Abstract
COVID-19 has affected the growth of every industry; in the meantime, an enormous amount of demand is present in the field of telecom and automobiles. In this chapter, the authors present case studies based on sales prediction for the Indian market. The analysis of the study is based on the various traditional methods like growth rate (GR), percentage growth rate (PGR), and the evolutionary techniques like genetic algorithms (GA). The data are collected for the report of telecommunication and heavy industry ministry (Republic of India). The results are used to analyze the sale of automobiles and telecommunication devices and to predict the growth at the time of the COVID-19 pandemic. The prediction is used to identify the upcoming sale and counterparts with demand.
TopIntroduction
Around the world, India’s population is the 2nd place as compared with other countries. The economic growth rate of India is also high which create huge demand in the field of automobile, telecom, and many other things (Priya et al., 2016). The prediction of growth is determined by various traditional and evolutionary techniques. During the time of COVID-19 the growth rate of every country is extremely reduced (Natraj 2012). Every industry wants to predict the upcoming growth in their respective field. Now, forecasting the growth of marketing we have to use the computer technology. There are various technologies are present in the field of prediction such as machine learning (ML), evolutionary computing, growth rate, (etc.). The evolutionary techniques are uses the feature of natural phenomenon’s such as chromosome mutation, crossover, selection, and updation. These steps are used to select the best solution from set of solutions however Machine learning (ML) techniques have different approach for choosing the best solution. In ML, mathematical regression and decision making process is performed to select the optimal solution based on the probability. In this chapter, we study traditional and evolutionary technique to predict the upcoming marketing sales for automobiles and telecom. The forecasting of sales based on previous data is a much essential part of the company (Sinha et al., 2020). The prediction is not limited in very few methods. So, here we discuss various mathematical models, machine learning, and evolutionary techniques for the prediction of upcoming growth in the sales.
Mathematical Finance Technique
In the year 1926, Ragnar Frisch the first economist developed the mathematical model in the context of economic demands and utility functions (Sandmo, 2018). Mathematical Finance or Quantitative Finance is a field of applied mathematics that is mostly concerned with the financial markets. Generally, Mathematical Finance (MF) will reduce and stretch the mathematical/numerical models and establish a link with the financial theory which will take a market price as an input for simulation. Mathematical finance is a heavily intersected field in computational finance and financial engineering. The main focus is to develop the stochastic asset model for application and simulation. This model will be used for quantitative analysis and it will work as a tool for implementing the model (Widicus, 1966). We use the predicting model to allocate the sales for next year (Bruno, 1998). The fundamental theorems are Mean and Standard Deviation, Growth Rate, and Percent Growth is one of the vital prediction techniques for the upcoming sales.
- 1.
Percent Growth Rate: The annual sales are increasing at every period. It is valuable to calculate the percentage of sales increasing every year. So that it will give a rough idea about what increment is done in the sale every year. Percentage Growth Rate is the method that is used for calculating how much amount is increased annually, half-yearly, or quarterly (Sinha et al., 2018).
- 2.
Growth Rate: The sales are recorded in discrete periods of times in quarterly or yearly. It is often useful to model the dynamic economy in a discrete period. Growth Rate is calculated as an output for every year divided by the number of population in that year of any industry sales (Sinha et al., 2018).
- 3.
Mean, Variance, and Standard Deviation: The mean value is used to replace the existing item in sample space, and have the best result of all. The calculation of variance and the standard deviation is used to predict how much sale amount is deviated from the current sale and produce the tentative sale for the future (Sinha et al., 2018).
Key Terms in this Chapter
Budget: It is a fiscal plan which is used to estimate the revenue and expenditure for a certain period.
Optimization: It is the process of making the best or most effective use of a situation or resource.
Marketing Strategy: This means the overall game plan for reaching the desired customers and turn them into customers of products the business provides.
Genetic Algorithm: It is a meta-heuristic technique inspired by the natural selection process.
Prediction: it is the process of forecasting future events based on experience or knowledge.
Sales: It is related to selling or sold several goods in the targeted period.
Buying Behavior: It is the decision process and act of people involved in buying and using products.
Forecasting: It is the process of making predictions of the future based on past and present data.