I just wanted to provide a visual equivalent to Avinash’s excellent explanation about the difference between Visits to Transaction and Path Length. Visits to Transaction is in the E-commerce > Time to Purchase report section, whereas Path Length is part of Multi-Channel Funnels.

Here is an actual example of Visits to Transaction:

Looks like 30 out of 33 transactions were completed in one visit. Maybe I don’t have to “worry” about attribution at all? Compare that to Path Length:

Same number of transactions, but only 23 had a path length of 1. The difference is due to the fact that Visits to Transaction only counts visits from the last campaign, whereas Path Length shows you all interactions for the past 30 days. You can reconcile the different attribution models in the Top Conversion Paths report, where I added how the data would look in Visits to Transactions:

The Path Length report really gives you a richer understanding of how visitors interact with your site. The takeaway is that there are typically more interactions prior to a transaction than you might otherwise believe by looking at the Visits to Transaction report.

More interactions means that you should help your visitors achieve their immediate goals at each interaction, even if those are not actual purchases. If a visitor “needs” six different visits from an organic search before placing an order, so be it. You’ll want to provide enough value during each of those visits to get the visitor to come back (and hopefully place an order).

I have always been a big believer in tracking mini goals. Abandonment occurs earlier than you may think: you are not going to convert someone if she is not able to get to the product detail page first. Or maybe a visitor is not ready to pull out his credit card, but gives you permission to continue the dialog by subscribing to your newsletter. That is a worthy goal to track.

There is another reason to add upper funnel goals: more data to work with. If your e-commerce conversion rate is 2%, are you going to ignore the other 98%?

Where this clearly comes into play is Multi-Channel Funnels. You only get conversion data from the goals you set up.  If you only have data for relatively few transactions you would be really limiting yourself.

Try this for fun: Set up a goal for non-bounce visits. I like that goal in any case because many visitors who bounce off your site are never going to convert anyway. Set your sights on those who give you at least one click!

After you have collected some data, fire up your Multi-Funnel reports.

Again, if you only have an e-commerce goal funnel you’ll only get data on those conversions.

In this case I have 249 Sale Confirmations, but over 2,400 non-bounce goals! Far more data to work with.

Lets take a specific example: the (not provided) keyword issue. If I look for (not provided) keywords in any interaction, I get 19 different results. By the way, I set up some custom groupings to illustrate the point more easily and not reveal the actual keywords.

Contrast that to the non-bounce goal:

I have 5 times as many data points than before and I can now dig a little deeper. How do visitors interact with Adwords, brand and non-brand terms etc? I may never know what’s hidden in those (not provided) keywords, but at least we can know a little more about those people who interact multiple times with our sites via multiple channels.

In any case, the point of this post is to create more goals – if only for the plain reason to get more data. I know that Google Analytics will have to work a little harder when you throw in additional goals, but I am sure they don’t mind…

The site speed report in Google Analytics got a nice upgrade recently with the introduction of the performance report and the ability to track virtual pageviews.

The performance report shows us how the page load times are distributed and you can see that the average page load time may not the best statistic to describe that distribution.  The trouble is that you can have outliers with extremely high/non-sensical values:

and these will skew the overall average page load time with one single sample:

Far better in my opinion to use the median with page load times. Just add up the individual percentages until you get to 50%, and track that value over time. In the above example the median would be in 1.5 – 2 second bucket, which is not affected by the outlier.

The impact of the SSL Google Organic change can be nicely visualized using Motion Charts since we can use up to 5 dimensions at the same time. For example, are the (not provided) keywords more like branded navigational terms or more like longtail? What’s the bounce rate? What’s the percentage of new visits or the impact on revenue? And since this is a gradual rollout it’s interesting to see it unfolding over time…even if you don’t like what you are seeing

Forget all the fancy new report capabilities…the real reason why Google Analytics launched multi-channel funnels is because they got tired of having to explain why Google Analytics showed fewer conversions than Google Adwords reporting. OK I made that up, but in my years of doing web analytics this is probably THE number one question I get and I am glad I can now point folks to this post.

Despite the fact that Google Analytics and Adwords use different tracking approaches and attribution models I guess it feels “wrong” to many people that Google Analytics would show different/fewer conversions than Adwords. But instead of me trying to explain why this is the case I can just show you by way of a simple example.

I picked a week and looked at Adwords reporting

Then I looked at the same date range in Google Analytics.

Look familiar? Fewer conversions attributed to Adwords – there must be something wrong! Maybe bad tagging?

Enter MCF.

We know that Adwords takes credit for a conversion within 30 days of the initial click. Doesn’t matter if it was first touch, assist or last touch. To simulate this behavior I created a conversion segment where ANY interaction is paid search.

Now look at the data in the Assisted Conversions section.

Drumroll…..tada!

The Paid Advertising/Adwords channel actually shows fewer Last Interaction Conversions than Google Analytics – 204 vs 219, but we know from the Google Analytics attribution model that Direct visits do not overwrite a campaign cookie. Add Paid Advertising and Direct channels and you basically get the same number: 218 vs 219. Voila – that explains the Google Analytics data.

Now, add up all channels and you get 273 conversions vs 276 from Adwords reporting. I will take that margin of error any time.

Aren’t you just itching to delve into understanding the relationship between Paid and Organic channels? But even before we attempt that, we can now reconcile Adwords and Google Analytics reporting. That’s pretty sweet…

Segmentation is great, but segmentation in context is even better. Just wanted to point out one subtle update in the new version of Google Analytics.

Let’s say I want to compare engagement metrics for two different segments. Specifically, I want to compare buyers vs non-buyers who have viewed more than 20 pageviews in their visit.

Here is what it looks like in the “old” version of GA:

And here it is in the new version:

With the exception of the All Visits row, the bars for the two segments look quite different (nb: I removed the pageviews bars for clarity). How do you get from 1.11% to 36.35%?

In the old version each dimension is evaluated in the context of overall visits. Nothing wrong per se, except when you work with segments that are very small as is the case with the “new and buying” segment; it gets “dominated” by the All Visits segment and the small 1.11% can easily be overlooked or dismissed.

Contrast that to the new version, where each dimension is evaluated within the context of the segment itself and not overall visits. I can now see that over a third of visitors within the “new and buying” segment viewed 20+ pages. All we needed to see this valuable information is a subtle change of context.

In general you should be particularly concerned with tracking elements on your site where your visitors (hopefully) take action, such as adding an item to cart or filling out a form. Same thing with social buttons – you want to know if visitors click your Like button. These are mini-goals and should be tracked. So it’s great to see that Google Analytics now has a dedicated report for tracking social plugins, but it might be even better if you could add goal tracking to Social Actions, just like they did with event tracking.

There is of course a cost to adding external scripts on your site, such as increasing page size and giving out data to the button providers. Remember that external scripts can log valuable web analytics data such as referrer info, so you just have to weigh the pros and cons. But only add buttons if you actually want visitors to see and use them. There is no point adding buttons on your site, only to hide them inside tabs where they will never be clicked.

And of course, you should test whether having buttons at all helps your site goals. You’ll probably find that a small minority of users actually click on social buttons, but maybe just having them provides “conversion-boosting” social proof.

The Site Speed report in Google Analytics was announced about a month ago, so how about doing some analysis now that we have gathered some data?

First off some background. Load times are still only collected for Internet Explorer and Chrome, and the data is sampled to the tune of about 3% of pageviews for IE and 9% of Chrome. I don’t have a problem with sampling at all, but since the metric of interest is average page load time it would be nice to know a little bit about how the data is distributed. Does it vary a lot? What’s the typical range of values? Do some outliers massively skew the data? At this point we can’t say since we just get the average value…

Anyhow. Reducing page load times across the board may be a worthy goal in and by itself, but can we get some specific answers from the data itself? Specifically, can the data tell us slow page load = bad and fast page load = good ?

Fortunately we can make use of the fact that data is collected on all types of visits, including single pageview visits, aka bounces! So one interesting question might be:

Is bounce rate correlated with page load times?

Here is how I approached the question.

I created a Flat Table (cool new feature btw) custom report with Page and Landing Page as dimensions and added a bunch of metrics. See here.

(Why not just “Page” as in the default Site Speed report? Because with bounce rate I am concerned with Landing Pages and I want to try to include data only if the Page is a Landing Page. For non-bounce traffic there will be some cases where the page was viewed again during the same visit, which is what I don’t want, but I can at least make sure that the visit started on the same page. Not perfect, but better than just looking at Pages.)

Next, I created an advanced segment for bounced traffic. There is already a default segment for non-bouncing visits, so I applied both segments to the custom report. Finally, I wanted to get rid of data with too small a sample size, so I applied an advanced filter to only show data where I have more than 10 samples and I ended up with this nice report:

(Click image for larger version)

The only thing missing is the visualization… Can we say something about page load times in aggregate for both segments? I don’t have a nice way to visualize this data in Google Analytics so I just exported this report as a csv and loaded it into R. And my best friend there is the boxplot.

This is just one data set for one site over a short period of time, but what do you think in terms of the validity of process? This is observational data of course and not an experiment, but those segments sure look different.

It may be a bit premature to write about one’s impressions of the new Google Analytics because features that one is used to from the current version could be added back gradually rather than officially removed. For example, I miss weighted sort and I hope it’s just a temporary omission. GA also recently updated in-page Analytics and it’s nowhere to be found. Gone is also the $ Index metric for the Page type reports. On the road of progress there are bound to be some who are left behind. Also see Justin’s post.

To me this is anything but a cosmetic UI change. There are some big changes and some subtle ones, but it all feels to me like a lot has changed under the hood.

The new UI has so much space for expansion. You can easily visualize how additional report sections could be added in the left, yet the actual space for the graph in the main body is larger.

The top nav could host other types of functionality, and it just makes sense (easy for me to say with 20/20 hindsight) to separate Reports and Intelligence.

Reports: Past……………..Intelligence/Alerts: Present……………..Acres of space for Future/Prediction?

One shouldn’t speculate too much, but at least as far as Intelligence/Alerts is concerned it’s pretty much a given for me that there will be realtime capabilities in Google Analytics. Alerts have to be (near) realtime. No need to alert me about something in the past.

Back to the concrete. I love the expanded segmentation capabilities, but going into any detail would warrant a whole separate post.

Here is one example why it’s so powerful:

(Click image for larger version)

This is an Adwords report that shows me Keywords and Matched Search Queries, pivoted by Match Type. But what’s new is that I can further segment this report, which in this case I did for All Visits and Add to Carts. Now this is truly useful because I can get a much better sense of how these keywords perform in the sales funnel. For any given keyword I can see how many visitors made it at least to the cart page and the percentage of those who ended up buying.

Can you see that this is basically a segmented goal funnel? I have an overall cart completion rate of 27.6% for all Adwords traffic, but now I can see individual cart completion rates by keyword. My thanks go out to the segmentation and pivot table person(s) in the Google Analytics team!

Try and create this type of report in the “old” Google Analytics. Of course you can’t because you’d see this:

There you have it, one reason to use the new Google Analytics

Of course you know what your top landing pages are

But if you haven’t already done so, create an advanced segment for each of your main landing pages, including the homepage.

Then apply each segment in turn and make a note of what products visitors actually bought in the Ecommerce section. It can be quite eye-opening, as was the case in my example. Your data may look completely different, but the analysis is valuable all the same.

The 3rd most visited landing page sold a grand total of 10 different products:

The 2nd most visited landing page sold a grand total of 24 products:

And visitors who landed on the homepage ended up buying….drum roll…903 different products during the same time period!

Not only does the homepage have to work overtime to route visitors to the right product pages, but it has to do so without much help from descriptive referring keywords – how do you infer intent from visitors searching for your brand name? I had written about homepage segmentation before and we also know that visitors typically click in order, so how you do you arrange content on the homepage knowing that the homepage is the starting point for so many orders?

The answer I believe has to do with lift. If you highlight a product in the most prominent #1 spot on the homepage it will likely sell more because of that location. What you really want to know is what product gives you the most incremental revenue over any other product in the same location. This would be a great thing to test…

And while we are it, if you are sending paid search traffic to the homepage you should allocate that cost among the products you feature on the homepage, particularly if visitors click on it. Kevin Hillstrom calls it on site attribution in his almanac.

Want to be featured on the homepage? You have to earn it!