Toro Company S’No Risk Program Case Study

Assignment Instructions

Review the case study “The Toro Company S’No Risk Program” by David E. Bell (1994)

In the mid-eighties, the Toro company launched a promotion in which snow blower purchasers could refund a portion of their purchase if the next winter brought modest snowfalls. The amount of their refund was tied to snowfall amounts and so, the program was prey to certain risks and uncertainties. You will explore those risks and choices from a variety of perspectives.

Analyze the risks of the program from the following points of view:

  1. Toro
  2. The insurance company
  3. The consumers

Write a 6–8-page analysis paper that addresses the following:

  1. Why did the insurance company raise the rates so much? How would you estimate a fair insurance rate?
  2. 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?
  3. 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”? (Hint: Map the possible outcomes for the consumer.)
  4. From either Toro’s or the insurance company’s perspective, how would you frame your argument to achieve your desired objective?
  5. Was the program successful? Why or why not?
  6. If you were Dick Pollick, would you repeat the program? Assume you manage the Toro Company S’No Risk program and argue your case. To what biases are you susceptible in this case?

Sample Answer

Why Insurance Company Raised the Rate

In the beginning of the campaign, the American Home Assurance Company offered the company a premium of 2.1% of the retail value of snowthrowers sold but later increased this to 8%. The main reason for this change is due to the fact that the insurance company payout approximation in the first case was much less than the actual payout made or was supposed to be made based on the past statistics. In the year 81/82, the company total sales for the covered equipment was 30024217, the 2.1% of this was 630508.56. According to the exhibition 6, the total payout that the insurance company could have made to refund customers experiencing snow average of 50% as per the company provision was 3790509, which is about 12.6% of the sales and not 2.1%. This simply means that the insurance company was making a huge loss with the initial premium. The change of premium was founded on average payout that could have been done in a period of four consecutive years (Bell, 1994). According to Susan’s calculation, the actual payout from 1980 to 193 could have been 4%, 8%, 1%, and 19% whose average is 8%. This ensured that the insurance company did not run a risk of absorbing losses. It could have also given the company an opportunity to recover from the losses recorded in its first year of contract with Toro Company. Susan anticipated that there would be heavy snow in the year 1984, meaning the insurance company refund payout would be minimal. The sales were also anticipated to be high due to high equipment demand in heavy snowing seasons. Thus, Toro sales risk transfer would have minimal and hence with 8%, the company could have managed to recover some of the losses it registered in the 1983 deal (Bell, 1994).

In my opinion, a fair insurance rate needs to be based on previous statistics. The insurance company should not take advantage of the covered company and neither should it take the liability of the company risk that is beyond the company’s premium. In this case, a balance must be reached where by an average value is settled on. In case the yearly payouts goes below the average estimated rate the insurance company should enjoy the profits, while being ready to cover extra payouts in case the liabilities goes past the set rates in the future (Vanderhoof& Altman, 1999).

Customer’s Perspective

The current arrangement provides no risk transfer to the consumer at all rate. Consumers purchase the equipment due to their personal needs. They are added the advantage of guaranteed money back in case the product did not do the intended purpose as perceived by the customers due to weather aspects; something that is beyond the seller’s control. The guaranteed money back is offered while having an assurance of keeping the product for future use. This is like a double win to the consumers. Actually, the consumers would prefer the worse-case scenario where they purchase the product and it does not snow at all. They even go for the most expensive or the largest equipment to ensure that they benefit maximally in case the set refund conditions happen. The paybacks were structured such that in case the customers region snow below 20% of the average they were to get all their money back and retain the item purchased. In case the snow rate was less that 50% the customer will get a refund of (100-snow rate)and retain the product (Bell, 1994). This was a great arrangement. However, they might be restructured to a fixed refund cost of (100-snow rate) for all values below 50%, rather than offering a special offer of 100% for below 20% of the average. This will still entice the customers and reduce the insurance cost. The arrangement completely eliminates the risk of having unnecessary expenditure by purchasing expensive equipment which is not put into its use effectively or satisfactorily. The elimination of dead investment will entice customers to purchase more, especially the thought of having expensive equipment at half or less than half price or for free based on weather condition encourages people to purchase even more.

Common Decision Traps

Consumers’decision is highly susceptible to decision traps. Customers are highly likely to experience plunging in trap where they make decision based on the available information, without making critical evaluation on various aspects that influence purchase such as the need for the product to the buyer. The over estimation of lack of heavy snowfall compared to previous years, increasing the chances of getting a refund is likely to sway buyers decision. In this case, there are high chances for customers facing prospect theory pseudo certainty impact where he or she is either risk-acceptant or risk-averse founded on the patterns of snowfall. In this case the program provides no risks to customers and the chances of double advantages increase chances of purchasing. Others in risk-averse situation may lower the chances of light snowfall and opt out of the deal (Hammond, Keeney& Raiffa, 1999).

Consumer Regrets

The program in this case eliminates consumer regrets. The consumer benefits either way. Purchasing the snowthrower and experiencing heavy snow give appropriate use to the purchased equipment and an easy life for the customer to survive the winter season. Purchasing and experiencing low snowfall gives the buyer a chance to receive the refund and to keep the equipment. Thus the customer benefits either way. However, based on the fact that the tool can be kept for future use, most customers would prefer a situation where you make a purchase and the snowfall gets lighter for a refund. This gives the consumer a tool to use in the future at a reduce cost or no cost. Thus, a heavy snowing season would be less fulfilling to the consumer who made a purchase to benefit on the provided offer.

Program Success in Toro’s Perspective

Based on the case study, Toro Company had experienced a period of about three years of sales stagnation. During this time, the company’s distributers were not able to clear their inventories and the company experiencing downtime. This made the Toro Company to consider the insurance option, a choice that brought great onetime success to the company. In this case, Toro management only wished to come out of this stagnation and experience sales growth. The growth should also take place at a reduced operation cost possible. Thus, if working on behalf of ToroCompany, I would focus on obtaining the best possible deal that would bring positive change at the lowest cost possible. In this case, I would first base my argument on mathematical computation. In case the deal demonstrates chances of high benefits I would accept it. If the deal demonstrates chances for minimal benefits I would focus on pushing to increase winning possibilities. For instance in this case, I would restructure the refund structure to push the insurance company into reducing the premium rate. The restructuring would still be attractive to buyers. I would also consider arguing based on possibilities of no or a very minimal liability since snowing is a season that is hardly to be missed in most regions and hence the chances of having extreme refund cases is minimal.Based on the analysis the program was successful to Toro Company. The company was able to sell all it inventories and to manufacture 2500 more units to address the demand. In addition, the company was able to substitute the 10% discount costs with 2.1% insurance costs which offered 7.9% benefits from the first arrangement (Bell, 1994). The company was therefore able to make a high profit.

The main objective of any company is to maximize on the profit. Toro transfers its liability to the American Insurance Company. However, the company should carry the responsibility and ensure its profitability. In this case, I would argue based on the possibilities of having a light snowing season where more customers would come demanding for a refund. I would either work to increase the premium to ensure a worse-case scenario is covered or to change the compensation structure to minimize the number of beneficiaries (Kahneman& Tversky, 1984).The insurance company demanded a premium of 2.1% which was very low compared to the payout it had to pay in refund. The company managed a loss of about 10% by carrying Toro liabilities. This demonstrates that the program was not successful to the American Insurance Company. This is because the company recorded a huge loss.

Program Repeat

Repeating or not repeating this program will be determined by a number of factors. One of the main factors is the increase in the premium demanded by the company. The American Insurance company demands a premium of 8% in the future involvement. A survey to other insurance companies shows two more company one offering a rate of 6% and the other one at a rate of 10%. This means there is a company with better rated that the American Insurance (Bell, 1994).  Hence I would not consider repeating this with American Insurance since there are considerably expensive. Another factor to consider is the possibility of making high sales even without any marketing program in the following year. More snow is anticipated in the following year, meaning that more people will need snowthrower due to the situation. This means, they do not need to be persuaded too much to make purchases, but they will be forced by the condition. In this regard, I would postpone the program and reconsider it in a different time period. Another aspect to consider is that most people did unnecessary purchase due to offer. The company products are not highly perishable or weak to break easily. This means, with good maintenance, one may use it for a number of seasons. Thus there is a chance that too much sales will results to market saturation and making no difference in terms of level of the sales even with advertisement. For these reasons, I would consider not repeating the program for a certain period to observe the situation.

In case I was to argue my case to the manager, the most possible biases that I would susceptible to would be the currently recorded profits. The company managed to get out of three years of sales stagnation due to this program. Thus, it would be hard to convince the director that the program should be postponed. More biases would be shown in case the new rate of 8% would still demonstrate high profits and increased sales to the company based on the current computation.

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