1. Models for Strategic Marketing Decision Making

2. 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

3. Ch. 3 Decision Analysis (DA) Making decisions under conditions of uncertainty –Decision Theory Models A choice or sequence of choices must be made among various courses of action The choice or sequence of choices will ultimately lead to some consequence; but the decision maker cannot be sure in advance what the consequence will be, because it depends not only on his or her decision but also on an unpredictable event or sequence of events –Choice of action depends on the likelihood that the action will have various possible consequences and the desirability of the various consequences

4. Types of Decision Making Environments Decision making under uncertainty –the decision maker does not know the probabilities of the various outcomes Decision making under risk –the decision maker knows the probability of occurrence of each outcome models are based on two equivalent criteria: maximization of expected monetary value (EMV) and minimization of expected loss Decision making under certainty –the decision maker is certain about the consequences of every alternative or decision choice choice is based on the alternative that results in the best outcome

5. Steps in Decision Analysis Approach Structure the problem –state objectives, measures of effectiveness, restrictions 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

6. Decision Table Analysis Approach Assume we are considering the use of three different strategies to make our product available to prospects: Nationwide distribution, mail order, or sell patent. There 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.

7. Decision Table Analysis Approach Assume we are considering the use of three different strategies to make our product available to prospects: Nationwide distribution, mail order, or sell patent. There 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. Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature

8. Decision Table Analysis Approach Assume we are considering the use of three different strategies to make our product available to prospects: Nationwide distribution, mail order, or sell patent. There 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. Without information on the probability of market states, this would be considered decision making under uncertainty. How are decisions made? Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature

9. Decision Making Under Uncertainty Common Decision Rules –Maximax –Maximin –Equally Likely –Criterion of Realism –Minimax Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature

10. Decision Making Under Uncertainty Maximax –Choose the best of the best –Is a rule for risk takers Based on the maximax criterion, which alternative would be chosen? Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature Row Best Outcome ($) 95,000 48,000 25,000

11. Decision Making Under Uncertainty Maximin –Choose the best of the worst –Is a rule for those who are risk averse Worst if nationwide is chosen = -$26,000 Worst if mail order is chose = $19,000 Worst if we sell the patent = $25,000 Based on the maximin criterion, which alternative would be chosen? Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature Row Worstt Outcome ($) -26,000 19,000 25,000

12. Decision Making Under Uncertainty Equally Likely (Laplace Decision Rule) –Choose alternative with the highest computed average outcome Nationwide row average = (95,000+52.000+(-26,000))/3 = $40,333 Mail order average = (48,000+24.000+19,000)/3 = $30,333 Sell patent average = (25,000+25.000+(25,000))/3 = $25,000 Based on the equally likely criterion, which alternative would be chosen? Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature Row Average Outcome ($) 40,333 30,333 25,000

13. Decision Making Under Uncertainty Criterion of Realism (Hurwicz Criterion) –Compute a weighted average using a coefficient of realism , which is between 0 and 1 –When  is closer to one, the decision maker is optimistic about the future –criterion of realism =  (maximum in row) + (1 -  )(minimum in row) If  is.80, which alternative would be chosen under this rule? Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature

14. Decision Making Under Uncertainty Criterion of Realism (Hurwicz Criterion) –Compute a weighted average using a coefficient of realism , which is between 0 and 1 –When  is closer to one, the decision maker is optimistic about the future –criterion of realism =  (maximum in row) + (1 -  )(minimum in row) If  is.80, which alternative would be chosen under this rule? Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature Row Best Outcome ($) 70,800 42,200 25,000

15. Nationwide Mail order Sell patent High 95,000 48,000 25,000 Moderate 52,000 24,000 25,000 Low -26,000 19,000 25,000 States of Nature Decision Making Under Uncertainty Minimax –Minimize the maximum regret (opportunity loss) –The opportunity loss is the loss that occurs through not taking the best option for each state of nature. It can be shown in an opportunity loss table

16. Decision Making Under Uncertainty Minimax –Minimize the maximum regret (opportunity loss) –The opportunity loss is the loss that occurs through not taking the best option for each state of nature. It can be shown in an opportunity loss table Opportunity Loss Table Nationwide Mail order Sell patent High 0 47,000 70,000 Moderate 0 28,000 27,000 Low 51,000 6,000 0 States of Nature Row Maximum Loss ($) 51,000 47,000 70,000

17. Decision Making Under Uncertainty Minimax –Minimize the maximum regret (opportunity loss) –The opportunity loss is the loss that occurs through not taking the best option for each state of nature. It can be shown in an opportunity loss table Maximum regret for Nationwide = $51,000 Maximum regret for Mail Order = $47,000 Maximum regret for Sell Patent = $70,000 Based on the minimax decision rule, which alternative would be chosen? Nationwide Mail order Sell patent High 0 47,000 70,000 Moderate 0 28,000 27,000 Low 51,000 6,000 0 States of Nature Row Maximum Loss ($) 51,000 47,000 70,000

18. 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. There 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. Now let ’ s assume that we know the probability of occurrence of each market state. There is a 25, 30, and 45 percent chance that the state of the market will be high, moderate, and low, respectively. Now we are in decision making under risk. Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature

19. Decision Making Under Risk Choose the alternative with the highest expected monetary 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 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 of nature) Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature

20. Decision Making Under Risk 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 Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature Computed EMVs ($) 27,650 27,750 25,000

21. Decision Making Under Risk 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. Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature Computed EMVs ($) 27,650 27,750 25,000

22. 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. There 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. StarTech Research has approached us and claims that it can provide us with 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? Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature Computed EMVs ($) 27,650 27,750 25,000

23. Expected Value of Perfect Information 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 information before a decision is to be made 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) Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature Computed EMVs ($) 27,650 27,750 25,000

24. Expected Value of Perfect Information 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 information 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 $22,850 is the most we should be prepared to pay for this information. Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature Computed EMVs ($) 27,650 27,750 25,000

25. Summary of Alternatives Chosen Under 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 Probabilities Nationwide Mail order Sell patent High.25 95,000 48,000 25,000 Moderate.30 52,000 24,000 25,000 Low.45 -26,000 19,000 25,000 States of Nature Computed EMVs ($) 27,650 27,750 25,000

26. Ch. 4 Decision Tree Analysis A graphical device for analyzing decision under risk; used on models in which there is a sequence of decisions, each of which could lead to one of several uncertain outcomes – D (decision nodes) management has control over the course of action – C (chance node) decision maker has no control

27. Example: QSR Company ’ s New Product Introduction Decision 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 Low sales resulting in a net loss of $50,000 (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

28. New Product Introduction Decision Tree Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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

29. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C C D D D D C C D Take Market 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

30. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C C D D D D C C 10 D Take Market 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 Since the tree has already been constructed, we begin by working backward, from right to left, averaging out and folding back

31. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C C D D D D C C 10 D Take Market 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 Recall from our initial information that the possible outcomes include net profits from high sales, low sales, and no sales.

32. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C C D D D D C C 10 D Take Market 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 +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

33. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C C D D D D C C 10 D Take Market 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 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

34. Example: QSR Company ’ s New Product Introduction Decision Conditional Probabilities –We apply Bayes ’ s Theorem to calculate conditional probabilities p (A B) = p (B A) p (A) p (B)

35. Example: QSR Company ’ s New Product Introduction Decision Probabilities known from past experience –p (great survey high sales) = 0.6 –p (good survey high sales) = 0.3 –p (poor survey high sales) = 0.1 –p (great survey low sales) = 0.1 –p (good survey low sales) = 0.3 –p (poor survey low sales) = 0.6 –p (high sales) = 0.4 –p (low sales) = 0.6

36. Example: QSR Company ’ s New Product Introduction Decision 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

37. Example: QSR Company ’ s New Product Introduction Decision Conditional Probabilities based on Survey Results p (great survey) = 0.3 p (good survey) = 0.3 p (poor survey) = 0.4 To get p (high sales great survey) = = = 0.8 p (great survey high sales) p (high sales) p (great survey) (0.6)(0.4) (0.3)

38. Example: QSR Company ’ s New Product Introduction Decision Conditional Probabilities based on Survey Results p (great survey) = 0.3 p (good survey) = 0.3 p (poor survey) = 0.4 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)

39. Example: QSR Company ’ s New Product Introduction Decision Conditional Probabilities based on Survey Results p (great survey) = 0.3 p (good survey) = 0.3 p (poor survey) = 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)

40. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C C D D D D C C D Take Market 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 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

41. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C C D D D D C C D Take Market 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 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

42. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C C D D D D C C D Take Market 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 So the EMV after making a new product based on good survey results is EMV = (0.4)(84,000) + (0.6)(-66,000) EMV = -6,000 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) –6 C +84 –-66 –16 +84 –-66 –16 +84 –-66 –16 +100 –50 0

43. New Product Introduction Decision Tree Consequences ($1,000s) C C C C C C D D D D C C D Take Market 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 What is the EMV after making a new product based on poor survey results? EMV = (0.1)(84,000) + (0.9)(-66,000) EMV = -51,000 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) –6 C –51 C +84 –-66 –16 +84 –-66 –16 +84 –-66 –16 +100 –50 0

44. New Product Introduction Decision Tree Consequences ($1,000s) D D D D C C D Take Market 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 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 10 C 0C0C +84 –-66 –16 +84 –-66 –16 +84 –-66 –16 +100 –50 0

45. New Product Introduction Decision Tree Consequences ($1,000s) C C D Take Market 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 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 10 C 0C0C 10 D –16 D –6 D +54 D +84 –-66 –16 +84 –-66 –16 +84 –-66 –16 +100 –50 0

46. New Product Introduction Decision Tree Consequences ($1,000s) D Take Market 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 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 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C +84 –-66 –16 +84 –-66 –16 +84 –-66 –16 +100 –50 0

47. New Product Introduction Decision Tree Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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) 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

48. New Product Introduction Decision Tree Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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) 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

49. New Product Introduction Decision Tree Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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) 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

50. New Product Introduction Decision Tree Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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) 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

51. New Product Introduction Decision Tree Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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) 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

52. New Product Introduction Decision Tree Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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) 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

53. New Product Introduction Decision Tree Consequences ($1,000s) C C C C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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.

54. New Product Introduction Decision Tree Consequences ($1,000s) C C C C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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. 100,000 –-50,000 0 100,000 –50,000 0 100,000 –-50,000 0

55. New Product Introduction Decision Tree Consequences ($1,000s) C C C C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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. 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

56. New Product Introduction Decision Tree Consequences ($1,000s) C C C C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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) To approach this situation, we must reconstruct our tree for perfect information. 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

57. New Product Introduction Decision Tree Consequences ($1,000s) C C C C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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) To approach this situation, we must reconstruct our tree for perfect information. 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

58. New Product Introduction Decision Tree Consequences ($1,000s) C C C C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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) To approach this situation, we must reconstruct our tree for perfect information. 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

59. New Product Introduction Decision Tree Consequences ($1,000s) C C C C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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

60. New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D D D 10 C C D Take Perfect Information No Market Survey High Forecast Low Forecast No Additional Information (1.0) 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

61. Low Forecast New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C C D Take Perfect Information No Market Survey High Forecast No Additional Information (1.0) 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

62. Low Forecast New Product Introduction Decision Tree Consequences ($1,000s) 100000 C C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C C D Take Perfect Information No Market Survey High Forecast No Additional Information (1.0) 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

63. Low Forecast (0.6) New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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

64. Low Forecast (0.6) New Product Introduction Decision Tree Consequences ($1,000s) 100000 C C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C (0.4)(100,000) + (0.6)(0) C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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

65. Low Forecast (0.6) New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C (0.4)(100,000) + (0.6)(0) C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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

66. Low Forecast (0.6) New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C (0.4)(100,000) + (0.6)(0) C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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 EVPI = [(0.4)(100,000) + (0.6)(0)] – 10,000 100,000 –-50,000 0 100,000 –50,000 0 100,000 –-50,000 0 -50,000 C

67. Low Forecast (0.6) New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C (0.4)(100,000) + (0.6)(0) C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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 EVPI = [(0.4)(100,000) + (0.6)(0)] – 10,000 EVPI = 40,000– 10,000 100,000 –-50,000 0 100,000 –50,000 0 100,000 –-50,000 0 -50,000 C

68. Low Forecast (0.6) New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C (0.4)(100,000) + (0.6)(0) C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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 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

69. Low Forecast (0.6) New Product Introduction Decision Tree Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C (0.4)(100,000) + (0.6)(0) C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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 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

70. % 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.

71. 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

72. 2. Decision Making Under Risk Choose the alternative with the highest expected monetary 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 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 of nature) Sum of Decision Table Analysis (2)

73. 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)

74. 1. Read the problem carefully 2. List the info from the problem 3. 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

75. 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?

76. To get P(B), use theorem of total probabilities P(B) = P(B/Outcome 1 )P(O 1 ) + P(B/O 2 )P(O 2 ) 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. Sum of Decision Tree Analysis (3)

77. Sum of Decision Tree Analysis (4) What is the most the firm should pay for a survey In other words, what is the expected value of sample information (EVSI)? EVSI = expected value of best decision with sample information, assuming no cost to gather it expected value of best decision without sample information EVSI =(8,000 + 16,000) – 10,000 = 14,000 The most the firm should pay for any market survey is $14,000)

78. Consequences ($1,000s) +54 C –16 C –6 C –16 C –51 C –16 C 10 C 0C0C 10 D –16 D –6 D +54 D 10 C 8C8C 10 D Take Market 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) +84 –-66 –16 +84 –-66 –16 +84 –-66 –16 +100 –50 0 Sum of Decision Tree Analysis (5)

79. Sum of Decision Tree Analysis (6) 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

80. Low Forecast (0.6) Consequences ($1,000s) 100000 C 0C0C 0C0C 10 C 0C0C 10 D 0D0D 100000 D 10 C (0.4)(100,000) + (0.6)(0) C D Take Perfect Information No Market Survey High Forecast (0.4) No Additional Information (1.0) 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 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 Sum of Decision Tree Analysis (7)