Category Archives: Web Analytics

Apollo was the god of prophecy. His namesake is a digital insights time machine

Adam Greco has been doing digital analytics implementations since before Adobe bought Omniture (now Adobe Analytics). I go back nearly that far, and have religiously studied his blog posts on the topic. He’s saved me hours of work. So I was thrilled to talk to him the other day about a new product category invented by his latest employer Search Discovery: Apollo, an Analytics Management System. I’m impressed, but I’ll be calling it a Digital Insights Time Machine.

Here’s why:

Excellent Digital Governance

If you’ve been in my shoes, and Adam’s, you know the trouble an organization can get into if it doesn’t have buttoned-up digital governance. Like when an enterprise lacks a clear insights generation strategy.

You see, that strategy describes business goals and objectives. and from them, predicts the reports that will be needed. This measurement strategy answers the question, What user behaviors are needed to achieve our business goals? Many organizations skip this step, and go right to implementation … Often trusting analytics team members with little understanding of business (!). This leaves these implementation pros having to make a best guess at what reports the enterprise will need down the road.

Imagine you’re someone responsible for an implementation of Adobe Analytics, and the chat window lights up with a request from your boss’s boss. Or boss’s boss’s boss. “Can we get a report on XYZ?”

When there is excellent digital governance in place. the answer you give is almost certainly Yes. But if you don’t, you’re faced with telling someone in control of your career that the report requires metrics that are not currently measured. Worse, the implementation will take weeks or even months because a change to the digital analytics data layer will be needed, and IT has many hotter priorities.

If you’ve faced this moment of white hot panic, you probably have wished you could climb into a time machine and make sure those metrics get implemented out of the gate.

Apollo is that time machine.

The first thing you’ll see, if you get a demo like the one Adam showed me, is Apollo’s best practice library of business requirements, for reporting and insight generation.

The list is vast, literally hundreds of requirements.

Search Discovery — relying in part on Adam’s deep experience with clients in every industry — have provided building blocks for any type or hybrid of online business . From these business requirements flows all of the metrics and dimensions that will be needed to address them in reporting.

Then things get really interesting

This shows part of the flow that is followed as you set up your Apollo instance:

Everything following out of the Business Goals and Objectives of this Measurement Strategy value tree is prompted from you by Apollo. Keep in mind that I’ve avoided arrows in the graphic, that would typically connect boxes from one column to the next, but as you likely have guessed, there is a one-to-many relationship flowing from left to right, with all the relationships being contained and documented within the Solution Design Resource (SDR).

If you state a requirement such as “I want to report how many orders are placed each day, week, month, etc.,” you select that requirement as needed and Apollo automatically adds all of the variables, data layer objects, tagging, etc., that you’ll need to track within the SDR.

More about that SDR: Unlike all of the SDRs I’ve ever encountered, the one in Apollo is part of its relational database. That means it can be exported to Excel if you feel inclined, but lives as a dynamic document that revises itself every time you make a change to requirements, metrics or reports.

So when you join an organization using Apollo, you never have to encounter all of the lapses and omissions that come from an SDR that is only half-heartedly updated as an Excel file (often in several versions, causing you to wonder which contains the most “honest” implementation snapshot!).

Leveraging APIs to Adobe Analytics, Launch, and even Workspace

How does Apollo populate your instance of Launch? It’s connected via API to your instance of of both Adobe Analytics and Launch. Since it adds Launch tags, all that’s left is for you to do is refine its work and begin the testing.

And because most implementations require IT to install or update the data layer, Apollo auto-generates the JSON code for that data layer. This makes the work of IT easier, improving the odds of speedy deployment.

Finally, Apollo helps you at least two ways to debug the implementation once it is deployed. It uses those API connections to identify errors, and pushes to Adobe Workspace the reports that can make visual review of the data easier.

Speaking of Workspace, all of the reports that are specified in this digital analytics “time machine” are pushed there, so all you’ll have to do is review and refine them.

Apollo has impressed me so much that I can’t wait to get my hands on the working system, for my first client to use it. If you’re also intrigued, contact Adam for a demo. Like me, you’ll get a glimpse into the future of our industry, where we can spend more time on strategy and insight generation, and less on wrangling code and change requests.

Build a Google Analytics campaign spreadsheet that also crafts the links!

Dorcas Alexander wrote on the Luna Metrics blog recently about an important and often-overlooked topic: Organizing the campaign information you can gather in Google Analytics. I’m following up here with a way to document your campaigns. This method also solves the problem of constructing the special URLs used to create those campaigns in the first place.

If that seems a little opaque to you, read on. I suggest you start with this excerpt of Dorcas’ post:

It’s so easy to tag your campaigns for Google Analytics that you can quickly fill your reports with a mishmash of labels and end up with campaign tag soup! But what’s the best way to get organized? Even if you know what medium and source mean, it’s not always obvious how you should fit campaign info into those slots. And what about the extra slots we get for campaign tags like campaign and content and term?

It goes on to list four simple steps to preventing confusion. The fourth discusses documenting your work. It recommends how — by setting up a Google Docs spreadsheet, which can be shared among all content or analytics team members. He goes on to say, “Another good thing about using a spreadsheet is that a formula can pull all your labels together into a campaign-tagged URL.”

That’s a great idea, but how exactly can this be done?

Here’s my how-to, an addendum to that Luna Metrics post.

Above is the Google Spreadsheet I created for a former client (I needed to stop working with them when I joined Accenture). I’ve replaced the live information they were using with some of my own, to protect confidentiality. I’ll assume you already know how to set up a free Google Docs account, which includes the use of their cloud-based Excel competitor, named Spreadsheet.

  1. Create five columns: Output URL, Target URL, Formula, Campaign, Source and Medium. But wait!, you say. Where is that third column? It’s the Formula column, and is hidden here. I hid it because, a.) It looks identical to Output URL when you have live data in there, so it was redundant, and b.) I prefer to keep it hidden because each cell of that column contains the same formula — one that you definitely don’t want to accidentally change or delete. If I were setting up the system in Excel, I’d make those cells protected.
  2. Before “hiding” column C, place this formula in it: =((((((((B2&IF(ISERROR(FIND(CHAR(63),B2,1)),"?","&"))&"utm_campaign=")&D7)&"&utm_source=")&E2)&"&utm_medium=")&F2)) This formula confirms that the target URL (in cell B2) does not already contain a question mark in it. If it finds one already, none will be added. If it finds no question mark, it added one. After that it builds a trailing URL string that will be familiar to those who roll their own URLs, or use Google’s URL Builder. Once you’re done you’re safe to highlight the column and hide it.
  3. In the Output URL column, place a far smaller formula: =C2 Yes, that’s all. Just display the contents of the hidden cell C2 in the visible cell B2.
  4. Populate the Target URL cell in that row with the web address of the landing page you want to tag with campaign information.
  5. Finally, fill in the Campaign field, along with the Source and Medium fields. These are the unique names of the campaign you wish to credit that visit to, along with the web site or social app it was came from (e.g., Twitter, or Jason Falls’ Social Medial Explorer blog), and the general medium (e.g. social, or web).

That’s it! In the Output URL you’ll find the line. Copy it, and paste it wherever you are setting up a hyperlink on another site or digital channel. For example, that top line shows the URL I used when I was Tweeting about my recent blog post extolling the new release of an Excellent Analytics upgrade.

In the rows to the right of those I’ve shown, you can make notes about when it was used, why, and how you promoted the link. All of this can be helpful when you pull the campaign, source and media statistics for analysis.

I hope this helps. Let me know what improvements you might have experienced in how to catalog your campaign information.

Using Google Analytics’ New Report Dashboard

My work with Accenture has meant this blog has been silent since I joined. I’m loving my work there, by the way. But as for the central focus of this blog, I’ve been continuing to have fun in my off hours with web marketing analytics, especially using Google Analytics. If you use this app, you know they’ve launched a major upgrade of their reporting. It includes a way to create custom dashboards. Below you’ll find one small way I’ve used these new custom dashboards to save time and gain valuable insights.

Until I joined Accenture I was one of the contributors to Jason Fall’s exceptional social media marketing blog, Social Media Explorer. I miss being in such terrific company (they haven’t kicked me out of their Facebook group, something I’m very pleased about). I also miss those posts and the greater audience they had afforded me for my ideas on measuring social media.

But all was not well. I had always wondered how often people viewed my posts, the way I can with this blog. Yes, I could see which posts were the most likely to go viral. I could get that like anyone, from this summary of all of my posts there.

Then Jason shared with his contributors full reporting access to his Google Analytics metrics. Heaven!

Now I had a different problem: I could see aggregate information, but there was no easy way to view just the information about my pages. If the structure of the site had been, say, “domain.com/jefflarche/blogname,” I could view only the pages starting with /jefflarche/. That’s not the case, though. So I walked away, vowing to someday find a way to create a report that would give me a breakdown of my posts, at least for the KPI of Page Views. I got busy today by creating a new Dashboard for the profile. I then populated it with Widgets. Here you can see what the set up looks like for each widget I added (one per post):

Below are the steps taken in this form:

  1. I chose the widget called “Metric.” This shows one number only (along with a couple of others, for context), instead of a chart, a timeline or a table
  2. I chose the metric of Pageviews. But I needed to add a filter. For that, you can see I chose to only show the count for pages that contain a unique string. For this example, I chose the unique string social-media-awareness-measurement/ portion for this post’s URL
  3. I gave the widget the title of that post and linked to it so reviewing content for hints of popularity (or lack thereof!) would be easier

Pretty easy, no? Once I had added a widget for each, this is what I got:

So what insights can I glean from this? First of all, it took a while to build an audience. I learned as I went along, from the first post (lower right corner) to the latest (upper left). I knew this from other measures, which made it particularly sad for me to walk away from the posts. I saw a growth for 693 percent, comparing the views my first post got versus my last.

Turning Information Into Insights

Here are other insights:

  1. People love “how to” content, and respond to headlines that contain those magical words. (I knew this from my direct response days, but it’s cool how thoroughly this has been carried to the online world.)
  2. People like to read reviews of relevant books. That’s what I did with the extremely popular post Lessons from the Twitter Love Guru
  3. Sparklines can give valuable hints to user habits

This last one isn’t readily apparent. I’m going to assume you know what a sparkline is and just say that each of them above shows a sharp rise and fall in readership. After the week it has been posted you can see the view plateau very near zero. It’s to be expected. But there was an outlier, which you could only see if you viewed the full report. It’s shown above right.

Not only did this post not immediately “click” with readers (look at the leading tail), but once it did, its tail at the end is thicker, showing more ongoing popularity. If you’ve been a reader from the start, you’ve already read here and elsewhere about The Long Tail. Here it is in action!

This odd sparkline caused me to dig deeper, and I saw this report for all sources of visits to that page since it post (to the right).

It shows a significant number of links from referring sites and search engines. The referrers obviously liked the content enough to send their readers to it. And search engines? This is the ultimate long tail. I even got four visits from Google for the phrase “measure if people share your content on social media.” Believe it or not, this is hotly contested (I no longer show up for this phrase — at least in the top three pages).

By the way, “feed” stands for Feedburner, which means the fourth (or third, depending on how you look at it) source of visits is people who read Jason’s blog using an RSS reader.

As I said, it pays to be in cool company. By the way, here’s a shout-out to Argyle Social. They’re right near the top as a source for clicks to this page. Their latest post, Is Post Automation Effective? particularly fitting. I would say certainly say yes!

A Link To All of My Social Media Explorer Posts

If the headlines of the above got you curious about my content, I encourage you to visit this summary page, with links to all of them. I’ll be watching this new dashboard to see just how many of you do!

When Virtual Pageviews trump Events

There was much excitement when Google Analytics unveiled its Events metric. This meant web analytics could store several levels of information on a specific action, and associate that information with a unique web visit and visitor. Before that, if you wanted to — let’s say — record a download, you’d need to create a Virtual Page View.

So why did I recently blog on Jason Falls’ site about creating Virtual Pageviews when recording interest actions, such as “Send to a Printer,” or sharing actions, such as “Email a Friend?” or “Share on Facebook?” Why don’t I just create Events?

Using AddThis To Talk To Google Analytics

The answer is simple: If you consider sharing to be a goal of your site, you may want to set it as a Google Analytics (GA) Goal. Events, for all of their power, can’t be set as Goals.

Another action that Events are commonly used for is downloading white papers. Events seem perfect for this because you can set and capture a number of variables, such as title. In other words, you can set the Event Label as the title of the paper. But if you want to measure this as a Goal in GA, you’re out of luck.

Events don’t even “talk” to Goals. [This is no longer true – changes to GA allows any event to be used as a Goal – JL] Let’s say you want to generate a report showing how many people who downloaded a white paper remained on the site for three or more minutes. The time on site can be set as a GA Goal, but you can’t easily generate a report showing the percentage of those who downloaded that remained on the site for that time period.

You can do all of this with GA Virtual Pageviews.

My rule of thumb is this: If you need to identify more than one variable with an event (such as identifying various Actions and Labels), and you do not need to correlate these with GA Goals, used Events. For all else, stick with Virtual Pageviews.

How To Track Content Interest Index In GA Using AddThis

Here is that how-to post I was referring to:

How To Measure Interest Using Google Analytics and AddThis, posted on Social Media Explorer by Jason Falls.

Studying a Twitter ecosystem one user at a time

If you’ve been following my (roughly) monthly posts on Jason Falls’ blog you know that I’ve taken this tack: On his blog I cover the key concepts of a particular web analytics approach, then provide additional support for that idea here.

A recent example is from two months ago. I posted about the use of Brownie Charts as a way to report Content Interest Index. I posted a parallel piece here on another use of the technique (Using Brownie Charts to Measure Bounce Rates). You could say this blog has become my laboratory: Results of preliminary experiments are described here, while the “real” story is broken on Jason’s blog. Tomorrow will be a little different.

Tomorrow, on Jason’s blog, I’ll be posting on someone else’s innovation. It is a review of an extraordinary book: Hashtag Analytics. I’m a huge fan of its author, Kevin Hillstrom, and over the years I’ve spent way too many hours creating Excel-driven models in order to replicate and fully understand his findings.

I’ll be doing that again, this time in support of Kevin’s approach to monitoring Twitter communities. Check back at this tag (hashtag-analytics) to read updates on my “lab work.” Ill be reporting over the next several weeks.

When A Hashtag Community Member Is “Removed”

You may want to check Kevin’s blog as well — especially later this week, when Kevin reports on the future vitality of the hashtag community #measure.  He posted about it last week. Now he plans to theoretically whack an active member. Here’s an excerpt from his post, where he invites readers to suggest whom to “remove”:

In every e-commerce company, somebody is responsible for forecasting sales for the next twelve months, by day. So it makes logical sense that any community manager would want to know what the future of his/her community is, right? This is something you don’t find in any of the popular Twitter-based analytics tools. This is my focus. This is what I love doing, it’s completely actionable, and it’s an area of analysis not being explored!

Next week [the week starting January 24 — that’s today!], we’ll do something neat — we’ll remove one important user from the community, and we’ll see if the absence of the individual harms or helps the future trajectory of the community. If you are an active participant in the #measure community, please send me a user_id that you’d like to see removed in the forecast … I’ll run an example for the individual who gets the most votes.

And in two weeks, we’ll compare the #measure community to the #analytics community … competing communities doing similar work … which community is forecast to have a stronger future?

It’s a fun stunt / modeling experiment that has real world implications. It should serve as a proof of sorts of the predictive power of his Hashtag Digital Profiles and the statistical work behind them. More relevant to online community managers, it should illustrate why showing your participants “love,” lest they never return, is of tremendous importance.

What To Expect Here

I will be applying my own Hashtag Analytics to a different online group — one that has the advantage of weekly meetings. It’s a fairly new group, so the rules may not fully apply (Does an acorn sprout follow the same natural laws of growth as a full-grown tree?). To ensure I don’t jinx my test or influence the community — in a far more direct way than Heisenberg was referring to — its identity will remain unknown until I’ve gathered and analyzed a critical mass of data.

Do stop back.

January 25, 2011 Update:

Here are two related links I didn’t have yesterday. The first is Kevin’s post where he removes that member to the #measure Hashtag community. The second is my review of his book today on Jason Falls’ blog:

  1. Hashtag Analytics: Removing a Member of the Community
  2. Lessons from the Twitter Lover Guru