The S’No Risk Program Risk Analysis from point of view of Toro: For the particular year Toro first ran the program, they assumed little risk since they got such a good deal from the insurance company, American Home. On one hand, if there were little snowfall there would be little payout and would then increase their sales. Goodweather also quoted them 2.1% and this actually helped them save money by not paying out the 10% they normally would payout to dealers for their normal promotion (Bell, 1994). This gave Toro an increase of 7.9% in profit and for their dealers increased their total sales. Toro also benefited from this lower rate because after being quoted the 2.1%, after 2 years of marginal snowfall 1983 brought a much more severe winter that would have otherwise cost Toro hundreds of thousands of dollars more if they had been quoted a higher percentage. Risk analysis from the point of view of the insurance company: In this case, American Home assumed the most risk since they would be the ones to pay out in the event of significant snowfall for that winter. In this case, they did pay out a significant amount to Toro covering losses from the promotion. The total number of rebates for the promotion in 1983 was 19% (Bell, 1994). American Home assumed about 17% of these rebates and with it came a significant cost! In the event that American Home continued insuring Toro for the program they would need to significantly raise the insurance rate to try to gain back some of their losses. They also assume a significant risk in insuring the program because weather is unpredictable year in and year out and there is no way to predict how much snowfall will actually occur. Risk analysis from point of view of customer: The customer assumes very little risk, but like the insurance company faces uncertainty due to the unpredictable nature of weather. The customer assumes some risk from spending more to upgrade to the larger snow thrower in the event it snows a lot more than average; though in the end they would still receive a refund of 50% of the suggested retail price which would be roughly equal to buying the smaller single stage snow thrower outright at full retail price. For the customer, a promotion can’t get much better than this one was. If the promotion didn’t occur and the customer had bought either of the snow throwers and it did not snow they would be more likely to be unsatisfied with their purchase and could be less likely to purchase from Toro in the future. Conversely, if they didn’t buy and there was record snowfall they would also be unsatisfied and this could also have a negative future impact on Toro. Since the program did occur, it was a win-win for the customer. After a winter of heavy snowfall and incurring significant losses in rebate payouts, American Home was forced to raise the rate if they wanted to continue insuring the rebate program. They raised the rates such a high amount to equal the average rebate percent had the program been in effect the previous 4 years, including the one-year they did insure Toro. Had the insurance company did their due diligence before offering Toro a 2.1% in 1983 they would have realized a better offer would be 4.3% using snowfall statistics from the previous 3 years, which would have helped reduce the blow of the 19% payout from 1983 (Bell, 1994). If I were American Home, I would have done the same thing they did for their 1984 offer by averaging the last 4 years of insurance rates and using that number as my offer. The rebates were offered at a customer friendly sliding scale that was based on the amount of snowfall for that winter if purchased during a certain period (May 1-Dec 10). The promotional slogan stated it best, “If it doesn’t snow we’ll return your dough! And you keep the snow thrower.” (Bell, 1994). If it snowed less than 20% of the average annual snowfall (mean) for each individual reporting station Toro refunds 100% of the retail price. Under 30% refunds 70%, 40% refunds 60%, and lastly less than 50% refunds 50% of the retail price (Bell, 1994). At that refund percentage it is a win-win for the consumer to buy even with the risk of having less than average snowfall. There would be no need to restructure the promotion because the premiums would still be the same since no one could accurately forecast what the weather would do that year. The program influences decision to purchase by enticing customers to purchase based on the guarantee that at least 50% of the retail price would be refunded. If the customer thought ahead they would realize they could buy a snow thrower for at least half price (after rebate) and would subsequently get many years of use out of it, only magnifying the success of the program from the customers perspective. The common decision traps customers face are the anchoring decision trap by focusing on previous snowfall amounts, or in this case a lack of snowfall from the previous year, and purchase within the parameters of the program in hopes of getting up to a 70% rebate. Another decision trap consumers face is overconfidence. Customers could purchase a high end snow thrower “knowing” it is going to be another mild winter and a majority of their purchase will be rebated but instead end up being wrong and losing out on some of their expected rebate. In this case there is little regret to be had from the point of view of consumers since regardless of snowfall will be a significant rebate and will get many years of use from the snow thrower. For the risk-adverse customers, they may decide that even with the deep rebates available it still isn’t worth the purchase if they think there is little chance it snows more than previous years and will not buy. The rebate program though all but eliminates customer regret through deep rebates and a net positive value from the consumers’ point of view but does leave opportunity for dissatisfaction, as noted below. With Rebate | Purchase | No purchase | Snow | Satisfied (limited) | Not satisfied | No snow | Satisfied | Satisfied |

Without Rebate | Purchase | No purchase | Snow | Satisfied | Not satisfied | No snow | Dissatisfied | Satisfied |

To explain, with rebate if the customer purchases a snow thrower and it snows they are satisfied because they have the product, but their satisfaction is limited based on snowfall and the rebate amount; with no snow the customer is also satisfied to a greater amount because 70% of the purchase is rebated (assuming less than 20% average snowfall). With no purchase and snowfall the consumer is not satisfied because they are presumably without a snow thrower with lots of snow on the ground; conversely they are satisfied with no snow because there would be no need for the snow thrower. Without the rebate available the outcomes are the same with the exception of purchasing the snow thrower and it not snowing since at the present moment it can feel like money wasted. To frame the argument to achieve desired objective moving forward from Toro’s perspective I would restructure the rebate percentages to decrease the premium liability of Toro and to increase the chance of getting a better rate from American Home. Toro could also restrict purchase dates to a narrower time period or only offer the rebate on one model instead of two. They could also give dealers the option to use the rebate program or the regular 10% off rebate. Doing any and all of these would give Toro and American Home more flexibility and reduce their liability while still maintaining sales. From the perspective of Toro and the consumers the program was a success. Toro was able to increase sales after a couple years of decreases and customers were able to capitalize on extremely customer friendly rebates. It was such a success Toro had to manufacture 2,500 more snow throwers midseason to keep up with demand, only increasing their profits (Bell, 1994). For American Home is was an utter disaster! The low quote rate for Toro let them off the hook for only $630,508 instead of almost 5.9M that was rebated at 19% (Bell, 1994). If I were Dick Pollick I would not recommend repeating the program unless sales became stagnant again for a couple years because insurance premiums could continue to go up depending on the weather and program outcome each year. There is too much at risk for Toro to continue the program due to the uncertainty of annual snowfall that could raise rates the following year.

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