Conversion Rates Across Time

Customers. They mess up all of our conversion rate metrics, don't they?

Two weeks ago, I visited Zappos on a Monday night. I copied thumbnails of twelve pair of shoes that I liked, pasted the thumbnails into an e-mail, and sent the e-mail to my wife.

The next morning, my wife reviewed the thumbnails, and gave her thumbs up to one pair of shoes.

On Wednesday, I purchased the pair of shoes that my wife liked.

From a customer standpoint (i.e. from my standpoint), this process was a complete success.

From a metrics standpoint, this could be considered a flawed process. We have two visits with just one conversion. Your software tool might consider this to be a unique visitor with 100% conversion. Your software tool might consider this to be two visits yielding a 50% conversion rate.

From a Multichannel Forensics standpoint, we try to take some of the mystery out of conversion rate. Instead of looking specifically at the mystery of conversion rate, we look at how a customer with specific attributes behaves in the future.

Let's look at a very simple example. We take all visitors from the month of May, and categorize them into one of several segments, based on the depth of website activity during May:

• Homepage or Landing Page Visit Only.
• Multiple Pages Visited.
• Shopping Cart Abandoned.
• Purchaser.

Clearly, this segmentation strategy can be expanded upon (source = PPC or e-mail ... or looking at visitors from April or March, or looking at new buyers vs. multi-buyers, you get the picture).

The next step is to create a "grid". For all customers who visited the site in the month of May, we categorize them based on behavior during the month of June:

• No Subsequent Visit.
• Homepage or Landing Page Visit Only.
• Multiple Pages Visited.
• Shopping Cart Abandoned.
• Purchaser.

Our job is to populate the grid, categorizing customers based on May behavior and June behavior. The grid looks something like this:

		Home or		Shop
	No	Landing	Multi-	Cart	Pur-
	Visit	Page	Pages	Abandon	chaser!!!
Homepage/Landing Page	78.2%	10.3%	5.2%	4.3%	2.0%
Multiple Pages	49.6%	12.5%	22.4%	7.3%	8.2%
Shopping Cart Abandoned	32.3%	15.7%	24.4%	13.7%	13.9%
Purchaser	25.8%	20.4%	29.4%	9.3%	15.1%

This table is pretty simplistic, but it gives us a lot of "business intelligence", if you will.

For instance, take a look at the "No Visit" column. The converse of this column is the "Re-Visit Rate". The table suggests that 21.8% of homepage/landing page visitors re-visited.

Now look at the Shopping Cart Abandoned customer. The "Re-Visit Rate" for these customers is 67.7%. That's important. If you know that customers who abandon a shopping cart are unlikely to come back to your website, then shopping cart abandonment is a REALLY bad thing. If you know that two-thirds of the visitors will come back over the course of the next thirty days, well, you think about abandonment with a little bit less fear.

In my Multichannel Forensics projects with online marketers, the important word is always "context". In the table above, the customer who drills down into the site and then leaves is not necessarily considered a failure. More than half of the visitors in that segment come back to the website next month, with 8% converting. The data suggest an element of "engagement" that may not be easily conveyed by a 3% conversion rate or a 47% shopping cart abandonment rate.

Context is derived by knowing the state of a customer in pre/post timeframes. When we step outside of measuring campaigns, focusing instead on measuring customer behavior across time, we obtain a different level of customer understanding.