Pricing Analytics: Price Skimming - YouTube

The prices of several product classes - notably fashion and technology - tend to drop over

time.

There are three common reasons for prices of the same product to fall over time.

The first, and most obvious, is competition.

As the supply of a product or its perfect substitutes increases, prices are pushed down.

The second reason is commonly known as the "learning curve".

The theory is that as you build more units of a product, especially a high-tech product

like a microchip or an airplane, you'll get better at it and be able to reduce costs.

If you pass your manufacturing savings on to customers, you can create a virtuous circle

where lower prices will increase demand, while increased demand pushes you further and further

down the learning curve so you can manufacture the product more cheaply.

The third reason is what we're going to talk about this afternoon: price skimming.

Price skimming is based on a simple concept from economics: not every potential customer

puts the same value on your product.

If you start out by charging a low price for your product, you're going to give up potential

revenue from customers who value it more highly.

But as time passes, and the higher value customers have been satisfied, you have to drop the

price to attract the remaining customers.

Let's walk through a simple example with Excel that'll show us how to maximize our profits

by changing our product's price over time.

Our example product is going to sit at the intersection of fashion and technology: Rolex's

entry into the smart watch market.

We want to set up a pricing model for the next 12 months, covering the 10,000 watches

we want to sell during that time period.

On the first day of each month, we're going to drop our prices.

We're going to assume that our watches are so awesome, we can sell all 10,000 of them

during the year as long as we price them appropriately.

Start by opening a new Excel workbook.

We're going to enter a trial price for each month.

$10,000 is the rough starting price we've been asked to use.

As always, we're just making guesses about what the price might be at each point, because

the Excel Solver needs a value to get it started.

We don't have to be close, since Solver will find the correct values for us.

I've entered some guesses that seem reasonable to me - you can enter any guesses that seem

reasonable to you.

Next, we want a column that'll tell us the "highest value" customer that's left at the

end of the month.

This is the most we could conceivably charge and still sell any watches.

Logically, this value is going to be less than or equal to the price we charged in the

current month.

We can use a formula of our current price minus one to get this value.

Enter the formula for the first month, and accept it.

Select the formula cell, and drag it down into the remaining cells in the "Highest Value

Left" column.

The next thing we need to do is track how many watches we sell each month.

It's going to be the number of watches we started the month with, minus the watches

we sold during the month.

Enter the formula for the first month, and accept it.

Select the formula cell, and drag it down into the remaining cells in the "Units Sold"

column.

Now we can compute each month's revenue by multiplying the number of watches sold by

the price we charged that month.

Enter the formula for the first month, and accept it.

Select the formula cell, and drag it down into the remaining cells in the "Revenue"

column.

In cell E15, we're going to calculate the total revenue for the year by summing each

month's revenue.

Enter the SUM formula, and accept it.

Now that we have our model set up, we can use Excel's Solver tool to find the prices

we should charge to maximize our revenues.

Navigate to the Data tab in the ribbon, and choose Solver from the Analysis group.

We want to maximize our revenue by changing the prices we charge each month.

We're also going to use the Evolutionary solving method, since the optimum monthly movement

of prices probably isn't something that can be fit to a common formula.

We also want to set a few constraints by clicking the Add button.

Our first constraint is that the prices have to be integers - we're dealing with the kind

of people who only think in whole dollars.

Click OK to set the constraint.

Click Add again.

We can't charge more than the $10,000 we're using as a starting price.

Click OK to set the constraint.

Click Add one last time.

We also don't want to give product away for free, so our minimum price is $1.

Click OK to set the constraint.

Click the Solve button, and Solver will zip off and do the heavy lifting for us.

This one's going to take a while, so it's a good time for a coffee or bathroom break.

When it finishes, it'll update all of the prices in our worksheet to their optimum values.

If we follow this schedule of price reductions, it looks like we'll see a maximum revenue

of a little over $46 million for the 10,000 watches we're going to sell.