Quantitative and Qualitative Techniques in Organizational Demand Analysis

1. 0 Quantitative Techniques in Organizational Demand Analysis

Quantitative techniques are mathematical and statistical models describing a diverse array of variables' relationships, and are designed to administer with management problem-solving and decision-making like in this case, the demand analysis in organizations (Naude, Stray, & Wegner, 1991). It has roots in both the positivist view of the world, and the modern marketing viewpoint that marketing is an interactive process in which both the buyer and seller reach a satisfying agreement on the "four Ps" of marketing: Product, Price, Place (location) and Promotion.

As a social research method, it typically involves the construction of questionnaires and scales. People who respond (respondents) are asked to complete the survey. Marketers then use the information obtained to understand the needs of individuals in the marketplace, and to create strategies and marketing plans. The same goes for the demand analysis a business product or service. In building the analysis of demand of an organization, this similar method can be adopted by companies in understanding better the wants and needs of customers as well as being able to provide to them exactly what they want efficiently and effectively.

Types of Quantitative Techniques

Time Series

Time-series methods make demand forecasts based purely on historical patterns in the data. Past sales are taken into account and the buying pattern of a certain product or service by a customer is determined whether demand is high, low or intermediary. Time-series methods only use historical site visit data to make that forecast. Whether it is the customer purchasing the product or service through web or the customer himself is present at the location of the business entity to make a purchase or inquire more about a product or service that interests him. Time-series methods are probably the simplest methods to deploy and can be quite accurate, particularly over the short term. It can be used to monitor the demand forecast of a company over a period of months or years but it is most efficient if used on a month or yearly basis and not an accumulative time period of four years or so. Most quantitative forecasting methods try to explain patterns in historical data as a means of using those patterns to forecast future patterns. For instance, a bank had just introduced a new credit card to its customers a year ago, the bank then monitors the pattern of the demand in the past year using the time series method to forecast the future demand of probably the following year and more years to come depending on the pattern of the demand that the sales of the credit card has provided.

Simple time-series methods include moving average models as explained further in a formulated manner. In this case, the forecast is the average of the last "x" number of observations, where "x" is some suitable number. If the forecast is of monthly sales data, a 12-month moving average is used, where the forecast for the next month is the average over the past year.

Explanatory Methods

Explanatory methods use other data as inputs into the forecasting data. In the time series method, marketing data is included as input for a model to understand how they affect visit levels and to forecast future visits with those data. These types of techniques have been used for ages in the offline world to evaluate marketing activity's effect on brand awareness or sales.

1.1 Specific Applicability of Quantitative Techniques

In the business world, and in fact, in practically every aspect of daily living, quantitative techniques are used to assist in decision making. Quantitative research is normally to quantify the data and generalize the results from the sample to the population of interest; recommend a final course of action. This research will be done with the large number of representative cases. Their data collection should be structured as well.

A business basically will have the objective of reaching high sales level, so, the business should forecast and analyze their demand and sales statistic properly. Demand forecasting is an activity where it estimates quantity of products and services that customers would purchase. In order to satisfy this research, the business have to follow up quantitative techniques such use historical sales data, and current data from test markets. It is used in making pricing decisions, assessing future capacity requirements and others. When a business introduces a new product to market, it should be reasonable and satisfy customers' requirements. For an example, Citibank has offered credit cards to their customers for various types of transactions such as travel, reward points, cash debates, and business purposes. Citibank has satisfied their customers' consumption by providing useful and efficient services through their product. Research based on quantitative techniques has helped Citibank in rising their sales forecast year by year.

Besides that, a population has included in these element. A business should specify their target market or areas in order to gain demand and sales throughout it. Some products will attract consumers' attention when it's newly introduced at the area. Let say internet service is introduced at a rural area, of course, people will be eager to get the services when it's of good quality and usable. Through my research on Citibank, I have found out that most of the local customers are satisfied with services of Citibank which is why Citibank has its business running throughout the country. Their operations are not only limited to the Peninsular of Malaysia but also in Sabah and Sarawak. For the purpose of this research, an inquiry was made with customers of Citibank and it was found that most of the loyal customers range of up to almost 10 years and mostly are satisfied with the high level of professionalism shown by their customer service representatives.

Therefore, in conclusion, quantitative techniques can help a banking industry for the prediction of bank acquisitions whereas the banking industry has faced significant challenges due to globalization, financial innovation and global competition. Therefore, Citibank had adopted strategies to grow and expand their activities more efficiently by use of quantitative techniques as well.

1.2 Merits and Demerits of Quantitative Techniques in Organizational Demand Analysis

Quantitative research is mainly useful in allows the researcher to measure and analyze data. The