Looking back on the past decade in Web analytics, a major positive shift in recent years is the focus on so-called voice-of-the-customer programs.
For far too long, organizations had tended to be too site centric when optimizing online performance, believing that the solution lay in just knowing "what happened" and "when it happened" as opposed to "who did it" and "why they did it" - or more often than not - "why they didn't do it."
These days organizations are more aware of the need to complement and support their Web analytics data by having insight into why people come to their site and what they thought of the experience. This is evidenced by the growth of companies such as ForeSee Results at one end of the market, which recently reported a doubling in revenue over the last two years, and the adoption of free tools such as 4Q from iPerceptions at the other. But having gone through the process of selecting or implementing a voice-of-the-customer program, is it working hard enough for you?
Voice-of-the-customer programs can provide tracking data to look at performance over time as well as diagnostic data to drill down into specific issues. That data can be quantitative in nature such as customer satisfaction or Net Promoter Scores. The data can be qualitative such as comments left by visitors in response to open ended questions.
But like any measurement system, there needs to be an ongoing investment in voice-of-the-customer programs in order to extract value. I have seen organizations enthusiastically set up a voice-of-the-customer program and scour the numbers during the program's first few months. But over a while, the major focus becomes a few top-line numbers that end up populating a monthly or similar report. Here are some thoughts about how to extract longer term value from your investment in voice-of-the-customer programs.
Make It Somebody's Job
This might seem like an obvious statement but any measurement system needs ownership. Somebody somewhere must be responsible for managing the program and realizing the return on investment. One challenge: an e-commerce department, for instance, may not have the skills to set up and run such a program. Those skills may exist elsewhere in an organization. In any case, a voice-of-the-customer share program must have a home. Someone must be designated to ensure the program is running properly from a technical perspective, that the questions are relevant and up to date, and the right insight is being extracted at the right time.
Look to Integrate With Other Data
Any measurement system that sits in glorious isolation is not doing its job properly and voice-of-the-customer data is no exception. In the data world, two plus two really does equal five. At the simplest level, integration can mean working with two or more datasets side by side. An example? Look at the funnel from a Web analytics systems, such as reasons for not achieving goals from a voice-of-the-customer program. Alternatively it might be starting with some specific comments left by visitors in a voice-of-the-customer program and then investigating their behavior using Tealeaf or another customer experience measurement tool.
At a more complex but more powerful level, you can integrate the data from VoC program and Web analytics system. Many major measurement systems now have data integration capabilities. These mainly allow key data from a voice-of-the-customer system to be captured into the Web analytics database and reported alongside the usual Web analytics metrics. Because segmentation capabilities are built into most Web analytics systems these days, analysts are able to look for relationships and patterns between people's attitudes and options and their behaviors. For example, are people who land on our marketing landing pages more or less likely to achieve their goals than people who land on the home page?
Actually Listen to "The Voice"
While a lot of value can be gained from systematically tracking various different customer orientated scores, a huge amount of value can also be mined from the comments that visitors leave when asked for their opinions on issues or reasons why they did or did not do certain things. Depending on the program's scale, you might have a lot of valuable feedback that is often left to gather virtual dust. However, this data is often not structured, so it's difficult to extract meaning from it.
Some higher end voice-of-the-customer services are developing algorithmic approaches to analyzing free text data and are trying to quantify "sentiment." However if your program is more modest, then there is no substitute for the human brain in terms of making sense of free text. When you set up your program, read through the comments that people are leaving. You will quickly get a sense of the main themes. Note these themes and then quickly add up how many comments relate to each theme. This exercise will give you a sense of how important or widespread these issues are. If you do this on a systematic basis, every day or every week, you can also build up trends of how these themes are changing over time.
Hopefully these thoughts will help you to extract more insight from your investments in understanding who your visitors are, what they think, and why they do the things that they do (or don't do...).
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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.
March 19, 2014