Authors :
Gopi Sai Sri Mallu; Nandhini Devi Divi; P. Srinu Vasarao
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
https://tinyurl.com/ymekdvzd
Scribd :
https://tinyurl.com/2jmzfrd5
DOI :
https://doi.org/10.5281/zenodo.10785372
Abstract :
Nowadays most of the people are going out to
grab food, the majority of individuals relying on food
applications. Though Zomato is popular food app in
India, it deals with few challenges like – extreme market
competition, loss of market shares, negative impact on
brand etc. It experiences a major drop of orders during
the month of October, which is a festival season that
leads to drop down of revenue. Zomato came up with a
solution for this problem, during festival season Zomato
provides discounts and coupons on food that they
purchase and asked restaurants and food stalls to keep
open so that customers can make order even on festival
season. The few root cause for Zomato are delivery issues
and delay deliveries that can leads to unhappy customers
and opt other food apps, more competition from other
food apps which provides festival offers and discounts
can make the customers to switch other food apps,
customers doesn’t come forward to purchase when they
look high cost on food menu without discounts, and
during festival time people more likely to eat outside
rather than to order food from food apps. To increase
more and more deliveries Zomato should ensure with
good quality of food, providing transparency in order-
tracking and delivery of meals in time efficiently, and
finally to resolve competition over other apps Zomato
needs to provide personalized services and make sure the
deliveries speed and accurate. Zomato make use of
machine learning to predict real time challenges such as
allocating delivery partners, estimating time for
preparation of food and food delivery, and removing or
deducting fake customer reviews. It also uses ML to
digitalize the food menu and identifies the items included
in that. It includes Natural Language Processing (NLP)
to extract the structured data from unstructured data
from food menu.
Keywords :
Importing and Cleaning Data, Statistical Methods, Visualization Techniques, Carrying Out Statistical Tests, Model Prediction.
Nowadays most of the people are going out to
grab food, the majority of individuals relying on food
applications. Though Zomato is popular food app in
India, it deals with few challenges like – extreme market
competition, loss of market shares, negative impact on
brand etc. It experiences a major drop of orders during
the month of October, which is a festival season that
leads to drop down of revenue. Zomato came up with a
solution for this problem, during festival season Zomato
provides discounts and coupons on food that they
purchase and asked restaurants and food stalls to keep
open so that customers can make order even on festival
season. The few root cause for Zomato are delivery issues
and delay deliveries that can leads to unhappy customers
and opt other food apps, more competition from other
food apps which provides festival offers and discounts
can make the customers to switch other food apps,
customers doesn’t come forward to purchase when they
look high cost on food menu without discounts, and
during festival time people more likely to eat outside
rather than to order food from food apps. To increase
more and more deliveries Zomato should ensure with
good quality of food, providing transparency in order-
tracking and delivery of meals in time efficiently, and
finally to resolve competition over other apps Zomato
needs to provide personalized services and make sure the
deliveries speed and accurate. Zomato make use of
machine learning to predict real time challenges such as
allocating delivery partners, estimating time for
preparation of food and food delivery, and removing or
deducting fake customer reviews. It also uses ML to
digitalize the food menu and identifies the items included
in that. It includes Natural Language Processing (NLP)
to extract the structured data from unstructured data
from food menu.
Keywords :
Importing and Cleaning Data, Statistical Methods, Visualization Techniques, Carrying Out Statistical Tests, Model Prediction.
