I’ve been tracking page load times on my blog for quite some time now. So it’s about time that I published my findings with my setup (now that I have enough data and all). I’ve dug up the key insights for my site after looking at some reports unfortunately I still don’t have enough data to make certain observations (increasing subscriptions, comments, Twitter clickers etc) so I stuck to pages per visit as my engagement metric.

Let’s investigate… Read more →

Conversion rates can change day to day without any definite explanation - and someone in your organisation, comparing time periods might ask you – “Why isn’t our conversion rate X% higher as you said it would be?” Or worse yet - “Why haven’t we seen any lift in our Google Analytics conversion rate?”

I’m going to show you how you can easily present lift in a conversion rate optimisation campaign, in laymans terms. How it works: Read more →

I frequently get asked why I choose Google Website Optimizer (GWO) over other platforms (Optimost, Visual Website Optimizer & Adobe Test & Target). It’s as though people believe that using a paid platform, they’re going to see much larger increases in conversion rates over time than if they used GWO for example. Read more →

While there’s some consensus that conversion rates should be measured by unique visitors, in some industries it can be naive to ignore a conversion rate calculated based on visits - especially under (very rare) specific circumstances. Tools like Google Website Optimizer rely solely on unique visitors, meanwhile Google Analytics relies upon visits to calculate conversion rates.

Let’s see why this is a problem…

First, an example

Let’s say you’ve built a fantastic site about health insurance and you’re testing three different variations. Google Website Optimizer (using unique visitors to calculate its conversion rate) tells you there are no winners.

In your own investigation however, Google Analytics’ goal conversion rates shows one variation performing dramatically better. You might end up with the following scenarios:

Assuming one variation increases the number of visits that each visitor makes to the site and another variation decreases the number of visits to the site, this could result in a higher or lower CPA (if visitors return to the site through a costly medium such as PPC). But you wouldn’t see this in Google Website Optimizer! Check out the difference in CPA that could be achieved here (yes it’s drastic, but it proves a point):

While GWO shows no clear winner calculating conversion rate on unique visits, an conversion rate calculated using visits (or clicks) might show a different winner in terms of CPA

Worse yet… You might place yourself in the position where Google Website Optimizer tells you one thing and Google Analytics tells you another thing:

In the example above, whilst “Visits CR Low” looks nice in GWO, you’d be better off choosing the losing variation.

Assumptions in the cases above

The examples above make a number of assumptions:

1. Subsequent visits return to the site through branded search (not resuming their session after having their cookies expire)
2. The number of return visits increases significantly
3. CPCs are unforgivably high
4. You’re heavily CPA driven

Likelihood of encountering this?

This is a very rare circumstance. In fact, I’ve encountered only one or two sites where this is of SOME concern. If you’re in doubt, you’re most likely going to want to measure your website’s conversion rate in terms of unique visitors.

How can you measure/keep-an-eye-on this?

First you’ll need to start tracking your Google Website Optimizer tests in Google Analytics and second…

Well to tell you the truth, I don’t know how to measure this very accurately myself.

Any ideas?

What good are your Analytics if you can’t make sense of the data and suit it to better your needs? Sometimes, you can get more meningful information out of the reports by customising the data to suit your needs. For instance, have you ever wondered what the value of comments are on your pages? How about determining the longevity of posts? Or seeing which author rocks up the higher number of pageviews per month? Read more →

The problem with most lead generation sites is that while most of them track leads converting on the website, they do not marry them up with the conversion rate of those leads on the backend.

This creates a problem - For instance, you might serve a major accounting practice which attracts 50 leads per month - while the original variation may be sending you more leads, most of those may be rubbish compared to the fewer quality leads converting on Variation 1 in your test. If you just tracked the number of leads, you may be making the wrong decision. Read more →

Now there’s a big emphasis on page load times, people are beginning to take more scrutiny about the elements they add to a webpage. I myself have been guilty of whether or not to add different tools to my page.

I finally figured I’d take a look at what impact Google Website Optimizer actually has on page load times and I was pleasantly surprised - particularly given I live in Australia where the internet is notoriously horrible. Read more →

Normally, cross domain tracking is a nightmare in Google Analytics. Transferring the cookie data from one domain to another can create nightmares for companies, especially as they have to use things like:

• _getLinkerURL on iframes
• _linkByPost on all forms posting users cross domain
• _link on URLs sending users cross domain

Citricle seems to have come up with a solution (at least for sites that have their carts on a 3rd party domain name like Netsuite - but it looks simple enough to work on more complex setups). The idea is simple. To help, I’ve drawn a little diagram to explain it (seriously whipped this up in 5 minutes, certainly not winning any awards for this!): Read more →

I see a lot of people who start off in conversion optimisation making several critical mistakes when choosing what to test.

Here are a few that I see frequently:

• Testing content or elements that are meaningless to customers
• Making small changes and expecting the world
• Running a test in a low trafficked part of the site
• Testing parts of the site that already work well before solving huge problems in the funnel
• Testing aimlessly around the site
• Testing without a clear objective

These are all big mistakes but at least they’re testing. Read more →

Latest update: Added some instructions about how I’m blocking traffic from Slideshare from showing up in my reports.

Update: After tweeting about it a couple of times and posting my site on Digg and Hacker News, a representative from Slideshare contacted me (see comments under post) and my code has removed my code from their site - Luckily, I shouldn’t exceed the free quota for Analytics. Regardless I’m still feeling the aftermath through Google’s cache of their site.

Update: I found my Analytics code in this file - http://public.slidesharecdn.com/v3/scripts/combined.js

Hi Slideshare,

I had an interesting surprise as I checked my Analytics today - Visits had jumped up over 800,000% from the previous day. Read more →