Let's get these two columns down because these are important. This column comes from that previous data and it gives you the percentage of people who feel the price is not too low. That means it might be low but it's acceptively low. That's coming exactly from the data we saw two slides ago right, so those are the numbers given right here and this 100% come from the fact that we know 25 is 100%, so we can just fill in the hundreds after that. We know all of these numbers are going to be 100% of 25 is 100%. And then, we have the percentage who fill the price Is too high okay? And those are the percentage could come in from the previous slide and the lowest number on the previous slide was this $20 that we can just fill in zeros for the lower prices okay is nobody thinks that's too high and nobody down the number were lower than that So for all prices between $1 and $55, they were written down by any one as being too low or too high. We have the people who feel it's acceptably low and the people who feel like it's too high. Now, what do we do with that? We subtract one from the other. So what it means is we're now going to find the prices that are acceptably low okay they're okay but not too high. So we're going to take this column right here and subtract it from this column right here and the interpretation of this column is this is the sweet spot. Not too hot not too cold right? The courage is not too hot it's not too cold the price is not too low, it's not too high and it gives you the percentage of people that are in each one of these buckets so what that means is 72.7% of the target population Finds that $20 is not too low so low that they would consider the quality poor and not to high if they otherwise like it they would be willing to pay that price. And you can look at this and say, well looks like the maximum occurs at 90.9% and that implies a price of $15. Now, Is the maximum kind of what we're going for here. Well, maybe in some cases yes. But notice what this doesn't take into account. It gives Peoples willingness to pay and the number of people that would pay that but it ignores issues of what, how much does it cost us to make. Right? So, we would kind of need to know that, as well. I mean, it's great that lots of people want to pay 15 dollars for it, but if it costs us 20 dollars to make it's clearly not going to be the profit maximizing price right? Even if people would like it. So, what do we have to add into this information to really get a good sense of what the best price is given by this survey data? We have to enter in costs. So here are the cumulative percent that's just that right hand column right that I had before. I've just sort of cut and pasted it that over to this column. Now in the cost column I'm just going to assume that whatever were selling cost $5 clearly an example I don't know how much this cost but we're going to call it $5 And it's five all the way down because the cost doesn't change relative to the price. It cost whatever it cost. Then, the margin in this column, it's simply taking the price that we're going to charge and subtracting the overall cost. That's why you get a negative four here. If you go down to 31 dollars and it cost you five, then you make 26 dollars. So that's going to be your overall. Margin. Then to get a profit this final column all of them doing is taking this margin information and multiplying it by the cumulative percentage of people that would accept that individual price right. So the negative 0.36 is just the negative four multiplied by 9.1% or in decimals it's 0.091, right. That would be 9.1%. Now, if I look down this column, what do I find? Well, profit is maximized $16.54 and the price it implies is $31. So given this survey data, in our sort of indirect way that we asked willingness to pay, what this survey data implies is The best price if the cost is $5, would be $31, and it's a better way than asking people just directly how much they’d be willing to pay. This has been tested in different industry situations and these kind of results have been found to be more valid than asking people directly. Now Is it perfect?,it is not perfect, it still have some problems. When you ask people how much they will be willing to pay or ask them what price is too high or price is too low it might erase the importance of price in their mind relative to what would relay be happening if they were making a choise. When making a choice other things might come to play the atmosphere of the store, etcetera. It might lower the overall important surprise. Right here you're kind of sticking it in front of their face so they really are going to pay attention to it. So that can be a problem. In so doing you are kind of isolating price from all the other attributes of the product. And those attributes might become very important, maybe even more important than price in the overall choice process. But we can't really do it in a simple survey like this. Now, what is the solution to this? The solution is to do something called conjoint analysis which is going to combine attributes of the product with the price of a product To get a more robust willingness to pay measure. Now if conjoint analysis is better in a lot of situations, why am I even presenting this survey? Going back to what I said earlier. Sometimes you don't have the time, you don't have the money, you don't have the resources to do a contour analysis. The paper survey or the online survey, it's quick, it's cheap and it can give you a good first look. At consumers' willingness to pay for your products.
