Guest Post: How much is enough when it comes to Voice of Customer?

jl1Jonathan Levitt has spent the last 5 years as a pioneer in the voice of customer analytics space. Through his speaking, writing, and evangelism, he was instrumental in legitimizing voice of customer analytics at a time when traditional web analytics still dominated the online business intelligence conversation. Jonathan has worked with world leading brands like Bank of America, Verizon, Dell, Procter & Gamble, Ford, and Reebok and has been featured in several industry publications including 1to1 Magazine, ClickZ, DM News, and MediaPost.

One of the best sources of business intelligence for companies of any size is raw Voice of Customer data.

This is particularly true for start-ups, where early, frequent, and consistent interaction with customers is critical to getting off the ground. The more customer-centric your decision making processes are from day one, the more likely you will get to the next stage in the development and maturation of your business plan.

This explains the recent growth in the selection of free and low cost Voice of Customer collection tools. User Voice, Kampyle, Survey.io, 4Q Survey (disclaimer: I helped conceive and build 4Q) — all of these are examples of popular Voice of Customer collection tools that can provide site owners with a pipeline of cheap and actionable visitor-sourced insights.

Once you put on the VoC practitioner’s hat, however, questions about respondent count size inevitably come up. Simply put, you need a way of knowing how much data is enough.

At what point can you act on the findings coming through your shiny new tools, with full confidence that you have collected a representative sample of your audience? If you’ve been running a User Voice customer feedback tool for 3 weeks and you’ve only collected 20 respondents, is that enough to act on? These are certainly agonizing questions for a data-centric marketer.

Now’s the time to start glancing over enviously at the big sites, because they don’t have this problem. The laws of probability are such that feedback from 500 respondents is usually enough to deliver reliable data at even the strictest confidence intervals. A big site like Dell.com can pull in 500 respondents within a day or two; at that clip, statistical significance comes through in a heartbeat.

But since your traffic generation muscle isn’t likely to match Dell.com’s anytime soon, I’ll let you in on a little secret: for small, startup websites that want immediate answers to their questions, the size of your sample almost doesn’t matter.

Here’s why. [Read More]

Places and tasks

I have a problem with web analytics.

The whole notion of a web visit as a rigid set of steps that users follow is incompatible with how we use the web today. Visitors browse around the site, taking their time, exploring and interacting. Occasionally, they complete some kind of action we want—inviting their friends, buying something, and so on.

For a couple of years, I’ve been thinking about web visits in terms of two fundamental building blocks: Places and tasks. If you look at your site as a series of places and tasks, you’ll think differently about how and what you should be watching.

[Read More]

[Web Analytics] My, How Things Have Changed

I’m currently in the middle of writing the Web Analytics chapter for the book, and my gosh – things have changed so much in fifteen years.  This screenshot is from the program “GetStats”, one of the first web analysis tools to exist.  I ran it using watchingwebsites.com logs (I had to parse them through sed & awk to change their log format to CLF for it to work).  Notice how it took 7 and a half minutes to process as many lines!

I was talking to the author, Kevin Hughes about GetStats and the state of web analytics when he first wrote it.  “Actually getstats wasn’t the first Web server log analysis tool, but it was very influential in terms of the way the data was presented and summarized.  Roy Fielding with wwwstat was the first as far as I can recall to present statistics in an easy-to-read paragraph summary form, that I think was written in Perl.  I also took ideas from Thomas Boutell (wusage) and Eric Katz (WebReport).

Web analytics tools began by telling us how many hits we had on the site, but that doesn’t do much today to tell us what’s really happening with our sites.  The tools went through many evolutions before they got to where we are today – simple metrics, a few KPIs and actionable information.  I’ll touch a bit on this in the book; we’ll also cover implementation methods, advantages, limitations and deployment impact of web analytics tools.

The book is days away from having a completed 1st draft.  I can’t wait to send the complete manuscript out to the reviewers!