Pricing: The goal to break-even in the first year sounds a little unreal. Indeed, looking in product lifecycle and analyzing the data of other baseball clubs (both professional and minor) it may be unreachable. However, considering several effects we may still do it. Worst case scenario is 2 years; best case scenario is 1 year. Let’s analyze the data first. The main point is that the survey was already sorted and we do not have any chance to pull the data we need. That’s very important, for example straight out of the first question we may divide fans and non-fans and then sort these 2 groups by income, gender, price preferences etc. Since we do not have this opportunity we will operate the data given. Out of 150,000 residents and 55,338 households 25% are below the poverty line. They are not our market. Next, we have 60% with children younger 18. That’s 33,000 households and according to the stats they are 3 times as likely to purchase the tickets as others. The case clearly states that the data is fairly representative and (very important) heavily weighted towards our target market (households with kids
Pricing: The goal to break-even in the first year sounds a little unreal. Indeed, looking in product lifecycle and analyzing the data of other baseball clubs (both professional and minor) it may be unreachable. However, considering several effects we may still do it. Worst case scenario is 2 years; best case scenario is 1 year. Let’s analyze the data first. The main point is that the survey was already sorted and we do not have any chance to pull the data we need. That’s very important, for example straight out of the first question we may divide fans and non-fans and then sort these 2 groups by income, gender, price preferences etc. Since we do not have this opportunity we will operate the data given. Out of 150,000 residents and 55,338 households 25% are below the poverty line. They are not our market. Next, we have 60% with children younger 18. That’s...