Probability prediction influencing consumer buying behavior w. R.T conventional marketing

Probability Prediction influencing Consumer Buying behavior w. r.t Conventional Marketing

Dr. MANOJ KR. JHA

Department of Business Administration, ITS College, GZB

Emil id: prof.manoj02983@gmail.com

Contact No: 8285615491

SHAILESH DHYANI

Research Scholar, Mewar University, Chittorgarh

Email id: Shailesh.dhyani@hotmail.com

Contact No. : 9891651226

Abstract

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.

Key words

Digital Marketing, Buying Behavior, Standard Deviation, Regression Line, Probability.

Introduction

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.

Literature Review

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 Methodology

· 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.

Automobile Companies

Feb 2017 (X)

Feb 2018 (Y)

Maruti Suzuki

120″,599

136″,648

Hyundai Motor India

42327

44505

M&M

20717

22389

Tata Motors

12272

17771

TKM

11543

11864

Honda Cars India

14249

11650

Ford India

8338

9041

Source :economictimes.indiatimes.com/news/industry/complete-auto-sales-analysis-feb-2018

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.

Now”,

Standard Deviation= σ

[image: ]

where”,

μ=Mean

N= No. of events

xi= individual events

μ(Y)= (Y1+Y2+…Y7)/7

=(136″,648+44505+22389+17771+11864+11650+9041)/7

=(2″,53″,868)/7

=36266.85714

σ(Y) = 43478.01 (Standard Deviation for Y values i.e. Feb 2018)

μ(X)= (X1+X2+…X7)/7

=(120″,599+42327+20717+12272+11543+14249+8338)/7

=(2″,30″,045)/7

=32863.57143

σ(X) = 38683.10 (Standard Deviation for X values i.e. Feb 2017)

Variance (var) = Σ (xi – μ)2

n-1

Var(X)= (10474673522.61)/6

= 1745778920

Var(Y)= (13232361305.31)/6

= 2205393551

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:

y=1.1351709649536x-1038.9149475367

[image: ]

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

Conclusion

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.

References

1. Sivasankaran S., ” Digital Marketing and Its Impact on Buying Behaviour of Youth” International Journal of Research in Management & Businesss Studies , Vol. 4, Issue 3 (SPL 1) , jul-sept. 2017.

2. K. Gabriel Jenyo, M. Kolapo Soyoye, “Online Marketing And Consumer Purchase Behaviour: A Study Of Nigerian Firms”, European Centre for research Training and Development UK, Vol.3, No.7, pp.1-14, September 2015.

3. Lodhi Samreen, Shoaib Maria, “Impact of E-Marketing on Consumer Behaviour: a Case of Karachi, Pakistan” IOSR Journal of Business and Management , PP 90-101 Volume 19, Issue 1. Ver. V (Jan. 2017)”,

4. Mahalaxmi K. R., Ranjith P., “A Study on Impact of Digital Marketing in Customer Purchase Decision in Trichy” , International Journal for Innovative Research in Science & Technology, Volume 2, Issue 10 , March 2016 .

5. Pawar Sudarshan, Naranje Sunil, “A Study on Factors Influencing on Buying Behaviour of Customers”, Research journal -Institute of Science, Poona College of Computer Sciences ISSN2394-1774 Issue II, 2015

6. Kumar Hemanth A. H, John Franklin S. , Senith S. , “A Study on factors influencing consumer buying behavior in cosmetic Products”, International Journal of Scientific and Research Publications, Volume 4, Issue 9, September 2014 .

7. Monga Nikhil, Chaudhary Bhuvnender and Tripathi Saurabh, “Car Market And Buying Behavior- A Study Of Consumer Perception”, IJRMEC Volume2, Issue 2(February 2012.

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