Effect of Sales Forecast on Marketing Decision Making
CHAPTER ONE
OBJECTIVE OF THE STUDY
The main objective of this study is effect of sales forecast on marketing decision making. But for the successful completion of the study; the researcher intends to achieve the following sub-objectives;
- To investigate the effect of sales forecaston marketing decision making.
- To determine if the forecasting method adopted is used consisting by the company.
- To ascertain if sales forecast is relevant to marketing decision making.
CHAPTER TWO
REVIEW OF RELATED LITERATURE
FORECASTING MARKET SIZE
Market size is influenced by environmental factors such as economic conditions, population,abilityto purchase, social trends, technological change, or government legislation. For example, demographic factors such as the size and age distribution of the population, distribution of disposable income, culture, and religious factorsinfluence the market for alcoholic beverages. Econometric methods have been used for environmental forecasting. Econometric researchers have devotedmuch effort to short-term forecasting, an area that has yielded unimpressive results. Econometric methods would beexpected to be more useful for long-range forecasting because the changes in the causal variables are not swamped
by random variations), as in the short run. Armstrong (1985, Chapter 15) reported seven empirical comparisons ofmethods used in long-range forecasting. In all comparisons, econometric methods were more accurate thanextrapolations. Fildes (1985) located studies on long-range forecasting; he found where econometric methodswere more accurate than other methods, ties, and showing econometric forecasts to be less accurate.Improved environmental forecasts should lead to more accurate market forecasts. Surprisingly, research inthis area indicates that forecasting errors are not particularly sensitive to the accuracy of environmental forecasts.Measurement error in the causal variables (e.g. the environmental inputs to a market forecasting model) had littleimpact on the accuracy of an econometric model in the few studies done on this topic (Armstrong, 1985). Moreover,conditional econometric forecasts (those made with actual data on the causal variables) have generally been found tobe no more accurate than unconditional forecasts (where the causal variables themselves must be forecasted). Of 18studies found, only 3 have shown conditional forecasts to be more accurate, 5 showed no difference, and 10 showedthem to be less accurate (Armstrong, 1985; Rosenstone, 1983; and four studies from Fildes, 1985). A possibleexplanation for these strange findings is that the unconditional forecasts may have included subjective revisions thatmight have reduced the error in estimating starting values (current levels).
CHAPTER THREE
RESEARCH METHODOLOGY
Research design
The researcher used descriptive research survey design in building up this project work the choice of this research design was considered appropriate because of its advantages of identifying attributes of a large population from a group of individuals. The design was suitable for the study as the study sought to effect of sales forecast on marketing decision making.
Sources of data collection
Data were collected from two main sources namely:
Primary source and Secondary source
Primary source:
These are materials of statistical investigation which were collected by the research for a particular purpose. They can be obtained through a survey, observation questionnaire or as experiment; the researcher has adopted the questionnaire method for this study.
Secondary source:
These are data from textbook Journal handset etc. they arise as byproducts of the same other purposes. Example administration, various other unpublished works and write ups were also used.
CHAPTER FOUR
PRESENTATION ANALYSIS INTERPRETATION OF DATA
Introduction
Efforts will be made at this stage to present, analyze and interpret the data collected during the field survey. This presentation will be based on the responses from the completed questionnaires. The result of this exercise will be summarized in tabular forms for easy references and analysis. It will also show answers to questions relating to the research questions for this research study. The researcher employed simple percentage in the analysis.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
Introduction
It is important to ascertain that the objective of this study was to ascertain effect of sales forecast on marketing decision making
In the preceding chapter, the relevant data collected for this study were presented, critically analyzed and appropriate interpretation given. In this chapter, certain recommendations made which in the opinion of the researcher will be of benefits in addressing the challenges of sales forecast on marketing decision making
Summary
This study was on the effect of sales forecast on marketing decision making. Four objectives were raised which included;to investigate the effect of sales on marketing decision making, to determine if the forecasting method adopted is used consisting by the company, to ascertain if sales forecast is relevant to marketing decision making, to ascertain the relationship between sales forecast and marketing decision making. In line with these objectives, two research hypotheses were formulated and two null hypotheses were posited. The total population for the study is 200 staff of Dangote group of company was selected randomly. The researcher used questionnaires as the instrument for the data collection. Descriptive Survey research design was adopted for this study. A total of 133 respondents made up managers, human resource managers, senior staff and junior staff was used for the study. The data collected were presented in tables and analyzed using simple percentages and frequencies.
Conclusion
Significant gains have been made in forecasting for marketing. Advances have occurred in the development of methods based on judgment, such as role playing, opinions surveys, and bootstrapping.As a result, the significance of all business forecasting depends upon forecast theory.
Recommendation
- When making forecasts in highly uncertain situations use more than one method and combine the forecasts using equal weights.
- When possible, forecasting methods should use data on actual behavior, rather than judgments or intentions, to predict behavior.
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