In this video, evaluate price elasticity given a demand curve.
- [Instructor] I'm in the 03_03_Begin Excel file. One of the first steps to optimizing price for your firm, is to get an idea of what level of sales you can expect in a given price point for your product. To do that, you'll need to collect some data, of course. What I've got here, in columns A and B are series of prices for a particular product and the sales in units, associated with each of these prices. As we see in row two, a price of $100 leads to sales of 1,000 units, for example.
A price of 110 leads to sales of 920, etc. In order to figure out our price elasticity we're going to need to go through and graph this data. To do that, I'm going to insert a scattered plot, from the Insert tab, and now I'm going to add a trend line to this data. Now, if you recall, we have a couple of different types of lines that might fit with our product.
In particular, we could have either a linear demand curve of a power demand curve. Both types of demand curves are very common within a variety of different industries. In this case, I'm going to stick with a linear demand curve. I'm going to add an equation that'll give me an idea of what level of sales I can expect, with any given unit price. Now, I'll move that equation up, so we can see it a little bit better. But as we see, in one unit change in the X variable here, changes the Y variable by 4.15 units.
So, what that means, our X variable here is our price, and Y is sales. So in this case, increasing price by $1 leads to a 4.15 unit fall in the number of sales. Now we need to go through and translate this into revenue so that we understood what percentage change, a one unit change in price has on sales, but this is a good starting point. Now, let's see whether we should be using a linear demand curve, or power demand curve here, shall we? Let's try adding another graph, and this time we'll fit it with a power demand curve.
So we've got the same set of data points, now I'm going to add a trend line again, but in this case, I'm going to use the power demand curve. And again, I'll add in the equation and the R squared value. Which one looks like a better fit to you? Well, if you said the one on the left, I'd agree with you. And we can see that statistically based on the R squared. So the R squared over here in the left hand side, where our sales are represented by a linear demand curve is 0.9945.
That tells us that we have an almost perfect fit with our data. In contrast, the sales with the power demand curve, only have an R squared of 0.87. What that tells us is that the data is not a particularly good fit, comparatively speaking. It looks like a linear demand curve is a better fit for our particular model. This is the first step of understanding what our price elasticity is.
- Reviewing the pricing strategies available to firms
- Analyze pricing relationships
- Identifying different types of price discrimination
- Using pricing and revenue drivers to maximize profits and revenues
- Assessing the impact of competition and the competitive landscape
- Using variance analysis walks to analyze price and do cost analysis
- Gathering data to build pricing models and assess profit impact