BUS 620 Week 2 Assignment

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 1/20

Chapter 4

Evaluating Market Demand and Forecasting

Belinda Images/SuperStock

Learning Outcomes

By the end of this chapter, you should:

Recognize the differences between market poten�al, available market, and sales poten�al. Explain the significance of the product usage gap and brand usage gap. Know the uses of short-range, medium-range, and long-range sales forecasts, as well as the limita�ons of related forecas�ng methodologies. Know the differences between qualita�ve and quan�ta�ve forecas�ng techniques and be able to describe the strengths and weaknesses of each.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 2/20

Ch. 4 Introduction This chapter examines the role of forecas�ng in evalua�ng market demand. The first half inves�gates the es�ma�on of market demand and market poten�al, referred to collec�vely as demand measurement. This is concerned with the determina�on of current levels of market demand and market poten�al. The focus is exclusively on the analysis of exis�ng markets in total without regard to market segmenta�on or brand-specific measures of demand. The second half of the chapter inves�gates sales forecas�ng. The methodologies discussed in this sec�on focus on es�ma�ng future sales at the product and brand levels of analysis. These forecasts are focused on predic�ng what will happen to markets and brand sales in future periods.

Accurate forecasts of product sales and market poten�al are essen�al to survival when confron�ng uncertain environments. In the same way that other forms of market research provide the means for organiza�ons to reduce risk in the face of dynamic markets, forecas�ng is a strategy for mi�ga�ng market-related hazards by providing �mely informa�on on the changes taking place. Although forecas�ng is primarily understood as a strategy for threat reduc�on, it can also provide insights on the poten�al of new market opportuni�es.

***

When we hear the word forecas�ng, many of us first think of weather forecasts on our local television sta�ons. Our second thought is usually about those all-too- frequent occasions when the meteorologists got it completely wrong and our family picnic was rained out.

Well, sales forecas�ng is also pre�y far from being an exact science. Maybe you have heard the story about the two market forecasters who went deer hun�ng together. When a deer appeared 120 feet from their stand, the first forecaster carefully squeezed off a shot that missed by 4 feet to the le�. His hun�ng partner hurriedly fired a�er him, but missed by 4 feet to the right . . . at which point the first forecaster began jumping up and down, shou�ng: "We hit it! We hit it!"

Yep . . . it's just like Yogi Berra said: It's tough to make predic�ons, especially about the future.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 3/20

Apple markets products well and accurately forecasts market poten�al and sales. This has consistently resulted in lines of eager customers at store openings and product debuts.

Demo�x/Corbis

4.1 Demand Measurement More accurate informa�on will inevitably help marke�ng managers improve the design and performance of marke�ng plans. Historical data on pa�erns in the market (e.g., market shares, aggregate growth trends) and brand-specific informa�on (e.g., sales, customer reten�on rates) can provide significant insight into previous marke�ng decisions. To effec�vely plan for the future, however, managers need access to general market and brand-specific informa�on for both the present and future. Es�mates of future demand can be created based on short-range, medium-range, or long-range forecas�ng horizons, as will be discussed later in the chapter.

The founda�on required to create reliable forecasts, without regard to the �me period in ques�on, is a thorough and accurate understanding of the prevailing current or near-term market condi�ons. This requires a rigorous study of the individual components that make up the cumula�ve or aggregate level of total market demand available to the firm.

Assessing Market Demand

Whether evalua�ng the profit poten�al associated with a new market opportunity or considering revisions to an exis�ng marke�ng program, marke�ng managers need to understand the basic parameters that define the markets in which they compete. Market demand is the total volume or amount of a specific product purchased by consumers over the complete range of prices at which it is sold. Market demand is usually expressed in terms of both the specific market and the �me period. Consider, for example, the market for tablet personal computers in 2011. The level of market demand can be expressed in either total unit sales volume (e.g., 22 million) or dollar sales volume ($15.4 billion). The dollar volume metric is usually expressed in retail sales.

Market demand provides a snapshot in �me of the market's overall size. More detailed informa�on about current pa�erns can be disaggregated from the whole by examining how segments within the market behave in response to different price levels. Market research can provide insights on how other segment descriptors relate to sales (e.g., age, income, family size). Markets can also be analyzed geographically if those characteris�cs are relevant. For all marke�ng managers, it is essen�al to understand elements of current market demand, although some factors will be more important than others.

Market Demand Measures

The ini�al assessment of market demand provides the pla�orm for subsequent financial analysis and projec�ons. Whether based on the extrapola�on of trends over �me or rooted only in current period data, all forecasts rely on measures of current market condi�ons as a jumping off point. In situa�ons where market demand is not par�cularly vola�le, however, current period data are some�mes used in lieu of any forecasts.

The dimensions highlighted in an ini�al evalua�on of market demand depend on the intended use of the informa�on. Entrepreneurs seeking investment funding from venture capitalists will need to demonstrate the financial poten�al of a market. Produc�on and opera�ons managers need informa�on about poten�al unit sales so that they can align manufacturing capacity with demand. Human resource managers for retailers will need to derive the demand for part-�me hiring in the current period as a func�on of prevailing sales levels.

The most common and rou�ne use of market demand metrics is as a scorecard to evaluate the effec�veness of the firm's marke�ng efforts. Determining which elements of the marke�ng mix contribute to the current period's sales, however, is not always a straigh�orward task. Some elements of the marke�ng mix may have an immediate impact on sales (e.g., sales promo�ons), others have a lagged effect (e.g., image-oriented adver�sing), and all of them have the poten�al to interact with each other. For example, inten�onal interac�ons o�en occur between changes in pricing strategy and adver�sing over �me as managers gradually shi� the posi�oning of a brand over several repor�ng periods. Consequently, current levels of market demand may reflect the results of one-�me marke�ng events, the cumula�ve impact of marke�ng efforts over �me, or the interac�on of one organiza�on's marke�ng ini�a�ves with those of compe�tors. Invariably, all three types of effects are

usually present in the sales results from the current period.

Accurate market demand measures set the stage for financial projec�ons including the assessment of market poten�al and sales forecas�ng. The realis�c evalua�on of market poten�al includes both the appraisal of the available market for a product category and the sales poten�al that is specific to a given brand.

Measuring Market Potential

Market poten�al is a measure of the maximum total possible sales of a given product over a fixed interval of �me. It is the mathema�cal product of the total number of poten�al buyers in a market mul�plied by the average quan�ty purchased per buyer. This yields an assessment of market poten�al in product units. Total dollar sales poten�al can be determined by mul�plying this measure by the average price of one unit. Total market poten�al may be stated in dollars or expressed in unit sales.

The available market for any product is made up of those buyers who possess both the interest and means to purchase the product under current market condi�ons. The target market for each brand is, of course, a subset of this group. Sales poten�al is a term that is o�en confused with market poten�al. However, sales poten�al is a brand-specific metric that provides an es�mate of the por�on of total market poten�al that a specific brand can reasonably expect to reach. In

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 4/20

Interna�onal markets provide poten�al growth opportuni�es for companies like Burger King. What must marketers consider when evalua�ng an interna�onal marketplace?

Associated Press

short, it is an es�mate of the size of the brand's target market. When expressed in percentage terms, sales poten�al indicates the size of the brand's target market rela�ve to the available market.

The basic conceptual formula for calcula�ng market poten�al remains the same in every circumstance. However, the applica�on of the model must be uniquely tailored to suit the situa�on. Some�mes secondary data in the form of census informa�on, other government sta�s�cs, or readily available data from other published sources (e.g., trade associa�ons) will be sufficient to meet the needs of the analyst. In other instances, market research studies are needed to develop primary data specifically collected for this purpose. Internal company data is a third source of sta�s�cal informa�on that is some�mes overlooked if marke�ng managers fail to consider how some types of historical data may relate to the evalua�on of current opportuni�es. For example, the firm's market- specific sales databases may be applied to the study of product development opportuni�es. Similarly, the assessment of the market poten�al for a market development opportunity may be related to historical pa�erns in product-specific sales data.

Geographic market boundaries are largely irrelevant to the market poten�al for many products that are sold online. However, geographic concep�ons of market poten�al s�ll hold significance for many brick-and-mortar retailers, agribusiness firms, and service companies. The delinea�on of an organiza�on's geographic trade area will be defined by the convergence of several factors such as the size and number of nearby compe�tors, the uniqueness of the product or service being offered, and the mobility of prospec�ve buyers within the region.

The challenges related to geographic considera�ons increase substan�ally when companies begin to evaluate the market poten�al of foreign countries. Although the complexity of the related tasks is amplified, the fundamental conceptualiza�on of market poten�al does not change.

Measures of Potential in International Markets

Given the simplicity of the concept, the challenges encountered in developing metrics and models to es�mate market poten�al can be surprisingly complex. For example, one of the first steps that marke�ng managers must take when evalua�ng new interna�onal markets for their products is determining the financial poten�al represented by compe�ng opportuni�es. Typically, analysts are charged with assessing the market poten�al across na�ons with widely disparate economies and cultures. Rather than simply comparing apples to oranges, it becomes a ma�er of comparing rambutan to jabo�caba. The challenge is par�cularly daun�ng when trying to iden�fy measures that accurately reflect the true market poten�al of developing or emerging na�ons.

Figure 4.1: Interna�onal Market Poten�al Index for 2010

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 5/20

Based on the informa�on in the indexing study above, which countries seem to have the most and the least market poten�al and why?

This figure shows the dimensions that help influence market poten�al. No�ce that some dimensions hold more weight than others. Why do you think this is the case?

h�p://globaledge.msu.edu/resourcedesk/mpi/ (h�p://globaledge.msu.edu/resourcedesk/mpi/)

To address this problem, the Michigan State University Center for Interna�onal Business and Economic Research (MSU-CIBER) annually publishes its Market Poten�al Index for emerging markets. As illustrated in Figure 4.1, this index value is calculated for the fastest-growing emerging global markets using the eight dimensions of market poten�al iden�fied in Figure 4.2.

Figure 4.2: Dimensions and measures of market poten�al for 2010

h�p://globaledge.msu.edu/resourcedesk/mpi/ (h�p://globaledge.msu.edu/resourcedesk/mpi/)

The use of mul�ple factors reflects different facets of market poten�al. Each factor is determined by mul�ple measures, and each is weighted rela�ve to its importance to the overall poten�al of any given market. Some measures such as market size and economic growth rate make generally posi�ve contribu�ons to overall poten�al. Factors such as limited economic freedom and country risk (e.g., high poli�cal instability) reduce the Market Poten�al Index value.

Though this is far from being a perfect representa�on of market poten�al across all countries and products, it does provide analysts with a preliminary assessment. This is clearly a task worth pursuing, insofar as these countries with underdeveloped economies account for more than half of the world's popula�on, and represent a huge sales poten�al for many types of products.

Think About It

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 6/20

Assessing market poten�al within an interna�onal context can be very difficult. However, the challenges confron�ng small-business owners and entrepreneurs are no less demanding. Assume that you are the owner of a small retail pet store in Columbia, South Carolina.

What types of market poten�al measures would interest you?

What inexpensive methods might you use to es�mate the market poten�al?

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 7/20

Arizona Iced Tea products, once only popular in a small consumer niche, have increased in popularity and now appeal to soda and tea drinkers alike.

Associated Press

4.2 Comparing Performance to Potential: Gap Analysis As noted earlier, the most common use of market demand metrics is as a scorecard to evaluate the effec�veness of the firm's marke�ng efforts. Measures of market poten�al provide important baselines or benchmarks against which an organiza�on can assess its performance. One of the most useful techniques for comparing sales poten�al to actual results is gap analysis.

Gap analysis is a term used to describe a range of contrasts of interest to marke�ng managers. Each of these comparisons examines actual performance rela�ve to poten�al performance. Different types of gaps reveal different areas of poten�al improvement or growth for the organiza�on. Marke�ng managers are par�cularly concerned with two market-related gaps that reflect a difference between current sales and sales poten�al: product usage gap and brand usage gap.

Product Usage Gap

The product usage gap is defined as the difference between total market poten�al and actual or current product usage by all customers within a given market. This contrast is based on two measures of industry-wide volume and is not directly related to the sales of any given brand. Significant gaps between market poten�al and actual usage indicates that there is an opportunity to s�mulate primary demand for sales.

Product Usage Gap = Total Market Poten�al – Current Product Usage

Consider the market for prepared iced tea. Not very long ago it was regarded as a niche market in the so� drink industry, limited to consumers who already enjoyed brewed iced tea but who might enjoy the convenience of bo�les and cans. Marketers' percep�ons in this regard limited es�mates of market poten�al. Current usage was almost exclusively a func�on of product development— introducing a new varia�on of the product to current consumers. The introduc�on of brands like Snapple and Arizona Iced Tea increased the size of the total poten�al market through market development. That is, new users and higher rates of consump�on were added to the market as new varie�es were promoted to a wider audience.

In most instances, closing the usage gap is a higher priority for market leaders than companies that hold small market shares. The firms that have already achieved industry leadership are the most likely to benefit from expanding the size of the market as a whole. Market leaders some�mes opt to acquire the innova�ve emerging brands that pioneer new market segments as a cost-effec�ve path to defending overall market share and rounding out the company's por�olio of brands within a category. This was the case when the Dr. Pepper beverages group acquired Snapple in 2008.

A concept that is closely related to the product usage gap is the market penetra�on index. This measure is simply the ra�o of current market demand to poten�al demand. Low market poten�al index values indicate substan�al growth poten�al for all the firms. High market penetra�on index values suggest the costs of bringing those rela�vely few new prospects into the market will be

rela�vely high.

Brand Usage Gap

Most companies do not sell brands into every segment of a product market. This may be the inten�onal consequence of strategic planning or the result of limited resources. In either circumstance, product-level analysis of the usage gap between total market poten�al and current usage by all customers within a given market will yield a distorted picture of the firm's performance. If segments of the market where a company does not ac�vely compete are included in the baseline measure of total market poten�al, the actual effec�veness of the marke�ng efforts on behalf of the exis�ng brands will be underes�mated. That is, an assessment of the compe��ve impact of brand-specific marke�ng programs will be diluted by contras�ng current brand sales to a level of poten�al market sales that includes segments where the brand is not intended to compete. The calcula�on of a brand-specific measure called the brand usage gap is intended to compensate for that poten�al error.

Brand Usage Gap = Target Market Poten�al – Current Brand Usage

Brand usage gap is defined as the difference between poten�al sales to the target market and the current level of brand usage by customers within the targeted segment of the market. The gap provides an indica�on of how much poten�al growth is possible from taking market share away from compe�tors. In this sense, it relates most closely to market penetra�on strategies.

In a compe��ve market, it is highly unlikely that one brand can drive the brand usage gap to zero by elimina�ng the compe��on. Further, it is important to note that the presence of a gap does not necessarily indicate a problem or marke�ng failure of any kind. The value in calcula�ng brand usage gap values over �me is to

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 8/20

help managers remain aware of the outstanding or residual financial value remaining in the market. The implica�ons differ from simply tracking market share stability over �me, especially in growing industries.

Consider the case of a leading brand in the young adult mul�vitamin and mineral supplements market. If the company's brand held a steady market share of 27 percent for five years, it might suggest that the market is stable and offers few opportuni�es for growth. However, if the number of young adults has increased by 45 percent over that interval, the brand usage gap as measured in dollars will have grown substan�ally. Consequently, brand managers may wish to pursue a more aggressive market penetra�on strategy since the total dollar volume and corresponding profits have grown so quickly. The cost of acquiring each addi�onal percentage point of market share may have remained virtually unchanged, but the value of each incremental point has increased considerably.

An alterna�ve measure that is closely related to the brand usage gap is the firm's share penetra�on index. This measure is simply the ra�o of the brand's current sales to its poten�al sales. If the index values for this measure are low, the company may have opportuni�es to greatly expand its market share. Marke�ng mix strategies to build brand awareness and preference could yield substan�al gains at rela�vely low cost. Much like the market penetra�on index, however, high share penetra�on index values suggest the incremental cost of acquiring addi�onal share points will be rela�vely high.

Each of the gaps iden�fied in this sec�on suggests opportuni�es for growth that stem from a disparity between what consumers demand and the characteris�cs of the products offered by compe�ng companies. It is important to remember, however, that not every gap can be bridged collec�vely by the sum of the alterna�ve brands. Some sets of demands are simply beyond the market's scope or ability to meet. Similarly, each brand cannot realis�cally expect to meet the demands of every consumer in its defined target market.

One of the many valuable insights that can be gleaned from gap analysis is a be�er understanding of how the sales volume of a brand compares to its poten�al performance. These types of contrasts provide marke�ng managers with the opportunity to evaluate areas of possible improvement. Sales forecasts, however, serve a dis�nctly different purpose. These predic�ons about future sales are primarily intended to help marke�ng managers improve the match of company resources to an�cipated brand-specific demand and improve the efficiency of marke�ng programs.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 9/20

The accuracy of forecas�ng affects every aspect of the sales planning process. Perhaps the most cri�cal part of forecas�ng is the sales forecast. How does forecas�ng affect the marke�ng of a product?

4.3 Sales Forecasting Sales forecas�ng is generally regarded as a specialized subfield within the domain of market research that provides predic�ons about markets, customers, and compe�tors. Almost all forms of marke�ng-related forecasts relate either directly or indirectly to predic�ng future sales. Predic�ons are most o�en based on historical data, observed trends, and expert opinion. Accurate forecasts are essen�al to the efficient management of current opera�ons, assessing new market opportuni�es, and evalua�ng the effec�veness of ongoing marke�ng programs. Reliable predic�ons of how brand sales will respond to future changes in marketplace dynamics are essen�al to reducing uncertainty, and consequently risk, to the lowest levels possible. Forecasts can span different �me frames depending on the purpose of the forecast and availability of informa�on.

Sales Forecas�ng

Short-Range Forecasting

Short-range forecas�ng refers to making predic�ons less than one year into the future. These are fairly rou�ne forecasts that o�en reach managers' desks in the form of monthly or quarterly projec�ons of sales. They provide some of the most cri�cal data that are used in managing the opera�ons of the firm. Short-range forecasts are essen�al to making scheduling and alloca�on decisions about near-term produc�on capacity. An�cipa�ng sales levels also permits manufacturers to keep the costs of carrying large inventories of raw materials and unfinished goods to a minimum. Accurate forecasts are par�cularly cri�cal in labor-intensive manufacturing industries where human resource costs represent a large percentage of unit costs. Matching capacity to demand not only improves efficiency but also reduces the likelihood of stock-outs and shortages that result in dissa�sfied customers.

Medium-Range Forecasting

Medium-range forecas�ng refers to annual forecasts or predic�ons. These projec�ons are typically used as primary inputs to the annual budget planning process. This is the level of data most o�en used in se�ng or revising the marke�ng plan for each brand and SBU within the organiza�on's por�olio. Alloca�ons and adjustments to the marke�ng variables are o�en made in direct response to data from these forecasts.

The promo�onal mix elements (e.g., adver�sing, personal selling) are o�en the features of the marke�ng plan most directly impacted by sales projec�ons. Although sales forecasts play a pivotal role in the establishment of sales budgets, they are not the same thing. Forecasts predict future sales, and budgets specify the planned alloca�on resources to accomplish the organiza�on's goals. When used correctly, there is a cause-and-effect rela�onship between the two. The sales, adver�sing, and marke�ng budgets for a brand should be based on the goals for the brand as well as the corresponding level of forecast sales.

Long-Range Forecasting

Long-range forecas�ng refers to making predic�ons for periods greater than one year into the future. The most common use for these long-term projec�ons is strategic planning. Since these predic�ons extend farther into the future, they are generally more difficult to construct and tend to be less accurate. However, they provide essen�al inputs to the process of shaping the strategic direc�on of the organiza�on over the long run.

Both the providers and users of long-range marke�ng forecasts recognize the constraints and limita�ons associated with predic�ons about events more than a year in the future. These projec�ons are necessarily more specula�ve in some markets than others. However, the discipline of having to specifically quan�fy the

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 10/20

Small companies, such as this local bakery, as well as large companies use sales forecasts to help them make decisions that will lead to improvement and success.

Ambient Images Inc./SuperStock

Outsourcing manufacturing can reduce produc�on costs and improve the firm's ability to compete on price in global markets.

Imaginechina/Associated Press

expecta�ons associated with various op�ons and scenarios is essen�al to good strategic planning. It improves the rigor and strengthens the commitment that par�cipants bring to the process. However, even under the best of circumstances, the accuracy of short-range, medium-range, and long-range forecasts is necessarily limited by a range of complica�ng factors.

Think About It

As with other decision making aids we have looked at in previous chapters, marke�ng managers cannot afford to let forecasts do their thinking and decision making for them. There are risks associated with becoming too dependent on long-range predic�ons, just as there are nega�ve consequences associated with ignoring short-term sales forecasts.

What can managers do to avoid these kinds of problems?

Limitations of Sales Forecasting

Sales forecasts are used extensively as cri�cal inputs to decision making throughout both small and large companies. Accurate forecasts of sales are essen�al to making good decisions in areas related to opera�ons management, human resource planning, and marke�ng. In marke�ng, these predic�ons are used to evaluate the poten�al profitability of new ini�a�ves, evaluate the effec�veness of exis�ng programs, and establish future direc�ons for the firm. In light of the importance of sales forecasts, it is necessary to recognize the limita�ons and poten�al problems that are built in to the process of crea�ng them.

Sales forecasts can only take into account a limited number of market-related condi�ons. Most forms of market forecas�ng rely on very limited sta�s�cal models of the environment. By defini�on, models are incomplete, abstract representa�ons of the environment they represent. The real world is far messier, and it is very difficult to an�cipate or assess all of the factors that will impact brand sales.

A closely related problem is that most forecas�ng models assume that the basic condi�ons and events observed in past periods will con�nue into the future. However, compe��ve environments are dynamic and subject to change. New entrants to the market, the introduc�on of subs�tute goods or new technologies, significant changes in the marke�ng mix decisions made by compe�tors, and one- �me events or shocks to the market or economy cannot be an�cipated by forecas�ng models.

The accuracy and reliability of sales forecasts is also limited by the quality of available data. For example, some forecas�ng models require informa�on on the marke�ng expenditures and alloca�on decisions made by compe�tors. Few firms have any incen�ve to make this type of informa�on publicly available. In B2B contexts, prices and other terms of trade may change significantly from one customer to the next. Consequently, the data used to construct profiles of compe�tor behavior o�en need to be es�mated before being subsequently used to construct forecasts. This added layer of poten�al error can render medium- and long-range forecasts unreliable in many industries.

Finally, market-related variables are always interac�ng in fairly complex ways that are difficult to capture in sales forecasts. Consider the case of a major appliance manufacturer from China that wishes to improve its U.S. market rank from fi�h to third for convec�on ovens. In the span of 12 months, the company may opt to reduce retail prices, decrease adver�sing, and increase sales promo�ons to wholesale distributors. Assume that this is happening against the backdrop of a slowing na�onal economy. The impact of any one of those changes is dependent on the others to a greater or lesser extent. That is, the net impact is almost certainly different from the sum of the individual changes. The interac�on of these changes with the compe��ve response from other brands adds a second order or �er of complexity.

Think About It

Consider the case of the toy store owner who developed her sales forecasts based on economic indicators within her rapidly growing community and previous years' sales trends. She was an�cipa�ng 15 to 18 percent growth in the coming year, but actually lost nearly half her sales over the prior period.

Assuming that her forecas�ng model was specified correctly, what internal and external factors could possibly account for the huge discrepancy?

Though never perfectly precise in its predic�ons, the complex task of sales forecas�ng provides informa�on that is essen�al to the firm. Short-, medium-, and long- range projec�ons are used in many se�ngs and applica�ons. As with many other facets of the organiza�on, being very good at forecas�ng future events provides a

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content=… 11/20

dis�nc�ve competence or capability that can be translated into a compe��ve advantage for the company. The sec�on that follows examines the range of quan�ta�ve and qualita�ve forecas�ng techniques available to market analysts.

Why Good Forecasts Go Bad

Sales forecasts some�mes seem much more objec�ve and unbiased than they are because they produce such precise quan�ta�ve projec�ons. However, it would be naive to believe that the processes and outcomes related to the sales forecas�ng are immune from personal bias. "I have never seen (nor heard of) a sales forecast that was accurate, except by accident. The reason is simple: the poli�cs of self-interest makes accurate forecasts virtually impossible" (Geoffrey, 2011).

It's human nature. Whether inten�onal or uninten�onal, sales forecasts are necessarily biased by the experience, priori�es, and goals of the people involved in their crea�on. Consequently, the people most in�mately involved with the prepara�on of sales forecasts o�en have much more faith in their accuracy than the sales force that is o�en most directly affected by them.

So, how do bad forecasts emerge from the offices of professionally qualified managers and experienced execu�ves? The inaccuracy of many sales forecasts can be a�ributed to the unrealis�c expecta�ons of execu�ves and their failure to acquire accurate input from the salespeople in the field (Warner, 2012). James Geoffrey (2011), the author of the world's most-visited sales blog, humorously describes the chao�c process of crea�ng forecasts as a nine-step process:

Step #1: Top management needs a story to tell investors, and thus asks the sales manager for a forecast.

Step #2: The sales manager asks the sales reps what they think they'll sell, based upon their current gut feeling.

Step #3: The sales reps make a wild guess at what they might actually sell, and then subtract 10 percent as a fudge.

Step #4: The sales manager compares that forecast to the sales quota and adjusts both to match.

Step #5: The marke�ng group issues its own forecast, based upon sta�s�cs from a "market research" firm.

Step #6: The manufacturing group ignores all of the above and forecasts what it plans to manufacture.

Step #7: Top management looks at the three forecasts, throws them out, and then makes up numbers that will look good.

Step #8: The accountants juggle the books so that, regardless of what was sold, the quarterly report resembles the CEO's story.

Step #9: If, for some reason—like the laws of mathema�cs—Step #8 isn't good enough, the CEO fires the sales manager.

Though somewhat exaggerated, this nine-step model clearly illustrates how accuracy and forecas�ng discipline can be sacrificed to the serve the self-interest of mul�ple par�cipants in the process. Accurate sales forecas�ng begins with a commitment to making effec�ve use of tangible, objec�ve sources of data. The ini�al forecast can be created from mathema�cal models that rely on trend analysis based on previous years' sales, seasonal buying pa�erns, recent marke�ng expenditures, and general economic factors. Once fine-tuned and tested against historical data, this type of model can provide a baseline to build from.

The refinement of mathema�cally driven sales projec�ons by subjec�ve judgment is both unavoidable and essen�al in many instances. To minimize the impact of self-interest bias at this stage of the process, Geoffrey suggests two basic strategies.

First, decouple forecasts from quotas. Sales quotas are valid tools in managing and mo�va�ng the sales force. However, they should not have a role in driving the development of forecasts. The de-coupling of sales quotas and forecasts, though difficult, should remove pressure from the process and enhance the accuracy of the informa�on provided by the sales representa�ves in the field.

Second, eliminate unnecessary influencers from the process. If everyone within the organiza�on gets involved in the forecas�ng process, it will necessarily reflect the sum of everyone's personal and professional biases. Star�ng with an independent, mathema�cally driven ini�al sales forecast, only marke�ng, manufacturing, and top management should be involved in the considera�on of subsequent revisions. Any substan�al changes necessarily require the evalua�on of suppor�ng data so that informed revisions can be made rather than changes driven by self-interest (Geoffrey, 2011).

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 12/20

This line graph shows an increase in sales over �me. Using a graph like this allows marketers to understand their products' trends and to predict their products' future success.

4.4 Forecasting Techniques A range of approaches and methodologies can be applied to the predic�on of future sales growth. Some are dependent on modeling sta�s�cal rela�onships between data, while others rely on the expert opinion of business professionals. The choice of a sales forecas�ng technique in any given situa�on depends on several factors, including the required level of predic�ve accuracy, the availability of reliable data, and how far into the future projec�ons need to be made. None of the exis�ng techniques for forecas�ng sales is superior under all condi�ons. Both quan�ta�ve and qualita�ve methodologies have specific strengths and weaknesses. However, the appropriate applica�on of mul�ple measures and techniques to any given problem will invariably serve to refine and fine-tune the accuracy of the final forecast.

Quantitative Forecasting Methods

Quan�ta�ve forecas�ng methods primarily rely on the analysis of historical data to predict future sales. The sta�s�cal techniques and data sets used in crea�ng quan�ta�ve forecasts make the process rela�vely objec�ve in contrast to methods that depend on the expert opinion and judgments of people. In general, most marke�ng managers are more confident in quan�ta�ve approaches when reliable data are available, the market environment is rela�vely stable, and their previous experience with specific forecas�ng models has demonstrated that the past can be a good predictor of the future events.

Simple Trend Extension

The simplest method for forecas�ng future sales from past sales is called naive forecas�ng. It assumes no vola�lity in sales whatsoever and simply predicts that sales for the next period will be the same as sales for the previous period. Though simple, the assump�on of no change is seldom realis�c.

St+1 = St where S = sales and t = �me period

In rela�vely stable markets, there are situa�ons in which the overall trend in sales growth is clear, but not flat. For example, year-to-year sales growth for pens and mechanical pencils typically mirrors the rate of change in popula�on. Consequently, the rate of growth is slow, but posi�ve and steady. If sales data over �me exhibit a generally linear trend, the sta�s�cal method of simple linear regression analysis may be used to determine a trend line sales predic�on for the future period's forecast. (The general principles of regression analysis are discussed in the sec�on on mul�variate regression analysis.)

Figure 4.3: Simple trend extension

In simplest terms, this approach to forecas�ng the next period's sales is to graph the pa�ern of sales over �me and simply extend the observed trend line for one addi�onal period as shown in Figure 4.3.

Time Series

Quan�ta�ve methods of forecas�ng are based on an analysis of historical data specific to one or more intervals called a �me series. A �me series of data is a set of quan�ta�ve observa�ons collected at successive points in �me over a specified period. For example, the monthly sales of a given brand over a two-year interval would make up a set of �me series data with 24 observa�ons.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 13/20

This sca�er plot diagram shows the components of forecast demand. With a diagram like this, a marketer can decipher the trend from the fluctua�ons and random noise of the market sales.

If the historical �me series data set is used exclusively to predict future values of the same variable (e.g., 2007–2011 unit sales data are used to forecast 2012 unit sales), then the process is called a �me series study. However, if the historical �me series data are combined with other �me series data sets related to the variable that we wish to forecast, the analysis is called a causal study. For example, if you combine the 2007–2011 unit sales data with a comparable data set on product pricing over the same interval to predict 2012 unit sales, it would be considered a causal analysis.

The purpose of both �me series methods is to sta�s�cally isolate the underlying trends in the data from random noise and fluctua�ons. Once the trend is iden�fied, the historical data provide the baseline measures from which predic�ons about future levels can be projected. Whether building a rela�vely simple �me series study or a highly sophis�cated causal model, forecasts derived from this approach typically feature four major elements: trend, seasonal, cyclical, and irregular components.

The trend component accounts for the gradual shi�ing of the �me series data over a long period of �me. This can be thought of as the core element of the forecast that provides the general projec�on of growth or decline for the product.

The seasonal component of a �me series accounts for regular, recurring pa�erns of variability at specific �mes of the year. Many products exhibit some seasonality in sales as func�on of when they are used (e.g., snowblowers), specific purchase occasions (e.g., Easter baskets), or annual events (e.g., children's back-to-school lunch boxes). Figure 4.4 illustrates the linear trend and seasonal components of forecast demand.

Figure 4.4: Components for forecast demand

The cyclical component incorporates any regular wavelike pa�ern in the sequences of values. When graphed, these cyclical influences o�en appear as arcs rising above and dipping below the core trend line. These cycles are independent of any seasonal effects and can be very difficult to detect. Analysts need to understand the source of the pa�ern to determine whether these cyclical components can be expected to reliably recur in future periods.

The irregular component of a �me series study is caused by short-term, unan�cipated, and non-recurring factors that impact the observed values in the data set. The impact of these random, unpredictable events (e.g., a natural disaster, labor dispute) on the forecast values of the model cannot be predicted in advance.

The �me series study approach to forecas�ng assumes that future demand can be predicted from the analysis of historical data. In this way, fairly objec�ve methods are applied to mi�gate the risks posed by future uncertainty. Time series studies of all kinds, however, can some�mes be fine-tuned to make them more accurate by transforming the data used as inputs to the sta�s�cal analysis process. Exponen�al smoothing, for example, is a process for weigh�ng the observa�ons from previous sales periods to place greater emphasis on the most recent data. This reflects the pa�ern most o�en observed in sales data: that historical observa�ons gradually lose their value as �me passes and market condi�ons change.

Mul�variate Regression Analysis

Mul�variate regression analysis is the most common technique used to carry out causal �me series analyses. It is a sta�s�cal forecas�ng tool that iden�fies rela�onships between sales (the dependent variable) and one or more influencing factors (independent variables). If only one independent variable is used to forecast sales (e.g., household income), the model is called a simple linear regression, and the results can be illustrated by a line graph.

This mathema�cal rela�onship can be used to predict future values of sales based on changes in the independent variable. When more than one independent variable is considered, however, it is called a mul�variate regression.

The mul�variate regression approach to forecas�ng permits analysts to iden�fy mul�ple variables that have a causal influence on future sales. Consider, for example, how many factors might contribute to the retail sales of word processing so�ware. Among the variables that would have the most direct influence is computer sales. However, a simple model using historical sales data on word processing so�ware and retail computer sales could be made more accurate by including other factors that are not as directly related to so�ware sales. Growth rates in college enrollment or even the popula�on of high school students might

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 14/20

Microso� Windows dominates the word processing so�ware market. To be this profitable, Microso� examines the variables that affect its success in the market, such as college enrollment and business pa�erns.

Associated Press

have predic�ve validity. For forecasts within a specific geographic market, popula�on growth, employment levels, and pa�erns in home business startups might have explanatory power as well. The power of mul�variate regression analysis to inves�gate these rela�onships is based on the iden�fica�on of variables that are significantly correlated with retail so�ware sales.

Forecasters can incorporate a wide range of possibly useful variables in ini�al model trials to test their explanatory power against historical data. The sta�s�cal models in the form of mathema�cal formulas will indicate which variables have significant correla�ons with the dependent variable. Some will have a posi�ve rela�onship with sales (e.g., compe�tors' prices), while others may have an inverse rela�onship (e.g., own brand prices). By running prac�ce tests against different subsets of �me series data, some correla�ons between the two will emerge as truly causal in nature. The final model that emerges from this itera�ve trial-and-error process will include the set of mathema�cal rela�onships between variables that are the most effec�ve in predic�ng future sales.

Mul�variate regression analysis is a poten�ally powerful tool. It can incorporate a wide range of variables and evaluate their reliability as sales predictors. Among the marke�ng mix variables most commonly included in these causal models are changes in adver�sing expenditures, sales promo�ons, and pricing at various points in the channel of product distribu�on. Causal approaches to forecas�ng tend to be complex and provide the means to recognize a wide range of poten�al influences on product sales, including interac�ons between independent variables.

Qualitative Forecasting Methods

Quan�ta�ve forecas�ng methods rely exclusively on inferences from the extrapola�on of trends in historical data to predict future sales. By contrast, qualita�ve forecas�ng techniques u�lize the judgment and expert opinion of knowledgeable professionals to generate forecasts. A primary advantage to these methods is that they can be applied in situa�ons where historical data are not available. However, even in contexts where historical sales data are readily available, qualita�ve forecas�ng methods can be used to qualify or fine-tune sta�s�cal projec�ons.

Expert opinion is a subjec�ve approach to forecas�ng that relies exclusively on the judgment of individuals who possess specialized knowledge of buyers, compe�tors, and products. Companies o�en rely on experts from a variety of sources to provide their unique insights on the market. These sources may include customers, suppliers, channel intermediaries, trade associa�ons, or consultants. The opinions provided by these experts may be influenced by quan�ta�ve forecasts, but their forecast is typically an independent extension of judgment beyond projec�ons grounded in historical data.

The complexity of compe��ve business environments poses a significant challenge to sta�s�cal forecas�ng. It simply cannot account for all of the factors and interac�ons between factors that influence market-related behaviors at any given �me. The advantage of expert opinion and judgment over sta�s�cal forecas�ng methods is the ability of the human mind to synthesize informa�on from a wide range of qualita�ve and quan�ta�ve sources.

The Delphi method is a qualita�ve method that relies on the interac�on of mul�ple expert sources to develop forecasts through group consensus. Although the specific steps in the methodology may differ from one organiza�on to the next, the process is rela�vely simple. Ini�ally, the company that wants to develop a forecast contacts a group of experts from different backgrounds to answer the same set of structured ques�ons about the future of the product or brand of interest. Each of the experts completes his or her responses independently and returns them to a coordinator. Subsequently, each par�cipant in the process receives feedback about the anonymous responses of the other forecasters. In this second itera�on, the team members return their revised responses based on the input from round one.

This Delphi process is an itera�ve one that con�nues un�l there is a rela�vely high degree of agreement and certainty among the forecasters' responses. Although there are many varia�ons on the specific procedures used, in every instance the objec�ve of this endeavor is to build a consensus view of future events.

Expert panel methods are similar to the Delphi method insofar as they rely on the opinions and judgment of many professionals. However, expert panels are assembled and brought together to directly share and compare views. Individuals' forecasts are each given due considera�on before the par�cipants begin working to converge on one common forecast for the future.

In most instances, the expert panel is drawn from diverse professional backgrounds (e.g., academics, corporate execu�ves, consultants). However, there are �mes when the forecas�ng task requires a gathering of individuals with a common background. Some companies rou�nely gather their sales representa�ves to provide insights on the market and sales forecasts for the next period. Since members of the sales force are in immediate contact with both exis�ng customers and prospec�ve buyers on a daily basis, they can be a valuable and reliable source of marke�ng intelligence.

The gathering of the sales force described above is some�mes combined with simple quan�ta�ve forecas�ng. In this version, called the sales force composite method, each salesperson prepares forecasts for his or her own territory. Prior to gathering together as an expert sales panel, the forecasts are consolidated across products, brands, and geographic regions and distributed for subsequent discussion by the whole group.

Another varia�on on the basic model of expert panels is the jury of execu�ve opinion. In this version, par�cipa�on is limited to the top execu�ves within a company. Though farther removed from the field than salespeople, execu�ves possess a perspec�ve on the larger trends and market developments that is essen�al to making good decisions on new products and emerging technologies.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 15/20

Accurate forecas�ng requires both the analysis of historical data and subjec�ve judgments about the future.

Photodisc/Thinkstock

Simula�on is a final varia�on on the use of expert opinion that can be combined with any of the procedures described above. A simula�on consists of providing experts with several wri�en, conceptual scenarios of the future based on clearly defined assump�ons. Par�cipants are asked to respond to the scenarios by indica�ng which they believe is most likely to occur. In many instances, the discussions about the validity and reliability of the assump�ons built into each of the scenarios provide valuable insight by itself. However, the final product of this procedure is the same as with other forms of qualita�ve forecas�ng: to build a consensus predic�on of future events.

Market methods of sales forecas�ng are qualita�ve techniques that do not depend on the expert judgment of business professionals, but rather rely on the opinion of current customers and prospec�ve buyers. However, it is worth no�ng that market surveys are not typically designed and executed only for the sake of forecas�ng, since many of the same insights can be gleaned from a combina�on of the company's internal data and external published sources.

Similarly, market tests are used to gauge consumer responses and assess the viability of a wider product introduc�on or market rollout. The results of these field tests for new product concepts are intended to assess the interest of poten�al customers in buying the product. The actual sales

forecasts and other measures of a product's poten�al are completed prior to field tes�ng a new product concept.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 16/20

Ch. 4 Conclusion Quan�ta�ve forecas�ng methods provide predic�ons of future sales based on the analysis of historical sales data and an�cipated future market condi�ons. Qualita�ve approaches allow marke�ng managers to incorporate the judgment and opinions of experts within the field. Forecasts enable companies to evaluate current performance and prepare for future sales levels. The predic�on of slowing or declining sales provides an opportunity for firms to re-evaluate their marke�ng plans and explore alterna�ve opportuni�es for growth.

Accurate predic�ons of future demand growth enable organiza�ons to compete and func�on more effec�vely by making their opera�ons more efficient. Reliable sales forecasts can facilitate more efficient produc�on planning, improve customer service, and generally improve the alloca�on of the firm's resources to match consumer demand. In this way, the compe��ve advantage provided by be�er sales forecas�ng can result in lower costs, higher margins, and improved levels of customer sa�sfac�on and brand loyalty.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 17/20

Ch. 4 Learning Resources

Key Ideas

Key Terms

Click on each key term to see the defini�on.

available market (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

Those buyers who possess both the interest and means to purchase a specific product under current market condi�ons.

brand usage gap (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The difference between poten�al sales to the target market and the current level of brand usage by customers within the targeted segment of the market.

causal study (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

An analysis in which historical �me series data (e.g., sales) are combined with other �me series data (e.g., adver�sing expenditures) related to the variable being forecasted (e.g., future sales).

cyclical component (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The element in a �me series study that incorporates any regular wavelike pa�ern in the sequences of values.

Delphi method (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A qualita�ve forecas�ng method that relies on the interac�on of mul�ple expert sources to develop forecasts through group consensus.

expert opinion (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A subjec�ve approach to forecas�ng that relies exclusively on the judgment of individuals who possess specialized knowledge of buyers, compe�tors, and products.

expert panel methods (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A qualita�ve forecas�ng method that relies on the opinions and judgment of many professionals. Unlike the Delphi method, however, expert panels are assembled and brought together to directly share and compare views.

exponen�al smoothing (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A process for weigh�ng the observa�ons in �me series data to place greater emphasis on the most recent observa�ons.

gap analysis (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

SLIDE 1 OF 12

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 18/20

A variety of comparisons that examine the difference between actual performance and poten�al performance on measures of interest to marke�ng managers.

irregular component (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The element of a �me series study that is caused by short-term, unan�cipated, and non-recurring factors that impact the observed values in the data set.

jury of execu�ve opinion (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The top execu�ves within a company serve as an expert panel to consider long-range trends that may impact the organiza�on.

long-range forecas�ng (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

Making predic�ons for periods greater than one year into the future.

market demand (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The total volume or amount of a specific product purchased by consumers over the complete range of prices at which it is sold.

market methods (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

Qualita�ve sales forecas�ng techniques that rely on tapping the opinion of current customers and prospec�ve buyers.

market penetra�on index (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The ra�o of current market demand to poten�al demand level.

market poten�al (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A measure of the maximum total possible sales of a given product over a fixed interval of �me.

Market Poten�al Index (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A mul�-factor index of market poten�al for emerging global markets created by the Michigan State University Center for Interna�onal Business and Economic Research.

medium-range forecas�ng (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

Making annual forecasts or predic�ons.

mul�variate regression analysis (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The most common technique used to carry out causal �me series analyses. This sta�s�cal forecas�ng tool iden�fies rela�onships between sales (the dependent variable) and one or more influencing factors (independent variables).

naive forecas�ng (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The predic�on that sales for the next period will be the same as sales for the previous period.

product usage gap (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The difference between total market poten�al and current product usage by all customers within a given market.

qualita�ve forecas�ng techniques (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

Using the judgment and expert opinion of knowledgeable professionals to generate forecasts.

quan�ta�ve forecas�ng methods (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

Using the analysis of historical data to predict future sales.

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 19/20

sales force composite (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A two-part qualita�ve forecas�ng technique. The organiza�on's sales force serves as the expert panel. Addi�onally, however, each salesperson prepares forecasts for his or her own territory. Prior to when the expert sales panel comes together, the forecasts are consolidated and distributed for later discussion by the group.

sales forecas�ng (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A specialized subfield within the domain of market research that provides predic�ons about markets, customers, and compe�tors. Almost all forms of marke�ng- related forecasts relate either directly or indirectly to predic�ng future sales.

sales poten�al (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The por�on of the market poten�al that a firm can reasonably expect to reach. An es�mate of the size of the brand's target market.

seasonal component (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The element of a �me series study that accounts for regular, recurring pa�erns of variability at specific �mes of the year.

share penetra�on index (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The ra�o of the brand's current sales to its poten�al sales.

short-range forecas�ng (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

Making predic�ons less than one year into the future.

simple linear regression analysis (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A sta�s�cal forecas�ng tool that iden�fies rela�onships between sales (the dependent variable) and one correlated factor (independent variable).

simula�on (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A qualita�ve forecas�ng procedure that begins by providing a panel of experts with several wri�en, conceptual scenarios of the future based on clearly defined assump�ons. Par�cipants interact to reach consensus on which scenario and set of assump�ons represent the most likely depic�on of future events.

�me series (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

A set of quan�ta�ve observa�ons collected at successive points in �me over a specified period or interval.

�me series study (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

An analysis in which historical �me series data are used exclusively to predict future values of the same variable.

trend component (h�p://content.thuzelearning.com/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12.1/sec�ons/front_ma�er/books/AUBUS620.12

The element in a �me series study that accounts for the gradual shi�ing of the �me series data over a long period of �me.

Web Resources

This website is an excellent general reference site on forecas�ng and forecas�ng methodologies. It includes a forecas�ng dic�onary as well as the Standards and Prac�ces for Forecas�ng. This is a statement of 139 principles that are used to summarize knowledge about forecas�ng. h�p://www.forecas�ngprinciples.com/ (h�p://www.forecas�ngprinciples.com/)

This website is owned by an independent research firm that ac�vely monitors consumer-related trends in more than 120 countries worldwide. The site includes free access to a monthly trend briefing and an archive of previous findings on informa�on related to emerging trends and pa�erns in consumer markets around the world. h�p://www.trendwatching.com (h�p://www.trendwatching.com)

This website is a white paper briefing on the unique challenges of sales forecas�ng that confront manufacturers. It provides a good overview of the value and obstacles related to accurate forecas�ng based on a market research study conducted in December 2009. In total, 274 manufacturing execu�ves and managers

4/1/2019 Print

https://content.ashford.edu/print/AUBUS620.12.1?sections=ch04,ch04introduction,sec4.1,sec4.2,sec4.3,sec4.4,ch04conclusion,ch04_eoc&content… 20/20

employed in their opera�ons, sales, IT, supply chain, or manufacturing produc�on business units par�cipated in the study. h�p://www.right90.com/whitepapers/answering_the_sales_forecas�...ge_for_manufacturersFINALPRINT.pdf (h�p://www.right90.com/whitepapers/answering_the_sales_forecas�...ge_for_manufacturersFINALPRINT.pdf)

The Michigan State University Center for Interna�onal Business and Economic Research (MSU-CIBER) publishes its Market Poten�al Index, which marketers use as a preliminary assessment to evaluate emerging global markets. h�p://globaledge.msu.edu/resourcedesk/mpi/ (h�p://globaledge.msu.edu/resourcedesk/mpi/)

The Census Bureau website provides the most comprehensive source of U.S. demographic and economic sta�s�cs. Resources on this site include the American Fac�inder, Current Industrial Reports, Economic Census, Industry Series, and Sta�s�cal Abstracts. h�p://www.census.gov/ (h�p://www.census.gov/)