Tuesday, 14 May 2013

Web Analytics and Testing: Summary so far

It's hard to believe that it's two years since I posted my first blog post on web analytics.  I'd decided to take the step of sharing a solution I'd found to a question I'd once been asked by a senior manager:  "Show me all the pages on our site which aren't getting any traffic."  It's a good question, but not one that's easy to answer, and as it happened, it was a real puzzler for me at the time, and I couldn't come up with the answer quickly enough.  Before I could devise the answer, we were already moving on to the next project.  But I did find an answer (although we never implemented it), and thought about how to share it.

Nevertheless, I decided to blog about my solution, and my first blog post was received kindly by the online community, and so I started writing more around web analytics - sporadically, to be sure - and covering online testing, which is my real area of interest.

Here's a summary of the web analytics and online testing posts that I've written over the last two years.

Pages with Zero Traffic

Here's where it all started, back in May 2011, with the problem I outlined above.  How can you identify which pages on your site aren't getting traffic, when the only tools you have are tag-based (or server-log-based), and which only fire when they are visited?

Web Analytics - Reporting, Forecasting, Testing and Analysing
What do these different terms mean in web analytics?  What's the difference between them - aren't they just the same thing?

Web Analytics - Experimenting to Test a Hypothesis
My first post dedicated entirely to testing - my main online interest.  It's okay to test - in fact, it's a great idea - but you need to know why you're testing, and what you hope to achieve from the test.  This is an introduction to testing, discussing what the point of testing should be.

Web Analytics - Who determines an actionable insight?
The drive in analytics is for actionable insights:  "The data shows this, this and this, so we should make this change on our site to improve performance."  The insight is what the data shows; the actionable part is the "we should make this change".  If you're the analyst, you may think you decide what's actionable or not, but do you?  This is a discussion around the limitations of actionability, and a reminder to focus your analysis on things that really can be actionable.

Web Analytics - What makes testing iterative?
What does iterative testing mean?  Can't you just test anything, and implement it if it wins?  Isn't all testing iterative?  This article looks at what iteration means, and how to become more successful at testing (or at least learn more) by thinking about testing as a consecutive series, not a large number of disconnected one-off events.

A/B testing - A Beginning
The basic principles of A/B testing - since I've been talking about it for some time, here's an explanation of what it does and how it works.  A convenient place to start from when going on to the next topic...

Intro To Multi Variate Testing
...and the differences between MVT and A/B.

Multi-Variate Testing
Multi Variate Testing - MVT  - is a more complicated but powerful way of optimising the online experience, by changing a multitude of variables in one go.  I use a few examples to explain how it works, and how multiple variables can be changed in one test, and still provide meaningful results.  I also discuss the range of tools available in the market at the moment, and the potential drawbacks of not doing MVT correctly.

Web Analytics:  Who holds the steering wheel?
This post was inspired by a video presentation from the Omniture (Adobe) EMEA Summit in 2011.  It showed how web analytics could power your website into the future, at high speed and with great performance, like a Formula 1 racing car.  My question in response was, "Who holds the steering wheel?" I discuss how it's possible to propose improvements to a site by looking at the data and demonstrating what the uplift could be, but how it all comes down to the driver, who provides the direction and, also importantly, has his foot on the brake.

Web Analytics:  A Medical Emergency

This post starts with a discussion about a medical emergency (based on the UK TV series 'Casualty') and looks at how we, as web analysts, provide stats and KPIs to our stakeholders and managers.  Do we provide a medical readout, where all the metrics are understood by both sides (blood pressure, temperature, pulse rate...) or are we constantly finding new and wonderful metrics which aren't clearly understood and are not actionable?  If you only had 10 seconds to provide the week's KPIs to your web manager, would you be able to do it?  Which would you select, and why?

Web Analytics:  Bounce Rate Issues
Bounce rate (the number of people who exit your site after loading just one page, divided by all the people who landed on that page) is a useful but dangerous measure of page performance.  What's the target bounce rate for a page?  Does it have one?  Does it vary by segment (where is the traffic coming from? Do you have the search term?  Is it paid search or natural?)?  Whose fault is it if the bounce rate gets worse?  Why?  It's a hotly debated topic, with marketing and web content teams pointing the finger at each other.  So, whose fault is it, and how can the situation be improved?

Why are your pages getting no traffic?

Having discussed a few months earlier how to identify which pages aren't getting any traffic, this is the follow-up - why aren't your pages getting traffic?  I look at potential reasons - on-site and off-site, and technical (did somebody forget to tag the new campaign page?).

A beginner's social media strategy

Not strictly web analytics or testing, but a one-off foray into social media strategy.  It's like testing - make sure you know what the plan is before you start, or you're unlikely to be successful!

The Emerging Role of the Analyst
A post I wrote specifically for another site - hosted on my blog, but with reciprocal links to a central site where other bloggers share their thoughts on how Web Analytics, and Web Analysts in particular, are becoming more important in e-commerce.

MVT:  A simplified explanation of complex interactions

Multi Variate Testing involves making changes to a number of parts of a page, and then testing the overall result.  Each part can have two or more different versions, and this makes the maths complicated.  An additional issue occurs when one version of one part of a page interacts (either supports or negates) with another part of the page.  Sometimes there's a positive reinforcement, where the two parts work together well, either by echoing the same sales sentiment or by both showing the same product, or whatever.  Sometimes, there's a disconnect between one part and another (e.g. a headline and a picture may not work well together).  This is called an interaction - where one variable reacts with another - and I explain this in more detail.

Too Big Data

Too big to be useful?  To be informative?  It's one thing to collect a user's name, address, blood type, inside leg measurement and eye colour, but what's the point?  It all comes back to one thing:  actionable insights.

The current online political topic:  how much information are web analysts and marketers allowed to collect and use?  I start with an offline parallel and then discuss whether we're becoming overly paranoid about online data collection.

What is Direct Traffic?

After a year of not blogging about web analytics (it was a busy year), I return with an article about a topic I have thought about for a long time.  Direct traffic is described by some people as some of the best traffic you can get, but my experiences have taught me that it can be very different from the 'success of offline or word-of-mouth marketing'.  In fact, it can totally ruin your analysis - here's my view.

Testing - Iterating or Creating?
Having mentioned iterative testing before, I write here about the difference between planned iterative testing, and planned creative testing.  I explain the potential risks and rewards of creative testing (trying something completely new) versus the smaller risks and rewards of iterative testing (improving on something you tested before).

And finally...

A/B testing - where to test
This will form part of a series - I've looked at why we test, and now this is where.  I'll also be looking at how long to test for, and what to test next!

It's been a very exciting two years... and I'm looking forward to learning and then writing more about testing and analytics in the future!

No comments:

Post a Comment