What does Salesforce Marketing Cloud mean for marketing automation giants Marketo and Eloqua?

This is the question I found myself pondering while heading home from Dreamforce on Thursday afternoon. Earlier that day I’d watched a demo presentation on Salesforce’s vision for the Exact Target Marketing Cloud. The presentation centered around use of a product called Journey Builder that combined multi-channel intelligence on prospect behavior together with email marketing, targeted social advertising, mobile push notifications and internet-of-things-style device integrations to provide a custom-tailored marketing approach for each prospect. To illustrate how the system might work for particular prospect, the presentation followed a fictional guy that is interested in buying an orange Ford convertible.

Marketing Cloud Demo at Dreamforce 13

I have to admit that I was pretty impressed. The interface looked super slick, and while some of the features weren’t particularly novel (like a nurture email featuring the make and color of the car the prospect was looking at) some of them were really exciting, like the ability to send a push notification to the prospect’s phone informing him that he was near a dealership where he could test drive the car he wanted.

After the presentation ended, I wanted to figure out how much of what had been demonstrated was actually available and what sort of a threat the Marketing Cloud posed to other players in marketing automation, such as Marketo and Eloqua.

What are Exact Target Marketing Cloud and Journey Builder?

The Marketing Cloud consists of a number of Salesforce acquisitions, including Exact Target, Social.com, Buddy Media and Radian6. These products can be purchased separately or together. Salesforce lists the different Marketing Cloud packages available, ranging from Basic to Enterprise (see chart below). At the Basic and Professional levels, the Marketing Cloud is essentially a social engagement and listening tool. Marketing automation is available at the Corporate and Enterprise levels.

Journey Builder is in beta and not widely available yet. A Salesforce rep at the Marketing Cloud area told me that Journey Builder is expected to be available in January, although I have not yet been able to verify this with other sources. If this is true, then that means that the release has been slightly delayed from the Q4 2013 release target mentioned in Exact Target’s September 17, 2013 press release announcing Journey Builder.

Salesforce Marketing Cloud Packages

Why Salesforce acquired Exact Target

This quora answer from Jason Lemkin, co-founder of Echo-Sign suggests that the acquisition was part of Salesforce’s strategy to get to a billion in marketing revenue, and that the more mature Exact Target, with $300M in ARR compared to the next best Marketo and Eloqua (now acquired by Oracle, so no longer on the table) at $100M ARR would be a safer bet to help them reach $1 billion. However, that’s just one possible explanation. This article in Marketing Automation Times posits a few other convincing perspectives, namely that the acquisition provides Salesforce with an entrance into the B2C customers and a way to beef up the CRM’s currently limited email capabilities. (Having managed email communications in both Salesforce and Marketo, I can personally attest to Salesforce’s email short-comings in terms of dynamic content, scheduling and sending limits)

How does Pardot fit in?

Right next to the main Marketing Cloud area at the expo was a smaller circle for Pardot, a marketing automation company that was acquired by Exact Target prior to Exact Target itself being acquired by Salesforce. And here’s where I started getting confused. If Journey Builder represents an attempt to build Exact Target into a marketing automation suite, where does that leave Pardot, which already is a marketing automation platform? I asked this question of a couple of different booth staffers in each circle, and got a couple of different answers. A woman with Pardot suggested that Pardot will likely remain more suited for B2B companies while Exact Target and Journey Builder will serve B2C customers. A guy with Social.com on the Marketing Cloud side told me that the difference would likely come down to price and scale, with Pardot being a better fit for SMBs and the full-service offering of the Marketing Cloud predominantly serving large enterprise customers.

So what does the future hold for Pardot? Will Salesforce help it develop to be a strong competitor against Marketo and Eloqua, or will it fade further into insignificance behind Exact Target? Some of the reps at Pardot suggested that they hope to eventually become fully integrated with Salesforce, eliminating the need to sync data between a marketing automation platform and the Salesforce CRM.  Since data syncing issues account for a good portion of the headache associated with administering a marketing automation system, a complete integration would indeed be a game-changer and would make Pardot much more competitive. However, such a move would be costly and time consuming (we’re talking years, not months here) and given that acquiring Exact Target’s $300M ARR and developing Journey Builder is a big bet for Salesforce, it’s unclear to what extent Salesforce will continue to invest in further developing Pardot.

(In response to the question, “Will Salesforce Invest Enough in Pardot to make it competitive with Marketo?”, Mike Volpe, CMO of Hubspot, votes “no”.)

How does the Exact Target Marketing Cloud compare to Marketo and Eloqua?

So the more I learn about the Marketing Cloud, the more I am under the impression that the Marketing Cloud and the Journey Builder app transform Exact Target into a true marketing automation platform like Marketo and Eloqua, but do not cause it to surpass Marketo or Eloqua in any meaningful way (at least not at this point). This year has seen a lot of growth in marketing automation and a trend towards developing marketing automation clouds or suites of integrated marketing services. The Exact Target Marketing Cloud is really a suite of different apps, and includes Fuel, a platform for the development of new Marketing Cloud app. This isn’t unique. Marketo has the Launchpoint ecosystem of Marketo-compatible apps, and Eloqua has the AppCloud.

For an interesting case on why this recent “Marketing Cloud” trend might be bad for innovation, check out Tom Wentworth’s blogpost at Acquia.com.

In the future, I’d like to write a post directly comparing the features included the Exact Target Marketing Cloud with other major marketing automation platforms. In the meantime, I do have some impressions regarding some key differences in philosophy. From what I can gather, the Exact Target Marketing Cloud focuses more on social and mobile marketing than Eloqua or Marketo, but, since it seems to be envisioned as more of a B2C than B2B platform, it does not seem to provide same lead prospecting capabilities. (They may exist, but they definitely don’t seem to be a focus. The presentation at the Dreamforce expo emphasized that Journey Builder is trigger-based, rather than score-based.)

(To see a comparison of functionality between four major marketing automation providers, check out this marketing automation review on R2i. Unfortunately, they haven’t included the Exact Target Marketing Cloud yet.)

What do the competitors have to say?

“It’s been a super competitive market since we founded the company six years ago and we’ve been the fastest growing and we continue to be the innovator and the leader. So we’d expect probably some kind of move from Salesforce with this new acquisition, but we’ve competed with ExactTarget for some time like the way we compete and expect to continue to compete really favorably with them going forward.” — Phil Hernandez, CEO of Marketo

Finally, as the last part of my research, I wanted to find out how other marketing automation companies had responded to news of Salesforce’s Marketing Cloud.

About a year ago, when the Salesforce Marketing Cloud consisted only of Buddy Media and Radian6, a number of CMOs from marketing automation companies responded to the question “Will Salesforce’s Marketing Cloud be a threat to marketing SAAS companies like HubSpot and Marketo?” At the time, the responses were generally positive and combined “a rising tide raises all ships” sentiment regarding the added buzz around marketing automation with an observation that, at the time, the Marketing Cloud did not have the core components of a marketing automation platform.

Mike Volpe, CMO of Hubspot said that “SFDC is not very competitive with other solutions… today”, but also guessed that “at some point in the next 12-24 months they will acquire additional companies or build new features internally to have a more complete marketing solution”, a prediction that has proved true. But simply acquiring marketing automation features is only part of the challenge. A year ago, Marketo CMO Sanjay Dholakia put it this way: “The stuff above [marketing automation] is very sophisticated stuff and not easily replicated.  It’s even harder to put it into an easy to use package that every marketer can use.  That makes us a very complementary solution with Salesforce.” Even after news of the Exact Target acquisition, Marketo’s tune didn’t changed much. In an interview with Jim Cramer in June, Marketo CEO Phil Hernandez has this to say about competition from Salesforce in the marketing automation space: “It’s been a super competitive market since we founded the company six years ago and we’ve been the fastest growing and we continue to be the innovator and the leader. So we’d expect probably some kind of move from Salesforce with this new acquisition, but we’ve competed with ExactTarget for some time like the way we compete and expect to continue to compete really favorably with them going forward.”

Conclusions

So after this research, what do I think? What does the Salesforce Marketing Cloud mean for other players in the marketing automation space?

I’ve summarized my opinions into three main points:

- Competition is growing among large corporate players (Oracle, Adobe, Salesforce) for the enterprise marketing automation market, but it remains to be seen who will best capture the SMB market and whether any independent marketing automation companies (like Marketo or Hubspot) will continue to be competitive in the long term.

- The resources behind the Salesforce Marketing Cloud may be significant, but the marketing automation component is still in its infancy and has not yet demonstrated that it truly has comparable functionality and usability when compared to more mature products. It will also likely take a year or two for the recent Exact Target and Eloqua acquisitions to be fully integrated with Salesforce.

- Finally, Salesforce’s decision to focus on Exact Target and the more B2C-focused Journey Builder app suggests that the strategy behind the Marketing Cloud may be less about directly competing with the more B2B-focused marketing automation establishment, than about opening up into the relatively uncharted area of B2C marketing automation. If that’s the case, I’m curious to see who will try to be their competition in that new arena.

What do you think the future has in store for marketing automation? Share your predictions in the comments!

Source Tracking Part II: Using UTM parameters for online source attribution

Google Analytics’ source tracking is primarily URL based, and unfortunately, some of your important traffic sources may not be correctly identifiable based on their default referring and destination URLs. But there is a solution! Read on to learn how you can leverage native Google URL parameters to deliberately label your traffic sources in a way that aligns with your objectives.

Typically, when determining the source of a visit, Analytics looks at the referring URL domain, page and any additional parameters (such as search parameters) that may be included in the URL. Read Source Tracking part I to learn more about how Analytics interprets source information from referring URLs. However, Analytics can also recognize source information contained in the destination URL in the form of UTM parameters. These parameters are native to Google and are already used to communicate campaign attributes from Adwords to Analytics. You can leverage these UTM parameters to communicate custom tracking information for your advertising campaigns to Analytics – read on to see how.

Historical note: UTM stands for Urchin Tracking Module and references a web traffic tracking and logging system developed by Urchin Software Company and eventually acquired by Google to become Google Analytics. Although Google no longer uses the Urchin program, the acronym is still used to refer to URL parameters that are interpreted by Analytics for source information.

When UTM parameters are added to a destination URL, they have the affect of causing Analytics overwrite whatever source information Analytics might otherwise have populated by default. This means that to use UTM parameters to improve ad tracking, you can simply add parameters designating source information about, say, a display ad, to the destination URL (this works for online ads – I’ll explain how it works with offline ads later).

Important: Do NOT use UTM parameters in destination URLs in AdWords or AdSense, as Analytics already receives all of the available source information, so long as the accounts are linked. If you try to manually add UTM parameters, they will most likely not show up in Analytics.

Available parameter slots

There are five UTM parameter slots that you can leverage to label your traffic sources, described in the table below.

Parameter Name UTM Code Description
Medium utm_medium How—via what channel—the visitor found your site. E.g., paid search, online display or email
Source utm_source Who—what domain or publisher brought the visitor to your site. E.g., Google, Amazon or Buzzfeed.
Campaign utm_campaign What campaign brought the visitor to your site. May be a campaign targeted at a specific audience or keyword group or may represent a promotion. For Adwords traffic, this is the name of the Ad Campaign.
Ad Content utm_content What ad content brought the visitor to your site. In Adwords, this is the copy in the displayed search ad, but you can use it to indicate anything you want about the content of an ad.
Term (Keyword) utm_term What search term prompted the ad—most relevant for Adwords.

So what does use of the parameters look like in practice? First, you identify the destination landing page. Then, you follow the landing page URL with a question mark. The question mark indicates that any following characters are parameters, and are not part of the base page URL. After the question mark, add your parameters in the form utm_source=your-source-name. All individual UTM parameter phrases must be separated from one another with an ampersand (&) sign.

Here’s an example:

URL-parameter-illustration

Important: Not all parameters are created equal. The first two parameters—source and medium—are required parameters and both MUST be used in order for any customized UTM source data to register in Analytics.

Not just for online display!

This method is not limited to online display advertising. It can also be used for Yahoo/Bing Search advertising (since that account won’t natively sync with Google Analytics in the way that Adwords will). It can also be used for any other type of electronic traffic-generation campaign where visitors click on a URL to get to your site, such as email, press releases, guest blog posts and social media.

Devising and organizing your labeling strategy

For your source tracking to be really useful for marketing analytics, it has to be logical and consistent.

Think about what sources of traffic you have and how you would ideally like to organize them for reporting purposes. You may want to be able to identify traffic generated by via the medium of email. You may also want to be able to segment what portion of email traffic came from invites, nurture campaigns or auto-responses—this type of information could be considered the “source” of the traffic. Or, maybe you’re running cross-promotional email campaigns with another vendor. In that case, maybe the “source” shouldn’t be the type of email (you could call that campaign, instead), but rather the vendor or partner that generated the email. There’s no right answer, but maintaining a consistent, labeling scheme that is compatible with your reporting needs will save you a lot of headache.

You’ll also want to make sure that your labeling system not only makes sense, but that it’s applied consistently, down to the exact spelling of the terms used. You’ll end up with sloppy data if you, say, call paid search “cpc” in one place and “sem” in another. In order to keep track of what tracking you’re running and to maintain consistency of terminology, I recommend using a spreadsheet to catalogue all of the destination URLs (complete with tracking URLs) and parameter values you use. This spreadsheet can also build the URLs for you, and can maintain consistent parameter values by getting all parameter values from a look-up table.

Save source info to your CRM or marketing automation platform

An additional benefit of using URL parameters to improve your source tracking is that it may make it easier to save that source information to your CRM or marketing automation platform. Your marketing automation platform may save a visitor’s landing page URL by default, and that landing page URL now contains useful traffic source information. You can simply parse out the parameter values from those landing page URLs to gain insights into unknown traffic. If some of your common landing pages also include forms that submit to your CRM, you can edit your form to save the page URL as well. You can then write workflows to parse out the source and medium of a converting visit for a known individual in your database.

Source Tracking Part I: Default labeling and account linkages

Out of the box, Google Analytics source tracking is incomplete. Some sources – such as visitors that come to your site from offline sources – aren’t attributed meaningfully at all. Some other traffic sources, such as traffic from paid Yahoo/Bing search or non-Google online display ads come in with sloppy labeling.

First, I’ll go over some of the common sources of imperfection in Google’s source tracking, and then I’ll show you a few tricks you can use to make your tracking a lot cleaner.

Where (and why) Google Analytics default source tracking gets it wrong

Google’s source tracking is primarily URL based. When a visitor comes to your site, Google will look at the URL of the site they came from, and label the source of that visitor based on the referring URL. If the visitor came from a Google or Bing search listing, Google will see a URL that looks something like this:

http://www.bing.com/search?q=google+analytics&go=&qs=bs&form=QBLH

Google Analytics parses the URL to identify the following characteristics:

1. Source. The source is the domain name or publisher that brought the visitor to your site. This is equivalent to “WHO” led the visitor to your site. By default, Analytics records the domain name of the referring page as the “source”.

2. Medium. You can think of the medium as “HOW” the visitor found your site. Google looks for clues in the referring URL to identify whether the visitor came as a result of natural search, paid search, referral. If there’s no referring URL, the visit is labeled as a “direct” visit with a source of “none”. In the URL above, “search?” indicates that prior to visiting your site, the visitor made a search query. By default, Google labels this traffic as “organic search”. If Google recognizes the domain name of the referring site as a social network site, it will label the medium as “Social Media”. For all other referring URLs, Google records the medium as “Referral”.

3. Keyword. In addition to source and medium, which label the “who” and the “how” of a visitor’s source, Analytics can also parse the search term out of the referring URL to determine WHAT generated a visit. Someone who clicks on a search will provide a keyword (in above URL see the text after “q=”). This attribute is only relevant for visits generated by searches, and is applicable whether or not the visitor clicked on a paid search ad or organic search result.

You might have noticed by now that there are some major traffic sources missing in the descriptions above – namely any sort of online or offline advertising. This is because in GA’s model, these types of traffic look like other types of traffic. Someone that hears a radio ad might go directly to your site (direct traffic) or might search for your brand (paid or organic search, depending on what result is clicked). Someone that clicks on a display ad to get to your site might be labeled in one of a number of different ways, depending upon whether that display ad was served on a search listing page, social network or referring site. Even a click on a search ad, will not–by default–be distinguishable from a click on an organic search result because the referring URL takes the same form.

Needless to say, not being able to track advertising sources is a HUGE problem. Tracking advertising sources is critical to being able to identify how many visits and conversions particular ad buys are generating. Fortunately, there are a number of different solutions for setting up tracking for different types of advertising properties.

How to add tracking for Google Adwords

By default, your Google Analytics account will not recognize traffic generated by your Adwords account as paid advertising, and will instead identify it as organic search traffic. However, it’s really easy to link your Adwords and Analytics accounts. After linking your accounts, Analytics will properly label traffic with source=Google and medium=ppc and will also communicate a couple of additional attributes, specifically:

Campaign – the name of the Adwords campaign that generated the ad
Ad Content – the copy that the search ad displayed

Sounds useful, right? Here’s how you set up the link.

Click on the “Admin” tab in the top right of your Google Analytics screen.

Then, in the far left column under “Account Management”, click “Adwords Linking”.

adwords-linking-admin-menu

On the following screen, click “New link”.

adwords-linking-new-link

Then, select the Adwords account you want to link. Analytics should already recognize those Adwords properties for which you have Admin access. If you see a message that says that you do not have the permissions needed to link the desired Adwords account, you will need to either request Adwords Admin access, or else ask someone with Admin access to link the account for you.

adwords-linking-select-account

Once you have selected which Adwords account to link, then select which Analytics view to link to it to. Check the “Data Sharing” checkbox, if you would like to be able to view Analytics data from Adwords.

link-accounts

Do not be alarmed if you do not immediately see evidence of proper account linking. It may take up to 24 hours for the accounts to be properly synced and for you to begin seeing proper Adwords source tracking in Analytics.

So once you’ve linked your Adwords account, you will have proper source tracking for that advertising medium. You can also follow the same steps to link a Google AdSense account, if you have one. But what about other online display ads or offline advertising? Read more to learn how to use UTM parameters to fix advertising source tracking.

How to add Google Analytics to your site

Before you actually implement the Google Analytics tracking code, there are a couple of questions you should ask yourself first. You will need to decide which version of Google Analytics to use – standard or Universal Analytics – and you will need to identify how many different sites you need to track.

Standard vs. Universal Analytics

Google recently released Universal Analytics as the new official version of Google Analytics, and generally speaking, if you are adding Google Analytics tracking code to your site for the first time, I recommend that you implement Universal Analytics. That said, the standard version is still available.

Universal Analytics offers a number of exciting new features that are not available on standard, and as the official platform, will continue to benefit from new and updated features. (Stay tuned for a review of new features in Universal Analytics.) Although, as I said, in most cases I would recommend Universal Analytics, there are two main reasons why you might consider implementing standard if not instead of, than at least in addition to Universal. The first reason would be to take advantage of integration with AdSense, content experiments, DFA and remarketing, which exist in Classic and are not yet available in Universal. The other main situation in which you might want to use Classic would be if you had an existing older web site already using Classic, and wanted to implement Google Analytics on a newer web site you own and be able to have consistent implementation and reporting between the two properties. In either case, using Classic would not preclude you from using Universal as well.

What is a property and how many do you need?

At the very highest level in Google Analytics, you have Accounts. There is one account – i.e., person, company or organization – that owns the one or more sites being tracked. When you use Google Analytics, you place a unique cookie on each of the individual sites that your account (you or your organization) wants to track. One cookie pertains to one “property” in Google Analytics.

The most basic setup of a Google Analytics account would have one cookie on one website. However, there are cases in which your organization might want to have multiple websites tracked and even more than one cookie on a particular website. Most websites have a staging server. It’s a good idea to implement a separate cookie on staging so that you can test changes to your tracking implementation without risking interfering with the data for your production site. You would also create a separate property for any separate websites you own. Let’s say you own www.apples.com, and you also own www.bananas.com. Each site would get its own Google Analytics property.

To create a new property, login to Google Analytics or create an account, and then click Admin.

GA-admin-toolbar-screenshot

You should see a screen that’s divided into three columns, one column each for “account”, “property” and “profile”. To make sure you’re in the right place, look at the gray word or words above your account name (in the image below, “AnyaLamb”). If that gray word is simply “Administration”, you’re in the right place. If there are words following “Administration”, just click on the word “Administration” in the sequence to get back to the top level.

To create a new property, click on the pull-down menu immediately below the word “property” and select “Create new property”.

create-new-property

On the following page, select whether you would like to track a website or an app, select Universal or Classic.

select universal or standard analytics

Then enter your website name, URL, time zone, and industry. Click “Get Tracking ID”. Follow the instructions to place the tracking code on your website – usually in the header file.

enter-website-information

What is a profile and how many do you need?

A property represents a body of data collected about a site. A profile is a filter of that data that can then be reported upon. By default, Google Analytics will create an “All Web Site Data” profile for you when you create a property. You can, however, edit this profile and add other profiles.

The important thing to note about a profile, is that it does not affect data collection. Each particular profile is simply a lense with which to look at the body of data that is being collected on the property level. The profile settings don’t affect what data is or is not collected, they simply affect what that profile does or does not show you about that data. Furthermore, not only do different profiles within a property all refer to the same master dataset, they certain custom filters, such as advanced segments, that can be shared across profiles.

There are several really useful things you can do with filters that I’ve covered in previous blog posts, such as how to use filters to exclude internal traffic and use separate profiles to look at subdomains and subdirectories separately. Keep reading to learn more about filters!

The X and the Y: Dimensions and Metrics

Once, in elementary school, one of my teachers gave a very popular lesson on graphs using candy. She gave groups of us students bags of skittles and asked us to count how many of each color of skittle we had in our packets. On the chalkboard was a big empty X-Y axis. On the X axis was a list of all of the colors possible in a bag of skittles. We then had to mark an X above each color label on a plot for every skittle we had that matched that color label.

 skittles-graph

The X-axis, or the color of the skittle, was the dimension of the data – the attribute by which the teacher had us sort the “data” – i.e., skittles. The number of skittles of each color – that’s the metric. 

All of the reports you make in Google Analytics can be thought of in this same dimension vs. metric framework. You can save yourself a lot of trouble with imprecise reports by remembering to ask yourself what dimension and metric you’re interested in, and whether the metric makes sense for that dimension.

Let me show you some examples

Take a look at the default reports in Analytics. See if you can identify both the dimension and the metric.

Here’s the audience overview report – probably one of the most familiar reports in Analytics.

Visit Report

The metric is obvious – visits – but what’s the dimension? Time! Specifically, in this graph, the dimension is “day”. Although in dashboards you can change some graph orientations, by default, graphs in Analytics have the dimensions on the horizontal axis and the metric on the vertical axis.

Let’s look at something a bit more complicated. This is a source report.

Source Report

You can see the dimension on the left hand column of the table – the visit source. But what’s the metric? In this report there are actually multiple metrics. All of the columns besides the left hand column are metrics. This layout is also pretty consistent across Analytics – the dimension is in the left column, and any metrics are to the right.

Commonly-Used Metrics:

  • Unique Visitors
  • Visits
  • Pageviews
  • Goal Conversions

Commonly-Used Dimensions:

  • Geography (City, Metro, Region, Country)
  • Browser and Operating System
  • Device
  • Source and Medium

MacGyvering dimensions with advanced segments

If you haven’t checked out advanced segments yet, then oh my goodness, you really, really should. They’re awesome. I think my mind was kind of blown when I first discovered them. I basically thought they were the answer to everything. It turns out that they’re not (more on that in a future post), but they are pretty darn powerful.

So what are they? They’re basically a way to look at a specific subset of your data identified via certain criteria that you specify – across all of your reports. So you can say, “I want to look at all of the paid search visits in California that landed on this landing page”, and then look at that same group of visits across all of your reports and dashboards. But there’s something else that makes advanced segments even cooler – and relevant to this article.

You can look at multiple advanced segments at once – side by side! This means that you can set up your own “custom dimension” using advanced segments. There is a drawback though – Google Analytics only allows you to look at up to four advanced segments at once. I know, it’s a bummer.

Advanced Segments

Variations on Metrics – Calculated Metrics

A metric like “visits”, is simply a count that applies to a particular set of data. However, some metrics are more complicated than simple counts. Some metrics available in Google Analytics are calculated in order to show relationships between different metrics.

Average time on site, exit rate, conversion rate, bounce rate – all of these are examples of calculated metrics. Average time on site shows the relationship between the total amount of time spent on site and the total number of visits.

Sometimes calculated metrics go both ways

Although most attributes in Google Analytics are treated exclusively as either a dimension or a metric, some attributes may be metrics in some contexts, and dimensions in others. In every example I can think of, these are all calculated metrics (if you can think of a counter-example, go ahead and prove me wrong!)

Pageviews per Visit, Visits  per Visitor and Average time on Site can all be metrics (for dimensions such as source, geography, medium and device). However, you can also bucket visits, visitors and other metrics based upon the value of a different metric, as in the following chart which shows a volume of visits based upon the bucket of elapsed days since previous visit.

Days Since Last Visit

 

What to do when standard reports aren’t enough

Remember when you first started working with Google Analytics? I bet you got into the interface, started poking around, and thought, “Cool! There’s so much data here, I don’t even know where to start!” It might have seemed like there was nothing the interface didn’t already do. You might have simply been overwhelmed by all the pretty graphs.

But then at some point, the honeymoon period ended, and you found yourself asking a question of your data, and realizing that there’s just not a report for that. Heck, there might not even be data for that. Facepalm.

But there’s good news! For any question you’re asking of your data, there’s most likely a way to configure Analytics to either give you the chart you want with the data you have, or, if you’re not collecting the data yet, get you the data so that you can then get the chart you want. (Any question within reason, of course – obviously Google Analytics is not going to be able to tell you how many of your visitors are wearing blue underwear or what they ate for breakfast this morning)

Odds are, you’re frustrated with your Analytics reporting for one (or more)  of four basic reasons. Figuring out which one can help you identify your next steps.

Potential Reason 1: Your reports are showing you too much

You want to look at a more specific subset of information. Let’s say you don’t want to look at all of the visits from your entire site, you just want to look at visits from iOS devices in the Bay Area.

Solutions to Reason 1:  Filters!

What you need is a filter. A filter shows you a particular subset of your data based upon certain criteria that you define. In Google Analytics, there are three different types of filters based upon how widely you want to apply your filter criteria, ranging from the report-level filter, which only applies the filter to one table or widget, to the profile-level filter, which is applied across your entire profile. Learn more about filters.

Potential Reason 2: You’re seeing all the data you want, just not in the format you want.

The standard reports, for example, have one report area for visits and another report area for conversions. Wouldn’t it be useful to see visits and conversions by source – side by side?

Solution to Reason 2: Customize your reports and dashboards.

Potential Reason 3:  The default dimensions don’t line up with your business objectives.

Let’s say that you don’t want to look at visits segmented by state, you’d rather look at visits segmented by your company’s sales regions, which might involve combinations of different states and metro areas.

Or, to give another example, the default source and medium tracking in Google Analytics might not be telling you the right information about your traffic. You may be running online display ads that are being recorded as “Referral” or “Direct” traffic with the source listed as the website hosting the ad.

Solution to Reason 3: URL tracking parameters or custom variables

The best solution depends upon the nature of the problem. If you want to set up a custom configuration of a dimension related to traffic generation sources, you may want to look into using url parameters to improve tracking. If you want to set up a dimension related to audience demographics, such as custom geography or audience engagement, such as , you may  be better served by implementing custom variables.

Potential Reason 4: You’re not tracking the data

Let’s say you want to know more about how your visitors are interacting with your video content. Which videos are they watching? What fraction of visits contain video views? By default, Google Analytics is simply not set up to tell you that. The cookie doesn’t track video plays. But guess what?

Solution to Reason 4: Event Tracking

You can set it up your Analytics tracking code to track such specific events as video plays, form field completions, and other behaviors. Some people even get really clever track whether or not visitors read pages or just skim them.  Read more about how to set up event tracking.

Did I miss something? Let me know if you don’t think your Google Analytics frustration can be attributed to one of my four reasons.

 

 

 

Filters for Subdomains and Hostnames

Separate Profiles for Separate Subdomains

You can also filter based on subdomains. Let’s say that there’s a part of your site that you want to track separately from the rest. Maybe it’s a blog, or maybe it’s a section that only receives traffic from your email marketing.  Or, for example, it may be a members-only part of the site – members.yoursite.com. To track the members-only section of the site separately, you could create a separate google analytics profile and then set up two filters – one in a “regular” profile and one in your members-only profile.

In your regular profile, you would set up the following filter:

Exclude – subdomain – members.yoursite.com

include-subdomain-filter

In the new members-only profile, you would set up the opposite filter:

Include – subdomain – members.yoursite.com

exclude-subdomain-filter

Don’t count  the posers – excluding hostnames

I know it’s not nice, but sometimes  people steal content. And when they do, sometimes they’re really sloppy about it, and copy your Google Analytics code along with it. When this happens, visits to the poser’s site will show up in your Google Analytics tracking –  unless you filter them out.

Now here’s the thing – I don’t necessarily recommend filtering out all hostnames besides your own. Sometimes other hostnames represent legitimate traffic that you  might be interested in tracking. If someone uses Google Translator to translate your page, for example, their session will be attributed to the translator tool hostname. I might think that that’s pretty cool, and want to count those visitors. Even if you decide you’re not interested in counting these sorts of visits and only want to count actual visits to your hostname in your reporting, I recommend that you set up a separate profile that records everything so that you can keep tabs on traffic from other hostnames and identify posers that are stealing your content.  (Another alternative is to only exclude hostnames as you discover them and determine them to be illegitimate).

Stop Taking Selfies!

How to use filters to exclude internal traffic from reporting

Yes, you. Stop taking pictures of yourself. I know you might not mean to, but see, that picture there? Yeah, that one. We see your reflection in the window. Kind of ruins it.

What I’m talking about, is the problem of showing your own visits to your site in Google Analytics. Because let’s face it, compared to every other visitor of your site you and most other people that work on your site or at your company are weird stalkers with totally erratic, outlier behavior that shouldn’t be factoring into your web analytics.

This is super obvious if you’ve started a site by yourself. If you have, my experience with the first iteration of anyalamb.com might sound familiar.

The very first time I used Google Analytics was for the first iteration of this site about a year and a half ago. I had written a handful of posts and wanted to look in Google Analytics to see if they’d gotten any traffic. Unsurprisingly, they hadn’t gotten much – I saw several dozen visits from one user, and a handful of other uniques – mostly spam and close friends I’d send links to. I immediately realized that all or most of the returning visits must be mine. “There’s got to be some good way to filter this stuff out!”, I thought.

It turns out that there is a very quick and easy way to filter out your own traffic – and certain other types of traffic you might not want to include in your reporting.  Here’s how it works:

Filtering out an IP address

Click on “Admin” and then click on “Filters”. Name your filter so you can easily keep track of what you’re excluding from your Analytics reporting and why. For example, if you want to create a filter to exclude all IP addresses from your company headquarters, you might label it “Internal Widgets, Inc. IP Addresses”. If I want to exclude traffic from hy home IP address, I might name my filter “Anya’s home ip”.

filter out own traffic

To simply filter out individual IP addresses, you can leave “predefined filter” selected. Then “exclude” from the first dropdown menu and “IP address” from the second. Then simply fill in the IP address you want to exclude. It’s that easy.

(Don’t know your IP address? If you’re dealing with multiple IP addresses, you can ask IT directly. If you’re just trying to exclude the IP address you’re currently using, check www.whatismyip.com – and yes, you could have just googled that. )

Once you do this, traffic from this IP address will not appear on your google Analytics reports. And guess what? It works retro-actively too. Traffic from the filtered IP address will be excluded from reports even for dates prior to the implementation of the filter.

Why it’s important

Setting up filters is so quick and easy and obviously good for data quality that it doesn’t require much justification. But just in case you’re curious, here are a couple of other reasons I’m a fan of filtering out your internal traffic.

Now if you’ve got a lot of volume on your site, excluding your own visits might not seem very important – they might be a drop in the bucket and when you take a high-level look at things, they might not seem to interfere with your analytics very much. And this may very well be true – most of the time. However, those self-visits tend to be visible in high-profile places.

Say, for example, that you’ve created a new landing page and you’ve distributed it for internal review but haven’t started sending ad traffic to it yet. That page might garner a lot of “internal” traffic that might distort your analytics once the page actually starts receiving traffic.

It also might cause some confusion around when the page actually “launched”. If you filter out the internal traffic, you’ll only start reporting data for the page when the page actually starts receiving external traffic, and there will be a nice clear indication in your visit data of when that happened. Ideally, you’ll add an annotation to your Google Analytics timeline when that happens anyway, but just in case you forget, you’ll have an easy way to look it up.

What other IP addresses should you exclude?

Remember – yours might not be the only IP address you might want to filter out. Are there contractors that work on your site? Make sure you filter them out too. What about other office locations? Ditto. And of course, don’t forget to update your filters when you move offices or otherwise change or add internal IP addresses.

Don’t forget (friendly) bots!

If you’re intentially running a bot on your site, such as StillAlive, you’ll want to exclude it too, usually by IP address.

What if you’re using dynamic IP addresses?

That’s ok. You can still filter out internal traffic from dynamic IP addresses, but instead of using the IP address to filter, you will use a cookie. Lunametrics has a great post about how you can use cookies to label your internal traffic.

Learn about more cool things you can do with filters