models for strategic marketing decision making. market entry decisions to enter first or to wait...

Models for Strategic Marke ting Decision Making

Market Entry Decisions

• To enter first or to wait• - Sources of First Mover Advantages

– Technological leadership• - Experience curve effect, patents, R&D success

– Preemption of scarce assets– Switching costs

Ch. 3 Decision Analysis (DA)

• Making decisions under conditions of uncertainty– Decision Theory Models

• A choice or sequence of choices must be made among var ious courses of action

• The choice or sequence of choices will ultimately lead to s ome consequence; but the decision maker cannot be sure

in advance what the consequence will be, because it depe nds not only on his or her decision but also on an unpredic

table event or sequence of events

– Choice of action depends on the likelihood that the action will have various possible consequences an

d the desirability of the various consequences

Types of Decision Making Environments

• Decision making under uncertainty– the decision maker does not know the probabilities of the vari

ous outcomes

• Decision making under risk– the decision maker knows the probability of occurrence of ea

ch outcome• models are based on two equivalent criteria: maximization of ex

pected monetary value (EMV) and minimization of expected loss

• Decision making under certainty– the decision maker is certain about the consequences of ever

y alternative or decision choice• choice is based on the alternative that results in the best outcom

e

Steps in Decision Analysis Approach

• Structure the problem– state objectives, measures of effectiveness, restric

tions on actions, chronology of events

• Assign probabilities to possible consequences– subjectively or based on past system behavior

• Assign payoffs to consequences– state preferences to possible outcomes

• Analyze the problem– average and fold back

Decision Table Analysis Approach

Assume we are considering the use of three different strategies to make ourproduct

available to prospects: Nationwide distribution, mail order, or sell patent. Th ere are

three states of the market that are possible. The expected profits for each alternative

under each market state is presented in the payoff table below.






• Payoff Tables (also called a Payoff Matrix)– A payoff table is a table that gives the outcome (e.g. profits)

of a decision under different conditions or states of nature.

NationwideMail orderSell patent

High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of Nature






Without information on the probability of market states, this would be considered

decision making under uncertainty. How are decisions made?


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of Nature

Decision Making Under Uncertainty

• Common Decision Rules– Maximax– Maximin– Equally Likely– Criterion of Realism– Minimax


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of Nature


• Maximax– Choose the best of the best– Is a rule for risk takers

Based on the maximax criterion, which alternative would be chosen?


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of NatureRow Best Outcome

($)95,00048,00025,000


• Maximin– Choose the best of the worst– Is a rule for those who are risk averse

-Worst if nationwide is chosen = $26,000 19000Worst if mail order is chose = $ , 25000Worst if we sell the patent = $ ,

Based on the maximin criterion, which alternative would be cho

ssss


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of NatureRow Worstt Outcome

($)-26,00019,00025,000


• Equally Likely (Laplace Decision Rule)– Choose alternative with the highest computed average outco

s s

ssssssssss row average = (95,000+52.000+(-26,000))/3 = s40333,

ssss sssss sssssss = (48,000+24.000+19,000)/3 303= $ ,33

ssss ssssss sssssss = (25,000+25.000+(25,000))/3 25= $,0 0 0

Based on the equally likely criterion, which alternative would bechosen?


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of NatureRow Average Outcome

($)40,33330,33325,000


• Criterion of Realism (Hurwicz Criterion)– Compute a weighted average using a coefficient of realism ,

0 1which is between and– ssss is closer to one, the decision maker is optimistic about

t he f ut ur e

– cr i t er i on of r eal i sm= - 1(maximum in row) + ( )(minim um in row)

If is .80, which alternative would be chosen under this rule?


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of Nature


• Criterion of Realism (Hurwicz Criterion)– Compute a weighted average using a coefficient of realism ,

0 1which is between and– ssss is closer to one, the decision maker is optimistic about

t he f ut ur e

– cr i t er i on of r eal i sm= - 1(maximum in row) + ( )(minim um in row)

If is .80, which alternative would be chosen under this rule?


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of NatureRow Best Outcome

($)70,80042,20025,000


High

95,00048,00025,000

Moderate

52,00024,00025,000

Low

-26,00019,00025,000

States of Nature


• Minimax– Minimize the maximum regret (opportunity loss)– The opportunity loss is the loss that occurs through not takin

g the best option for each state of nature . I t can be shown i n an opportunity loss table




Opportunity Loss Table


High

047,00070,000

Moderate

028,00027,000

Low

51,0006,000

0

States of NatureRow Maximum Loss

($)51,00047,00070,000




Maximum regret for Nationwide = $51,000 Maximum regret for Mail Order = $47,000 Maximum regret for Sell Patent = $70,000

Based on the minimax decision rule, which alternative would bechosen?


High

047,00070,000

Moderate

028,00027,000

Low

51,0006,000

0

States of NatureRow Maximum Loss

($)51,00047,00070,000

Decision Making Under Risk Assume we are considering the use of three different strategies to make our

product available to prospects: Nationwide distribution, mail order, or sell patent. Th

ere are three states of the market that are possible. The expected profits for each alt

ernative under each market state is presented in the payoff table below.

Now let’s assume that we know the probability of occurrence of each marketstate.

There is a 25, 30, and 45 percent chance that the state of the market will behigh,

moderate, and low, respectively. Now we are in decision making under risk.

ProbabilitiesNationwideMail orderSell patent

High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000

States of Nature

Decision Making Under Risk

• Choose the alternative with the highest expected monet ary value (EMV)

• EMV– The weighted sum of possible payoffs for each alternative EMV (alternative i ) = (payoff of the first state of nature) x (probability of first state of nat

ure)

+(payoff of second state of nature) x (probability of second state of nature) +(payoff of third state of nature) x (probability of third state of nature)

+. . . +(payoff of last state of nature) x (probability of last state of nature)


High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000

States of Nature


• Calculate expected monetary values (EMV)

- EMV (Nationwide) = (.25)(95,000) + (.30)(52,000) + (.45)( 26,000) = 27,650 EMV (Mail Order) = (.25)(48,000) + (.30)(24,000) + (.45)(19,000) = 27,750 EMV (Sell Patent) = (.25)(25,000) + (.30)(25,000) + (.45)(25,000) = 25,000


High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000

States of NatureComputed EMVs

($)27,65027,75025,000


• Calculate expected monetary values (EMV)

- EMV (Nationwide) = (.25)(95,000) + (.30)(52,000) + (.45)( 26,000) = 27,650 EMV (Mail Order) = (.25)(48,000) + (.30)(24,000) + (.45)(19,000) = 27,750 EMV (Sell Patent) = (.25)(25,000) + (.30)(25,000) + (.45)(25,000) = 25,000

Since the EMV for Mail Order is the greatest, it is the alternative that would be chosen.


High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000


($)27,65027,75025,000

Expected Value of Perfect Information

Assume we are considering the use of three different strategies to make our product

available to prospects: Nationwide distribution, mail order, or sell patent. Ther e are



St ar Tech Resear ch has appr oached us and cl ai ms t hat i t can pr ovi de us wi th perfect

information regarding the states of the market, thus permitting us to be in a decision

situation of certainty. What is the most we should pay for this information?


High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000


($)27,65027,75025,000


• Expected Value of Perfect Information (EVPI)– the expected value with perfect information minus the maximum EMV

• Expected Value with Perfect Information (EVwPI)– the expected or average return, in the long run, if we have perfect infor

mation before a decision is to be made EVwPI = (best outcome or consequence state of nature) x (probability of first state of na

ture) +(best outcome of second state of nature) x (probability of second state of natu re) +(best outcome of third state of nature) x (probability of third state of nature) +.

. . +(best outcome of last state of nature) x (probability of last state of nature)


High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000


($)27,65027,75025,000


• Expected Value of Perfect Information (EVPI)– the expected value with perfect information minus the maximum EMV

• Expected Value with Perfect Information (EVwPI)– the expected or average return, in the long run, if we have perfect infor

mation before a decision is to be made EVwPI = (.25)(95,000) + (.3)(52,000) + (.45)(25,000) = 50,600

- Thus, the EVPI would be 50,600 27,750 = 22,850, and $22,850 is the most we should be prepared to pay for this information.


High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000


($)27,65027,75025,000

Summary of Alternatives Chosen Un der Different Decision Environments

and Decision Rules• Decision Making Under Uncertainty

– Maximax: Nationwide– Maximin: Sell Patent– Equally Likely:Nationwide– Criterion of Realism ( 8= . ) = Nationwide– Minimax: Mail Order

• Decision Making Under Risk– EMV: Mail Order


High

.2595,00048,00025,000

Moderate

.3052,00024,00025,000

Low

.45-26,00019,00025,000


($)27,65027,75025,000

Ch. 4 Decision Tree Analysis

• A graphical device for analyzing decision und er risk; used on models in which there is a seq

uence of decisions, each of which could lead t o one of several uncertain outcomes

– D (decision nodes) management has control over the course of acti

on

– C (chance node) decision maker has no control

Example: QSR Company’s Ne w Product Introduction Decisi

on• Purpose: to determine whether to introduce a new product• Key points of information

– Two possible outcomes from introduction• High sales resulting in net profits of $100,000 (excluding survey costs) or• 50000Low sales resulting in a net loss of $ , (excluding survey costs

)

– Probability of high sales = 0.40– Market survey costs $16,000– - Market research results great, good, or poor

• - 60 percent of its high sales products in the past had great survey results• - 30 percent of its high sales products had good survey results• - 10 percent of its high sales products had poor survey results• - 10 percent of its low sales products had great survey results• - 30 percent of its low sales products had good survey results• - 60 percent of its low sales products had poor survey results

New Product Introduction Dec ision Tree

Consequences ($1,000s)

+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10 C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey

Great Survey Results (0.3)

Good Survey Results(0.3)

No Additional Information (1.0)

Poor Survey Results (0.4)

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Time Sequence of Events+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



C

C

C

C

C

C

C

C

D

D

D

D

C

C

D

Take M

arket Survey

No Market Survey

Great Survey Results

Good Survey Results

No Additional Information

Poor Survey Results

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Time Sequence of Events



C

C

C

C

C

C

C

C

D

D

D

D

C

C

10D

Take M

arket Survey

No Market Survey


Good Survey Results


Poor Survey Results

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Since the tree has already been constructed, we begin by working backward, from right to left, averaging out and folding back



C

C

C

C

C

C

C

C

D

D

D

D

C

C

10D

Take M

arket Survey

No Market Survey


Good Survey Results


Poor Survey Results

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Recall from our initial information that the possible outcomes include net profits from high sales, low sales, and no sales.



C

C

C

C

C

C

C

C

D

D

D

D

C

C

10D

Take M

arket Survey

No Market Survey


Good Survey Results


Poor Survey Results

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0

Outcomes:High sales = 100,000 + (- 16,000)Low sales = -50,000 + (-16,000)No sales = -16,000



C

C

C

C

C

C

C

C

D

D

D

D

C

C

10D

Take M

arket Survey

No Market Survey


Good Survey Results


Poor Survey Results

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Next, we determine the probabilities of possible consequences

For example, What is the probability of High Sales given Great Survey Results?

What is the probability of Low Sales given Great Survey Results?

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0


on• Conditional Probabilities– We apply Bayes’s Theorem to calculate conditional probabilit

ies

p (A B) =

p (B A) p (A)

p (B)


on• Probabilities known from past experience– p (great survey high sales) = 0.6– p (good survey high sales) = 0.3– p 01(poor survey high sales) = .– p (great survey low sales) = 0.1– p (good survey low sales) = 0.3– p 06(poor survey low sales) = .– p 04(high sales) = .– p 06(low sales) = .


on• Probabilities we need to determine– Survey outcomes based on theorem of total probabilities– p (great survey)

= p (great survey high sales) p (high sales) + p (great survey low sales) p (low sales)

= (0.6)(0.4) + (0.1)(0.6) = 0.3

– p (good survey) = p (good survey high sales) p (high sales) + p (good survey low sales) p (low sales)

= (0.3)(0.4) + (0.3)(0.6) = 0.3

– p (poor survey) = p (poor survey high sales) p (high sales) + p (poor survey low sales) p (low sales)

= (0.1)(0.4) + (0.6)(0.6) = 0.4


on• Conditional Probabilities based on Survey Results p (great survey) 03= . p (good survey) 03= . p (poor survey) 04= .

ss sss p (high sales great survey) =

= = 0 .8

p (great survey high sales) p (high sales)

p (great survey)(0.6)(0.4)

(0.3)



To get p (high sales poor survey) =

= = 0 .1

p (poor survey high sales) p (high sales)

p (poor survey)

(0.1)(0.4)

(0.4)



To get p (low sales good survey) =

= = 0 .6

p (good survey low sales) p (low sales)

p (good survey)

(0.3)(0.6)

(0.3)



C

C

C

C

C

C

C

C

D

D

D

D

C

C

D

Take M

arket Survey

No Market Survey


Good Survey Results (0.3)



Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Include probabilities onto the tree. Next, we determine the EMV at each node.

For example, What is the EMV after making the new product based on good survey results?

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



C

C

C

C

C

C

C

C

D

D

D

D

C

C

D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Recall that

EMV = (payoff) x (probability of first state of nature) + (payoff of second state of nature) x (probability of second state of nature) + . . .

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



C

C

C

C

C

C

C

D

D

D

D

C

C

D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

So the EMV after making a new product based on good survey results is

EMV = (0.4)(84,000) + (0.6)(-66,000)

EMV = -6,000Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

–6 C

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



C

C

C

C

C

C

D

D

D

D

C

C

D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

What is the EMV after making a new product based on poor survey results?

EMV = (0.1)(84,000) + (0.9)(-66,000)

EMV = -51,000Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

–6 C

–51 C

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



D

D

D

D

C

C

D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Now we fold back to the decision node before these chance nodes to indicate the optimal EMVs at each of the second decision nodes Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



C

C

D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Now we fold back to the decision node before these chance nodes to indicate the optimal EMVs at each of the second decision nodes Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Again, we calculate the EMVs for the first chance node set

EMV = (0.3)(54,000)+(0.3)(-6,000)+(0.4)(-16,000)Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Now that the decision tree is complete, how do we use it to make a decision?

What decision would you make? +84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

What is the most the firm should pay for a survey? In other words, what is the expected value of sample information (EVSI)?

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

EVSI =

expected value of best decision with sample information, assuming no cost to

gather it

expected value of best decision without sample information

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

EVSI = (8,000 + 16,000) – 10,000 = 14,000

The most the firm should pay for any market survey is $14,000.)

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

What is the most the firm should pay for perfect information? In other words, what is the expected value of perfect information (EVPI)?

The EVPI sets an upper bound on what to pay for perfect information.

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

By perfect information, it means a forecast such that

p(high forecast high sales) = 1

p(low forecast low sales) = 1

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



C

C

C

C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

To approach this situation, we must reconstruct our tree for perfect information.



C

C

C

C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0



C

C

C

C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High

Sales Low

No Sales

Sales High

Sales Low

No Sales

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


What is the probability of high sales from a high forecast?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0



C

C

C

C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High

Sales Low

No Sales

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


What is the probability of high sales from a high forecast?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0



C

C

C

C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High

Sales Low

No Sales

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


What is the probability of high sales from a low forecast?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0



C

C

C

C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


What is the probability of high sales from a low forecast?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0



C

C

C

C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Averaging out, what are the EMVs?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0



100000

C

0 C

0 C

10C

0C

10D

D

D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast

Low Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Averaging out, what are the EMVs?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast



100000

C

0 C

0 C

10C

0C

10D

0 D

100000 D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

folding back

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast



100000

C

C

0 C

10C

0C

10D

0 D

100000 D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

What are the probabilities of a high forecast and a low forecast?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast (0.6)



100000

C

0 C

0 C

10C

0C

10D

0 D

100000 D

10C

C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

What are the probabilities of a high forecast and a low forecast?

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast (0.6)



100000

C

C

0 C

10C

0C

10D

0 D

100000 D

10C

(0.4)(100,000) + (0.6)(0)C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Averaging out to get EVwPI

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast (0.6)



100000

C

0 C

0 C

10C

0C

10D

0 D

100000 D

10C

(0.4)(100,000) + (0.6)(0)C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

EVPI = EVwPI – Maximum EMV

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast (0.6)



100000

C

0 C

0 C

10C

0C

10D

0D

100000 D

10C

(0.4)(100,000) + (0.6)(0)C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


EVPI = [(0.4)(100,000) + (0.6)(0)] – 10,000100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast (0.6)



100000

C

0 C

0 C

10C

0C

10D

0 D

100000 D

10C

(0.4)(100,000) + (0.6)(0)C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


EVPI = [(0.4)(100,000) + (0.6)(0)] – 10,000

EVPI = 40,000– 10,000100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast (0.6)



100000

C

0 C

0 C

10C

0C

10D

0D

100000 D

10C

(0.4)(100,000) + (0.6)(0)C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


EVPI = [(0.4)(100,000) + (0.6)(0)] – 10,000

EVPI = 40,000 – 10,000

EVPI = 30,000

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

Low Forecast (0.6)



100000

C

0 C

0C

10C

0C

10D

0D

100000 D

10C

(0.4)(100,000) + (0.6)(0)C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


EVPI = [(0.4)(100,000) + (0.6)(0)] – 10,000

EVPI = 40,000 – 10,000

EVPI = 30,000

30,000 is the value of perfect information

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C

% Efficiency Of SI

• % Efficiency of Sample Information = EVSI x 100

EVPI

= 14 x 100 30= 46 %

• When % Efficiency is 30%-60%, it means that sample information is relatively efficient compared to perfect information.

Sum of Decision Table Analysis

1. Decision Making Under Uncertainty: 5 Rules

1. Maximax: Choose the max of the max of each row

2. Maximin: Choose the max of the min of each row

3. Equally likely: Choose the max of the avg of each row

4. Criterion of Realism: Choose the max of the weighted avg of each row Criterion of Realism= (max in row) + (1-)(min in row)

5. Minimax: Choose the min of the max opportunity loss of each row

2. Decision Making Under Risk• Choose the alternative with the highest expected mo

netary value (EMV)• EMV

– The weighted sum of possible payoffs for each alternative ssssssssssss ( i ) = (payoffofthefi r st st at e of nat ur e) x (pr obabi l i t y of fi r st st at e of

nature)+(payoff of second state of nature) x (probability of second state

of nature)+(payoff of third state of nature) x (probability of third state of

nature)+. . . +(payoff of last state of nature) x (probability of last state ofnature)

Sum of Decision Table Analysis (2)

3. Decision Making Under Certainty (or Perfect Information)– What is the most we should pay for this information?– Buy or not buy perfect information?– EVPI = EVwPI – Max EMV

EVwPI = (best outcome or consequence state of nature) x (probability of first state of nature) +(best outcome of second state of nature) x (probability of second state of nature)

+(best outcome of third state of nature) x (probability of third state of nature) +. . . +(best outcome of last state of nature) x (probability of last state of nature)

– If research company charges > EVPI, not buy perfect info.

– If research company charges < EVPI, buy perfect info.

Sum of Decision Table Analysis (3)

1. Read the problem carefully2. List the info from the problem3. Construct the tree

• To get numbers (EMVs) at nodes C, do averaging out (just like we usually do to get EMVs).

• To get numbers at nodes D, do folding back (just pick the one that is higher).

Sum of Decision Tree Analysis

Sum of Decision Tree Analysis (2)

• To calculate EMVs at the first set (from the right) of nodes C, we need to get conditional probabilities of the possible outcomes-high sales, low sales- first by using the following formula:

p (A B) =p (B A) p (A)

p (B)

For example, What is the probability of High Sales given Great Survey Results?

What is the probability of Low Sales given Great Survey Results?

• To get P(B), use theorem of total probabilities

• P(B) = P(B/Outcome1)P(O1) + P(B/O2)P(O2)

• So now, we can get conditional probabilities of the possible outcomes

• Then, do averaging out and folding back by working backward until you reach the last decision node. At that node you will know what the best alternative is.



• What is the most the firm should pay for a survey• In other words, what is the expected value of sample i

nformation (EVSI)?

EVSI =expected value of best decision with sample information, assuming no cost to gather

it

expected value of best decision without sample info

rmation

EVSI = (8,000 + 16,000) – 10,000 = 14,000

The most the firm should pay for any market survey is $14,000)


+54 C

–16 C

–6 C

–16 C

–51 C

–16 C

10C

0C

10D

–16 D

–6 D

+54D

10C

8C

10D

Take M

arket Survey

No Market Survey





Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (0.8)

Sales Low (0.2)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

Sales High (0.1)

Sales Low (0.9)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)

+84

–-66

–16+84

–-66

–16+84

–-66

–16+100

–50

0



• What is the most the firm should pay for perfect information?

• In other words, what is the expected value of perfect information (EVPI)?

• The EVPI sets the most to pay for perfect information.

• By perfect information, it means a forecast such that

P(high forecast / high sales) = 1

P(low forecast / low sales) = 1

• Reconstruct the tree

Low Forecast (0.6)


100000

C

0 C

0C

10C

0C

10D

0D

100000 D

10C

(0.4)(100,000) + (0.6)(0)C

D

Take Perfe

ct Inform

ation

No Market Survey

High Forecast (0.4)


Make New Product

Don’t Make

Make New Product

Don’t Make

Make New Product

Don’t Make

Sales High (1.0)

Sales Low (0.0)

No Sales (1.0)

Sales High (0.0)

Sales Low (1.0)

No Sales (1.0)

Sales High (0.4)

Sales Low (0.6)

No Sales (1.0)


EVPI = [(0.4)(100,000) + (0.6)(0)] – 10,000

EVPI = 40,000 – 10,000

EVPI = 30,000

30,000 is the value of perfect information

100,000

–-50,000

0

100,000

–50,000

0

100,000

–-50,000

0

-50,000

C


models for strategic marketing decision making. market entry decisions to enter first or to wait...

Documents

decision choice choice

riskthe decision maker

uncertaintythe decision

payoff matrixa payoff

probability of market

expected profits

arethree states

various possible consequences