Mayday Mayday Mayday - 67% of online shoppers are abandoning ship!
We’ve probably all done it. We arrive at our website of choice, browse through the range of products available, add one or more items to the shopping cart and then – just at the point of conversion from prospect to customer – we abandon ship.
It’s an all too common occurrence. Estimates vary, but according to research by the Baymard Institute, around 67% of shopping carts are abandoned before a sale is completed. Of course, that means 33% of customers go on to convert, but the two thirds who go through the motions and then think twice represent a huge swathe of lost revenue.
So what lies behind the conundrum of the abandoned shopping cart? Why do so may customers go to the trouble of selecting items for purchase only to change their minds at the last moment?
In fact there are many reasons, including:
• It may be, for instance, that the shopping cart is a useful tool. As the customer works through the site, the cart allows them to collect together items that he or she “might” want to purchase. At this stage no final decision has been made and the customer isn’t as yet committed to buying. The shopping cart is an alternative to the “wish list.”
• Or it could be that the customer really intends to buy but at the last moment “pre-sale remorse” sets in. Collected together maybe all the items just look too expensive.
• Perhaps there is a problem with the site. The delivery options aren’t clear. Or the customer is unsure whether it’s possible to pay with an American Express card or Paypal and there’s no upfront information to clarify the situation.
There could be dozens of different reasons, but here’s the tricky bit. Shopping cart abandonment rates are easy to measure but simply knowing that your site is running at, say, 65% is not going to help you much unless you know why people are opting out.
The same is true elsewhere on the site. Simply knowing that the bounce rate on your home page averages at 50% isn’t particular useful unless you also have actionable data on why it’s happening. Equally there may be a point on the customer journey that shows up on the stats as a problem. For instance, maybe there’s a particular page where customers get stuck and ultimately tend to drop out. Again you need to know why?
One way to approach this problem is to make improvements by trial and error. If there’s a high shopping card abandonment rate, you speculate on the reasons, make changes, and if the rate comes down you can chalk up a success.
But a much more scientific approach is to make use of the unstructured data that you collect every day through your interactions with customers.
Every time a customer talks to one of your agents you have the potential to collect vital intelligence about the customer experience. For instance, if a number of customers tell you they are having a particular problem with a section of the site and those comments correlate with an unusually high drop-out rate at that particular point, you have the information you need to make changes that will drive improved performance.
Of course, many customers will simply drop out when they encounter a problem. They won’t ring a call centre. They’ll just go.
That’s where pro-active targeting comes in. By using analytics you can identify customers showing signs of distress and invite them to talk to an agent, for instance, via chat. This serves two purposes. First and foremost you identify the problem and fix it for the customer at that point.
Equally important, you begin to develop a deeper understanding of the customer and his or her experience of your site. What are the problem areas? What are main reasons for abandoning a shopping cart? And once you know that you can set about making real improvements. The key is to put processes in place to allow you to engage with customers and to collect and analyse the unstructured data you collect.
The result over time will be a much better understanding of your customers and improved performance.
For more information on how to gain insight on visitor behaviour, download this free data sheet: