Customer behavior in changing markets when acquiring a car
Stock, Marko (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025120131034
https://urn.fi/URN:NBN:fi:amk-2025120131034
Tiivistelmä
The thesis analyzes customer behavior in financing markets when acquiring a car. In the thesis I will analyze customer behavior from the perspective of credit scoring and compare the data to inflation, GDP and customer confidence indicators. The objective of the thesis is to see how customers react in changing markets in comparison to expectations. The thesis is done as com missioned to the financing company and its purpose is to predict how customers react to certain market changes. The research questions are “How do credit applications compare with the mac roeconomic situation?” and “How do customers react to changing markets to forecast the future?”.
Research method is quantitative research method. I did not use any other research method be cause the data that was acquired from the financing company was massive and provided all the data that was needed. The data was edited to be able to be used in thesis. The subjects re searched were age, employment type, income level, financed amount, monthly payment, credit time, residence type and marital status. I was able only to analyze approved credit decisions, as rejected credit decisions get anonymized after a certain time period.
Theoretical framework of the thesis provides relevant concepts to the thesis and the data itself is compared at the end of the thesis. Understanding how credit decisions work and what credit scoring is crucial to understand the researched subject. Theoretical framework also describes relevant economic indicators in what the data is contrasted in. I chose the beforementioned eco nomic indicators based on that they are among the most common economic indicators. In age group and employment type I used more specific economic indicator and in other groups overall indicator.
The year 2022 was most substantial according to results, but other years also provided infor mation in relation to market changes. Key findings were lower customer confidence leads to bet ter performance in credit scoring in general, inflation affects greatly customer behavior in short term but not in long-term, GDP affects customers in short-term but not in long-term, in times of high inflation the credit scoring gap increases among different income levels and in certain re searched groups the expected highest and lowest performers actually does not perform the best and the worst and therefore there is a cutoff line among certain segments.
In conclusion, in certain segments performed in expected ways and certain did not. GDP and CPI did not affect credit scoring in the long term, and customer confidence showed unexpected results in form of most confident being the strongest applicants and the least confident being the strongest applicants. Among the applicants, in economic downturn, the credit scoring widens among income groups, meaning that higher income levels perform better and lower income levels perform lower.
Research method is quantitative research method. I did not use any other research method be cause the data that was acquired from the financing company was massive and provided all the data that was needed. The data was edited to be able to be used in thesis. The subjects re searched were age, employment type, income level, financed amount, monthly payment, credit time, residence type and marital status. I was able only to analyze approved credit decisions, as rejected credit decisions get anonymized after a certain time period.
Theoretical framework of the thesis provides relevant concepts to the thesis and the data itself is compared at the end of the thesis. Understanding how credit decisions work and what credit scoring is crucial to understand the researched subject. Theoretical framework also describes relevant economic indicators in what the data is contrasted in. I chose the beforementioned eco nomic indicators based on that they are among the most common economic indicators. In age group and employment type I used more specific economic indicator and in other groups overall indicator.
The year 2022 was most substantial according to results, but other years also provided infor mation in relation to market changes. Key findings were lower customer confidence leads to bet ter performance in credit scoring in general, inflation affects greatly customer behavior in short term but not in long-term, GDP affects customers in short-term but not in long-term, in times of high inflation the credit scoring gap increases among different income levels and in certain re searched groups the expected highest and lowest performers actually does not perform the best and the worst and therefore there is a cutoff line among certain segments.
In conclusion, in certain segments performed in expected ways and certain did not. GDP and CPI did not affect credit scoring in the long term, and customer confidence showed unexpected results in form of most confident being the strongest applicants and the least confident being the strongest applicants. Among the applicants, in economic downturn, the credit scoring widens among income groups, meaning that higher income levels perform better and lower income levels perform lower.