Nowadays there are high amounts of reviews, tests and opinions about any car model so that consumers are much more likely to get influenced on the way to their purchase.
Post purchase Behavior
This is a crucial point for any product. A satisfied consumer is more likely to purchase the product again and also tends to give out positive referrals of that product or the product’s brand. Personal information sources are rated highly effective and therefore it is from highest importance to keep the consumer happy and satisfied even after the purchase. Dissatisfied consumer may not only abandon or return a product, they can also take public action or warning other consumers (Kotler & Keller, 2016). Bad news usually spread wider than positive news due to the psychological fact that fear is stronger than the satisfaction a product might give. Some consumers will explore dislikes of a product of attribute that they have not recognized earlier. With the internet those information spread much faster, such as the recent cases of Samsung Galaxy Note 7 phones that are exploding. Even though this is a production error, it is now known by almost every single person that is interested in buying smartphones from any brand. Those “small” mistakes in the construction cost Samsung’s smartphone sector customers and reputation, even after years and several product lines of successful and safe phones.
In conclusion, with the background of a car purchase, it is important to further satisfy the consumer as long as he owns a certain vehicle. Any small disadvantages that do not occur at the competition have huge impacts on the overall reputation of the brand and future consumer purchase decisions.
2.2 Google Trends as a Source of Consumer Behavior
Existing forecasting models usually combine survey based indicators and macro-economic indicators. That way they can cover up macro-economic and psychological aspects of consumer behavior. Problems and inaccuracies that come along with survey data are plenty, such as a sampling bias. In previous research remarkable results have been
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data had found of not significant value (Schmidt & Simeon, 2009).The Google Trends data is unbiased sample of Google search data. It is anonymized, categorized and aggregated. It allows to measure interest in a particular topic across search, from around the globe, down to the city-level. Real time trends data is a random sample of searches from the last 7 days and not subject of this work. The full Google dataset is non-real time and starts from 2004. The relative search interest of a topic, which can also be a specific set of search terms, is compared to itself, over time and where it is most-searched (Rogers, 2016).
It is mentionable that most of the existing forecasting models have been conducted for developed countries such as the U.S, where a broad range of data and observations are available. Most researchers use this data-rich environment to conduct their research and prove/disprove theories. There is no doubt that those results are useful, also for other countries. However, it has to be realized that models cannot easily be generalized and simultaneously maintaining a healthy amount of validity. Some of the most prosperous markets are the hardest to analyze, to obtain valid data and to request for further data. This is the point where Google Trends becomes handier. With a few exceptions, most countries use Google as their primary tool for searching the internet.
Globally the market share of Google’s search engine is 79.8% (Seach Engine Journal, 2016).
In Thailand however the market share is 98%, which furthermore shows the significance and convenience of Google data in this country (Return On Now, 2015).
The data created through searching, also called googling, gets delivered directly to Google, unfiltered by authorities. The data becomes publicly available and gives deeper insights of the internet searching behavior of people using Google. Internet access, which includes access to Google, is increasing. That makes working with Google Trends a modern and promising way of obtaining consumer data.
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A good approach to look at Google Trends data in relation with traditional data shows the following:
Macro-economic variables indicate the consumer’s ability to purchase a good. Google Trends is providing a measure for the consumer’s preparatory steps to the purchase decision making.
How to use Google Trends?
The tool is modern so that a large amount of people are not familiar with using it. For this reason there is an introduction of how to utilize Google Trends to obtain data. The most important features are explained in the following:
Search Term is/are the keyword/s that consumers have entered into Google search.
Several search terms can be used at the same time and different language inputs are supported.
Country Selection provides all the countries where Google is being used.
Time Range allows the user to choose between various options. Daily data provides the results for every minute; Weekly data provides hourly results; Custom Range shows weekly or monthly data, which depends on how far the time period ranges.
Category is responsible to create the index score. A category should be included to avoid ambiguities of search terms. The category used in this work is “Autos & Vehicles”.
Type of Search allows the user to choose between “Web search”, “Image search”,
“News search”, “Google shopping” and “Youtube search”.
Furthermore the user is able to divide the country into sub regions and display related topics for the used search term. The output is presented via diagram and is exportable as a CSV file.
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(2012). They used different methodology while maintaining a logical explainable variable selection that consists of macro-variables. Among their models or methods were Ordinary Least Squares, Quantile Regression, Support Vector Machine, Decision Trees, K-nearest-Neighbor and Random Forest. The variables were picked under certain criteria, which will be explained in 3.2.2. An important finding of this work is the importance of considering special effects, such as the car-scrap bonus in Germany and the irregular behavior of the US-American market. Since this thesis is mainly about the improvement of a forecasting model by Google Trends, estimates of special events will be dealt with in the discussion (Hülsmann, Borscheid, Friedrich, & Reith, 2012).
The work of Choi & Varian (2012) is encouraging future researchers to engage with Google Trends as a complimentary tool for forecasting. Examples that are dealing with simple seasonal AR models that include relevant Google Trends, in various fields.
Models about Motor vehicles and parts, initial claims for unemployment benefits and consumer confidence that are including Google Trends variables tend to outperform models, which exclude Google Trends, by 5% to 20%.
A seasonal AR-1 model is used as a baseline for car sales. The model can explain 71.11% of the data variation (adj. R²). The next step of Choi & Varian’s (2012) work is to include Google Trends categories in the regression. The in-sample fit can be significantly increased by doing so, reaching 80.8% adj. R². The execution of an out-of-sample forecast shows a 10% increase in forecasting accuracy when using Google Trends, measured by the Mean Absolute Error (MAE) during normal economic period (Choi & Varian, 2012)
However, the research concentrates more on “contemporaneous forecasting” that predicts the present. The time difference between obtaining Google Trends and the car sales, accounts for only about 4 to 6 weeks. They used the search results from the first Saturday to Sunday, each month. The sales data was obtained 2 weeks after the end of each month. According to the reasoning of Kinski (2016) and the consumer purchase