Understanding Customer Preferences and Predictive Sales Trends in the Automobile Sector: An Analytical Study with Power BI

Abstract

In the rapidly evolving auto industry today, knowing the customer\'s preference is crucial for forecasting future car sales trends and creating business strategies. With this research study, it concentrates on detecting consumer preference and foretelling car sales with Power BI, a contemporary business intelligence platform for data analysis and visualization.The main objective of this study is to examine existing customer trends and predict sales patterns based on survey information. The research also seeks to determine the most desired car models and options, investigate the effect of customer demographics including age, income, and lifestyle, and approximate future demand for electric and hybrid cars. It also investigates the effect of variables such as price sensitivity, fuel efficiency, safety, and smart technology on car purchases.Surveys were conducted using a standardized questionnaire that captured multiple sides of customer behavior, such as preferred vehicle types, price point, fuel type, importance of sophisticated features, and future buying plans. Power BI was used to analyze the responses and reveal latent patterns and visual trends through bar charts, pie charts, filters, slicers, and forecasting models.The findings of the study indicate high customer demand for fuel efficiency, safety features, and technology integration. It also shows increased awareness and interest in electric vehicles, particularly among environmentally aware and younger buyers. Power BI\'s interactive dashboards offered greater insights by graphically mapping customer segments and sales trends.This study illustrates the effective utilization of business intelligence tools such as Power BI in assisting data-driven decision-making within the automobile sector. Knowing what drives customer choices helps car manufacturers and dealers plan for better marketing, enhance inventory control, and provide products that more closely meet consumers\' expectations. This study, in general, provides valuable information on how to align business processes with customer needs in a fast-changing market.

Introduction

The automotive industry is rapidly evolving, with consumer preferences and market trends continuously changing. In this competitive landscape, understanding customer desires is crucial for boosting sales and developing robust business strategies. When purchasing a car, consumers now consider various factors, including price, brand reputation, comfort, safety, fuel efficiency, and advanced features like AI assistance and connectivity.

This study focuses on analyzing customer preferences and forecasting car sales trends using Power BI, a business analytics tool known for its data visualization and forecasting capabilities. By examining consumer decision-making and behavior, the research aims to provide automotive companies, marketers, and dealers with data-driven insights to enhance decision-making processes.

The primary objective of this study is to identify current customer preferences and apply them to predict future automobile sales. Additionally, it explores secondary topics such as determining the most popular car models and features, assessing how demographics (age, income, lifestyle) influence purchasing decisions, and forecasting demand for electric and hybrid vehicles. The study also investigates the impact of price sensitivity, fuel efficiency, and technological advancements on purchasing choices.

Data was collected through a structured questionnaire addressing key areas such as car type, fuel preference, budget, desired features, and purchase intentions. Power BI was utilized to analyze the responses, creating interactive dashboards with graphs, filters, and charts. These visualizations facilitated the identification of trends and the drawing of conclusions from the data. Power BI's forecasting functions also enabled the study to predict future sales trends based on customer interests.

Key findings indicate a growing popularity of electric vehicles, an increasing desire for fuel efficiency, and a significant influence of price and technology on purchasing decisions. This research demonstrates how business intelligence tools like Power BI can simplify complex data and support decision-making in the automotive industry. By gaining a better understanding of consumer preferences, companies can improve product development, marketing strategies, and inventory management, leading to enhanced business growth.

Review of Literature:

1. Customer Preferences and Buying Behavior in the Automobile Industry
   Sharma, P., & Gupta, S. (2019)
   This study examines how factors like price, fuel efficiency, and safety features influence car-buying behavior in India. It highlights that customer preferences are strongly influenced by fuel prices and brand image, with demographics such as age and income directly impacting purchase decisions.

2. Role of Business Intelligence in the Automotive Industry
   Kumar, R. (2021)
   This paper explores the growing role of business intelligence tools like Power BI in analyzing consumer data. It discusses how visual dashboards assist companies in understanding customer behavior and making faster, data-driven decisions, emphasizing BI's use in sales forecasting and operational planning.

3. Impact of Demographic Factors on Car Purchasing Decisions
   Mehta, A., & Singh, R. (2018)
   The study investigates how age, income, occupation, and residential location affect customer choices. It concludes that younger buyers prefer technology-rich cars, while older customers focus on durability and brand trust, highlighting the importance of demographics in market segmentation.

4. Predictive Analytics in the Automotive Sector
   Johnson, L. (2020)
   This paper discusses how predictive models can estimate future demand for specific car types. It explores tools like Power BI and Tableau for creating data visuals that assist managers in sales planning, also touching on integrating machine learning algorithms with business intelligence.

5. A Study on Consumer Behavior towards Electric Vehicles
   Patel, N. (2022)
   This research analyzes why some customers choose electric vehicles over conventional cars. It identifies environmental concerns, lower maintenance costs, and government subsidies as major drivers, while noting that awareness and affordability remain challenges for EV adoption.

6. Using Power BI for Visual Data Analysis in Business
   Thomas, D. (2021)
   The paper showcases Power BI as a user-friendly tool for handling complex datasets. It explains how slicers, filters, and real-time dashboards improve data understanding, demonstrating real-world use cases including sales forecasting and customer segmentation.

7. The Influence of Fuel Efficiency on Consumer Buying Behavior
   Rao, M., & Verma, S. (2017)
   This research reveals that mileage is one of the most important factors for Indian consumers. It notes that even high-end buyers consider fuel savings over time, concluding that fuel efficiency can be a deciding factor in a highly price-sensitive market.

8. Technology Features and Car Buying Preferences
   Iyer, S. (2020)
   This paper discusses the importance of smart features like AI dashboards, lane assist, and connectivity in attracting modern buyers. It finds that tech-savvy customers are more likely to choose brands offering innovative features, noting that digital influence affects decision-making.

9. Automobile Sales Using Visualization Tools
   George, A. (2021)
   This study uses Power BI to create visual forecasts for vehicle sales based on historical data. It demonstrates how trends can be visualized using charts and forecasting tools, supporting the use of business intelligence tools for better planning and strategy alignment.

10. Business Intelligence as a Tool for Competitive Advantage
    Chatterjee, D. (2022)
    This paper explains how business intelligence tools help companies gain a competitive edge by turning raw data into actionable insights. It highlights the usefulness of dashboards in various industries, including the automobile sector, arguing that BI adoption leads to more customer-focused strategies.

Objectives of the Study:

• Primary Objective:
  To utilize Power BI to forecast future trends in automobile sales and analyze current customer preferences.

• Secondary Objectives:

  • To identify the most popular vehicle models and features influencing consumer purchasing decisions.

  • To examine how demographic factors such as age, income, and lifestyle impact car sales trends.

  • To predict future demand for hybrid and electric vehicles based on shifting consumer preferences.

  • To assess the influence of pricing, fuel efficiency, and technological advancements on purchasing decisions.

  • To leverage Power BI's forecasting and analytical capabilities to display and analyze sales trends for informed decision-making.

Scope of the Study:

This research focuses on understanding customer preferences and analyzing sales trends in the automobile sector using Power BI, a modern business intelligence and data visualization tool. The study aims to explore key factors influencing customer decisions when purchasing a car, such as price, fuel efficiency, design, brand trust, and advanced technology features. It also examines how consumer variables like age, region, employment status, and income level influence purchasing decisions. By utilizing Power BI to visualize this data through dashboards and forecasting tools, the research seeks to facilitate better business decision-making and simplify the prediction of future trends in automobile sales. Overall, the study offers a data-driven approach to understanding consumer behavior and forecasting sales in the dynamic automotive industry.

Research Methodology:

This study employs a quantitative research methodology, focusing on factual analysis and numerical data to comprehend consumer preferences and predict future trends in automobile sales. Data from a structured questionnaire serves as the foundation for the research, with Power BI utilized for analysis.

Data Collection Technique:

Primary data was collected through a survey using a Google Form. The questionnaire was distributed to prospective and existing car customers, featuring both closed-ended and Likert scale questions about expectations for technology, budget, fuel type, preferred car models, and anticipated purchase dates. This approach ensured that the responses were quantifiable and suitable for analysis.

Data Modeling:

A single structured table containing the cleansed dataset was loaded into Power BI. Relationship modeling was unnecessary due to the flat and non-relational nature of the survey data. To segment responses

Conclusion

Using Power BI, this study effectively examined consumer preferences and forecasted future trends in auto sales, providing insightful information for the automotive industry\'s decision-making. According to the report, younger consumers are becoming more interested in electric and hybrid cars, while the 26–35 age group is the most active car-buying market, with a strong preference for SUVs and hatchbacks. Spending capacity was found to be influenced by income levels, with mid-range budgets (?10–25 lakh) being the most common. All age groups place a high value on technological features, but internet-savvy millennials especially do, and there is a direct link between greater willingness to spend and digital enthusiasm. Despite the low level of interest in EVs at the moment, demand for them is expected to grow in the future, particularly in the one to three year purchase period, suggesting a shift in consumer behavior toward more environmentally friendly purchases. Power BI turned out to be a useful tool for anticipating sales trends, segmenting consumer behavior, and displaying patterns. The study illustrates how business intelligence technologies may improve strategic planning in marketing, inventory management, and product positioning using interactive dashboards and data-driven insights. Overall, the results highlight how automakers and dealers must prioritize affordability, intelligent features, fuel economy, and environmentally friendly models in order to match their product offerings with consumer expectations. Remaining competitive in the ever changing automotive sector will require ongoing customer preference monitoring using analytics solutions like Power BI.

References

[1] Kumar and P. Sharma, Consumer Behaviour in Automobile Industry, New Delhi, India: Sage Publications, 2018, pp. 112–120. [2] N. Gupta, Data Visualization Techniques using Power BI, Mumbai, India: TechPress, 2020, ch. 5. [3] ] S. Mehta, “Understanding millennial car buying preferences in India,” Auto Market Research Journal, vol. 14, no. 3, pp. 42–49, Jul. 2021. [4] R. Patel, “A predictive analysis on electric vehicle adoption,” Int. J. Sustainable Mobility, to be published. [5] L. Thomas, “Integrating Business Intelligence in Automotive Marketing (Submitted for publication),” IEEE Trans. Ind. Informatics, submitted for publication. [6] V. Krishnan, Personal communication, Automobile Customer Behavior Survey, Chennai, India, Mar. 2025. [7] M. Ramesh, R. Iyer, and D. Nair, “Power BI-based dashboards for customer segmentation,” IEEE Transl. J. Data Sci. Visual., vol. 5, Jan. 2022, pp. 88–91. [8] A. Singh, Modern Trends in Automobile Marketing, 2nd ed. Pune, India: Wheels Press, 2019. [9] S. Roy. (2023, Oct. 12). Forecasting Car Sales Using Power BI and AI [Online]. Available: https://www.automobileinsights.com/pbi-car-sales [10] T. Joseph. (2022, Feb. 5). Understanding Customer Purchase Behavior in the EV Segment [Online]. Available: https://www.evmarketresearch.org/behavioral-study

Copyright

Copyright © 2025 Gokulraj P. M., Dr. D. Kotteswaran. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.