Let’s say I have an idea for a new product I want to take to market, and I want to understand (naturally) what the price points for this particular product might be.  Let’s pretend further, for the sake of having an example, that my new product is a fiction book.
I may say to myself, “Now days, fiction books may not be very profitable, because with the advent of kindles and e-readers people aren’t willing to pay more than around .99 cents for an electronic copy of a book.”  And if I did say this to myself, I would be terribly wrong.  Let’s see how a quick 1 minute of research can help clear up this erroneous idea.

First, let’s take a trip over to the master database of product pricing – Amazon.com.  We notice a wonderful bit of functionality across the top, called the Search Bar, in which I will place the often unruly wildcard character (*).  In essence, I’m telling Amazon, “give me everything you’ve got.”

And it turns out Amazon has about 115 million products it returns.  Ample data, I would say, for pricing.  However, in this particular case we recall that we want to understand the price points of fiction books, so we will employ the analysts first rule of success – segmentation.  And thankfully, Amazon makes it easy for us – affording a glance to the left navigation bar, we will very cleverly click through the following series of topics to get to the data of interest: “Books -> Science Fiction and Fantasy -> Fantasy”

Hmmm. . . at a first glance, I may think that my fist notion was correct.  I’m seeing a lot of titles selling for “$0.00”.  It would seem that the kindle’s ruthless digitization of old fashioned paperbacks has doomed my dreams of ever selling a new fiction.  Until, of course, I decide to segment further by clicking on either the “New Releases” or “Best Sellers” buttons running along the top.

Aha!  Now I have a list of fiction books that people are actually buying, and I like the new price points I see – $18.40, $16.99, $7.99, ect.  A lot better than $0.00.   Most of the “analytics” is already done, because my previous erroneous notion has been proven false, and I now have a good estimate of where books that sell set their prices.

We can take this one quick step further, just to add a slight bit of data processing to our conclusions.  We will use two free tools that will make our data investigation so easy and fun it may feel like we’re eating red velvet cake.  Tool one: Google Docs.  Tool two: Google Chrome Scraping Tool.  If you don’t currently have this tools, please take a moment to enhance your life and get them.

Now back on our Amazon page, we do something easy and intuitive – highlight with the mouse the piece of information we want to analyze (the price), left click, and hit “scrape similar.”

Upon the click, we will find ourselves with the following screen:

Now, we have a complete list of every book price on that web page.  What if we want to pull out an average and a median?  Enter Google Docs.  Open up a new spreadsheet, and type in the following formula:    =importxml(“webpage“,”xml“).  You will be replacing the bold webpage with the URL of the Amazon page, and the xml with the highlighted xml that the scraping tool gave you, highlighted above.  (Also, please don’t forget the quotes (“”) in the formula, or this will all end in disaster.)  Once finished, you will end up with something like the below:

Wow!  That was easier than expected.  Don’t forget, you could also go back to Amazon, click on more results, and use those URLs in the same manner to give you more data.  If this 45 seconds of work is starting to wear on you, however, you can just grab the average and median of this current set, using the properly named functions  =average() and =median() in the Google Doc.

As it turns out, I would want to set my price somewhere around $14.00, much better than the $0.99 I had originally thought.  That’s a big difference in margin and profits coming in as a result of 1 minute of analysis!  And I could go deeper into the analysis, looking at the distribution curves, standard deviations, etc. etc.  But the analytics should never be more sophisticated than necessary for the task at hand (that would be a waste of precious time and effort!), and the task has been accomplished – I’ve given myself a ballpark, reasonable range for pricing a new fiction book.  Done.