Probability Prediction influencing Consumer Buying behavior w. r.t Conventional Marketing
Dr. MANOJ KR. JHA
Department of Business Administration, ITS College, GZB
Emil id: firstname.lastname@example.org
Contact No: 8285615491
Research Scholar, Mewar University, Chittorgarh
Email id: Shailesh.email@example.com
Contact No. : 9891651226
This paper focuses on importance digital marketing along with traditional marketing in order to assure an individual making decision to buy any product or to subscribe to any service. Many times we make quick decisions while on the other hand we follow the crowd or co-operate rather than compete. In this paper, relevance of regression line is calculated and considered in order to predict the future aspect of sales. This in turn will calculate the probability of change in sale for the next year, which in one way or the other is the result of a customer buying behavior. These calculations enable to calculate the future aspects as well as help in risk analysis of any business.
Digital Marketing, Buying Behavior, Standard Deviation, Regression Line, Probability.
Today, Marketing has become a conventional tool to convince a customer to buy any product or service. With the progression of technology, the fever for purchase of products has gained importance. Be it a traditional marketing strategy like newspaper or digital marketing strategy such as social media, email etc. Since a customer prefers digital marketing for review of a product, comparison, rates etc., but he prefers to go to an outlet to look, feel and test the product, following the online payment mode.
This puts an impression that traditional and digital marketing strategies go hand in hand to affect the buying behavior of a customer. In order to judge the buying behavior of an individual, it is important to find out the probability of the sale of that particular product for the upcoming period of time.
Youth have always been a major target for marketers. This group of individuals has influenced the sales data to a greater extent as compared to the people of age group more than 35. (Priyanka Mehra (2009) ). It has also been studied that how customer buying behavior differs with the difference in their living area. The state that a customer living in urban area have technology as a positive factor to do research for buying decision rather than a customer living in a rural zone(Khan and Mahapatra (2009)). Explanaition are also given from the point of view of a customer that there is no huge difference in the car brands taken into account any particular segment and performance simultaneously. Also different expectations and expectations from customers like socioeconomic, psychological, political, geographical, demographic and Product & Technology has also been concluded. Factors like the interception of local and global brands of automobiles in the market, their marketing strategies and difference in their data sales are also taken into consideration Vikram Shende (2014).
Objectives of The Study
· To study the customer buying behavior
· To analyze the influence of customer behavior in car sale
· To calculate the probability of next purchase
· To predict the increase in Sales Revenue
· Research Design
The research is carried out to analyze and model exponential distribution. Secondary Data from a reliable article is taken into consideration.
· Area of Study
Data from different automobile Companies are taken into account from all over India.
· Sample Size
A sample size of 7 is taken into consideration for the sake of convenience and sales data availability.
· Research Technique
Identical exponential distributions are mixed by the probability distribution of the correct points. This can be taken into account while calculating and analyzing different statistical values.
Analysis and Interpretation
The given table shows the sales data of different Automobile Companies of two consecutive years 2017 and 2018 for the month of february.
Feb 2017 (X)
Feb 2018 (Y)
Hyundai Motor India
Honda Cars India
Figure 1: car sales (in units)for two consecutive years
The above Graph shows change in buying behavior from 2017-2018 due to conventional and digital Marketing Strategies.
Now, we will calculate the Probability of Car Sale for the next year Using the two Statistical data.
Let us assume the data for Feb 2018 as X and that of Feb 2017 as Y.
Standard Deviation= σ
N= No. of events
xi= individual events
σ(Y) = 43478.01 (Standard Deviation for Y values i.e. Feb 2018)
σ(X) = 38683.10 (Standard Deviation for X values i.e. Feb 2017)
Variance (var) = Σ (xi – μ)2
But, if e consider both X and Y values, i.e., both 2017 and 2018 values simultaneously, an equation could be obtained from the regression line.
The equation of the regression line is:
The correlation coefficient r =∑((X – μ(Y))(Y – μ(X)) / √((SSx)(SSy))
Where SSx = ∑(X – μ(X)2 = 12621668878.857
SSy= ∑(X – μ(Y)2 = 9761186607.714
r = 11080615620.571 / √((12621668878.857)(9761186607.714)) = 0.9983
Therefore, Probability calculated would be: 0.00000023
Thus it indicates that there would be a change in buying behavior of a customer for the upcoming sales and that would be of 0.00000023.
Moreover, Regression lines are widely used in the financial sector and in business in general.It is used by business analysts to forecast future behaviors of the dependent variable (in this case Values of Feb 2018) by inputting different values for the independent ones (Values of Feb 2017).
Therefore , we can conclude that buying behavior is a continuous process which keeps on changing with respect to the change of factors like marketing strategies also. But, Looking ahead, post-purchase activities are expected to be the next digital frontier. It also concludes that this value will increase tremendously years ahead due to the improvement in technology as marketers tend to spend more in digital marketing.
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Feb-17 Maruti Suzuki Hyundai Motor India M & M Tata Motors TKM Honda Cars India Ford India 120599.0 42327.0 20717.0 12272.0 11543.0 14249.0 8338.0 Feb-18 Maruti Suzuki Hyundai Motor India M & M Tata Motors TKM Honda Cars India Ford India 136648.0 44505.0 22389.0 17771.0 11864.0 11650.0 9041.0