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Risk analysis from the point of view of Toro

• Why did the insurance company raise the rates so much? How would you estimate a fair insurance rate?

• From the perspective of the consumer, how were the paybacks structured and how might they be restructured to entice you at an equal or lower cost of insurance? How does the program influence your decision to purchase?

• What are the common decision traps which each group in point (2) is susceptible to? Develop a matrix or decision tree in order to compare the groups. How does the program impact the consumer’s “regret”?

Risk analysis from the point of view of Toro:  

The Toro company  bears almost all nil to a  minimal risk on this S’ No risk program the year they ran it, as the most they are going to pay-out to the insurance company in 1983 is around $680K, while in-turn profiting $106K. (Bell,  2004).

It all started with the year when they ran the promotion, a confluence of elements came into play: the insurance company erroneously quoted them 2.1% of the retail value of the snow throwers covered; the snowfall was significantly higher than the year before, however due to the highest amounts of premium which was levied by the insurance company, it did not lose its shorts on its liabilities. Other than this they didn’t have to pay the external vendors the 10 percent discount they normally did in the fall. This resulted in an increase of 8% in profit for Toro.

American Home Assurance carried the most risk. According to the case study, they agreed to meet all claims from the program for only 2.1 percent of the retail value of the snow throwers covered (Bell, 2004). The total number of rebates that year of the promotion was 19 percent.   While Toro, hedged its losses, American Home ate 17 percent of the cost of rebates.   If Toro were to continue the program it would increase the premium to 8% of the total sales, which amounted to an average of the last four years of actual payouts by Toro (Bell, 2004).   Some of the brands like American Home would try to recoup their losses however in the case of Toro it would be different as they  would have to shoulder some of the risk it was heavy snowfall year.   This is a point where uncertainty and come s into play—snowfall is an event you can’t predict nor control.

Risk analysis from the point of view of the insurance companies

The consumers would have mixed perceptions in such situations when it comes to dealing with losses and risks where the consumer has a perception of almost a nil risk in the process of promotion. Also the consumer is also would also be able to maximize this perception by upgrading and buying a larger model (Bell, 2004). Without the S’No Risk program, the consumer would be unsatisfied if they...

During the same year S’No. Risk Program there were various elements as we have seen that the American Insurance company has made an error in quoting the 2.1% as a cover of the snow thrower retail value. The snow fall which was experienced was more than the previous year and also dur to the presence of the premium cap by the American Home Assurance Toro did not lose out on its liabilities.

Other than this the copany has also ended up in not paying the 10% discount to its 26 distributors during the fall which has ended up in resulting huge amount of profits to the company.

The American Home Assurance company was the one which has ended up bearing the highest risk with the running of the S’ No Risk program. The insurance company however had agreed to solve and clear all customers’ claims at a rate of 2.1% of the covered snow throwers retail value (Bell, 2004). The total amount of rebates which were offered as a part of the promotion year was 19%. The Toro brand was able to make up for its losses where the insurance company absorbed 17% of the rebates costs. In the event that Toro would continue with the S’ No Risk program the premium rates would increase to 8% of the sales total. This was the amount which was calculated out of an average of the previous four years of Toro’s actual payouts. The insurance company would have naturally attempted to recoup for its losses as Toro was forced to bear a part of the risk in the year in which they have experienced a heavy snowfall. These are some of the natural conditions of weather which can’t be controlled or predicted which has created an uncertainty factor of the insurance cover.

The consumers had mixed perceptions and the main perception of the promotion was that of no-risk and to that end, they were able to utilize the offer by purchasing and/or upgrading larger models of the equipment (Bell, 2004). In the absence of the S’No Risk program the entire program would leave a dissatisfied consumer in the event they purchased a snow thrower and there were no snow; and if the consumer opted not to purchase and there was record high snow falling. The program offered a win - win situation for the consumer as it eliminated the aspect of uncertainty in the purchasing decision-making process. This in turn is a positive aspect from a consumer’s point of view as he is benefitted at the end of the entire deal.

Risk analysis from the point of view of consumer

American Assurance Insurance devised options and they had to increase the premium rates for Toro so as to spread their risk over a group or individuals so as to remain in operation. Strategy which they have used was a lot different to the one which is generally expected as it is ok to use the past recording but they should also consider the overall environmental readings and should devise their present plans. All that the insurance firm has done was that they have used previous snowfall recordings and past claims to formulate the average number of purchases of snow throwers. In addition to this the fact was that there was a 19% payout in the period running in 1983 – 1984.  The insurance firm had to answer for this while it was presenting the new premium at 8% of sales (Bell, 1994). If the S ’No Risk program had been available the previous three years, then the insurance firm would have quoted a rate that was based on the 1979-1982 average payouts.

This would have resulted in a much lower rate as it would have been at a rate of 4.3 % as opposed to the quoted 2.1%. The unpredictability of the weather complicates the analysis of risk by any actuarial scientist and hence, I would have thought of devising the formula in a similar way and  I would utilize the same rates just the way the insurance firm in determining an insurance premium rate that was fair to the customers.

 The S’ No Risk pay back structure has also presented a win – win situation for the snow throwers customers. The main base for this has been in the structure of the climatic conditions which the report has given in different regions where snowfall was expected. This was the sole determinant factor in all these cases.

Influence of program on the purchase conditions which the people had and their uncertainty in decision making are the deciding aspects that would impact my decision as a customer to purchase a snow thrower. As discussed above weather is not a factor which can be predicted and not able to decide on the amount of snowfall that would fall within a given season would play a role in my purchase decision-making. As a true consumer my dedicated focus will be, as stated by Kahneman (1984), on mental accounting of the purchase of a unit within the snow season without analyzing its use in the following years. I would anchor my decision on the previous years’ snow fall amounts and either under or overestimate the current year amounts. (Simonson, 1992).

Insurance Rates

Decision traps which would decide on all the in the program are inevitable. The consumer faces the various decision traps on anchoring where he gives preference to the most prevalent information at hand. If he tends to ignore the amount of snowfall based on the previous year’s recorded amounts, he may get attracted to buy a snow thrower based on the sliding parameters of the S’, No Risk program. The consumer could also face the pseudo certainty effect of the prospect theory where he is either risk-averse or risk-acceptant based on the snowfall patterns. The customer, would not face any risk in the entire program.

The premium rates which were to be fixed for the present year were determined by the insurance company using anchoring had Toro opted to extend the promotion to the following year. The fact that the insurance company had lost money with the 2.1% rate, it used this as the main driver to set a disproportionate weight at 8%.

References

Bell, D. E. (1994). The Toro company s’no risk program. Harvard Business School. Case No. 9-185-017.

Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. American Psychologist, 39(4), 341–350. doi: 10.1037/0003-066X.39.4.341

Simonson ,I., Tversky, A. (1992) Choice in Context: Tradeoff Contrast and Extremeness Aversion. Journal of Marketing Research, 29 (3). pp. 281-295

Russo, J.E., & Schoemaker, P.J.H. (2002). Winning Decisions: Getting It Right the First Time. New York: Doubleday (ISBN 0749922850, pbk.). [abbreviated RUSSO-SHOEMAKER below] The best “how do” managerial decision making book available

. Allen, David (2009). Making It All Work: Winning At The Game Of Work And The Business Of Life. New York: Penguin (ISBN-10: 0143116622 pbk.).

Gladwell, M. (2005). Blink: The Power Of Thinking Without Thinking. New York: Back Bay Books (ISBN 0316010669 pbk.). Management bestseller; book view assignment (see below).

 Ayres, I. (2007). Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart. New York: Bantam Books (ISBN 0553384732 pbk.). A good popularized argument for the advantages of statistical and experimental analysis over intuitive judgment – a complement to Blink.

Surowiecki, J. (2004). The Wisdom of Crowds. New York: Doubleday (ISBN 0-34-911605-9, pbk.).

Optional: Hammond, J.S., Keeney, R.L., & Raiffa, H. (1998). Smart Choices: A Practical Guide To Making Better Decisions. Boston: Harvard Business School Press (ISBN 0767908864, pbk.). [abbreviated HAMMONDetal below] This is a non-technical overview of decision analysis, with many simple example applications (many MBAs say, “A fine book, too elementary for me, but my spouse loved it”).

 Optional: Bazerman, M.H., & Moore, D. (2005, 7th ed.). Judgment In Managerial Decision Making. New York: Wiley (ISBN-13: 978-0-470-04945-7 hardcover). [abbreviated BAZERMAN-MOORE below]

Optional: Heath, C. & Heath, D. (2007). Made To Stick: Why Some Ideas Survive And Others Die. New York: Random House (ISBN 10-1400064287, hrdcvr.)  BUS 38002, Syllabus 2010 (Hastie): Page 2

Optional: Allen, David (2002). Getting Things Done: The Art Of Stress Free Productivity. New York: Penguin (ISBN-10: 0142000280 pbk.).

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