Just a quick note to announce that I am now an Optimization Manager at Adobe.
What does that mean to you?
While analytics and optimization go hand in hand, going forward the emphasis will be more on the optimization side of things.
Also, now that I’m primarily using Test&Target as a split testing tool, I will be able to pass on any cool tips or ideas that I come up with on Test&Target.
If you have any Test&Target related question, feel free to ask me directly on this blog.
– Ophir
I can’t help but think about conversion rate optimization in my day to day life outside of work.
A friend of mine who studied film (making movies) in college once told me that he can no longer watch a movie without dissecting it in his head.
Every know and then I have a similar experience (usually as part of an “a-ha” moment) in my life where I realize something about human behavior – specifically how to get someone to do something.
I have quite a few of these in my head which I’ll start sharing on my blog.
Today it’s a lesson I learned from a crying toddler.
David is a 3 year old boy. He has a cat called Muffins. David and Muffins get along very well, but if Muffins bothers him at night while he’s sleeping, it scares him and we wakes up.
Last night when David started to cry, I saw Muffins run out of his room and immediately understood what happened. I went into his room and tried to calm him down.
David: Crying
Me: It’s OK David, Muffins won’t hurt you
David: Crying
Me: I’ll close the door so Muffins won’t come back. Daddy’s here.
David: Crying
Me: Did Muffins scare you?
David: Yes (and stops crying)
David calmed down at that point and quickly fell back to sleep.
As you can see, I was only able to get through to David once I acknowledged his feelings. This is parenting 101 but the point here is:
Acknowledging someone’s feelings is a very powerful way to get through to them.
Some examples off of the top of my head:
Personally, I’m not crazy about using “informercial” type headlines, but the reason they are so widely used is that they usually work. Of course like any good marketer you are testing your copy so ultimately you know what works best for your audience.
For those of you not following the GA API change log Google just added four new data points:
Dimension
Metrics
All of the new data points are essentially “calculated metrics”, meaning you could calculate this yourself if you were to download the data and do the calculations offline, but still, I applaud Google for continuing to make it easier to get the data without having to resort to offline processing.
Personally, I’m most existed about the dayOfWeek dimension. If you’ve never segmented your traffic by day of week, you really should. Do you know what day of the week has the highest conversion rates? Maybe you should be sending out your emails that morning :)
I’ve read my share of articles on “101 things to test to improve conversion rates”.
While most of the suggestions are usually sound, I find that these lists are often overwhelming and you don’t know where to start.
So here’s how to start with a a simple but often overlooked problem – your links / link visibility.
Specifically, do your links look like links? Do visitors know what will happen after they click on a link?
This goes back to one of my main mantras in conversion rate optimization – Don’t make me think.
Visitors don’t read web pages, they skim. And when skimming, you should make these two points very obvious:
While the answers to the above questions are obvious to you – the site creator, they aren’t always obvious to a first time site visitor.
Here’s how you can actually fix any issues your links might have.
First of all, print out your homepage (or other page you want to test). Take the printout to someone who has never seen your site before, if possible, someone who is similar to your target audience.
Now ask them to circle the links on the page with a pen or highlighter. For extra credit, use two pens. A blue one for elements they’re pretty sure are a link and a red one for elements they think are a link but aren’t sure.
This alone should unveil any major issues where visitors aren’t sure what actions they can take on page.
Next, ask them to mark any links where they aren’t 100% sure what will happen once they click on the link.
For example, a link labeled “HOT” might be confusing where “Most Popular Items” would not be.
Lastly, people know a link is a link based on two different criteria.
When viewing a page, what a link looks like will be the first thing a visitor notices. Is it a different color? Does it have an underline? etc.
Only after reading the link text will they factor in what it says. For example, “Click Here”, “More Info” or “Add to Cart”.
In order to make sure visitors can find links based purely on what they look like, we’ll use the “Greek Link Test”. The idea is to translate all of a page’s text to Greek and then see if people know what’s a link and what isn’t.
First thing is to go to Google Translate – http://translate.google.com/ choose English to Greek and enter the URL of your page.
For example, here’s what my blog looks like in Greek: http://goo.gl/EKwya
Now print the page (now in Greek) and do the same exercise as before. Ask someone who is not familiar with the site to mark all of the links on the page.
What’s Next?
Now that you’ve identified problematic links on your page, you have one of two possibilities.
Your best option is to actually split test problematic links with ones that look more like a link. This will tell you conclusively the effect of improving link visibility, it will look like you’re getting instant likes on Instagram. The first metrics you should look at are bounce rate (or exit rate), page views per visit and time on site per visit. You should also look at the conversion rates for your site’s main goals, but it will probably take longer to get statistically significant data.
Please note that if time on page goes down, this is NOT a bad thing. Sometimes increasing link visibility makes it easier for visitors to find what they’re looking for and they stay less time on a page.
Even if you can’t split test the links, I would still suggest trying to improve them by making them visually stand out more or improve the link text itself. Then repeat the above exercises and see if there is any improvement.
What are your thoughts?
Just a quick post on a very cool trick I recently learned from my colleague Mike Plummer.
If you’ve ever tried to bookmark a report in Google Analytics or share a report URL via chat, you might have noticed that some of the report’s criteria isn’t included.
For example, here is the URL in my browser bar for the top content report for Jan 1-31, 2011:
https://www.google.com/analytics/reporting/top_content?id={profile id}&pdr=20110101-20110131&cmp=average#lts=1296578679097
Now lets say I want to add a page filter (at the bottom of the page) to only show URLs with “google” in them. After I’ve added the filter and clicked Go, the report now only shows URLs with “google” in them, but the URL has not changed!
If I were to bookmark the URL and come back to it later (or send it to a colleague) the report would not include the “google” filter you just added.
But there is a simple solution!
1. Click on the “Email” button on the top of the report (next to Export button)
2. Click on the “Back to report” text link at the top of the page – right underneath the “Set Up Email:”
3. You’re done!
Now the link in the address bar looks like this:
https://www.google.com/analytics/reporting/top_content?
id={profile id}&pdr=20110101-20110131&cmp=average&
rpt=TopContentReport&segkey=request_uri&q=google&qtyp=0&tst=0
As you can see, the page filter information is now included in the URL and when returning to this URL you’ll get exactly same same view!
Thanks again to Mike for the awesome tip.
Google just added a boatload of new dimensions and metrics to the Google Analytics API:
http://googlecode.blogspot.com/2011/01/127-new-dimensions-and-metrics-through.html
I’ll spare you the technical details (you can read the official post) but I do want to comment on what I think is the most important change – 10 new Adwords dimensions.
Here’s why –
I admit I’m not an expert regarding Adwords administration and optimization tools, but until recently, they’ve had what I consider one very big flaw. Initially Adwords tools would look at the beginning of a visit; what happened on Google and the Google network such as impressions, CTR, CPC, etc and then what happened at the end of a visit, IF it ended with a conversion and you had Adwords conversion tags.
Then Google integrated Google Analytics goals into the mix which provided some additional data, but we’re still looking at the start and the end of a visit.
For sites that have a zillion visits and a few hundred conversions a day, you have enough data for analysis, though for the average small business site, there just isn’t enough data if you’re just looking at the end goal (sales, leads, etc).
In order to analyze the vast majority of visits that don’t end up in a conversion, you really need to look at metrics that serve as indicators for traffic quality such as bounce rate, time on site, page views, viewing key pages, etc.
This means that either the Adwords tool has it’s own internal analytics system (and you need to install yet another tag on your site) or it can take advantage of your existing analytics data.
I know a few vendors recently added Google Analytics metrics to the mix, which is a very welcome addition, but some key Adwords dimensions were still missing form the API.
Now that we have almost every Adwords dimension you could want in the API, I foresee a new wave of Google Analytics / Adwords integration, and eventually tools that will truly be able to automatically optimize your campaigns.
I’ve been fortunate enough to see and experience first hand the evolution of the Internet, from even before the web till today.
I’ll spare you a lengthy history lesson explaining how we’ve gone from brochureware sites to where we are today, but I do want to share some thoughts and perspective on where I think things are going.
When marketers started to understand the potential of dynamic web sites, there were two terms everyone was throwing around:
Personalization & Customization.
Fast forward to today (2011). The user experience is still exactly the same for all visitors (other than on a handfull of sites such as Amazon.com).
For the most part, web site Personalization has failed. Sure it sounds good in theory, but trying to tailor the web site experience at the individual level is extremely difficult. It is difficult both from a technological perspective but mostly by trying to create an optimal user experience based on data from a single individual.
There is no doubt in my mind that in the future (and to some extent today) the user experience when visiting a web site will be created dynamically based on what gets the best results, but based on “anonymous” information which is common to large groups of visitors, and not based on a single person.
This reminds me of the concept of Psychohistory from the science fiction series “Foundation” by Isaac Asimov.
Wikipedia explains it better than I can:
The premise of the series is that mathematician Hari Seldon spent his life developing a branch of mathematics known as psychohistory, a concept of mathematical sociology (analogous to mathematical physics). Using the law of mass action, it can predict the future, but only on a large scale; it is error-prone on a small scale. It works on the principle that the behaviour of a mass of people is predictable if the quantity of this mass is very large. The larger the number, the more predictable is the future.
I also like to think of this in terms of what usually happens at (successful) brick and mortar stores.
When you walk into a store, the salesperson probably doesn’t know you personally, but will probably try to help you based on certain public traits such as gender, age, if you’re by yourself or with someone else, etc.
Which brings me back to what actually prompted me to write this article in the first place :)
While I’ve been split testing since 2005 in order to improve conversion rates, the majority of the time, it’s still about what works best for the site as a whole, opposed to split testing together with segmentation (which is what we really want).
Until recently, there haven’t been many options out there to achieve this level of targeting and testing (at least not priced for small to mid sized businesses) but over the past few months, I’ve been starting to see more and more startups trying to bring this level of sophistication to the masses.
While I haven’t had a chance to use any of these services first hand, there is no doubt in my mind that business that truly embrace this level of targeting and split testing will eventually lead the pack and leave most one-size-fits all web sites in the dust.
Just a quick posting about using Google Analytics on pages that don’t use a fully qualified domain name.
If you’re using Google Analytics on a site with a URL like http://intranet/ or something like http://mydevserver:12345 it won’t work.
Specifically, the Google Analytics JS code will not send the tracking hit (__utm.gif) to the GA servers.
I don’t really know the specifics, but I’m guessing that the domain hashing code looks for at least one period in the hostname and won’t work if it doesn’t find one.
Two alternatives come to mind:
1. Use an IP address if one will work. If you’re testing on a local machine 127.0.0.1 should work fine (that IP always resolves to the machine you’re on)
2. Turn off domain hashing. Simply using _setDomainName("none") in your code should also fix the issue.
Hope that helps someone who might be pulling their hair out trying to figure out why the page is not being tracked :)
Just a quick post about a phenomenon I’ve personally seen happen but don’t recall ever seeing mentioned in split testing articles.
I’ll start by saying that ideally you should always look at the results from any split test by segmenting your visitors.
It’s not enough to know that overall version X did better than version Y. Ideally you should check how the different versions performed for various visitor segments. For example, users from organic search might behave differently than visitors from a referring site or direct traffic.
There is one segment though where merely the fact that you’re doing a split test can have an impact on the results:
New vs. Return visitors
Even if you weren’t doing a split test, you would probably see a difference between the two segments based purely on the fact that return visitors already know something about your product, service or site.
I’m talking about a different phenomenon though. The “something has changed” effect.
For new visitors, your site will be new regardless of which version of a test page they see.
For return visitors who have some level of familiarity with your site, if they see something new or changed on the site, they’ll probably pay more attention to it – merely because it’s different.
For example, if you’re homepage does not currently have any video on it and you test a new version with some video on it, return visitors who get the version with the video might watch the video simply because it’s something new.
Conclusion: Always segment visitors by new and return visitors.
If both groups show the same preference, it’s safe to say that you have a winner. If you’re seeing a large variance between new and return visitors, it might be worth it to let the test run for a while to see if the variance changes over time as more of the return visitors first visited the site after the split test started.
[UPDATE]
I was just thinking that this would be a feature that split testing tools can / should support. Segmenting not only new vs return users, but return users who’s first visit was before the split test started vs return users who’s first visit was after the split test started.
Heck – you should be able to only include visitors who’s first visit was after the test started if you want to. Are you listening optimizely, visual website optimizer, and the rest of the gang?
Just a short blog entry about something that’s been bothering me for many years.
I think the best way to explain the problem is by starting with a simple riddle.
My site had visits 31,000 for one month, and then 30,000 visits the next month but the overall trend for visits was up.
How could that be?
The answer is simple.
Not every month has the same number of days.
If I had 31,000 visits in January (31 days) and 30,000 visits in February (28 days) I’d say the overall trend is up.
July / August and December / January are the only consecutive months that actually do have the same number of days in the month.
All this means is that when comparing metrics for two different months, be sure to use relative number and not absolute ones.
For example, comparing average visits per day for the month is better than comparing visits for the entire month.
To make matters even worse, most sites will see a large variance between weekdays and weekends.
Therefore the BEST way to compare month to month will factor this in as well.
I’ve seen two different ways to handle this:
1 – Compare the month in question to a time frame exactly 28 days (4 weeks) prior to the first and last days of the month you’re looking at (yes, you’ll probably have a few days which are overlapped, but that’s OK).
For example, we can compare May which is 31 days to the time frame of April 3rd (May 1st minus 28 days) to May 3rd (May 31st minus 28 days).
2 – Use metrics like “visits per weekday” or “visits per weekend day”. Not perfect since there is still some variance between specific days, but this is better than “visits per day” for the entire month.
– Ophir
Analytics Impact 