Data
Recently, we shared how to extract data from food delivery websites and process and mold it into a
visual map with python. Let's discuss further to resolve a few queries, as shown below.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
Based on the input address shown in the map below, We filtered them into different categories
based on delivery fees in different colors. As predicted, we found that the higher the delivery fees of
the restaurant, the highest the delivery. As indicated in the map, you can see a circular boundary
with orange shading. Beyond that boundary, the delivery fee is greater than 5 USD. But, there are a
few points beyond the limit where the delivery fee is less than 5 USD.
To understand the factors influencing these outliers fees in a better way, we will separate points
outside the orange boundary and compare their features to those points inside the boundary. To do
so, we will choose the topics that create this orange boundary and use them to draw a polygon in
the script. Then, we will check the facts in the datasets and their location to analyze further.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
We observed the general trend in an outlined area with red shading, where delivery fees increase
with the input address distance. But, the points outside the mapped polygon don't follow the
pattern (black points are an exception), and we can consider them outliers. We need to convert
coordinate points for every restaurant into a geometric point to compare features of QSRs inside &
outside the polygon and find if these points are inside or outside.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
In the dataset, our function determines whether the restaurant is inside the polygon, and the last
column shows that. To study the characteristics of the restaurants in both categories, we will
separate the dataset into two groups.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
The most impactful factor on the delivery fees seems to be the distance between the restaurant and
the delivery location. We will consider this feature to begin.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
Most restaurants inside the polygon are situated 1 to 3 km from the input address, and their
delivery fees range from 0 to 7 USD. Apart from this, restaurants in the 4 to 7 km range have a
probability of delivery charges below 5 USD.
From the analyzed data, we observed that most of the restaurants located outside the polygon have
zero delivery fees, which means the delivery distance needed to be more accurate. We saw the
delivery fees of 7 USD for the restaurants within the polygon. This reflects that restaurants outside
the created polygon have considerably fewer delivery fees than inside restaurants. Moreover,
analyzing the potential impact on delivery fees, we selected a group of columns with our
assumptions and the output of the correlation matrix measuring the strength of relationships of
values.
df.corr()['deliveryFeeDefault'].sort_values(ascending=False)[2:]
Per the correlation matrix, delivery by Scoober is the main factor with a strong correlation with the
delivery fees. There is a possibility that other factors could have a weaker correlation because of the
existence of outliers. Therefore, we will further investigate by plotting the data.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
After plotting the selected data column groups, we see all the restaurants in the polygon plotting.
The columns' price ranges and new have the same distribution. The slightest order value also varied
more in this portion considerably. But no restaurant outside the polygon used Scoober delivery, and
the sponsored restaurant distribution looks more balanced.
Let us check the restaurant distribution that uses Scoober delivery, where we will plot two plots
parallelly using the below function. This will help us to compare both groups easily.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
The boolean value shows whether a restaurant is using Scoober delivery or not likely plays a
significant role in delivery fees of restaurants, as you can see in the shared red range in our study
earlier. The reason is that restaurants are making agreements with lieferando to use Scoober
delivery services, where Scoober will work as a food delivery aggregator. To further investigate the
relationship between delivery fees and Scoober, we can categorize the data for the restaurants
associated with Scoober delivery and study the link between delivery fees and other factors.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
Here, the distance is related to delivery fees on a positive note, where the output has grown from
.15 to .75
It is worth noting that the order's base value has NaN, meaning the order value is constant or
missions. Let's analyze these restaurants again for the case where lieferando doesn't use Scoober
delivery for food shipment, considering that these restaurants are using their delivery platforms

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
Here, we couldn't see any improvement with the distance remaining constant at 0.20, and the
minimum order value was 0.4, way higher than the distance.
When our team plotted the least order values for Huston-based restaurants to check whether they
use Scoober for delivery service, we got the results below.
It looks like lienferando has a base value for orders of 8 USD for each restaurant that offers scoober
delivery service. It may be because of the agreement between Scoober delivery and restaurants. It is
the same as another food aggregator Wolt as they also have a base order value of 8 USD for every
restaurant.
Our team created histograms to compare selected columns on whether restaurants used Scoober
delivery. This helps to quickly overview the notable variations between the two groups we created
for analysis.

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?
Introduction
web scraping tool.
We found that around 25 percent of existing restaurants don't offer Scoober delivery with their
order value between 8 to 45 USD and low delivery fees despite the delivery distance. In comparison,
the rest of the restaurants provide Scoober delivery, with a minimum order value being 8 USD and
higher delivery fees.
Do you want to analyze the food delivery platform for your restaurant business using our mobile
app scraping and web scraping services to work on food delivery data analytics? Do contact us at
Actowiz Solutions, and we will revert you instantly!
Key Takeaways

Introduction
Detailed Guide
information.
Introduction
this resource.
What is a Dataset?

A Detailed Analysis of Food Delivery Aggregator Data (1).pdf

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