Our recent Google Analytics training in Washington, DC sold out a week before the event (sorry - we were limited by the size of one of the rooms.) A lot of people have since asked about our next training and signed up for early notification. So we are going to do another Google Analytics training day in Washington, DC — same price ($285/person), same place (American Institute of Architects in the heart of downtown DC.) The venue was just fabulous - we had great wi-fi for everyone, electrical outlets at every seat so that laptops ran all day, not to mention stadium seating with tables in front of everyone.

We haven’t even finished compiling the evaluations forms (but read them all on the way home). A few things were clear: people really loved Jonathan’s new presentation on Google Analytics workarounds for analysts and techies; lots of attendees wanted a little more time learning about goals (OK, I’ll do that); many eval forms mentioned how nice it would be if attendees had a chance to work with each other. We are making one important change: the last hour of the day will be devoted to a Google Analytics lab, where you can bring your laptop and work with your own data. We’ll be there to answer questions and help you do your analyses, help you find the right data, help you configure your analytics (you’ll need admin privileges to your GA in order to do configuration.) Even if you don’t have a laptop to bring (or hate dragging it), we can still spend that time working on our laptops and helping you with your GA.

Well,– here’s the link to our October 3, 2008 GA Training in Washington, DC.

Robbin

At our Google Analytics training in Washington DC last week, one of the most burning questions we got asked was, “Why can’t I see my AdWords data in my Google Analytics?” And even though we’ve blogged about this problem before (here), we wanted to provide a step-by-step troubleshooting guide, complete with how-to’s and screenshots.

So - let’s start from the beginning! Log into your Analyltics account directly from www.google.com/analytics/home/ (rather than just tabbing over to Analytics from your AdWords account). Go to Traffic Sources / AdWords / Adwords Campaigns. If you see data there, but simply cannot access it from your AdWords screen, that is a separate problem (see steps #4 and #5 on this Google help page). However, if you see zeroes there (as in the thumbnail to the left which you can click to enlarge), follow the steps below:

Step #1: Checking Your Auto-Tagging

When your AdWords aren’t talking to your analytics, the first thing we suspect is that your Auto-Tagging may not be turned on.

To check this, log into your AdWords account from adwords.google.com/select/Login, click on the “My Account” tab, and click on “Account Preferences”. Under “Tracking”, you will see either “Auto-Tagging: Yes” or “Auto-Tagging: No”.

If you see “Auto-Tagging: No”, you’ve likely found your problem. Click on “Edit” and change it to yes. (Remember that once you do this, you’ll be collecting AdWords data going forward — you can’t recover data retroactively — so wait six to eight hours before you log into your Analytics account and look for your AdWords data.)

If your auto-tagging was already turned on, keep going on to Step #2 to find your problem!

Step #2: Make Sure Your Landing Pages Are Tagged

Now, you’re going to check whether you have Google Analytics code on the landing pages you’re sending traffic to. So go to one of your landing pages, and click on “View” / “Page Source”. (If you’re not entirely sure what landing pages your ads are going to, you can go to an ad group in your AdWords account, and just click on the blue underlined title in the Ad Variations tab all the way to the right — that will take you straight to your landing pages without incurring any click charges.)

After you select “View” / “Page Source”, you should see a bunch of HTML with a chunk of javascript like in the screenshot to the left. However, if you find that your landing pages aren’t tagged (since it’s easy to forget to add GA code if you have dedicated landing pages!), you’ve found your problem. On the other hand, if your landing pages are all properly tagged, continue on to Step #3.

(**Side note: If going to “View Source” and then hunting in the code for your GA code is not your cup of tea, never fear. Stephane Hamel has created a wonderful plug-in for just this purpose and it’s well worth the download!)

If you still haven’t solved your problem, you’ve most likely got a gclid problem. (And what the heck is a gclid, you ask? Actually, it’s just the tracking code that passes information from your AdWords to your analytics.)

To diagnose this problem, go ahead and click on one of your ads in the paid search results, and then look up in the URL. Do you see the letters “gclid” followed by a series of letters and numbers? If not, you’ve identified the problem at last!

Usually, this happens when the destination URL of your AdWords traffic is being automatically redirected to another page. To correct this problem, fix the destination URLs in your AdWords account so that each ad is going directly to the right landing page. Or, just have the server redirection retain the gclid parameter in the URL of the page the traffic gets redirected to. (You may need to find yourself a good Google Analytics geek to help with this last part!)

Best of luck, and happy linking!

-Traci Scharf

Apologies to anyone who wanted to go to our Google Analytics training next week in Washington DC, but we are now sold out. We are planning to do another training day in NYC and back in DC. I will have more information on dates and places soon. In the meantime, if you want to be notified of either training as soon as I have the dates, fill out this form and we’ll get back to you:

Sorry that we sold out! Be notified of upcoming NYC and/or DC Google Analytics Training Days. Email:

Robbin

Content - Navigation

About a week ago, I read a post by Avinash that answered GA questions; but when I got to the part about the navigation report (see screen shot, left), I just didn’t agree. The question was, “Navigation summary question - why is previous and next page often the same as the page you are viewing? ” Like this report on the left: Notice that 6.23% of pages that lead to the index page come from the index page, and 6.23% of pages that come from the index page go to itself. A little strange, no?

Why I was suspicious of the original answer.

In his post, Avinash wrote that someone at GA explained what caused this peculiar beharior. Here is how he described it — basically, it is about viewers that look at a regular tagged page and then look at a picture on the page in larger format (which isn’t tagged). Here is the example he gives:

Visitor Action One (view): /avinash/2007/09/rethink-web-analytics-introducing-web-analytics-20.html
Result: javascript hit generated (data collected)

Visitor Action Two (click): http://www.kaushik.net/avinash/wp-content/uploads/2007/09/web_analytics_1.0.png
Result: NO javascript hit generated (no data collected)

Visitor Action Three (back): /avinash/2007/09/rethink-web-analytics-introducing-web-analytics-20.html
Result: javascript hit generated

Visitor Action Four (click): http://www.kaushik.net/avinash/wp-content/uploads/2007/09/web_analytics_2.0.png
Result: NO javascript hit generated

Visitor Action Five (back): /avinash/2007/09/rethink-web-analytics-introducing-web-analytics-20.html
Result: javascript hit generated

To Google Analytics (or any other Analytics tool), it will look like this:

1) /avinash/2007/09/rethink-web-analytics-introducing-web-analytics-20.html - javascript hit generated

2) /avinash/2007/09/rethink- web-analytics-introducing-web-analytics-20.html- javascript hit generated

3) /avinash/2007/09/rethink-web-analytics-introducing-web-analytics-20.html - javascript hit generated

</Avinash>

This sounded plausible, but too neat. Much too neat for me. What if someone got to one of those pictures - one of those untagged .png pages - and decided to leave the site altogether? If just a single person bailed out, that would make the percentages different. In order for this explanation to work, every single person would have to exhibit the identical behavior - they would all have to look at two pictures and come back to the same page. It has to be perfectly symmetrical, and it is in the hands of thousands of humans to do it the same way.

Do you believe that? I didn’t. But I didn’t know the answer.

The Truth According to John (aka Google Analytics Gang Signing)

So yesterday, I was working with John and Jonathan here at LunaMetrics. “Did you see Avinash’s post a week ago?” I asked them, “Those numbers are WAY too clean. How could a page refer to itself and then refer to itself again every single time?”

John thought to himself for a couple of minutes and then said, “Oh, I get it. Here is what happens. Whenever the page is viewed twice in a row - like a page reload — the whole thing automatically works.” He put his hands together in the configuration on the left. Jonathan nodded wisely. I looked at them like they were nuts.

But ultimately, I understood what he meant:

If a page precedes itself, it also follows itself. That’s what John meant with his fingers — on one side of the report, we see a page preceding itself, on the other side of the report, we see the page following itself. It is just the same story, told twice.

The key is, you can’t think of that report like a clickstream when it involves the same page more than once. Once you stop thinking about it that way, it becomes intelligible. The page is the same no matter which of the columns of the report it appears in, and the numbers have to match exactly because of that.

Still lost? I know that some of you are sitting there nodding your heads, while others are saying, “What is she talking about?” So for the latter crowd, let me describe it in a different way. I hope you won’t mind if I use numbers instead of percentages, just to make this clearer.

Let’s say that Page A refers to itself via a page reload 100 times. And let’s say that the website has only one page — Page A. The report would look like this — in a conceptual way:

Notice how we get 200 pageviews in the middle of the page (and we know that that’s how many there are.) Notice how the number of pageviews on the left and on the right are symmetrical. And notice how these are two identical pictures, which meet in the middle — just like the picture of John’s hands above.

So I think I have run out of ways to explain this problem. It is sometimes caused by a reload, and sometimes caused by part of the explanation that Avinash gave. But it never requires thousands of people to exhibit the identical behavior.

And in closing, John wanted me to show off that he is really known for his good looks and not for his gang signs, so here is he is.

Robbin

My daughter really wants to go see the opening of Mama Mia! this weekend. But, while a good play, it is not exactly what I am looking for in a movie. On the other hand, I am dying (heh) to see Heath Ledger’s posthumous appearance as the Joker in The Dark Knight.

“It got a ‘Must Go” rating on Fandango last night,” I pointed out to her. Ever the analysts daughter, she retorted, “And that was out of three people?”

Well, no, that were actually hundredds of people who succeeded in seeing it before it opened (Hmm, maybe it opened in other countries in other time zones, the way that you could get an iPhone in New Zealand almost a full day earlier than here.) But it got me to thinking. If there are only five rankings: Must Go, Go, So-So, No and Oh No! — then how does anyone ever achieve a ranking at the ends of the scales? It’s like asking someone to take a survey and they can choose a number between 1 (lousy) and 5 (awesome) — unless everyone chooses a 5, how does anyone end up with an average of 5?

“Aren’t you assuming a lot about Fandango’s algorithm?” asked John Henson, famous creator of the GA Goal Copy tool. “Maybe it’s like the Google ratings,” pointed out SEO Jim Gianoglio, “They count more if you not only rate but also write a review.”

Well, analytics to the rescue. If you click through, you can actually see the rankings in buckets (sort of like the Google Analytics loyalty charts, but without all the misleading titles):

Obviously, you don’t have to get all “fives” to get a five. So let’s expand the system and pretend that Fandango weights all answers on a scale of 1-10, and you have to get between a 9 and a 10 to score a “Must Go.” And maybe each vote gets the top of its category (so if you vote “must go,” it is worth ten points, and if you vote, “go” it is worth eight points. We would have (in my made-up algorithm):

45*2, 26*4, 63*6, 96*8 and 991*10

all of which gets divided by the number of votes, 1221. For a weighted average, i.e. raking of 9.21376 (OK, that is a little overly precise given that I don’t know the algorithm.)

Late note: After publishing, I realized that this (made up) algorithm only works at the high end. What if you had a lot of Oh No! and a scattering of other rankings — if you gave a “two” to an “oh no!” ranking, you could never get a movie to rank, overall, as an “oh no!” So probably it is more of a sliding scale — but the concept is the same.

Well anyway, that is your web analytics movie lesson. Enjoy the weekend. Comment when you see the movie and tell me if I should go.

While some customers (and friends!) are ready to go out and spend gobs of money on their pay per click campaigns, I don’t usually hear that. More often, I hear, “I’d like to start very small, learn what works and what doesn’t, and then roll out in a large way.” It sounds like a great idea, but it doesn’t usually seem to work — in my opinion. Traci Scharf, our pay per click (PPC) specialist, disagrees, so after I write, she is going to do a rebuttal (and you’ll get to see both sides of the problem.)

Oh, before I get into this in a big way, let me not forget:  our next Google Analytics training is August 12 in Washington DC, it costs $285, and you can read more and register here.

Robbin’s opinion: It’s hard to start small with Pay Per Click:
So you want to start small with your pay per click campaigns and roll out after you know what works? Here has been my experience:

To make the numbers easy, let’s say that a click for the customer we are working with is $1.00, and his conversion rate for visits to the site is .5%. May be he wants to spend a million dollars eventually, but up front, he is starting with $100/day. He starts wtih 500 keywords and multiple ads.

If a click costs $1.00, and his budget is $100/day * 30 days, he has a monthly budget of $3000, i.e. 3000 clicks/month. With a .5% conversion rate, that’s 15 orders.

Those 15 orders will be spread over many keywords. There may be two keywords that got three conversion each, and three keyword that got two orders each, and another three keywords that got one each. Some of those may be branded keywords, too, like “LunaMetrics conversion rate.” When someone does a branded search, they are already looking for you (a topic for a different post.)

So what can we learn from this? My answer: just about nothing. We don’t have enough data to be able to say, “This keyword does well,” or “This keyword does poorly.” If everything is coming from the same AdGroup or campaign, we may be able to learn more there, but my experience has been that we generally learn what we already know — which products, or which areas of the site, draw the most visitors. Which products tend to sell the most. Whereas real learning would be, “When we use exact match on these five keywords, we have a higher quality score and get better conversion for less money, but on this AdGroup, we can’t get the kind of traffic we need with exact match, and so we need to use a different kind of match” (for example.) Or even, “These keywords suck!! We have to retool this whole campaign.” Now, that’s learning.

OK, Traci, your turn.
Traci’s Opinion: It’s Really Important to Start Small with PPC
I’ve always felt that limiting your initial spending in a PPC campaign is a smart move for most businesses. Let me give you a little analogy:

Say you want to lose weight, and someone says to you, “Hey, you can lose a lot of weight on the peanut butter diet.” You might be willing to give it a try, but probably don’t want to invest too much of your time and energy until you know whether it works for you or not. A rational decision, then, might be to try it just for three weeks, and then get on the scale and see whether your weight went up or down.

When client companies say they want to learn small, this is in effect what they’re proposing. They’re saying, “Let’s spend a set amount of money, and see if our investment is getting a positive ROI.” Because, just like a diet (”Are you losing weight or aren’t you?”), PPC is pass/fail (”Are you making money or aren’t you?”). Maybe you know that, given where your initial budget is set, you need 15 conversions to break even on your PPC campaigns over the course of three weeks. If you find that your campaigns are getting 30 conversions over the course of three weeks, you’ve learned one big important piece of information: “You’re making money - so go ahead and increase your budgets.”

But probably the most compelling reason I encourage companies to limit their initial ad spend is because they’ll want to have enough money to act on what they learn. Consider the company that runs PPC campaigns for three weeks and finds out they are getting, on average, three conversions a week, but need to be getting ten/week in order to break even. Well, assuming I’ve done my job in setting up their campaigns to drive qualified traffic to their site, we will want to look at what is going amiss with their landing pages. That will mean doing a best practices analysis on their landing pages, and then creating and testing alternate versions so that we can transform their conversion rate. However, if they’ve already blown their whole PPC budget, there is no place to go from there, except to cut losses and admit defeat - not the best strategy for getting ahead!

So the bottom line is, be leery when anyone tells you that you have to spend a lot of money in the beginning months of your PPC campaigns. Exercise the same caution you would with anything else, and remember that you can’t just throw money at PPC and expect success.

On a post way back in April on Custom Advanced Filters, Idris left a comment asking about the required/not required selection (seconded by Paul):

Hey, great articles. I am trying to do some advanced filtering, but I’m confused by the “Field X Required” option. If I say “Yes” to the requirement, which of the following two things does that mean?

a. If the regex in this field does not match, do not include this hit in the profile at all.

b. If the regex in this field does not match, skip this filter, and move on to the next, but still include these hits in the profile.

These two are obviously very different. Which does Google Analytics do?

The confusion is about what exactly is “required”. We were pretty sure we knew, but we did an experiment to confirm. It’s basically b from what Idris suggested.

Here are the details:

1. If the field is required and the regex matches, the output is written to the field you select.
2. If the field is required and the regex does not match, the output is not written to the field you select.
3. If the field is not required, the output is written to the field you select regardless of whether the regex matches.

In no case are the pageviews excluded from the profile entirely (you need an exclude filter for that). The filter just doesn’t apply if the field is required and it doesn’t match.

We did GA training in NYC earlier this month, and I got forms and emails and notes from people asking me when our next one was. They couldn’t make it that day (which was, bizarrely enough, good. We sold out. We printed 70 binders with all the slides to give away, and didn’t have a single one left for ourselves.)

So our next training, for all you folks who asked, and for a whole bunch of people who didn’t, is in Washington DC, August 12, at the American Institute of Architects.

We know that different audiences attend: Those who just want to use the analytics for analysis. And (and/or) those who need to configure them and/or implement. And not only are attendees driven by different tasks, they are at different levels, too. So we’ve tried hard to have the right mix of sessions, and you can see the agenda (and how the sessions are rated) here. And you can also read a more in-depth write-up of each session. You’ll see that the event includes tips and tricks, in-depth analysis, case studies, and even a session on GA 101 for beginners. For those who do configuration, we’ve got goals and filters and profiles, cross- and subdomains, e-commerce and user defined variables. Notice that there are also sessions on Google Website Optimizer and Google AdWords.

Don’t you hate it when people hide their prices? So we won’t: it costs $285. You can register now, and even if you have to cancel, we can refund your money in full until close of business on the Friday before the event. After that, you can transfer your registration to someone else, if you need to.

If you have questions about the event, or want to be sure that your GA problem gets solved there, send me email. Here’s the link where you can learn all about the event.

Robbin

Google is doing a triple event. I don’t really think that they need me to blog about it to our 1900 readers (which is why I never blog about new features, everyone has already heard about them by the time I get to my Wordpress dashboard.) But you do favors for your friends, and this is one of them.

In the PR, which you can also read on the GWO blog, they say that they will:

• Briefly introduce the products
• Highlight recent product releases and developments
• Discuss the benefits of using the products together
• Answer selected questions that attendees have submitted

So I believe it is a beginner seminar. Just ideal for the person who is beginning to work with one of those tools, or who works with one or two but doesn’t know the value of the others.

Here’s the What/Where/When:

TITLE: The Google Trifecta: Webmaster Tools, Analytics, Website
Optimizer
DATE: Tuesday, July 8, 2008
TIME: 9:00 - 10:00 am PT (Pacific Time)
JOIN US: Register to attend

Goals are one of the most useful things you can set up in your Google Analytics. Funnels go along with goals in helping you understand how visitors progress to your goal.

A Primer on Funnels

A funnel is just an ordered list of pages (up to ten) leading up to your goal page. For example, for a shopping cart, a funnel might be something like this:

1. Check out
2. Fill out shipping information
3. Fill out billing information
4. Confirm purchase

The funnel that make sense for your goal depends on your site and how you intend your visitors to progress to your goal. Not all goals have a natural funnel, and you aren’t required to define a funnel for a goal. However, for a goal like the shopping cart example above, defining a funnel can give you a lot of useful information about where people get hung up and decide to abandon the funnel, never reaching your goal.

You set up a funnel along with your goal, and the setup is the same — just put in the URLs for the funnel pages (using Head Match, Exact Match, and Regular Expression match just like the patterns for the goal URL). See How Do I Set Up Goals? in the Google Analytics Help for more information on setting up goals. One tip: Give the funnel steps descriptive names, since they’ll show up in the reports and you want them to be self-explanatory. “Step 1” and “Step 2” don’t help anyone much. Better to go with “Check out” and “Fill out shipping information.”

There’s a lot of confusion around how setting up a funnel affects your goal reporting, and what gets recorded in the funnel by Google Analytics. First of all, the funnel you define affects only the Funnel Visualization report. Your goal conversion counts and rates are still exactly the same as they would be without the funnel in the rest of the reports in GA. With that in mind, here are some answers and some troubleshooting for common problems with funnels.

Required Step

The “Required First Step” check box on the goal setup causes a lot of confusion. First of all, remember that this affects only the Funnel Visualization report. If you check off this box, here’s what happens: The Funnel Visualization report includes only conversions that passed through the required step. That’s it. Your other reports still include any visit that views the goal page, but the Funnel Visualization report only calls it a conversion if it visits the required funnel page.

The required step can be a way to separately measure multiple goals that have the same ultimate goal page, but start at different places. Simply set up more than one goal with a different required step for each one. (Again, remember that the differences will only be apparent in the Funnel Visualization report.)

Order of Steps and “Backfill”

Your funnel steps have an order, and they show up in that order in the Funnel Visualization Report. But the truth is, GA doesn’t care what order the steps occur in. It simply looks through the visit to see whether the funnel pages and the goal pages were viewed, and if they were, that’s represented in the Funnel Visualization report, regardless of the order they were viewed in. A visitor could view step 2, then step 1, then step 3, then the goal, but they’ll still show up in the funnel for each of the steps.

In fact, GA goes even further and “backfills” missed steps in the funnel. So if someone views step 1, skips to step 3, and then views the goal page, GA will actually show that they proceeded through step 2! It will fill in any pages between a visited step and the goal.

Converting More than Once

A conversion is when someone reaches your goal page. But what if someone
visits your goal page more than once? Whether they visit your goal page one
time or one hundred, GA will only report a single conversion for that visit.

Much like the scenario in “Order of Steps” above, GA simply looks through
the visit to see if the goal page was viewed, and if it was, the visit
counts as a conversion. So if a visitor repeats the funnel within the same
visit, you’ll only see one conversion.

Funnel Problems: 100% leave after a step, or 100% convert for several steps

Occasionally we see a funnel that looks like the one below. Something’s clearly wrong. 100% of the visitors leave after the first step, but the other reports clearly show goal conversions are happening.

This happens when you have a funnel step that matches the subsequent steps in the funnel. Remember you need to be careful if you are using Head Match or Regular Expression match in your URLs. If your funnel setup looks like the one below, you’ll end up with a Funnel Visualization report that looks like the one above, where everyone leaves after the first step, because all of the steps match the first one.

A related problem happens when only some of the later pages match a previous page in the funnel. Take a look here:

This problem is harder to detect, but 100% conversion across several steps looks fishy. You should expect to lose at least a few people, given enough data. So what’s happening here?

It’s similar to the first problem funnel setup. Here step 1 also matches
steps 2 and 3, but this time it does not match the goal page. Like the
first example, because steps 1 and 2 are the same, no visitors make it to
step 2, as far as GA is concerned. However, the goal page is different.
For every visitor that reaches the goal page, GA backfills into the previous
steps.

You can avoid problems with a step matching subsequent steps by using regular expression that have negative lookaheads to exclude the later steps.

Jonathan