Competitor Pricing Analysis

One of my newest clients is in a highly competitive business in which they sell similar products as other retailers. These days, many online retailers have a hunch that they are being “Amazon-ed,” which they define as visitors finding products on their website and then going to see if they can get it cheaper/faster on Amazon.com. This client was attempting to use time spent on page as a way to tell if/when visitors were leaving their site to go price shopping. Unfortunately, I am not a huge fan of time spent on page, since a page could have wide varieties of time spent on page due to many other reasons other than price shopping (i.e. working, going to the bathroom, yelling at kids-in my case, etc.). Because of this, I wanted to come up with an alternative way to see if price was a potential reason for lost business. However, before I share my idea, I want to add a disclaimer that there is no [legal] way to really know if people are leaving your site to buy something elsewhere due to price, but the technique I will show may shed some light on how pricing impacts your conversion rates.

Competitor Pricing – Step 1

The first part of my competitive pricing solution requires that for some or all of your products (SKU’s), you have detailed competitor pricing. Many of my clients have teams that are constantly monitoring competitive websites and documenting the current prices for some or all of their products. If your organization doesn’t have this, my solution will not work (so you can stop reading now!). If you do have this information, you will need to create a spreadsheet that has your product ID’s (values passed to the Products Variable) and your competitors’ price in the next column. If you have multiple competitors, you can add a new column for each one:

Next, you will have to talk with your Adobe Account Manager to create a new DB Vista Rule. As a refresher, a DB Vista Rule allows you to populate SiteCatalyst variables with values from a database lookup table stored on Adobe’s secure servers. This will allow you to pass in the competitor price for each product viewed and added to cart on your website via a server-side lookup. The Adobe Engineering Services team can walk you through how to upload the competitor prices to DB Vista and how to updated it over time. Keep in mind that you will need to have a process in place that updates competitors’ prices as they change, preferably within the hour so your data is accurate. This is often done by FTP’ing changes on an hourly basis. Creating a DB Vista Rule will cost you a one-time fee of a few thousand dollars, but that you can maintain it yourself thereafter. If you want to save some money, you can ask your internal developers if they can ping a similar competitor cost table in real-time as visitors are on your site, but in my experience, the work effort around that is much more than the cost of the DB Vista Rule.

Competitor Pricing – Step 2

Once you have a way to send competitor prices (by Product ID) into SiteCatalyst, where should it go? What I propose is that you pass the Product ID, your price and your competitors’ price, concatenated in a string to a new Conversion Variable (eVar). Since your visitors may view multiple products, you will also want to make this a Merchandising eVar using Product Syntax. I recommend that the data be passed when visitors view the product detail page or add a product to the shopping cart. For example, if a visitor views SKU # 10010100 and your price is $30.00 and your competitors’ price is $29.50, you would pass this:

In this case, the product ID is available on the page, as is your current price. The only data point you don’t have is your competitors’ price, which can be added to the string via the DB Vista Rule. This allows you to capture all of the key elements needed to do analysis. For example, if you add the Product Views success event to this new eVar report and filter for the above product ID, you will see all of the different pricing permutations between you and your competitor for the selected date range:

Next, you can add Cart Additions or Orders to the report to see how often each product converted with the given pricing spread:

In this fictitious example, you can see that Orders per Product View was up significantly when pricing was the same or better than the competitor for the product in question.

But there is even more information you can glean when we apply SAINT Classifications. For example, you can classify the product with just the pricing range difference to boil this data down to a finite number of rows in a way that is a tad easier to interpret:

Taking this concept one step further, you can apply another SAINT Classification that takes the Product ID out of the equation to see how the pricing spread impacts all products:

For those that really need things spelled out for them, you can use SAINT to create the highest level view of your pricing by boiling the data down to cases where you were higher, lower or the same with respect to pricing:

Obviously, the last few reports can still be viewed by Product by simply using the Products variable breakdown, but I think they show a good high-level view of pricing impact. Keep in mind that each of these rows can be trended over time in SiteCatalyst or ReportBuilder to see a long-term effect.

Product Margin

For those of you who like to kick things up a notch, you can also use the same DB Vista Rule to incorporate your product margin to the new eVar. If you upload your product costs to the DB Vista table, you can have the rule calculate the difference between your price and your cost and add the result as another parameter to the eVar. Then, via SAINT Classifications, you can split this out and see cases where your price is higher than your competitor broken down by your margin:

In this case, the product in question has a cost of $26.00 so the difference is passed as the last parameter to the eVar so we can include it in our analysis. This allows us to create new SAINT Classification where we can see Orders/Product View (or Cart Addition) for all products by the product margin amount:

Since all SAINT Classifications can be broken down by each other, this also allows us to see our conversion rates by price difference broken down by product margin amount:

Keep in mind that all SAINT Classifications are eligible for use in Segmentation, which means that you can now build a segment using pricing differential to competitors and product margin as criteria when doing web analysis! Also, if you want to learn how to add product costs as a new metric with which you can calculate product margin as a KPI, check out my old blog post from 2008 on how to do that.

Final Thoughts

As I stated early on, there is no way to make a direct connection between people looking at your site and then price shopping on another site, but my theory is that if you consistently under-perform when you are priced higher than your known competitor(s), this approach may give you some data to validate your theories. Obviously, there are other factors such as shipping, taxes, etc. that can have a major factor, but some of those can be included in this solution as well by simply adding additional parameters to the eVar shown above. Other ways to do similar competitive analysis include using Voice of Customer surveys to ask your visitors if they are price shopping, or moving all SiteCatalyst and competitive data into Adobe’s Data Workbench product. Either way, if you like the concept, you can give it a try or contact me if you want some assistance. If you have other ways to do this, feel free to leave a comment here. Thanks!