Marketing Report: Google Trends for New Car Sales in India & Korea

This report investigates the application of Google Trends as a complementary tool for forecasting new car sales, with a specific focus on the markets of India and South Korea. The research addresses the challenges faced by car manufacturers in accurately predicting volatile market demands. The methodology involves an experimental research design, utilizing both primary data collected from Google Trends and secondary data for statistical analysis, including regression and correlation. Ethical considerations are addressed to ensure data confidentiality and proper citation. The report explores the relationship between Google search queries and new car sales, aiming to provide insights into how sentiment analysis can aid car manufacturers in making informed decisions. It also examines the customer journey and the importance of market forecasting in the automotive sector. The report concludes with an analysis of the research findings and recommendations for future studies.

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Table of Content
Introduction......................................................................................................................................2
Literature..........................................................................................................................................3
Research Problem and Question......................................................................................................3
Methodology....................................................................................................................................4
Data collection.............................................................................................................................4
Data analysis................................................................................................................................4
Ethical Consideration...................................................................................................................5
Research Timetable.....................................................................................................................5
References........................................................................................................................................7

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Title: Google Trends as Complementary Tool for New Car Sales Forecasting: A Cross-
Country Comparison along the Customer Journey
Introduction
The proposed research gives a detailed idea about the use of Google Trends as the
analytical tool for forecasting new cars sales in India and South Korea. It can be mentioned that
Google trends contribute to the decision-making process of car manufacturing brands in relation
to customers’ expectation, dynamic customer and market needs. Vehicle manufacturers across
the globe are in the rush of preparing themselves in the most effective way to deal with the
external environments. It has also been identified that automotive sector is the most effective
sector in India with sales unit of 3.8 million in 2019 (Önder& Gunter, 2016), and the scenario is
no more lagging behind in South Korea because South Korea’s motor vehicle sales reached
117,628 units in 2020 (Pavelková, et al., 2018). Even though sales growth is high in both nations,
marketers should not take it for granted because as the customer demands are changing rapidly,
importance and efficiency of demand planning for different car models can be challenging, as
such tailored approaches require dealing with the vast classification of data, which affects the
organization’s performance. In addition to this, these two markets are not immune to the external
challenges like social factor, economical factor and demographic facto, etc. Therefore, the
organisations are in the need of suitable sale forecasting tool like Google trends. The context of
Google Trends and its importance in New Car Sales forecasting have been chosen as the context
of the proposed study because even though the organizations are experiencing a good and
positive sales vibes in new car sales segment in India and South Korea, market demands are
volatile. Moreover, customers’ demands about new cars can change any time. Thus, it is

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important to explore how sentiment analysis or Google Trends are actually helping the car
manufactures to predict sales.
Literature
As put forward by Vosen and Schmidt (2011), sales forecasting in the automotive sector
is important as vehicles cars in the existing system are either built-to-delivery or built to forecast
and nonetheless, the later one can lead to a bullwhip effect because of uncertainty in demand and
inaccurate forecasting. Although, if vehicles are developed to delivery and accurate forecasting
could still to plan and allocate the resource better (Choi & Varian, 2012). According to Hand &
Judge (2012), social media act as word of mouth and enable organizations to gather large scale
as well as updated data, whichwould represent honest customers’ opinion. It has also been
identified that customers have spent a large amount of time searching for information regarding a
potential vehicle. In this context, Carrière‐Swallow and Labbé (2013) mentioned that a large
percentage of customers tend to spend more than 10 hours to particularly identify the best and
most effective vehicle for the requirements. Findings of this study also stated the fact that use of
Google trends in the emerging nations are highly effective because Google trends provide daily
and weekly reports based on the volumes of queries about car manufacturing sector like India. It
is not yet clear how Google trends can predict the future (Önder& Gunter, 2016).
Research Problem and Question
It has been identified that car-selling sector in South Korea and India is most likely to see a
growing margin in sales of new cars but the external market environment has been dynamic and
market demands are volatile. Consequently, car manufacturers lack confidence in relation to
fluctuating demands of new cars. Thus, the car manufacturers are in the need of a suitable

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market-forecasting tool to analyze the demands. The process of market casting should be
evaluated in the following section since it will help the organizations to provide better products
through molten metal and give the customers their favorite cars with their desired shape.
 What is the role of importance of market casting for new car sales in the automotive
sector?
 How does Google Trends serve the purpose of forecasting new car sales in India and
South Korea?
Methodology
Research methodology refers to the action taken in the course of a research that deals
with the application of various techniques in the course of conducting the research.
Design Framework:
In order to identify how Google Trends are effective for extracting suitable market data
related to new car sales, an experimental research design would be used in the study. This kind
of research design is generally conducted with a scientific approach where a particular set of
variables are kept intact while the other set is being measured according to its reaction on the
first set of variables. This kind of research design has high level of casual validity. It has been
seen that regarding the effectiveness of the Google trends in extracting the suitable market, there
are not much data available. Thus, the research cannot be done by using an explanatory or
exploratory design cannot be used since there is very less scope of analyzing the previously
established theories or variables. The research in this case requires to discover the facts by
conducting experiments on the available variables so that one a decision can be concluded. In
this case, the experimental research design can be implemented by analyzing a cause and effect

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phenomenon. The research will carry on conducting research in understanding the direct
feedbacks with help of the Google trend respondent. The high casual validity of the
experimental design will help in the determination of the cause effect relationship. Experimental
research design is an effective choice, even though there are several other tools for market
forecasting like Social media or Word of mouth, Google Trends are hardly experimented in the
study.
Data collection
In order to identify and experiment how Google trends are contributing to sales
forecasting new car sales and to explore demands through Google trends, both primary and
secondary data analysis should be considered. Primary data will be collected directly from
Google Trends, in order to obtain the information that is best suited to meeting the objectives of
this study. The external variable is one of the major sources of posing threat to the validity of a
research. Thus, the proper handling of these informations is essential for the purpose. The
random assigning of various test units and gathering informations about randomized decision
will help in understanding the statistical control Of Google trend in the decision making. The
research design then can be implemented by the sorting of the Google trend responses and the
recent market demand.
Data analysis
In order to find the relationship between two variables such as Google trends and car
sales percentage, statistical analysis would be performed with SPSS. Regression and correlation
will be performed between the variables. The co relation will be measured in accordance with the
Google trend and the trend in the sales of cars in the target markets. This will eventually help in
turning up a cause effect relationship from which the decision can be deduced.

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Ethical Consideration
As primary data would be collected in the proposed report, ethical issues are possible
outcomes and ethical issues can lead the research to failure. Therefore, to address the ethical
concern, certain steps would be taken on the part of the researcher. For the clause of
confidentiality of data, it will be ensured that the data used is well protected and stored and
efforts will also be made to ensure that it is not published anywhere, to avoid what may be
termed as the duplication of content. As per the Data Protection Act 2018, the information that is
obtained from Google Trends will be kept confidential. All of the data that is used in this project
will be cited using an appropriate citation style. It is also important to note that this is a
project that is being undertaken for reasons of scholarship alone and not for any personal
reason or for reasons of profit.
Research Timetable
Task Week
1
Week
2
Week
3
Week
4
Week
5
Week
6
Week
7
Week
8
Week
9
Selection of topic
and search for
justification
Constructing
literature
Selecting
appropriate
methods
Data collection

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Data analysis and
representation
Reviewing the
outcomes
Conclusions and
recommendations
Submitting draft of
the project
Printing and final
submission

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References
Carrière‐Swallow, Y., &Labbé, F. (2013). Nowcasting with Google Trends in an emerging
market. Journal of Forecasting, 32(4), 289-298.
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic record, 88,
2-9.
Hand, C., & Judge, G. (2012). Searching for the picture: forecasting UK cinema admissions
using Google Trends data. Applied Economics Letters, 19(11), 1051-1055.
Önder, I., & Gunter, U. (2016). Forecasting tourism demand with Google Trends for a major
European city destination. Tourism Analysis, 21(2-3), 203-220.
Pavelková, D., Homolka, L., Vychytilová, J., Ngo, V. M., Bach, L. T., &Dehning, B. (2018).
Passenger car sales projections: Measuring the accuracy of a sales forecasting model
1. EkonomickyCasopis, 66(3), 227-249.
Pavelková, D., Homolka, L., Vychytilová, J., Ngo, V. M., Bach, L. T., &Dehning, B. (2018).
Passenger car sales projections: Measuring the accuracy of a sales forecasting model
1. EkonomickyCasopis, 66(3), 227-249.
Vosen, S., & Schmidt, T. (2011). Forecasting private consumption: survey‐based indicators vs.
Google trends. Journal of forecasting, 30(6), 565-578.

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