Google Trends a Complementary Tool for New Car Sales Forecasting - Marketing Assignment Help

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Abstract

The objective of this thesis is to analyse the reasons behind the consumer preference and taste for goods and services between India and South Korea. After that, it discusses the different model frameworks to highlight the differences between the conceptual ideas of the various authors. It emphasises on the customer-oriented study to make more approachable for the customer-oriented analysis. The state of the economies was assessed since the purchasing power of the consumers depends on the state of the economy. Furthermore, the study also provided the relationship between new car models and car prices in both countries under study.

The study used a quantitative research method. The study used regression analyses and correlations to determine the significances of variables as well as concluding about the stated hypotheses. The study found out that seasonality has got a weak relationship between Google trends index on car model sales. Finally, it highlights the importance of the Google Trend Application in the Market of South Korea and India. The results of the study help decision-makers within the automotive industry an evaluated tool to use during predictability across countries and thus reduces the usage of outdated and traditional approaches while forecasting. The patterns identified in the internet data are specified for some cars models and countries and acts as a catalyst variable to justify changes in the planning capacity of a company.

List of Abbreviations

NCS New Car Sales

SPSS Statistical Package for Social Scientists

UN United Nations

SIAM Society of Indian Automobile Association

IMF International Monetary Fund

1.0 Introduction

This chapter provides a brief background of the study, problem statement, research objective, research questions, hypothesis, and definitions of relevant concepts in the thesis.  

1.1 Background of the study

The automotive industry is one of the essential industries in India and South Korea since these two countries are emerging economies (Vosen & Schmidt, 2011). Forecasting of sales within the automotive industry is essential because new cars in the present generation are built to either deliver or forecast. However, forecast usually leads to the effect of bullwhip since there are demand uncertainty and inaccuracies in forecasting (Karlgren et al. 2012). Although some cars are built for delivery, the issue of accurate forecasting is important for managers and decision-makers to efficiently and effectively allocate and plan their resources. 

Google trend is a tool employed by various companies in both developed and developing countries to collect updated and large sums of data on how consumers respond to new car sales (Carley et al. 2013).  After realizing how important this development is, many automotive companies have paid great attention to Google search and other social media platforms to develop ideas and implement positive and significant decisions (Karlgren et al. 2012; Liu 2012).

Typically, consumers spend much of their time searching for new and potential vehicle models. Kandaswami and Tiwar (2014) conducted a study and discovered that the average time spent by people on Google to search for cars of their choices was 10 hours a day. In Asian countries, including China, India, South Korea, and Japan, the number approached 70%. Currently, the registered search engines like Google.com have a more extensive search platform for decision information on the new car sales. Past authors have conducted researches on the forecasting power of Google trends on car sales. However, their results differ from time to time (Barreira et al. 2013; Geva et al. 2017). Geva et al. (2017) discovered that the forecast models for best car models sales join Google trends data, forum mention and Forum sentiment. In their research, they found that Google trends and Forum sentiment have the same predictive power and volume. According to information provided by Trading Economics global macro models, the use of Google Trends as a tool for forecasting new car sales across India and South Korea, the customer journey of AIDA model is equally essential (Gensler et al. 2017).

AIDA implies the four procedures that the customer considers during the process of buying. First A means attention. Attention is always attained through publishing activities, sales promotion, that may be done, for instance, in Google adverts. Letter I means interest that is portrayed in the customer’s search behaviours it may be registered through Google Trends (Vosen & Schmidt, 2011). D means desire. The desire evolves in people’s sentiment that may be registered through positive and negative Google expressions. The last letter ‘A’ means action, which is the customer’s real purchasing power. 

1.2 Problem statement

The process of decision making in companies is affected by the efficiency of forecasting methods. This is because it reduces the dependency on probability and acts as a scientific way to cope with external events (Waheed et al. 2014). The manufacturers of different cars are forced to be prepared for future and plan for the increased demand for new car models, especially within countries with a high developing rate. The demand efficiency across countries leads to several problems because approaches are needed to handle a wide range of data that affects the functioning of the automotive industry (Waheed et al. 2014).

The automotive industry is composed of fast-changing needs of customers reflected by the dynamics of demand patterns that are observed as a threat to predict future requirements. Dharmani et al (2015) discussed on the value of current statistical forecasting tools in enhancing the planning and decision making and having knowledge of the whole market. The value of suitable forecasts is encouraged by the “Institute of Business forecasting and planning”. This is because of the reduction in forecast error by only a point reduces the average saving rate. However, many manufacturers of cars still based on traditional and local tools of forecasting that may not be able to manage the rising complexity. 

The decision of purchasing a car is determined by extensive information search by the customers, although there are other factors that are relevant in the new car buying process, including the national culture. In recent years, the involvement of customers and the way they move into various stages of the customer journey. However, various sources, such as professional dealerships and personal contacts, are considered before the decision of buying is reached. Much attention is given to the presence of a time lag between the attention given to a car model on the Internet and the purchase decision made by the decision that requires to be emphasized in the sales predictions.

Researchers like Artola et al (2015) explain that the common use of internet in recent years has changed the performance of traditional activities including the way how financial transactions are conducted together with the online buying of products. In addition, Ernst and Young (2015) explored that customers put much time in online searching for new car trends before buying a car in comparison to any other product (Goel et al. 2010). The decision of purchasing is always made in the store.

Thence, the assumption that the interests of people are revealed in their behaviours online, and in the main words they send to search engines. Prediction accuracy has been improved through various fields of application by adding web data within a model. However, internet data, especially Google Trends, is rarely applied in forecasting sales that might arise out of issues of reliability and validity concerned with big data. Nevertheless, accessibility and volume of internet data act as a suitable solution to solve the increasing slow development in approaches of forecasting and help decision-makers to solve complex problems within the changing environment in the automotive sector. 

1.3 Research objective and research questions

Comparing the search engine-based prediction of new car sales in India and South Korea has extraordinary potential. India produces about 3.8 million passenger cars annually as well as South Korea produces about 3.7 million passenger cars per year. This implies that these two countries produce approximately equivalent amounts of passenger vehicles annually, and both countries are considered to have different cultures according to the habits of consumers. In this paper, quantitative research is employed to assess the relationship between the interests of people for a specific car model and the data of new car sales. This is done using various linear regression models and analyses that are cross-sectional. 

The main goal of this thesis is to determine whether Google Trends tools are efficient forecasting for New Car Sales in India and South Korea. The Google Trends tool enables achieving customized search query data that connect to a given time frame, country and relevant keywords. Although identification of the difference in internet searches across different countries is important in forecasting sales, less attention has been given to this issue. The paper also investigates on the predictive power of optimal time lag in search engine data as some authors previously verified the Twitter data and other social media phenomenon. In this research, time lag refers to the time between product information research on the internet and the final decision made on purchases.

The incorporation of time lag into the model helps in quantifying changes inaccurate prediction of the model in India and South Korea. The current high value of search engine data by determining new relationships and patterns significantly increases sales performance and forecasting on new car models. Besides, the practical value of free internet data (Google Trends tools) as a complementary tool to determine economic variables across countries is equally reviewed.

This research study also intends to create awareness to other researchers and decision-makers to base on raw data of search engine and to provide ways on how to handle issues concerned with internet data that are reliable and valid. Taking into the consideration of theoretical framework also improves the prediction value that enables in determining the difference in a time lag of the buying process that is related to the price of cars, the segment of vehicles and the respective model. Majorly, emphasis in this study is given to the use of Google Trends as a complementary tool for forecasting new car sales and car purchasing decisions made by people in India and South Korea. This is intended to develop insights that allow positive response to demand volatility across countries. During this study, the specific research question and sub-questions below will be answered;

1.4 Specific Research questions

• What is the importance of the state of the economy towards Google trends predictive power on the new car models?

• What is the relationship between the state of the economy and car prices in India and South Korea?

• Can the seasonality presence impact on the predictability of Google trends volumes as well as prices of cars in both countries? 

• Does the average time lag length differ between high-priced and low-priced cars in both India and South Korea? 

• How does the accurate prediction of new car model sales differ in South Korea and India?

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