he statistics are compelling. Here’s an example: “Poor online user experience, coupled with a lack of insight about why customers are abandoning websites, is costing businesses billions of dollars / pounds. Companies able to quantify site abandonment estimate they are losing the equivalent of 24% of their annual online revenue due to a bad online experience. This equates to more than $50 billion lost in the US and around £14 billion lost in the UK in the last year.” (Source: Econsultancy/Tealeaf report 2011). There are many others out there like that that show the impact that poor customer experiences have on customer loyalty and ultimately business performance. But as the old saying goes, “If you can’t measure it, you can’t manage it,” so organizations need to build up their ability to track and diagnose the customer experience.
Tracking the customer experience, though, is a multi-faceted challenge. Experience is broadly an attitudinal outcome that results in a set of behaviors. The digital marketing industry has historically focused on tracking the customer experience through behavioral observation using tools like web analytic systems. This has had limited success, as it’s possible to see what is going on (like a shopping cart abandonment), but it’s not that easy to see why it’s happening (poor usability, price issues, etc.). If measuring and understanding the customer experience is a multi-faceted challenge, then a multi-faceted approach to the problem is required.
The diagram below maps out the customer experience data ecosystem. The ecosystem is two-dimensional. One dimension is from behavioral data to attitudinal data, and the other dimension is from tracking to diagnostic. There are five main classes of data/tools that sit in the ecosystem. [Read the rest]
