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Business Discussion
– ## WEEKEDISCUSSIONECON.docx
– Demand-Driven_Forecasting_A_Structured_Approach_to…_—-_Chapter_2_What_Is_Demand-Driven_Forecasting_.pdf
**## WEEKEDISCUSSIONECON.docx**
Discussion no more than 300 words Week 3: Demand Analysis and Optimal Pricing, and Estimating and Forecasting Deman
1. Why is knowing (or estimating) the product demand so crucial for a firm? What are the differences between estimating and forecasting demand?
In your response, include an example of a business that has suffered from poorly forecasting the demand of its products. Evaluate how or why the business made such a mistake.
To keep our discussion more interesting, please use examples that are not from our textbook.
2. Respond to CM with no more than 200 words
Accurately predicting demand is essential for any business, as it directly influences production schedules, inventory control, pricing decisions, and overall profitability. If a company overestimates demand, it can lead to overproduction, excess stock, and wasted resources. On the flip side, underestimating demand may result in stockouts, lost sales, and frustrated customers. There’s a key difference between estimating and forecasting demand. Estimating tends to be more of a quick, rough judgment based on available data or past experiences, typically used for short-term needs. In contrast, forecasting is a more structured approach that uses historical data, trends, and statistical models to predict future demand, often on a longer-term basis. A real-world example of poor demand forecasting is Target’s ill-fated expansion into Canada. The company assumed that demand levels from the U.S. would directly translate to the Canadian market, which led to mismatches in inventory. As a result, some items were overstocked while others were in short supply. Target also underestimated regional differences and consumer preferences, relying on inaccurate data and moving too fast without adjusting their forecasts. This resulted in empty shelves, customer dissatisfaction, and ultimately the closure of Target’s Canadian operations just two years after opening. This example underscores how crucial it is for businesses to make well-informed, data-driven demand forecasts, rather than relying on assumptions or simplistic estimates. Accurate demand forecasting helps companies avoid these costly mistakes and make smarter, more strategic decisions.
3. Respond to RW with no more than 200 words
Understanding how much demand there is for a product is important for any business. It helps them figure out how much to produce so they do not end up with too much or too little. This also plays a big role in setting prices. If many people want the product, the company might charge more, but if demand is low, they might lower the price to attract buyers. Knowing demand also helps businesses decide how to use their resources, like labor and materials, more effectively. It is also crucial for planning budgets and predicting future growth. Marketing strategies also benefit from this knowledge, as it helps target the right audience. When we talk about estimating demand, we look at current data and trends to understand how much of a product people want right now. This is important in making short-term decisions. On the other hand, forecasting demand is about looking ahead and predicting future trends. This helps businesses prepare for changes in the market and plan long-term strategies. While estimating gives a snapshot of the present, forecasting helps anticipate the future, and both are key to making smart business decisions.
Week 3: Demand Analysis and Optimal Pricing, and Estimating and Forecasting Demand
**Overview:**
Welcome to Week 3.
The demand function is one of the key managerial decision–making tools for determining the magnitude of the demand for their products. An accurate estimation of the demand for a product or a service is based on defining the underlying variables that impact it. Theoretically, determinates of demand include
1. Own price: Remember the law of demand.
2. Income: A product is a normal good if an increase in income results in an increase in the quantity demanded for that product and vice–a–versa. A product is an inferior good where an increase in income lowers its sales and vice–a–versa.
3. Substitute goods: These are direct or indirect competitive goods or services, and therefore, an increase in demand for such a good results in a decreased demand for its substitutes.
4. Complementary goods: These are the types of products or/and services that are jointly purchased and consumed with a given item, and therefore, an increase in demand for such a good results in an increase in demand for its complements.
5. Population: Changes in population affect the number of consumers and also in the quantity of purchases.
6. Demographics: Characteristics of a population, such as racial and ethical compositions and age distribution, also have a significant impact on demand for a product.
7. Tastes and preferences: These preferences refer to those of a large group of consumers, not an individual. These change over time for music, food, fashion, health consciousness, recreation, travel, drugs, etc., resulting in changes in the quantity of demand.
8. Price elasticity of demand: price elasticity is a measure of consumers’ price sensitivity, or a measure of how responsive consumers are to price changes.
From the Law of Demand, we understand that an increase or a decrease in the price of a product always causes in a decrease or an increase in the quantity demanded. Price elasticity provides managers with an insight to the magnitude of change in quantity demanded due to change in price.
Scientific research investigates relationships among variables. These relationships may be causal, meaning that the changes in one variable (dependent variable) depend on the changes in another variable (independent variable). The regression method is used to quantify these models. However, such scientific processes demand data. The required data can be collected through secondary methods (published data) or/and primary methods including (a) observations, (b) interviews, (c) focus groups, (d) pilot studies, and (e) surveying and sampling. From such data sources, firms are in the position of the necessary information to construct a demand equation for their product.
A purpose of demand estimation is for forecasting future sales and prices. Estimation of demand functions is most often accomplished using the technique of regression analysis. Typically, in the demand estimation, quantity demanded for a product or a service is stated in relation to its price, the level of income, price substitute(s), price of complement(s), population, and tastes and preferences.
The method of estimating the parameters of an empirical demand function depends on whether the price of the product is market–determined (perfect competition) or company–determined (monopolistic competition, oligopoly, and monopoly). Managers of price–taking firms do not set the price of the product they sell; rather, prices are endogenous or "market–determined" by the intersection of demand and supply. Managers of price–setting firms set the price of the product they sell by producing the quantity associated with the chosen price on the downward–sloping demand curve facing the firm. Since price is manager–determined rather than market–determined, price is exogenous for price–setting firms.
**Weekly Objective(s):**
LO1: Explain the relationship between price elasticity of demand and revenue
LO2: Discuss how a firm can maximize its profit by using optimal markup pricing and price discrimination
LO3: Explain how empirical analysis is both "art and science"
LO4: Explain the statistics that are generated by regression analysis
Income Level of Consumers
The first potential demand shifter is the income level of consumers. In general, when consumers have a higher purchasing power, they will have a tendency to increase their purchase of most products, thus shifting their demand curve to the right. As I just mentioned, this is true for most products that are thus called normal goods. The opposite can be observed for products that are called inferior goods. They are products or goods that consumers purchase when they cannot afford more expensive alternatives. If or when their income increase, they replace them with more desirable alternatives.
Substitute Goods
The second factor that has the potential to shift the demand curve for a product or a service are similar products offered by the competition. They are called
**substitute goods**. For instance, if a competitor comes up with an innovative product that is similar to what a firm produces, the demand for the original product will decrease. In other words, an increase in demand for good results in a decreased demand for its substitutes, because consumers purchase one or the other.
Complementary Goods
After substitute goods, let me discuss
**complementary goods**. These are the types of products or/and services that are jointly purchased and consumed with a given item, and therefore, an increase in demand for such a good results in an increase in demand for its complements. For example, if the demand for hot dogs increased during the summer, the demand for hot dogs buns would increase as well. They are complementary goods.
Size of the Population
Of course, the size of the population is an important demand shifter. More consumers mean more demand, pure and simple. It is worth mentioning that the characteristics of a population, such as racial and ethical compositions and age distribution, may have a significant impact on the demand for a product.
Consumer Tastes and Preferences
Finally, the tastes and preferences of consumers can create massive shifts of the demand curve. For example, the rising concern of many people for the protection of the environment has shifted the demand for automobiles toward hybrid and electric vehicles, resulting in massive industry investment in that area.
Price Elasticity
It goes without saying that managers are keenly interested in the responsiveness of the demand they face to a change in the price of their product. Would most people stop buying their product if they raise the price by say 1%, or would such an increase go by unnoticed and not influence sales? The answer to such questions is provided by calculating the price elasticity of demand: it measures how responsive consumers are to a change in price. Let me define this more precisely.
From the Law of Demand, we understand that when the price of a product increases, the quantity demanded decreases, and vice-versa. Price elasticity provides information on the responsiveness of consumers to a change in price, i.e. by how many percents the quantity demanded would decrease if the price increased by 1%. This concept applies to any of the precedent variables, by the way. It is possible to calculate the income elasticity of demand, for example. It would measure the
**change in quantity demanded** in response to a change in the general income level of the population.
Estimating and Forecasting Demand
The precedent discussion about elasticities, and the discussion of demand analysis in chapter 3 of our textbook (Samuelson & Marks) requires knowledge of the demand curve of the market managers are interested in. The goal is not so much to understand what has happened in the past but to use the information from the past to predict what is likely to take place in the future so the best pricing and production decisions can be made.
There are several ways to try and predict future demand. Some methods are qualitative and represent an intuitive approach to forecasting based mostly on opinions, while quantitative methods require data gathering and statistical analysis to build some type of model. While the latter is more precise, it is also more costly and time-consuming, and a well-designed qualitative analysis may be a useful first step in demand analysis. Let me discuss it briefly, before summarizing the main principles behind the statistical tool that is used most often, regression analysis.
The easiest way to forecast demand using qualitative analysis is to rely on the personal insights of well-informed individuals. It is clearly a subjective approach, but it can provide valuable information, especially when it is not based on the opinion of a single individual, but on the informed opinion of a group. It will avoid the bias a single individual may have, and guarantee that more information will be taken into account to formulate a forecast.
Qualitative Analysis
It goes without saying that a group of experts may not come to a consensus easily. Furthermore, if one or a few of them have a forceful personality, their opinion can shape the consensus of the group, even if it is not the best one.
To avoid these issues, a method called the Delphi method is sometimes used. Individuals have to answer separately to a series of questions relating to the forecasting problem. Responses are analyzed by an independent party, who provides an anonymous summary of the forecasts with the reasons provided by each expert. Then, experts are encouraged to take into account everyone’s observations and to revise their answers accordingly. The range of forecasts tends to decrease, and the results can be averaged to determine a forecast that is fairly consensual.
The second type of qualitative analysis that is often used is survey techniques. As many of us have probably experienced, surveys are interviews or questionnaires that can provide managers with valuable information, provided they are well designed. It may be the only available tool in certain situations; like, for example, when a firm tries to estimate the demand for a new product (quantitative analysis is not possible since there is no data available).
In general, though, surveys are used as a complement to quantitative analysis. One of the reasons is that quantitative models generally assume that consumer’s tastes have not changed, so past data can be used to predict consumer’s behavior. However, if tastes are actually changing, survey data can provide information on the way these tastes and preferences have changed.
References
Samuelson W.F. & Marks S.G. (2015).
_Managerial Economics_. Hoboken, NJ: Wiley & Sons Inc.
Chase, Charles W..
_Demand-Driven Forecasting : A Structured Approach to Forecasting_, John Wiley & Sons, Incorporated, 2013.
_ProQuest Ebook Central_,
[https://ebookcentral.proquest.com/lib/apus/detail.action?docID=1315864](https://ebookcentral.proquest.com/lib/apus/detail.action?docID=1315864).
**Demand-Driven_Forecasting_A_Structured_Approach_to…_—-_Chapter_2_What_Is_Demand-Driven_Forecasting_.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/demand-driven-forecasting-a-structured-approach-to-chapter-2-what-is-demand-driven-forecasting-pdf)
**Demand-Driven_Forecasting_A_Structured_Approach_to…_—-_Chapter_2_What_Is_Demand-Driven_Forecasting_.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/demand-driven-forecasting-a-structured-approach-to-chapter-2-what-is-demand-driven-forecasting-pdf)
**## WEEKEDISCUSSIONECON.docx**
Discussion no more than 300 words Week 3: Demand Analysis and Optimal Pricing, and Estimating and Forecasting Deman
1. Why is knowing (or estimating) the product demand so crucial for a firm? What are the differences between estimating and forecasting demand?
In your response, include an example of a business that has suffered from poorly forecasting the demand of its products. Evaluate how or why the business made such a mistake.
To keep our discussion more interesting, please use examples that are not from our textbook.
2. Respond to CM with no more than 200 words
Accurately predicting demand is essential for any business, as it directly influences production schedules, inventory control, pricing decisions, and overall profitability. If a company overestimates demand, it can lead to overproduction, excess stock, and wasted resources. On the flip side, underestimating demand may result in stockouts, lost sales, and frustrated customers. There’s a key difference between estimating and forecasting demand. Estimating tends to be more of a quick, rough judgment based on available data or past experiences, typically used for short-term needs. In contrast, forecasting is a more structured approach that uses historical data, trends, and statistical models to predict future demand, often on a longer-term basis. A real-world example of poor demand forecasting is Target’s ill-fated expansion into Canada. The company assumed that demand levels from the U.S. would directly translate to the Canadian market, which led to mismatches in inventory. As a result, some items were overstocked while others were in short supply. Target also underestimated regional differences and consumer preferences, relying on inaccurate data and moving too fast without adjusting their forecasts. This resulted in empty shelves, customer dissatisfaction, and ultimately the closure of Target’s Canadian operations just two years after opening. This example underscores how crucial it is for businesses to make well-informed, data-driven demand forecasts, rather than relying on assumptions or simplistic estimates. Accurate demand forecasting helps companies avoid these costly mistakes and make smarter, more strategic decisions.
3. Respond to RW with no more than 200 words
Understanding how much demand there is for a product is important for any business. It helps them figure out how much to produce so they do not end up with too much or too little. This also plays a big role in setting prices. If many people want the product, the company might charge more, but if demand is low, they might lower the price to attract buyers. Knowing demand also helps businesses decide how to use their resources, like labor and materials, more effectively. It is also crucial for planning budgets and predicting future growth. Marketing strategies also benefit from this knowledge, as it helps target the right audience. When we talk about estimating demand, we look at current data and trends to understand how much of a product people want right now. This is important in making short-term decisions. On the other hand, forecasting demand is about looking ahead and predicting future trends. This helps businesses prepare for changes in the market and plan long-term strategies. While estimating gives a snapshot of the present, forecasting helps anticipate the future, and both are key to making smart business decisions.
Week 3: Demand Analysis and Optimal Pricing, and Estimating and Forecasting Demand
**Overview:**
Welcome to Week 3.
The demand function is one of the key managerial decision–making tools for determining the magnitude of the demand for their products. An accurate estimation of the demand for a product or a service is based on defining the underlying variables that impact it. Theoretically, determinates of demand include
1. Own price: Remember the law of demand.
2. Income: A product is a normal good if an increase in income results in an increase in the quantity demanded for that product and vice–a–versa. A product is an inferior good where an increase in income lowers its sales and vice–a–versa.
3. Substitute goods: These are direct or indirect competitive goods or services, and therefore, an increase in demand for such a good results in a decreased demand for its substitutes.
4. Complementary goods: These are the types of products or/and services that are jointly purchased and consumed with a given item, and therefore, an increase in demand for such a good results in an increase in demand for its complements.
5. Population: Changes in population affect the number of consumers and also in the quantity of purchases.
6. Demographics: Characteristics of a population, such as racial and ethical compositions and age distribution, also have a significant impact on demand for a product.
7. Tastes and preferences: These preferences refer to those of a large group of consumers, not an individual. These change over time for music, food, fashion, health consciousness, recreation, travel, drugs, etc., resulting in changes in the quantity of demand.
8. Price elasticity of demand: price elasticity is a measure of consumers’ price sensitivity, or a measure of how responsive consumers are to price changes.
From the Law of Demand, we understand that an increase or a decrease in the price of a product always causes in a decrease or an increase in the quantity demanded. Price elasticity provides managers with an insight to the magnitude of change in quantity demanded due to change in price.
Scientific research investigates relationships among variables. These relationships may be causal, meaning that the changes in one variable (dependent variable) depend on the changes in another variable (independent variable). The regression method is used to quantify these models. However, such scientific processes demand data. The required data can be collected through secondary methods (published data) or/and primary methods including (a) observations, (b) interviews, (c) focus groups, (d) pilot studies, and (e) surveying and sampling. From such data sources, firms are in the position of the necessary information to construct a demand equation for their product.
A purpose of demand estimation is for forecasting future sales and prices. Estimation of demand functions is most often accomplished using the technique of regression analysis. Typically, in the demand estimation, quantity demanded for a product or a service is stated in relation to its price, the level of income, price substitute(s), price of complement(s), population, and tastes and preferences.
The method of estimating the parameters of an empirical demand function depends on whether the price of the product is market–determined (perfect competition) or company–determined (monopolistic competition, oligopoly, and monopoly). Managers of price–taking firms do not set the price of the product they sell; rather, prices are endogenous or "market–determined" by the intersection of demand and supply. Managers of price–setting firms set the price of the product they sell by producing the quantity associated with the chosen price on the downward–sloping demand curve facing the firm. Since price is manager–determined rather than market–determined, price is exogenous for price–setting firms.
**Weekly Objective(s):**
LO1: Explain the relationship between price elasticity of demand and revenue
LO2: Discuss how a firm can maximize its profit by using optimal markup pricing and price discrimination
LO3: Explain how empirical analysis is both "art and science"
LO4: Explain the statistics that are generated by regression analysis
Income Level of Consumers
The first potential demand shifter is the income level of consumers. In general, when consumers have a higher purchasing power, they will have a tendency to increase their purchase of most products, thus shifting their demand curve to the right. As I just mentioned, this is true for most products that are thus called normal goods. The opposite can be observed for products that are called inferior goods. They are products or goods that consumers purchase when they cannot afford more expensive alternatives. If or when their income increase, they replace them with more desirable alternatives.
Substitute Goods
The second factor that has the potential to shift the demand curve for a product or a service are similar products offered by the competition. They are called
**substitute goods**. For instance, if a competitor comes up with an innovative product that is similar to what a firm produces, the demand for the original product will decrease. In other words, an increase in demand for good results in a decreased demand for its substitutes, because consumers purchase one or the other.
Complementary Goods
After substitute goods, let me discuss
**complementary goods**. These are the types of products or/and services that are jointly purchased and consumed with a given item, and therefore, an increase in demand for such a good results in an increase in demand for its complements. For example, if the demand for hot dogs increased during the summer, the demand for hot dogs buns would increase as well. They are complementary goods.
Size of the Population
Of course, the size of the population is an important demand shifter. More consumers mean more demand, pure and simple. It is worth mentioning that the characteristics of a population, such as racial and ethical compositions and age distribution, may have a significant impact on the demand for a product.
Consumer Tastes and Preferences
Finally, the tastes and preferences of consumers can create massive shifts of the demand curve. For example, the rising concern of many people for the protection of the environment has shifted the demand for automobiles toward hybrid and electric vehicles, resulting in massive industry investment in that area.
Price Elasticity
It goes without saying that managers are keenly interested in the responsiveness of the demand they face to a change in the price of their product. Would most people stop buying their product if they raise the price by say 1%, or would such an increase go by unnoticed and not influence sales? The answer to such questions is provided by calculating the price elasticity of demand: it measures how responsive consumers are to a change in price. Let me define this more precisely.
From the Law of Demand, we understand that when the price of a product increases, the quantity demanded decreases, and vice-versa. Price elasticity provides information on the responsiveness of consumers to a change in price, i.e. by how many percents the quantity demanded would decrease if the price increased by 1%. This concept applies to any of the precedent variables, by the way. It is possible to calculate the income elasticity of demand, for example. It would measure the
**change in quantity demanded** in response to a change in the general income level of the population.
Estimating and Forecasting Demand
The precedent discussion about elasticities, and the discussion of demand analysis in chapter 3 of our textbook (Samuelson & Marks) requires knowledge of the demand curve of the market managers are interested in. The goal is not so much to understand what has happened in the past but to use the information from the past to predict what is likely to take place in the future so the best pricing and production decisions can be made.
There are several ways to try and predict future demand. Some methods are qualitative and represent an intuitive approach to forecasting based mostly on opinions, while quantitative methods require data gathering and statistical analysis to build some type of model. While the latter is more precise, it is also more costly and time-consuming, and a well-designed qualitative analysis may be a useful first step in demand analysis. Let me discuss it briefly, before summarizing the main principles behind the statistical tool that is used most often, regression analysis.
The easiest way to forecast demand using qualitative analysis is to rely on the personal insights of well-informed individuals. It is clearly a subjective approach, but it can provide valuable information, especially when it is not based on the opinion of a single individual, but on the informed opinion of a group. It will avoid the bias a single individual may have, and guarantee that more information will be taken into account to formulate a forecast.
Qualitative Analysis
It goes without saying that a group of experts may not come to a consensus easily. Furthermore, if one or a few of them have a forceful personality, their opinion can shape the consensus of the group, even if it is not the best one.
To avoid these issues, a method called the Delphi method is sometimes used. Individuals have to answer separately to a series of questions relating to the forecasting problem. Responses are analyzed by an independent party, who provides an anonymous summary of the forecasts with the reasons provided by each expert. Then, experts are encouraged to take into account everyone’s observations and to revise their answers accordingly. The range of forecasts tends to decrease, and the results can be averaged to determine a forecast that is fairly consensual.
The second type of qualitative analysis that is often used is survey techniques. As many of us have probably experienced, surveys are interviews or questionnaires that can provide managers with valuable information, provided they are well designed. It may be the only available tool in certain situations; like, for example, when a firm tries to estimate the demand for a new product (quantitative analysis is not possible since there is no data available).
In general, though, surveys are used as a complement to quantitative analysis. One of the reasons is that quantitative models generally assume that consumer’s tastes have not changed, so past data can be used to predict consumer’s behavior. However, if tastes are actually changing, survey data can provide information on the way these tastes and preferences have changed.
References
Samuelson W.F. & Marks S.G. (2015).
_Managerial Economics_. Hoboken, NJ: Wiley & Sons Inc.
Chase, Charles W..
_Demand-Driven Forecasting : A Structured Approach to Forecasting_, John Wiley & Sons, Incorporated, 2013.
_ProQuest Ebook Central_,
[https://ebookcentral.proquest.com/lib/apus/detail.action?docID=1315864](https://ebookcentral.proquest.com/lib/apus/detail.action?docID=1315864).
**Demand-Driven_Forecasting_A_Structured_Approach_to…_—-_Chapter_2_What_Is_Demand-Driven_Forecasting_.pdf**
This file is too large to display. [View in new window](https://www.sweetstudy.com/files/demand-driven-forecasting-a-structured-approach-to-chapter-2-what-is-demand-driven-forecasting-pdf)
**## WEEKEDISCUSSIONECON.docx**
Discussion no more than 300 words Week 3: Demand Analysis and Optimal Pricing, and Estimating and Forecasting Deman
1. Why is knowing (or estimating) the product demand so crucial for a firm? What are the differences between estimating and forecasting demand?
In your response, include an example of a business that has suffered from poorly forecasting the demand of its products. Evaluate how or why the business made such a mistake.
To keep our discussion more interesting, please use examples that are not from our textbook.
2. Respond to CM with no more than 200 words
Accurately predicting demand is essential for any business, as it directly influences production schedules, inventory control, pricing decisions, and overall profitability. If a company overestimates demand, it can lead to overproduction, excess stock, and wasted resources. On the flip side, underestimating demand may result in stockouts, lost sales, and frustrated customers. There’s a key difference between estimating and forecasting demand. Estimating tends to be more of a quick, rough judgment based on available data or past experiences, typically used for short-term needs. In contrast, forecasting is a more structured approach that uses historical data, trends, and statistical models to predict future demand, often on a longer-term basis. A real-world example of poor demand forecasting is Target’s ill-fated expansion into Canada. The company assumed that demand levels from the U.S. would directly translate to the Canadian market, which led to mismatches in inventory. As a result, some items were overstocked while others were in short supply. Target also underestimated regional differences and consumer preferences, relying on inaccurate data and moving too fast without adjusting their forecasts. This resulted in empty shelves, customer dissatisfaction, and ultimately the closure of Target’s Canadian operations just two years after opening. This example underscores how crucial it is for businesses to make well-informed, data-driven demand forecasts, rather than relying on assumptions or simplistic estimates. Accurate demand forecasting helps companies avoid these costly mistakes and make smarter, more strategic decisions.
3. Respond to RW with no more than 200 words
Understanding how much demand there is for a product is important for any business. It helps them figure out how much to produce so they do not end up with too much or too little. This also plays a big role in setting prices. If many people want the product, the company might charge more, but if demand is low, they might lower the price to attract buyers. Knowing demand also helps businesses decide how to use their resources, like labor and materials, more effectively. It is also crucial for planning budgets and predicting future growth. Marketing strategies also benefit from this knowledge, as it helps target the right audience. When we talk about estimating demand, we look at current data and trends to understand how much of a product people want right now. This is important in making short-term decisions. On the other hand, forecasting demand is about looking ahead and predicting future trends. This helps businesses prepare for changes in the market and plan long-term strategies. While estimating gives a snapshot of the present, forecasting helps anticipate the future, and both are key to making smart business decisions.
Week 3: Demand Analysis and Optimal Pricing, and Estimating and Forecasting Demand
**Overview:**
Welcome to Week 3.
The demand function is one of the key managerial decision–making tools for determining the magnitude of the demand for their products. An accurate estimation of the demand for a product or a service is based on defining the underlying variables that impact it. Theoretically, determinates of demand include
1. Own price: Remember the law of demand.
2. Income: A product is a normal good if an increase in income results in an increase in the quantity demanded for that product and vice–a–versa. A product is an inferior good where an increase in income lowers its sales and vice–a–versa.
3. Substitute goods: These are direct or indirect competitive goods or services, and therefore, an increase in demand for such a good results in a decreased demand for its substitutes.
4. Complementary goods: These are the types of products or/and services that are jointly purchased and consumed with a given item, and therefore, an increase in demand for such a good results in an increase in demand for its complements.
5. Population: Changes in population affect the number of consumers and also in the quantity of purchases.
6. Demographics: Characteristics of a population, such as racial and ethical compositions and age distribution, also have a significant impact on demand for a product.
7. Tastes and preferences: These preferences refer to those of a large group of consumers, not an individual. These change over time for music, food, fashion, health consciousness, recreation, travel, drugs, etc., resulting in changes in the quantity of demand.
8. Price elasticity of demand: price elasticity is a measure of consumers’ price sensitivity, or a measure of how responsive consumers are to price changes.
From the Law of Demand, we understand that an increase or a decrease in the price of a product always causes in a decrease or an increase in the quantity demanded. Price elasticity provides managers with an insight to the magnitude of change in quantity demanded due to change in price.
Scientific research investigates relationships among variables. These relationships may be causal, meaning that the changes in one variable (dependent variable) depend on the changes in another variable (independent variable). The regression method is used to quantify these models. However, such scientific processes demand data. The required data can be collected through secondary methods (published data) or/and primary methods including (a) observations, (b) interviews, (c) focus groups, (d) pilot studies, and (e) surveying and sampling. From such data sources, firms are in the position of the necessary information to construct a demand equation for their product.
A purpose of demand estimation is for forecasting future sales and prices. Estimation of demand functions is most often accomplished using the technique of regression analysis. Typically, in the demand estimation, quantity demanded for a product or a service is stated in relation to its price, the level of income, price substitute(s), price of complement(s), population, and tastes and preferences.
The method of estimating the parameters of an empirical demand function depends on whether the price of the product is market–determined (perfect competition) or company–determined (monopolistic competition, oligopoly, and monopoly). Managers of price–taking firms do not set the price of the product they sell; rather, prices are endogenous or "market–determined" by the intersection of demand and supply. Managers of price–setting firms set the price of the product they sell by producing the quantity associated with the chosen price on the downward–sloping demand curve facing the firm. Since price is manager–determined rather than market–determined, price is exogenous for price–setting firms.
**Weekly Objective(s):**
LO1: Explain the relationship between price elasticity of demand and revenue
LO2: Discuss how a firm can maximize its profit by using optimal markup pricing and price discrimination
LO3: Explain how empirical analysis is both "art and science"
LO4: Explain the statistics that are generated by regression analysis
Income Level of Consumers
The first potential demand shifter is the income level of consumers. In general, when consumers have a higher purchasing power, they will have a tendency to increase their purchase of most products, thus shifting their demand curve to the right. As I just mentioned, this is true for most products that are thus called normal goods. The opposite can be observed for products that are called inferior goods. They are products or goods that consumers purchase when they cannot afford more expensive alternatives. If or when their income increase, they replace them with more desirable alternatives.
Substitute Goods
The second factor that has the potential to shift the demand curve for a product or a service are similar products offered by the competition. They are called
**substitute goods**. For instance, if a competitor comes up with an innovative product that is similar to what a firm produces, the demand for the original product will decrease. In other words, an increase in demand for good results in a decreased demand for its substitutes, because consumers purchase one or the other.
Complementary Goods
After substitute goods, let me discuss
**complementary goods**. These are the types of products or/and services that are jointly purchased and consumed with a given item, and therefore, an increase in demand for such a good results in an increase in demand for its complements. For example, if the demand for hot dogs increased during the summer, the demand for hot dogs buns would increase as well. They are complementary goods.
Size of the Population
Of course, the size of the population is an important demand shifter. More consumers mean more demand, pure and simple. It is worth mentioning that the characteristics of a population, such as racial and ethical compositions and age distribution, may have a significant impact on the demand for a product.
Consumer Tastes and Preferences
Finally, the tastes and preferences of consumers can create massive shifts of the demand curve. For example, the rising concern of many people for the protection of the environment has shifted the demand for automobiles toward hybrid and electric vehicles, resulting in massive industry investment in that area.
Price Elasticity
It goes without saying that managers are keenly interested in the responsiveness of the demand they face to a change in the price of their product. Would most people stop buying their product if they raise the price by say 1%, or would such an increase go by unnoticed and not influence sales? The answer to such questions is provided by calculating the price elasticity of demand: it measures how responsive consumers are to a change in price. Let me define this more precisely.
From the Law of Demand, we understand that when the price of a product increases, the quantity demanded decreases, and vice-versa. Price elasticity provides information on the responsiveness of consumers to a change in price, i.e. by how many percents the quantity demanded would decrease if the price increased by 1%. This concept applies to any of the precedent variables, by the way. It is possible to calculate the income elasticity of demand, for example. It would measure the
**change in quantity demanded** in response to a change in the general income level of the population.
Estimating and Forecasting Demand
The precedent discussion about elasticities, and the discussion of demand analysis in chapter 3 of our textbook (Samuelson & Marks) requires knowledge of the demand curve of the market managers are interested in. The goal is not so much to understand what has happened in the past but to use the information from the past to predict what is likely to take place in the future so the best pricing and production decisions can be made.
There are several ways to try and predict future demand. Some methods are qualitative and represent an intuitive approach to forecasting based mostly on opinions, while quantitative methods require data gathering and statistical analysis to build some type of model. While the latter is more precise, it is also more costly and time-consuming, and a well-designed qualitative analysis may be a useful first step in demand analysis. Let me discuss it briefly, before summarizing the main principles behind the statistical tool that is used most often, regression analysis.
The easiest way to forecast demand using qualitative analysis is to rely on the personal insights of well-informed individuals. It is clearly a subjective approach, but it can provide valuable information, especially when it is not based on the opinion of a single individual, but on the informed opinion of a group. It will avoid the bias a single individual may have, and guarantee that more information will be taken into account to formulate a forecast.
Qualitative Analysis
It goes without saying that a group of experts may not come to a consensus easily. Furthermore, if one or a few of them have a forceful personality, their opinion can shape the consensus of the group, even if it is not the best one.
To avoid these issues, a method called the Delphi method is sometimes used. Individuals have to answer separately to a series of questions relating to the forecasting problem. Responses are analyzed by an independent party, who provides an anonymous summary of the forecasts with the reasons provided by each expert. Then, experts are encouraged to take into account everyone’s observations and to revise their answers accordingly. The range of forecasts tends to decrease, and the results can be averaged to determine a forecast that is fairly consensual.
The second type of qualitative analysis that is often used is survey techniques. As many of us have probably experienced, surveys are interviews or questionnaires that can provide managers with valuable information, provided they are well designed. It may be the only available tool in certain situations; like, for example, when a firm tries to estimate the demand for a new product (quantitative analysis is not possible since there is no data available).
In general, though, surveys are used as a complement to quantitative analysis. One of the reasons is that quantitative models generally assume that consumer’s tastes have not changed, so past data can be used to predict consumer’s behavior. However, if tastes are actually changing, survey data can provide information on the way these tastes and preferences have changed.
References
Samuelson W.F. & Marks S.G. (2015).
_Managerial Economics_. Hoboken, NJ: Wiley & Sons Inc.
Chase, Charles W..
_Demand-Driven Forecasting : A Structured Approach to Forecasting_, John Wiley & Sons, Incorporated, 2013.
_ProQuest Ebook Central_,
[https://ebookcentral.proquest.com/lib/apus/detail.action?docID=1315864](https://ebookcentral.proquest.com/lib/apus/detail.action?docID=1315864).
**Demand-Driven_Forecasting_A_Structured_Approach_to…_—-_Chapter_2_What_Is_Demand-Driven_Forecasting_.pdf**
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