PPC Without Pity

Earlier this month, Google released a beta version of new code snippet that enables asynchronous tracking in Google Analytics. So what exactly is this, you ask? Well, under the current setup of Google Analytics, code is read sequentially by the user’s browser. When a page loads, first the header is rendered, then the body, then all of the elements in the body, etc. Most people put their Google Analytics snippets just before the close body tag, so the analytics script is one of the last things executed by the browser. If a user is having issues with Javascript or slow load times, sometimes the code snippet won’t be executed and you won’t get accurate data. You could move the snippet higher in the code to solve the loss of tracking fidelity, but due to the sequential nature of browser rendering, there may be a page load delay as the browser tries to execute the Google Analytics Javascript before it executes the rest of the HTML code.

With this new asynchronous tracking snippet, the Javascript code is executed separately from the rest of your scripts and HTML content. This means that you can put your tracking snippet higher up in the page code and not experience a page load delay as the browser executes the code. Think of it this way: the old Google Analytics code was a one-lane road, where cars (or in this case, scripts) couldn’t pass each other. Asynchronous tracking opens up another lane, where your Google Analytics code can zip by the rest of your sluggish code without impacting site load times.

So what does this all mean for PPC? Well, the main benefit of this asynchronous tracking is an improvement in site load times. And site load time is a commonly overlooked factor in landing page optimization. Consider this paper by Ron Kohavi and Roger Longbotham. In a web experiment, they tested the effect of site load times on Amazon.com. They found that for every 100 millisecond increase in site load time, sales decreased by 1 percent. One hundred milliseconds! That’s only a tenth of a second. It’s barely perceptible, yet somehow has a drastic effect on the psychology of e-commerce. A poorly implemented analytics tracking snippet could probably hold up your site loading time by this amount.

Sure, the new asynchronous tracking snippet promises greater accuracy in Google Analytics (always a good thing), but I think it’s the improved site load time that’s going to make the real impact. Good PPC marketers should always pay attention to what their analytics programs are telling them. But maybe we should be paying attention to what our analytics snippets are doing to our pages, as well. Having a web page that loads slowly and awkwardly could be costing you sales and conversions, and you wouldn’t even know it.

This week in Google Analytics news: German officials are trying to ban websites based in their country from using Google Analytics (you can read the full story at TechCrunch). They claim that Google is collecting personally identifiable information without users’ consent, and that this could potentially break privacy laws. To me, this is a great example of people being terrified of technology that they don’t understand.

Before all the German villagers bust out the pitchforks and torches, let’s get one thing straight: Google Analytics does not collect any personally-identifiable information. Period. As a frequent user of Google Analytics, the most I can tell about someone is what city they (or most likely, their internet service provider) is from, what pages they’ve visited on the site, and how long they spent there. Sure, there’s a lot else that I could do with that information to improve my website or marketing campaigns, but there is no freaking way that I could decode an individual’s identity from my Google Analytics data. And that’s assuming that I would have the time or inclination to do so, since there’s no way I could possibly benefit from that information. The value of Google Analytics comes from analyzing web traffic data in the aggregate, not on an individual level.

Of course, the majority of the German government’s ire is likely directed at the data that Google itself collects. Everyone likes to paint Google as this totalitarian vacuum of internet data, parsing our identities and dirty little secrets in their data centers. Let me ask you this: assume you were leading a company that collected petabytes of data every day. How the hell could you even look for one individuals data in all of that mess? What possible benefit could you get from that? Picking the needle of one user’s data out of the mile-high haystack of Google data would take hours (maybe even days) and cost a ridiculous amount of money in payroll and resources. Even if Google wanted to figure out your individual browsing history, it would not make economic sense for them to do so. Like I said before, the value of Google data is in the aggregate, not in the individual.

If Germany is so worked up about web analytics data, why aren’t they going after Omniture, Woopra, or any number of other web analytics providers? I’ll tell you why: because the bureaucrats who want to make this happen probably don’t even know they exist. Everyone knows who Google is. Just say that Google is collecting data from web users, and the average layman knows what that means. But try to say that similar analytics solutions can be had by installing a javascript tag or looking at web logs? Only us geeks would get that.

Web analytics is not a threat to online privacy, and causing a ban on it would do a great disservice to web users everywhere. Without analytics data, you can’t improve for usability, or determine what the most-needed features of your website are. I sincerely hope that the folks in the German web industry wake their government up to what a stupid, ill-advised idea this is, and stop the Google witch hunt.

When managing a pay per click advertising account, it helps to have a lot of software tools to help you manage your account, discover new keywords, track success, and perform split-tests for you. There are a lot of people out there who would love to sell you an expensive software package to accomplish these tasks. But, in my experience, everything you need to do in a PPC account can be accomplished through free tools that are easily available online. Read on for my choices of the six best free PPC tools:

6. Google Insights For Search – This tool gives you access to the vast amount of data that Google has collected on keyword searches and internet traffic trends. Simply type in the search terms you want to learn more about, select the time frame and geographic area you want to analyze, and you can view trends and relative popularity of any search term that has enough search volume to matter. This is great for discovering how popular your PPC keywords could be, as well as getting a look at likely seasonal trends before they happen.

5. MSN AdCenter Desktop – MSN AdCenter usually has the lowest traffic of the big three PPC providers, but this will soon change once Yahoo’s web properties switch to Bing search. If you’re doing PPC on either Yahoo or Bing right now, you’d better learn MSN’s tools now before your traffic increases drastically. Fortunately, MSN has recently released a desktop editor for their MSN platform, although it is still in beta. If your account meets the right requirements, you could be eligible to download it – see the above link for the steps you need to take. This desktop editor can help you make mass changes to your MSN AdCenter account, like adding multiple keywords, creating text ads in bulk, or other mass campaign/ad group changes. It certainly makes managing an MSN account a lot faster, since you don’t have to wait for multiple pages to load and re-load like in the web interface.

4. SEO Book Keyword Tool – There are lots of good keyword tools out there on the web, most of which are provided by the search engines themselves. But who wants to go back and forth between multiple keyword tools to make a single keyword list? SEO Book has a really awesome aggregator that pulls keyword data from multiple keyword tools like Google, Yahoo, and WordTracker. It’s one-stop shopping for all your keyword research needs. This tool does require that you have an SEO Book account, but registration is free. You also get access to a lot of other SEO Book resources, so it’s a pretty good deal.

3. Google Website Optimizer – Have you heard about how awesome split-testing and multivariate testing are, but the thought of doing all those statistics makes your head spin? Well, you’re in luck. With Google’s Website Optimizer tool, all you need to do is create a few variant pages, cut and paste some javascript code snippets, and the tool does the rest. It even crunches the numbers at the end to tell you conclusively which of your variant pages performed the best. And, with it’s multivariate testing feature, you can choose a set of elements (buttons, images, blocks of text, etc.), and the tool will automatically mix them up in different combinations to see which is the most effective. Split testing your landing pages couldn’t be easier.

2. Google AdWords Editor – After managing accounts with AdWords Editor, I can’t imagine doing it any other way. In fact, I hardly ever use Google’s web interface to work with PPC accounts anymore. This desktop application lets you download your account info, make whatever changes you need, then upload the changes to the web interface. It’s easy to copy and paste any element, from campaigns down to keywords. You can even select multiple keywords and change bids by percentage or dollar amount. I could do a dozen posts about all the features that it has, so it’s probably better to just read Google’s own documentation about this product. And, of course, it’s 100% free. Probably one of the best bargains on this list, considering the wide range of functionality it has.

1. Google Analytics – If you’re going to run a PPC account effectively, you must have a reliable web analytics system in place. Period. You need to be able to keep track of your web traffic, monitor how your organic and paid site traffic is interacting, and look at what keywords people are using to find your site. Google Analytics does all of this for free, and is incredibly easy to implement – just cut and paste a javascript snippet on to every page of your site, and you’re done. What’s more, there are infinite filters and segmentation formulas to allow you to customize your data. You can even set up alerts to let you know when key metrics are rising or falling. With all of this functionality, you’d probably expect to pay a hefty monthly fee for the privilege. But, unlike a lot of similar web analytics packages, this one is 100% free.

Got any more great free PPC tools? Let’s hear about them in the comments.

Whenever you’re split-testing factors in a pay per click advertising account, it’s easy to slack off and skip a rigorous analysis of your results. Option A got a higher conversion rate, so it must be better than Option B, right? Well, sometimes you just have to do the statistical legwork to verify your results. It’s easier than you think.

First, a quick primer on statistics. Whenever you create a research project testing two factors (like two different text ads, or two different match types for the same keyword), you’re bound to get different results for each factor by the end of the test. But you need to know whether this difference was caused by the factors actually being different, or if it just happened by random chance. Here’s where statistical testing comes in. Calculating the difference between two groups can be easily done by performing a “z-test.” You can get all the boring details at Wikipedia’s page on z-tests, or if you’re more interested in the result than the process, you can find a lot of online calculators that will do all the hard work for you. You can find a really good one at the Dimension Research z-test calculator here.

Calculating a z-test is easy. You just punch in your sample group size from your first group (in most cases, you’ll use the total number of clicks on your test factor), type in your frequency or percentage (number of conversions or conversion rate), then repeat the process for your second factor. Hit calculate, and you’ll get a result called your “confidence level.” The confidence level is the statistical chance that the result you are testing did not happen by random chance alone. Therefore, if you get a 95% confidence level on a z-test comparing Text Ad 1 to Text Ad 2, you’re 95% sure that one of the ads is better than the other, and it did not happen randomly. You’ll want to shoot for a 95% confidence level, since this is the acceptable level of confidence for most academic statistical tests.

The Dimension Research calculator also gives you confidence levels for one-tailed and two-tailed tests. Use the one-tailed confidence level if you’re trying to test if one factor is better than the other, and use the two-tailed level if you’re trying to test that the two factors are equal.

After running a few statistical tests, you’ll probably find that a lot of split-tests that initially looked significant aren’t very significant at all. By adding statistical rigor to your marketing tests, you can ensure that your analysis is accurate. You won’t make mistakes in judgment if you trust the numbers.

Now that everyone and their mother seems to have a smart phone or other internet-capable mobile device, it has become more important than ever to keep tabs on mobile traffic to your website. Google Analytics offers a “Visits From iPhones” segment by default, but if you want to get data on all of your mobile traffic, you’re going to have to cook up a solution on your own. Fortunately, the build-your-own-segment functionality in Google Analtyics can help you do just that.

Here’s a step-by-step guide on how to track all of your mobile visits in Google Analytics!

On your Google Analytics dashboard page, look for the button at the top right labeled “Advanced Segments”:

Advanced segments button in Google Analytics

Click on it to open up a menu box with your default segments, along with a few links. On the left side of this box, click on the link to “create a new advanced segment:”

Create a new advanced segment

On the next screen, open up the “Systems” option under the “Dimensions” menu. We’ll be adding a few “or” statements from this category. Let’s start with “Browser.”

Dimensions box in Google Analytics

Drag the “Browser” box over to the dotted box marked “Dimension or Metric:”

Dimension or metric

Now it’s time to add a value to this statement. You can leave the second field at “matches exactly” – the other options work better when we’re dealing with numerical factors. In the “Value” text box, you can either add in text of your own, or choose from the drop-down menu. Here, you can enter in several browsers that are mobile-only, like “Opera Mini” or “BlackBerry9530.” If you don’t see any mobile browsers in the drop-down, it means that you haven’t received any visits from users of these browsers yet. You’ll need to create a new “or” statement for every mobile browser you want to add. Just keep repeating the steps until you’re done.

Once you’re satisfied with your mobile browser selection, create another “or” statement and drag over the box marked “Operating System.” For this one, we’ll choose several operating systems of mobile devices: iPod, iPhone, Android, Danger Hiptop (powers the T-Mobile Sidekick and other devices), and any other mobile OS’s you might see in your drop-down selection. As with the browser selection, you’ll need to create a separate “or” statement for every OS you want to cover.

If you’re feeling particularly analytical, you can even add screen resolutions to the mix. Create another “or” statement and drag over the “Screen Resolution” box. Common mobile device screen resolutions include 320×396 and 320×480 (iPhones). Once you are finished creating “or” statements for all of your mobile conditions, you can test out your segment by clicking on the “Test Segment” button on the right side of the page.

Google Analytics does not support tracking visits from all mobile devices, but this segment should represent a good portion of your mobile visitors. Just activate this segment when reviewing your Google Analytics reports, and you can start figuring out what kind of behavior you’re seeing on your website from mobile visitors.

What keyword positions work the best in a PPC campaign? It’s a pretty common question, but there’s no straightforward answer. Like a lot of other factors in pay per click advertising, you need to do your research and check your web analytics numbers to see what works best for you.

If you have Google Analytics installed and linked to your AdWords account, it’s easy to get information on what keyword positions are working for you. Just follow these steps:

Getting to the Keyword Positions report in Google Analytics

1. Select your “Traffic Sources” menu on your left sidebar.
2. Open the “AdWords” drop-down menu.
3. Select the option “Keyword Positions.”

And you’re done! This report will give you traffic metrics (total clicks, impressions, CTR, etc.) for your AdWords keywords at the listed positions. If you have goal conversions set up (and you should), then you can even get information on what positions your keywords convert best at.

Once you get an idea of the positions that get you a good conversion and cost-per-conversion rate, then you can start making bid adjustments to make sure you hit those positions more often. This is yet another example of how accurate web analytics setups can really improve your ROI for pay per click marketing campaigns.

As helpful as PPC conversion tracking scripts are, they are far from infallible. While Google, Yahoo, and MSN have all been gracious enough to offer up free conversion tracking solutions for their respective pay per click advertising platforms, we all have our horror stories about how they’ve failed us. Maybe you’ve missed several days of tracking due to a glitch, or you are the owner of one of those cantankerous types of websites that just doesn’t want to play well with conversion tracking javascript. In any case, you should probably think about having a back-up method of tracking conversions in case your standard conversion tracking doesn’t work or experiences an error.

Fortunately, Google Analytics has a function that works pretty well. Just set up some conversion goals in your Google Analytics profile (you can find the how-to guide here) that are identical to your PPC conversion tracking goals. It’s easy to set up a simple conversion tracking goal. Just pick a catchy name for the goal, then copy the link to your “thank you” or confirmation page (or wherever you have your conversion tracking code) as the Goal URL. You’re done! Of course, you can always create fancy funnels and reports, but if you just want a simple backup for your existing conversion tracking that’s all it takes.

Not only will this help you keep track of conversions even if your account conversion tracking gets all wonky, but it will also help you get a second opinion of sorts to see if your conversion tracking is inaccurate. If you are running multiple PPC search engine accounts and directing them all to the same conversion page, the Google Analytics conversion goal reports will help you get an aggregate view of your total conversions without having to flip back and forth between accounts. You can use Google Analytics’ segmentation options in reports if you want to split your conversions up by search engine again.

This week, Google rolled out some new reporting features that are pretty interesting. If you run a performance report now, you might see some additional options for conversion reporting: “many-per-click” conversions, and “1-per-click” conversions.

Confused yet? I know I was when I first looked at this. Apparently, Google now has the ability to track multiple conversions that come from one click on your ads. For example, if you have newsletter signup and product purchase conversion events, and someone both signs up for your newsletter and purchases a product after clicking on an ad, you just got a “many-per-click” conversion. The “1-per-click” conversion tracks exactly what it says: one conversion on one ad click.

I can see where this might come in handy for someone with multiple conversion events in a campaign, but this is old news to anyone who’s familiar with setting up multiple conversion goals in Analytics. It could be useful if you want to see which keywords/ads are the most engaging, though. If you see that a keyword is getting a ton of many-per-click conversions and another is getting only 1-per-click conversions, that’s yet another metric to compare campaign elements for optimization.

While I’m never one to shy away from more analytical data, I’m not quite sure how to use this new functionality just yet. Got any ideas? If you do, let me know in the comments.

You can read Google’s official announcement regarding the feature here: Inside AdWords

Last week, I wrote about some changes going on with Yahoo demographic targeting. This week, I wanted to follow up with some more specific reasons why I think demographic targeting for pay-per-click campaigns is less useful than the search engines would have you believe.

There are three main reasons why I think web demographics are unreliable:

1. The inaccuracy of demographic statistics online – a great deal of user demographics that search engines use is pulled from third party data vendors, opt-in data, and algorithms that provide “estimates” of user data (source, source). None of these sources could possibly provide information with 100% accuracy. Third party data providers have an incentive to overestimate the value (and accuracy) of their data, opt-in demographic data is by definition incomplete (since you’re not gathering information on 100% of users), and I’d be surprised if my life could be quantified by a computer program. These are the building blocks that you would base your ad planning assumptions on. If you’re targeting 35-40 year old females, how can you be sure that any of these methods could accurately estimate the actual person who is seeing your ad fits into those categories? That brings me to…
2. One computer DOES NOT equal one person – I’m sure that there are plenty of households out there that share a computer. How many times have you looked up something on someone else’s machine? Despite all the big talk about demographic targeting, there’s really no way to truly target the person. Our ad process ends at the computer screen – you can’t control who is using it. You have no certainty that the user viewing your ad through the lens of the computer monitor is actually the owner of the machine, or the person that all of these fancy demographic programs have data on.
3. Reliance on voluntary data – Of the three sources mentioned in point #1, two of them rely on voluntary data – the third-party data (probably) and the opt-in data (definitely). For starters, you only reach a small percentage of people who will actually take the time out of their day to volunteer this data. I’m willing to bet that a vast majority of people prompted for this information either are too busy to fill it out correctly, or choose to provide inaccurate or invalid data due to privacy concerns. Then, you have to take into account the people who filled out the information, but provide misleading information either through carelessness or willful inaccuracy.

After you take all of this into account, what percentage of your audience do you think has completely accurate demographic information? Ten percent? Five? Maybe less? There’s probably no way to even know. Instead of relying on these vague demographics, do yourself a favor and spend more time analyzing the data that you know is correct – historical keyword reports and analytics tracking. These sources will provide a much greater wealth of insight than the lazy demographics put out by the search engines.

I can’t believe I’ve gone this long without discussing my favorite PPC management trick: the detailed keyword filter for Google Analytics. You can find simple instructions to set it up at this Semvironment blog post. It only takes a few minutes to set up, and is well worth the small investment of labor.

Once you set up the filter in your Google Analytics account, you’ll be able to view the exact search queries that users are reaching your site on. This is especially helpful if you’re running a lot of broad match keywords in your campaigns. Normally, you’d never be able to tell exactly what users are typing in before Google shows one of your ads. After getting a few weeks of data from this filter, you’ll realize that Google takes a very liberal interpretation of what is relevant to a broad match keyword. After all, more impressions equals more potential clicks and revenue for Big G, so what incentive do they have to make sure your broad match keywords are super relevant?

Now, I should mention that you could get this data out of a Search Query report in AdWords. The Analytics filter method has two advantages, though. One, you never have to worry about that heartbreaking “134345465 unique queries” entry in your report data. Two, you’re able to get detailed keyword data not only for Google, but for your other PPC campaigns that you have hooked into your Google Analytics profile (more on this later).

By using the keyword filter, you’re not only able to weed out ineffective keywords by adding negatives and changing match types, but you’re also able to get a feel for what users are actually searching for on your site. I’ve had a lot of success finding popular terms in the keyword filter, creating an AdWords ad group around them, and raking in the conversions. Not bad for a free web analytics package, huh?