1) Customer visits on 11/1, places item in shopping cart.
2) Customer visits on 11/5, browses, leaves item in shopping cart.
3) Customer visits on 11/7, deletes item from shopping cart, calls over the phone, and buys an item after talking to a customer service rep.
Question #1: Do you have a conversion rate or shopping cart abandonment rate problem?
Question #2: If your answer to question #1 is "No", are you accounting for this behavior in your web analytics tool, so that you can clearly see any real shopping cart abandonment rate issues?
Question #3: If the customer in (3) purchased online, do you have a conversion rate or shopping cart abandonment problem?
Discuss!
Question 1, 2, and 3: It depends on a particular definition of conversion rate or shopping cart abandonment. Are you defining shopping cart abandonment rate to be any body that fell off within in a session once they placed an item in the cart without completing the order? This kind of thinking is linear and we are expecting somebody to first, browse through products, select a product, read the product details, cart the item and then complete the order. Anybody that does not fit into this linear process is some kind of conversion problem to some folks/tools. To incorporate a nonlinear purchase life cycle process, we need to evolve and build tools that can make it happen. How many web analytics tools allow this kind of business logic on the underlying data?
ReplyDeleteHow many organizations have ability to trace real source of an order in real time? Even if they do, say, they have figured out 10% total orders come through email, 50% catalog, 20% organic, 20% paid-online, this kind of information is useless for predicting demand generation through targeting in market segments as each demand source falls in a different business model (CPM, CPC, CPA, and what not...).
Thanks for walking right through the door, Anonymous!
ReplyDeleteThe purchase process is non-linear. It is our job to illustrate the non-linear process.