Michael D. Geurts

Retailing, as a career: A comparative study of marketers

Abstract This note reports how positions in retailing—particularly store-based positions—compare with other marketing-related positions. While much of the past literature concerning work experiences in retail settings is primarily perceptual and anecdotal, the results of this nationwide study are based on an examination of actual workplace experiences in marketing-related positions. The findings suggest that workplace experiences in corporate retailing are positive and equivalent to other marketing-related careers. However, workplace experiences in retail store management are less satisfying. The retail store managers studied were paid less, experienced less variety and autonomy on the job, felt less satisfied and committed to their work, and had greater turnover intentions compared to the other marketing managers studied. Managerial implications and recommendations are presented.

Forecasting retail sales using alternative models

Abstract This paper explores the issues associated with adapting forecasting techniques used by manufacturers to produce accurate forecasts for retail sales. A case study is presented that is developed using a retail situation because retailers often view their sales forecasting problems as being very different from a manufacturers problems. Sales volumes are dramatically impacted by competitor promotional actions, discounts, store promotions and weather. Finally, consumption holidays like Christmas, Easter, Mothers day, have a large impact on sales as well as back to school shopping. The findings in this paper indicate that forecasting retail sales can be accomplished with a high degree of accuracy.

Using a randomized response research design to eliminate non-response and response biases in business research

A serious distortion of business survey research is that frequently the survey includes questions that the respondent views as sensitive or intimate. The respondent may refuse to answer or may give an inaccurate answer. This refusal to answer or tendency to falsify the answer can seriously distort the research findings and invalidate the interferences made from the survey. This paper discusses a sampling procedure that has recently been developed and used to reduce dishonest responses or nonreponses to sensitive questions. The procedure is called Randomized Response. As an example, it is used to ask sensitive questions of individuals who are marketing majors at the University of Hawaii concerning their behavior and attitudes toward racial discrimination, shoplifting, test cheating, and commodity hoarding.

A forecasting model to evaluate the profitability of price promotions

Abstract Experience over the years has led grocery store managers to spend almost all of their promotion money on adds in newspapers. The adds are generally advertised price reductions for specific products. These adds are usually cooperative adds in which the manufacturer whose product is featured in the display ad pay part of the cost of the ad. The rest of the cost is paid by the grocer. The grocer has a problem in evaluating the profitability of the price promotion. Different classes of consumers respond differently to the price promotions. To measure the impact of the price promotion becomes very difficult. In this paper the authors examine the different ways consumers respond to price promotions, and how the different responses affect profits. Using this data the authors develop a model that can forecast the profitability of specific price promotions. This model is then tested for three products at a very large grocery store chain in Finland.

Converging conflicting forecasting parameters in forecasting durable new product sales

Abstract Forecasting sales for a durable product prior to the products introduction is a necessary but difficult task. Most new product forecasting models require the estimation of parameters for use in the forecasting model. For a specific parameter the forecaster is often faced with estimated parameter values that are not in agreement. Dealing with this problem is the subject of this paper. Specifically, a methodology of dealing with conflicting parameter values is applied to a diffusion forecasting model. Diffusion models are widely used to forecast new product sales for durable products.

“In defense of ARIMA modeling”, by D.J. Pack

Abstract The comparative accuracy of ARIMA modeling remains a controversial issue. In the International Journal of Forecasting, Pack (1990, pp. 211–218) argued that some of the criticisms are misplaced, and in particular criticized a study by Geurts and Kelly that compared the accuracy of exponential smoothing with that achieved by ARIMA modeling when applied to departmental store sales. Here Geurts and Kelly respond to Packs criticism. Pack adds a final rejoinder. ∗

Causal partitioning and sales forecasting accuracy

This paper examines the benefits of partitioning a data set into components and then forecasting the components and adding (combining) the component forecasts as a method of forecasting a time series. The methodology is a modification of the combining forecasts methodologies that have proven in past studies to be a superior forecasting method. Three different partitioning methodologies are explored in this paper. For all three, an example is given where partitioning and then recombining produces more accurate results than the best nonpartitioned forecast. The paper also explores some reasons why combining methodologies produce more accurate results than a single best model forecast.

When and how much do average forecasts improve predictive accuracy

A composite forecast combines two or more individual forecasts into a single estimate by way of a number of different averaging schemes. The easiest way to combine forecasts is through using a simple average. In this paper, the authors show that in many instances the simple average of individual forecasts approximates the optimal combining scheme. Results are expressed in terms of the probability that a composite forecast will improve upon an individual forecast.

The Impact of Misrepresentative Data Patterns on Sales Forecasting Accuracy

The development of a sales forecasting system involves three major steps. The first step is to obtain prior sales data and to identify the model that will best forecast the patterns that exist in the data. The second step is to estimate parameter values for the selected model by analyzing the prior sales data. The third step is to test the accuracy of the model by use of the prior sales data. Each of the steps requires use of prior data.In all three steps, there is a basic assumption that the past data represent some underlying process that can be identified and modeled. In some cases the past data may not represent the underlying process, and the forecasting process is seriously distorted. Some frequent causes of distorted data are 1) accounting methods that are used to record or collect the data, 2) marketing tactics such as promotions which that create outliers, 3) limits on production capacity that cause stockouts.This paper looks at events and actions that may distort data used for sales forecasting and at the resulting impact the events and actions may have on forecasting accuracy.

Forecasting the consequences of negative atypical events: the case of tourism and terrorist attacks

In this chapter, we describe how time series analysis can often provide better insight than prior year data for predicting the total impact of an atypical event – including (1) taking into account other atypical events, (2) determining if the impact lasted greater than one season, and (3) adjusting for any performance/metric “rebounding” in subsequent seasons. We demonstrate using time series analysis to estimate the impact of the 9–11 terror attacks on the Hawaiian tourism industry. Terror attacks, in addition to the potential loss of life and property, can induce a post event fear factor that results in decreased revenue and profitability for businesses and their respective industries, insurers, and tax-receiving governments.