Web Analytics can reveal startling offline trends about you and your business

Getting Value from Referring Sites

Key insights from web analytics data help England Rugby star cater to a segment of fans

@MrsFastBloke is a Rugby fan like me (@fastbloke) but for somewhat different reasons. Last night @MrsFastBloke was browsing the interwebs 'researching' Ben Cohen - England and Sale winger. Seriously top rugger player.

Anyway, an interesting article popped up about Ben's gay following. Not surprising really - I guess you could say the guy is 'easy on the eye' without sounding like I'm announcing a lifestyle change. So, the question that piqued my interest in the interview was:

Outsports: What has been the reaction to the idea so far? Have you been surprised by the reaction?
Ben Cohen: I just noticed that I was getting more attention. My website stats were shooting up and when I looked where the links were coming from, I could see that 90% of them were from gay blog sites. The biggest surprise I got was knocking David Beckham off the top slot in the Sunday Times Gay Icon list. I was gob smacked quite frankly.

Cool! This is a great illustration of a key analytical lesson I deliver to clients - check out your top referring sites and see who your BFFs are (Best Friends For evah!). The traffic volume and quality will vary hugely but in Ben's case there was a clear 'hot' segment of referrals and kudos to Mr Cohen for responding to the demand:

...he announced plans for a night in London to honor his gay fans. The event will be held at the Dorchester on May 28.


Impressive stuff. Anyone using web analytics can get the same value from this type of insight. You might find a lucrative traffic segment that is of value through monetisation or brand awareness in Ben's case.

Web Analytics - Don't get caught out by averages and ratios.

The Scenario

We sent an email to a few thousand customers. It was utm tagged so the performance of the email could be measured.

If you don't measure it, you can't improve it!

The Headline Results

The graph of visits over time, below, from Google Analytics shows a familiar shape:



Now, visits are great - we like visits. Our customers are wonderful and it's great to know they like our website...enough to buy stuff? Show me the money! So, let's bring in some more metrics on the graph. Say, visits and revenue:



Okay, visits and blue and $$$ in orange. Looks about right. Yup. So, visits and transactions. How many orders does our email generate?



Great - clearly our back to back email testing is looking great! We wanted to drive more sales but what happened to our average cart value? Lets drag in another metric to compare with our visits:



Huh? That looks odd, don't you think? It seems as if our average order value seems to increase as our visits diminish...

First mistake

Look at the average order value metric over the life span of a campaign. If you haven't changed the campaign during the chosen time period, AOV will be independent of the visits metric. Don't look at AOV by day - it's an average so use a wider date range to pull a richer body of data together.

So, we understand a little more about how to use the average order value metric but the title of this missive is a warning about ratios as well as averages...let's look at visits compared to conversion rate:



Now this does look odd - isn't it somewhat counter intuitive that the holy grail of all metrics should go up...after visits trail off and then dip and rise again. Gah!

Think about what's happening here: We saw that our AOV was roughlyconsistent...rising slightly even. Well, we can see our visits peak early on and then exhibit the classic long tail pattern. Given that we have seen a close correlation between visits and transactions and our conversion rate is the ratio of transactions to visits, clearly we will see our conversion rate change with the inverse of transactions.

So, in the same way that we would examine the average order value over a multiple day period, we would apply the same analysis methodology to conversion rate.

So, as we were testing one email design against another, we would look at the average order value and conversion rate over a period of say, a week after the email was sent and look for a difference in the metrics. Then of course, seeing mathematical confirmation of statistical significance (say through a simple Student's t-test) is the next step to confirm (or refute) the hypothesis that gave rise to the test in the first place.