The five main classes of data and tools that sit in the customer experience ecosystem.
The 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.
Web Analytics Systems
Web analytics is a mainstay of the customer experience data ecosystem but it's not the only game in town. Web analytics data is great for tracking what's actually happening on a site but it can be limited in its ability to help the business understand why it's happening and so has limited diagnostic utility. This is mainly due to the fact that most data in web analytics systems is reported at the aggregate level and it can be difficult to isolate and understand individual user behavior.
Customer Experience Measurement Systems
This class of tools encompasses systems such as Tealeaf, ClickTale, and others. These types of tools are still mainly focused on tracking user behavior but have the ability to be highly diagnostic, as they can be used to isolate out and analyze either a small number of users behaving in a particular way or even reviewing individual sessions. These tools give the ability to do deep forensic analysis of a particular problem but are best utilized alongside other tracking systems to identify potential issues in the first place.
Voice of the Customer Programs
As can be seen from the diagram, a voice of the customer (VoC) program can straddle all four quadrants of the ecosystem and is therefore a vital component of any customer experience measurement approach. A VoC program can track both behaviors (or at least claimed behaviors) as well as attitudes over time. Tracking customer satisfaction or Net Promoter scores is a common output from any VoC program. A lot of the value from the program, though, is from the diagnostic capabilities either from an analysis of the quantitative data or from the rich insight often available from users' actual responses from open-ended questions such as "How could we have improved your experience today?" Smart companies are integrating VoC programs with customer experience measurement systems described above to get at the root issues behind customer dissatisfaction and then working out what needs to be fixed.
User testing, either in the laboratory or by using remote testing technologies, is a rich diagnostic approach and works well alongside other core tracking systems such as web analytics or VoC programs. Lab-based testing is often done with small samples with respondents being asked to complete a task while the consultant observes their behavior, but also can listen to what they are thinking by means of what is called the "think aloud protocol." Remote testing can use larger samples, which are robust enough to generate some metrics that could be used as part of a tracking system.
The experience research piece contains a variety of techniques that are often highly qualitative and attitudinal in their approach. Classic techniques include the use of focus groups, which can be offline or online, whereas other techniques such as ethnography are increasingly being used to get real insight into how users experience brands and services.
Social listening is a relatively new addition to the customer experience data ecosystem. Feeding off social media "buzz," it tends to be attitudinal in nature but can present challenges in "extracting the signals from the noise" when looking to understand the customer or user experience. It is often used for sentiment analysis and tracking but is probably more useful from a customer experience perspective for classification of comment into different groupings in the same way that you might classify survey comments.
Measuring and understanding the customer experience requires a multitude of different tools and systems generating different styles and types of data. Each on their own provides a limited perspective, but when aggregated together can give an organization the ability to measure and manage the customer experience.
Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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