June Dershewitz on Web Analytics

Career Advancement for Veteran Web Analysts (Article and Slides)

Whether you got your start in Web analytics just recently or ages ago, you will inevitably go through the “newbie” stage and come out the other side. You break into the field, gain some skills and eventually find your niche. So now you’re a veteran Web analyst, what’s your approach to career advancement?

That's the focus of an article I've contributed to Online Behavior, Daniel Waisberg's wonderful new marketing measurement portal: Career Advancement for Veteran Web Analysts.

The article was based on a presentation I gave at eMetrics San Jose earlier this year. If you just can't get enough of this topic, here are the slides from my talk:

Career Advancement for Veteran Web Analysts

Thoughts on Mobile Paid Search (and My SMX Slides)

Last week I participated in a panel discussion on Mobile Paid Search at SMX Advanced in Seattle; I have just posted my slides on SlideShare. As a practice, mobile PPC is still in its infancy and it was an honor to share the stage with mobile marketing thought leaders such as Cindy Krum and Greg Sterling.

When I wasn't speaking I was taking notes. Here are 3 of my favorite take-aways from the session:

• Use AdWords? Your PPC ads might show up on mobile devices already. You are opted in unless you have explicitly opted out. In my own presentation I gave an example of a paid search listing that ran in identical form on PC and mobile - and failed miserably on mobile. If you're going to take part in mobile search, make sure it's an intentional choice and cater to the medium.

• Mobile PPC is full of open questions, such as: Are mobile searchers closer to conversion point than PC? Are mobile search phrases shorter? Do short vs. long phrases convert better? Comparisons between traditional PPC and mobile PPC surfaced frequently during the panel, but the panelists' conclusions were often contradictory. We have a long way to go before mobile PPC benchmarking reaches a mature state.

• Trying to decide whether to jump in and start doing mobile PPC? These verticals already get a lot of mobile search activity:
  
  • Retail, entertainment, travel (& any business w/local component)
  • Financial
  • Mobile-specific goods & services

If you'd like to read more, check out my presentation.

 

Preserving Content Hierarchies in a Migration from Webtrends to Omniture

Blog posts are like shoes: some are sexy and some are practical. This post falls on the far side of practical, not unlike orthopedic clogs.

Here’s the premise: Say you’re currently tracking your site with Webtrends and you’re considering a switch to Omniture.  If you’re happy with the content hierarchy logic that you’ve built into your Webtrends implementation, how much of it will be portable to Omniture? Here are the most important factors to consider:

1) Webtrends Profile = Omniture Report Suite

The Webtrends profile and Omniture report suite both represent the top-level segments for a site. Assuming a given Webtrends profile genuinely maps to a visitor segment that requires full reporting it should be recreated as an Omniture report suite.

In some Webtrends implementations I’ve seen (especially log-based systems), administrators will create lots of profiles simply to obtain unique visitor counts for partial slices of populations. If this is the case it may be possible and appropriate to get the required metrics in Omniture without recreating every single profile as a report suite, since Omniture has more unique visitor reporting flexibility. Omniture also offers a free report suite roll-up feature; there’s no direct match for this in Webtrends.

2) Webtrends Directory Report ≈ Omniture Channel

The Webtrends Directory Report lists out visits by first-level directory based on URL structure. The Omniture s.channel variable serves a similar function, although it’s more likely to be based on business rules which may or may not match the literal file path. If the site has a clean URL structure this would be a simple and easy translation.

3) Webtrends Content Groups ≈ Omniture Hierarchy

In Webtrends most of the business-level content hierarchy reporting is managed using content groups. In my experience, ALL usable Webtrends implementations have at least a few content groups defined. The closest analogy in Omniture would be the s.hierN variables.

Keep in mind that Webtrends content groups go 2 levels deep (i.e. content groups and sub-groups) while Omniture hierarchy reporting goes 5 levels deep. Another important difference is that Webtrends has no strict limit on the number of content groups you can create, while a standard Omniture contract includes just 5 hierarchy variables (each with 5 levels). A migration would involve not only a mapping of content groups to hierarchy variables but a reassessment of whether the 2-level structure designed within Webtrends makes business sense now that you've got 5 levels at your disposal.

4) Other Considerations

In general, Omniture implementations tend to be more complex – and more flexible – than Webtrends implementations. Anyone considering a migration should take their Webtrends setup, translate functionality where parallels exist, then look to the additional Omniture features available to them.

What do you think? Am I right? Am I wrong? Am I missing something? I welcome your feedback, especially if it’s practical.

Top 10 Things I Wish I Knew When I Started in Web Analytics

Here's a short-but-sweet article I wrote 2 years ago for the Web Analytics Association, in which I reflect on my first job in web analytics - at a dot-com, almost 12 years ago. Ancient stale content, right? Actually it remains pretty timely so I thought I'd repost it:

I remember what it was like to walk through the door at my brand new job, my very first job as a web analyst, wondering what I’d gotten myself into. In retrospect, what did I wind up learning the hard way? What would been helpful to know up front? What should I have been prepared to expect? With that in mind, here are 10 things I wish I knew when I started in web analytics:

1. You will sit between the techies and the marketers. Figuratively, and maybe literally. Make friends on both sides of the fence.
2. You will learn all about your business. Not just the stats part. Not just the web part. The work you do in web analytics will only make sense once you’ve put it in the general context of your business.
3. Ahem, what is this thing you call a "Visit"? Know your standard web metric definitions by heart, and be able to recite them concisely for people who ask. They will ask.
4. Dirty, dirty, dirty. Numbers won't match, they won’t add up, they won’t make sense, sometimes they won’t even exist. Know how much dirt you’re willing to live with, then accept it and move on.
5. You will learn to love the query string. You will come to see it as a beautiful haiku. You will know it backwards and forwards. You will repeatedly explain its usage to people who need to append campaign codes to URLs.
6. CSV stands for "comma-separated value" ... it's a file format, every data analyst's friend, and - inexplicably - it doesn't even have to be comma-separated. Huh.
7. Operators are standing by. Know the support hotline number for your commercial web analytics vendor of choice, and don't be afraid to call. If you have one sticky note on your monitor it should be that number. Actually two sticky notes. The other one should say, “Patience is a Virtue.”
8.  Don’t fall into the “report monkey” trap. Manually-repetitious activities are not a good use of your time, so automate wherever possible. Strive to spend your cycles doing thinking fellers work, and leave robot work to the robots.
9. You are not alone. Right now there are other web analysts sitting at their own desks, somewhere between the techies and the marketers, and they’re facing exactly the same issues that you are. You will meet them at Web Analytics Wednesday.
10. Think long-term. From the very beginning, think about where you want your career to go and make every effort to develop in that direction. Your entry-level position in web analytics can/should/will lead to other things, so know what you're targeting and go for it. 

Photo credit: Ibai Acevedo

Beyond Web Analytics Podcast on Functionalism

In lucky episode #13 of Beyond Web Analytics you'll find a podcast with me on the topic of functionalism. It's a 40-minute recording of my conversation with Adam Greco, Rudi Shumpert and Gary Angel; we cover basic definitions, explore several examples and discuss implementation options.

Adam Greco's very practical post on page type pathing - written long before our podcast - describes a functional analysis technique that any Omniture customer can use.

Also, I have written several blog posts exploring the topic of functionalism; check out the series here.

Flickr Stats vs Other Photo Sharing Analytics

Just after the public launch of Flickr Stats back in 2007, I posted a positive review of the tool, musing that it could serve as a good model for other social media measurement systems.

Fast forward to 2010, I am no longer so content with Flickr Stats. I'm convinced we can do better. Perhaps my attitude is influenced by the recent buzz about how anyone can track stats on their Facebook Fan page using Google Analytics or Webtrends. Or perhaps I've just grown tired of the limitations of Flickr Stats.

Is Photobucket any better, or Picasa, or something else? This post serves as a summary of my quest to find the best photo sharing analytics. Here's what I found.

Flickr: A shortcoming and a workaround

For paying Flickr Pro customers, Flickr Stats currently reports a host of metrics including the number of views over time, favorite/comment activity, top content, referring domains/URLs and search keywords.

The thing I used to like best about Flickr Stats was the all-time referrers report, since it provided insight into my photostream's findability over time. Unfortunately, about a year ago Flickr chose to scale back the referrer stats it reported. Referrer information is now only available by day for the last month. Judging from the volume of support forum comments on this subject, I'm not the only Flickr member who's unhappy with this change.

As a workaround, one Flickr member has created an app called F*Stats that scrapes Flickr Stats and stores historic referrer information in a local database. This seems like a complicated solution, but right now it's the best way for Flickr members to retain their referrer info.

[March 3, 2010, 4pm Pacific. Update: Several hours after I published this blog post, Flickr announced the release of their shiny new Flickr Stats API. Also, members can now download CSV files containing all historic referrer data. Ah, timing.]

Photobucket: Easy social media sharing and pretty good stats

Less than a year ago Photobucket launched a stats dashboard for all members, with limited metrics in the free version and more in the Pro version. In terms of functionality, I'd rank the stats dashboard just below Flickr Stats. It does appear to provide referrer information over time, but it only reports referring domains - not referring URLs or search keywords.

On the positive side, the ability to easily share Photobucket content on Twitter, Facebook and other social media sites - and then get referrer stats - is an excellent feature and may be reason enough to use the service.

Picasa: Perhaps Google Analytics integration on the way?

Activity tracking on Google's Picasa photo sharing site is currently very limited - you simply get the number of views of each photo. However, there's quite a bit of user feedback regarding the desire to track Picasa with Google Analytics. In this wish list of most desired Picasa features, Google Analytics integration is one of the top requests. Come on Google, grant our wish.

Other Alternatives: Again, with GA integration

Although I've covered 3 of the most popular photo sharing sites, there are plenty of others to choose from. A quick search turned up 2 less well-known sites that currently allow Google Analytics tagging: SmugMug and PhotoSwarm.

What do you think? Are there better solutions that I have overlooked? Or, are photo sharing stats not worth anyone's time? I welcome your feedback.

3 Great URL Shorteners with Analytics

URL shorteners trim down regular links so they can be shared more easily on Twitter and other social media platforms. My favorite side benefit of URL shorteners is that every link click can be tracked as it's redirected to its final destination.Not all URL shorteners actually offer analytics, though, so if you need tracking you must choose wisely. To help you along, I recommend these 3 URL shorteners with analytics:

1) bit.ly

It's the Twitter default, and therefore very popular. Full-featured analytics are available to whoever creates a short URL and any other person who cares to look. The trick to seeing stats for any bit.ly link: add a "+" sign at the end of the short URL (for example, http://bit.ly/djG9TM becomes http://bit.ly/djG9TM+). Bit.ly also offers a Pro account with an enhanced dashboard for private use by link publishers.

• Best for you if: you want to stick with a popular choice and you don't mind sharing your stats with the world.
• Site: bit.ly
• Screenshot:

 

2) cli.gs

This URL shortener has many of same features as bit.ly, but stats for URLs are not public. In other words, you must be logged in to view your own stats, and you cannot view stats for URLs you did not create. Another differentiator: cli.gs displays geolocation data as a map overlay rather than a chart - nice if you're a "visual" person.

• Best for you if: you need full-featured analytics and privacy.
• Site: cli.gs
• Screenshot:

 

3) ow.ly

This tool takes on the problem from a different angle: the URL shortener and the associated reporting interface are embedded within Hootsuite, a social network management client. It takes a bit of work to set up an account and learn how to use it, but the stats are really well done. You get analytics for individual messages as well as a summary roll-up. Another very useful feature: an optional form helps you append campaign codes to your URLs before you shorten them.

• Best for you if: you're looking a comprehensive system for managing posts OR you want help appending tracking codes as you shorten URLs.
• Site: hootsuite.com
• Screenshot:

 

These 3 tools are my top picks. For a more complete review of URL shorteners (both with and without analytics), see Danny Sullivan's post from April 2009.

Review of All Android Analytics Apps

Out of curiosity as much as necessity, I recently evaluated all of the mobile web analytics applications that run on my Motorola Droid phone. As of January 2010, a search for "analytics" in the Android Market turns up the following apps:

1. Analytics Widget (free)
2. mAnalytics (free/paid)
3. DroidAnalytics (free 24-hour trial/paid)
4. Mobile GA for Android (free)

I have installed and reviewed each of these apps:

Analytics Widget

This very simple 1x1 widget sits on your mobile desktop and displays either page views or visits for the Google Analytics profile of your choice. The default (and only) date range is "today" - but this seems appropriate, since it functions as a ticker. The widget auto-refreshes periodically, or you can click to refresh it on demand. I now click it compulsively whenever I see it.

• I like: Very simple, basic and easy to use.
• Wish it had: A period-over-period percent change, or even a little sparkline to go along with the single number. The fact that I've had 11 page views today (on my lowly blog) is meaningless until I can compare it to past performance. Beyond that I'd like to click through to more detailed data. If I see a spike in traffic I might want to drill down and look at referrers or search terms.
• Bug factor: It force-quit on me a few times but always recovered on its own. There's currently a note on the Android Market page for this app indicating that this is a known problem, so hopefully it will be fixed soon.
• Stars: 4 out of 5
• Verdict: I'm going to keep it on my desktop (so I can continue to refresh it compulsively).

mAnalytics

Full-featured application (no widget) displays 7 top-line metrics from Google Analytics including visits, bounce rate, % new visits and more. It also provides detail reports for referrers and search terms. All data appears in tabular form, plus very basic charts for visit and page view trends. Date range choices include several presets (today, yesterday, last 7 days, last 30 days) as well as the option for a custom date range.

• I like: Just a little detail. It's an appropriate amount of data to display on my small mobile screen without feeling cluttered.
• Wish it had: A widget. Actually I wish I could merge Analytics Widget with mAnalytics so I could see a little overview on my desktop and then click through to detail. Other than that, it would be nice to see more of the reports and the drilldowns that I can get in the fixed web version of Google Analytics. More charts would also be a welcome addition. Turns out that there are more detail reports and more charts in the paid (vs free) version; if I develop a burning desire for these features I will buy the Pro license.
• Bug factor: No issues.
• Stars: 4 out of 5
• Verdict: I'm going to keep it. I might even spring for the Pro version. If another free app turns up with more of my desired functionality, though, I may replace it.

DroidAnalytics

This paid app has a 24-hour trial so you can try before you buy. It includes many of the same metrics and reports as the mAnalytics app, plus a whole lot more. There are basic trend charts for 6 main metrics - page views, visits, visitors, bounce rate, average time on site and pages per visit. Additionally, there are 9 detail reports listing out these 6 main metrics for every row of data. I found all of the data a bit overwhelming, but if you think you need all of this detail on-the-go, it's possible to find what you're looking for in the app.

• I like: As much detail as anyone would want on mobile. The settings are a nice touch; it's possible to customize your view quite a bit. I also like the fact that the developers have posted version notes; it's clear that this app has a good development path ahead of it.
• Wish it had: A slightly better user interface. I got the hang of things eventually, but it took more time than I'd like to figure out the icons and get a handle on what exactly was being displayed.
• Bug factor: I noticed several minor interface glitches. For example, rotation from portrait to landscape does not appear to work, and in one instance the date format changed on its own accord after I viewed (but did not edit) the settings.
• Stars: 3.5 out of 5
• Verdict: Although I liked this app, it's not sufficiently better than the free apps for me to keep it at this time. I am going to check back in a couple of months, though, because the developers are bound to include more features and fixes in future releases.

Mobile GA for Android

The screenshots of this app looked very promising but I was unable to get it working on my Droid. I tried a few times with no luck.

• I like: The product screenshots.
• Wish it had: Better recovery from errors.
• Bug factor: Terminal case.
• Stars: 1 out of 5
• Verdict: I'll try reinstalling it in a few months to see if the problems have been resolved.

Conclusion

I was surprised by the variety in functionality across these applications. For the time being I will keep using the Analytics Widget and mAnalytics in combination with one another; they both contain features I like, with a proper amount of detail for mobile.

It's also interesting (but not surprising) to note that Google Analytics is currently the only web analytics tool with apps in the Market. How long will it take before Omniture and Webtrends apps make their debut? There are already iPhone apps for both.

Speaking of the iPhone, here's a recent post from Lars Johansson in which he reviews all Google Analytics apps for iPhone. Hopefully we'll see more web analytics apps for the Droid in the near future.

[Feb 2, 2010. Update: I've been through a round of troubleshooting with the GA for Android developers, so hopefully I will get to test out and review a working release of that app before too long. Also, I have unearthed an Omniture Android app. It was in the Android Market all along; ironically, it just didn't contain the word "analytics" in the description.]

Greg Dowling joins Semphonic as Mobile Analytics Expert

I am thrilled to welcome Greg Dowling to the Semphonic executive team. I couldn't invent a new colleague with a more impressive background: head of analytics at Nokia, industry analyst at Jupiter, VP at Digitas, the list goes on. To round things out, he also spent time as a geologist.

Here's the press release we have just issued. You can also read Gary Angel's take on the new hire, plus a welcome message from Greg himself.

If you follow my blog because I write occasionally about mobile analytics, you'll be glad to hear that Semphonic's thought leadership in this space will really take off now that Greg has arrived. He's got deep experience as a mobile measurement strategist and equal ability as a technologist. I know he'll be a great asset to our company and to our clients. Welcome, Greg!

Functionalism Basics

In this post I'll describe the basic building blocks of functionalism. As you follow along, think about how you can apply these techniques to your own web site measurement and optimization projects.

Functionalism Defined

Functionalism is a methodology for web measurement. It includes:

• A way to understand what each part of a web site is supposed to accomplish.
• A specific set of measurements to determine how well the function is being performed.

Here's the cool part - functionalism is appropriate for any type of site: commerce, lead gen, media, branding, support, etc. 

Back when I was an on-staff web analyst at a large company with a very diverse collection of web sites - none of which had direct and obvious ties to revenue - I used to joke that, "I wish we just sold shoes." No offense to analysts who measure commerce, but you people have it easy. If I had known about functionalism back then, my group's web analytics projects would have been far more structured and consistent than, sadly, they were.  

So much for my flashback. Here in the present, functionalism can give you and your web analytics team a framework for measuring every component of every diverse web site that you own. Not only can it enforce business-wide consistency in basic measurement, it can also put some much-needed structure around the process of doing web analytics. 

The Approach

Functionalism has 2 major components:

• A set of page types that map to content on your site 
• A set of KPIs that map to each of the page types

Once these components are in place you can take any page on your site, give it a page type classification, then measure how well it's performing using the KPIs associated with that page type. In this way, functionalism provides a method for deciding when and how to use any KPI that you define.

Page Type Classification

Page types are designed to capture the natural function of a page. For instance, you'd call a page a "Router" if its function is to move the visitor to specific places on the site, and you'd call a page a "Convincer" if its function is to convince someone to buy your product or service.

As I classify pages I often refer back to a definitive list of the page types in my repertoire. It looks like this:

• Engagers: Pages whose primary purpose is to interest the visitor and get them to do something/anything on the site.
  
• Routers: Pages whose function is to move visitors into specific places on the site. One of the differences between an Engager and a Router page is that the latter is built with the expectation that the visitor has come to the page looking for a particular type of information/service. Search should often be treated as a special class of Router page.
  
• Convincers: Pages whose function is to “sell” the visitor on a product or service.
  
• Explainers: Pages whose job is to help the visitor understand some aspect of a product or service.
  
• Informers: Pages whose primary objective is to provide basic news and information about a product or industry. This differs from Explainers in that content is not specific to your own particular product or service.
  
• Billboards: Pages providing various content to visitors, but whose primary business objective is to display third-party advertisements on a Cost-per-Impression basis.
  
• Sponsors: Pages or series of pages which are themselves revenue-producing, whose content is mostly provided by a third-party who pays for inclusion within your website.
  
• Closers: Pages that are supposed to get visitors to enter a conversion process.
  
• Converters: Pages that are part of whatever is necessary to gather information/agreements and get a finished lead/sale/transaction.
  
• Re-Assurers: Pages built to re-assure the visitor about some potentially problematic issue or concern (privacy policies are a common example).
  
• Tools: Pages that are designed to collect or provide information as part of a nonsales process (like checking an account status or finding a location). There are many different kinds of tools and for a tool-rich site tool pages need to be subdivided into more granular types.
  
• Completers: Thank you pages – designed to signal the completion of a process and – in some cases – drive to additional engagement.

This is the Cliff Notes version of the full page type listing that you can find on page 19 and following in Gary Angel's freely-available Functionalism White Paper.

Make a list like this for yourself. Keep in mind that, as long as you have defined the right page types, every page on your site can be classified. Don't feel bound by the list I've provided here - it's totally fine to create your own page types.

Page Types & Associated KPIs

Within the framework of functionalism, each page type is associated with one or more KPIs. In this way, the measure of a page's effectiveness is tailored to the function it performs.

For instance, a "Router" page will be associated with KPIs that measure how well it moves visitors to specific places on the site. Typical router KPIs will measure the "next page" behavior for that page - the percent of clicks on body content, percent of clicks on navigational links, exit rate, etc.

Similarly, a "Convincer" page will be associated with KPIs that measure how effectively it convinces a visitor to buy. The most common convincer KPI is percent of next page clicks on call-to-action links - you can call this "Conversion Rate" as long as your business audience is clear on the definition.

Here's a sample mapping:

  

As with page types, the KPIs can be customized to suit your needs. Pick KPIs that 1) make sense as a measure of functional performance, 2) are meaningful to your business audience, and 3) are actually obtainable from the web analytics tool that you use.

KPIs are tool-neutral and designed to be implemented in a range of solutions including Omniture, Webtrends, Google Analytics or any other tool. So as long as you've got a basic, valid visitor behavior tracking in place on your web site, you can use functionalism today without having to customize your implementation.

That's the basic setup. Once you've defined how you'll measure each page, you can use it to compare pages of one type to one another, to assess the effectiveness of redesigns, and to provide focus for ongoing optimization. I'll cover examples in future posts.

Functionalism is NOT ...

Now that I've outlined what functionalism is, here's a quick note on what it's not. Functionalism is not designed to provide any subjective analysis of pages: It includes no subjective design rules, no business specific classifications, no way to “score” pages subjectively. 

It simply provides a framework for performance measurement.

[This post is part of my series on Functionalism. In my next installment I will walk you through a how-to.]