The amount of data that can be collected and assembled during market research is astronomical. As a result, data organization and analysis play a vital role in market analysis. The various data analysis techniques enable entities to make sense out of data collected in marketing research thus granting firms the ability to make cogent and insightful data interpretation (Kumar, Dash, Trivedi, & Panda, 2017). To ensure successful marketing campaign and re-emergence into the automobile industry, Maruti Suzuki in India should employ quantitative market research tools and methods such as multiple regression, cluster analysis, factor analysis, and conjoint analysis.
This statistical method is utilized to determine the equation that best explains how dependent marketing variables shift in relation to the independent variables (Palmatier & Sridhar, 2017). For instance, Maruti Suzuki in India should use multiple regression methods to determine the relationship between the effects of advertisement on the sales revenue (the dependent variable) and the expenditure incurred in the advertisement process, in placing the ads, and the timings when the ads are aired. Additionally, the Suzuki Company can employ the discriminant analysis technique. This method is used to classify products into two or more groups. For instance, the method can be used to determine the advertising channels that are most effective for different car models and the target consumers. Notably, SUVs such as Mahindra XUV500, Honda CRV, GM Captiva, MSIL Ertiga and Swift Dzire are more appealing to the young generations which seek status, comfort, technological sophistication, power, versatility, and utility.
This arithmetical method is used to sequester objects into definitive groups that are mutually independent but homogeneous (Sekaran & Bougie, 2016). The technique is similar to market segmentation and strives to determine the similarities that help firms to group consumers into segments (Kumar et al., 2017). Essentially, it helps to define the attributes that make the market segment distinct.
This method can be used to determine the most robust underlying dimensions of a broad set of variables that are inter-correlated (Grigsby, 2016). In scenarios where many factors are intertwined, factor analysis identifies the most potent relationship or combination of factors that significantly influence the profitability of the business. The method helps the firm to make a strategic decision regarding labor, capital, and advertisement investment (Grigsby, 2016). Through the factor analysis method, Maruti Suzuki in India can determine the combination of variables that are more appealing to particular consumers by reducing the marketing data collected down to a few variables that are most appealing to the consumers.
Conjoint Analysis Technique.
The method unpacks consumer preferences in respect to different offers on the market (Sekaran & Bougie, 2016). This technique determines the inferred utility functions of each attribute and the relative importance of the preferred characteristics to the consumers.
The above-stated methods are recommended by the Corporate Strategy Board (CSB) which has successfully used data analysis tools to revive business in global firms such as IBM, Proctor and Gamble, and Shell (Grigsby, 2016). Remarkably, the tools helped shell to regain market dominance in East African countries, leading with 18.5% of the total sales volume, despite the stiff competition posed by other international oil and fuel companies such as Kenol Kobil and Total which came second and third at 18.3 and 17.4% respectively (Grigsby, 2016).
Considering works completed by my peers, I have noted that it is vital to include descriptive statistics such as histograms frequency counts, bar charts, data coding techniques, and other basic techniques such as the measure of the central tendency in my data analysis. Essentially, histograms can be used to determine data distribution while the bar chart can be used to compare prices of several car models. This will help to determine the most competitive yet consumer friendly price for various models of automobiles.
Grigsby, M. (2016). Marketing analytics: A practical guide to real marketing science. Edinburgh, United Kingdom: Kogan Page Stylus.
Kumar, A., Dash, M. K., Trivedi, S. K., & Panda, T. K. (2017). Handbook of research on intelligent techniques and modeling applications in marketing analytics. Hershey PA: IGI Global, Business Science Reference (an imprint of IGI Global).
Palmatier, R. W., & Sridhar, S. (2017). Marketing Strategy: Implementing Marketing Principles and Data Analytics. Marketing Strategy, 259-278. Doi: 10.1057/978-1-137-52624-3_9
Sekaran, U., & Bougie, R. (2016). Research methods for business a skill-building approach. Chichester, West Sussex, United Kingdom: John Wiley & Sons.
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