Google Analytics report bookmarking hacks

Help HiPPOs!
Even though Moneyspyder is firmly behind the anti-HiPPO movement we recognise it is still important to help HiPPOs. After all, they do rule the business world.

Generally speaking it is best to deliver insight to HiPPOs. Make the news - don't just deliver the news! However, there is merit (on occasion) to furnish them with 'vanity metrics' or 'outcome proxies' as we tend to think of them via the medium of 'the dashboard'.

Better Dashboards
The dashboard in the context of Google Analytics will likely take the form of a custom report. Custom reports rock. F.A.C.T. A custom report based dashboard can lift the value of the deliverable. You can move from report 'puking' to actually delivering insight by placing the report in context. You can do this in a few simple ways:

• Multi-tabbed


• Use advanced segments


• Use relevant date ranges


• Use comparison date ranges

Express delivery!
Okay, you have the dashboard setup - it offers context as well as just raw numbers. It yields actionable insight (so go do some insightful actions already!).

You'll probably want to schedule the delivery of this report via a monthly PDF attachment in an email. Simple in Google Analytics.

I suggest dropping a note in with the email via the description:



The note should start delivering context for the HiPPO. Help them already before they open the attachment! Now, the real meat of this post. Provide a link to the report.

Scary controversial opinion alert!

Invite the HiPPO into Google Analytics...Give them a specific read-only login to one profile that contains the dashboard/custom report.

Use the options in the report URL to customise and control what they see.

Here is a standard custom report link:

https://www.google.com/analytics/reporting/setup_email?id=4867638&seg0=-1&pdr=20091201-20091216&cmp=date_range&trows=50&gdfmt=nth_day&rpt=CustomReport&segkey=medium&tchcol=1&tst=0&tscol=v0&tsdir=0&mdet=WORLD&midx=0&gidx=0&cid=26&afs=false&seg=1&fmt=0

Here is the link with some easily customisable options in the URI:

https://www.google.com/analytics/reporting/custom?id=profile id&pdr=primary date range&cmp=advanced segments&trows=50&gdfmt=nth_day&cdr=comparison date range&segsegment number=-segment number&rpt=CustomReport&segkey=medium&tab=tab number&tchcol=1&tst=0&tscol=v0&tsdir=0&mdet=WORLD&midx=0&gidx=0&cid=Custom report id&afs=false

So, what are the customisable options? this is not an exhaustive list - these are just the ones I find useful right now - this list may grow.

Custom report id
Open your custom report. Write down the id in the url. Use it.

Primary date range
This is the date range that you want to look at. It's optional. If absent, the report will show the default last 30 days. It is in the format: yyyymmdd-yyyymmdd (eg. 20091101-20091116) where the first date must obviously be before the last... ;-)

Comparison date range
This is the date range to compare with - great context. Look at 'the same period last week/month'

Advanced Segments
How cool! Load a report showing only the segments you want to see! The format for the NVP is segn=-m. So, the default will show 'all users' would be ....&seg0=-1&...the default advanced segments follow the order in the drop down list so 'Non-bounce visits; would be seg0=-12. You can show multiple segments in the format: ...&seg0=-3&seg1=-5&seg2=-12&...which would show 'Direct Traffic','Visits with Conversions' and 'Non-bounce Visits'.

Tab number
Have you got a multi tab report? Do you want to default to a tab other than the first? Specify the default tab number here. Simples ;-)

Statistical significance in A/B testing - a little tool to help

Rationale

I read a tweet recently by @tclaiborne about a great blog post on the subject of Easy Statistics For Adwords AB Testing And Hamsters. With a title like that, how could I not take a peek?

It so happened that I was working on a small project to build a tool in Javascript to enable easy analysis of two data sets to compare them for statisticaly significant differences, specifically in the context of A/B and MV Testing.

This post is introducing the prototype of that tool. Just to be clear, this tool is a mash up of Javascript snippets that have been published. I didn't write the whole thing so I'm not taking credit here - I'm just looking to share a cool tool!

A Simple Test to Introduce The Tool

Let's say we've run a test using Google Website Optimiser. We made a change to a page to increase the number of outcomes. We have 6 days worth of data. Here are the conversion rations for the 6 days for the original and the test variation:

Test	1	2	3	4	5	6
Original	6%	6%	5%	6%	7%	6%
Test Page	9%	6%	7%	6%	9%	8%

So, from 5 days worth of data, can we see if the difference in the conversion rates are significant? It's a small data set...the numbers seem to be different but as the blog post referred to earlier says, we humans are really bad at looking at data sets and making accurate judgements.

We need some stats. Enter jsstat.



So, we can drop in our two samples of data as comma separated values. They don't have to be the same size or integer values. Let's hit that 'oh-so-tempting' import button to see what wonders we can find:



Ah, such insights, knowledge and power are ours! We can deliver meaning and value to our clients! Ahem, enough whimsy - what the heck does this mean?

I'm going to keep this high level:

• The differences could have happened by chance.


• The green text tells us the truth


• The results are conclusive.


• The new page converts 1.5% better than the old on average

Try the test yourself using '1,2,3,4,5,6,7,8,9' as both data sets. NOT SIGNIFICANT!

Moving swiftly on

It's a prototype okay? It might not work in crufty old browsers. Stick with a later version Chrome or FireFox to be safe. The graphing is adding little value right now but box plots are coming!

I'd really like a direct export from Google analytics or Website Optimiser into something like this...Hmmm.

Now, the new Google Analytics Intelligence functionality is very similar to this. It's great, don't get me wrong! Different in some ways but based on the same theory...mostly.

We are looking at taking this tool a lot further to supplement Multi-Variate testing results analysis and click stream data analysis.

I'll keep you posted.