June Dershewitz on Web Analytics

Analytics Resolutions for 2012

Winter break is a time for slacking. I'll admit, I've been binging on peppermint bark and Words with Friends. Winter break is also a time when we all pause to contemplate the year ahead. Although I'm not usually prone to new year's resolutions, I do have a short list of things I'd like to work on, professionally, over the next 12 months:

1) Proactive, not reactive

It's all too easy for an analytics group to assume a help desk role within their company. You tell us what you need, and we serve it up. It's much harder (but much more valuable) to proactively approach business constituents with data they need - before they even know they need it. If an analytics practice is known solely as that-place-you-submit-a-ticket-and-get-a-spreadsheet-back, it's falling far short of its potential to serve the business.

2) Outward, not inward

"Siloed" is an overused term in the analytics industry, but it's an apt way to describe the way we typically feel. As analysts we often spend too much time talking amongst ourselves rather than communicating with - and collaborating with - others. In large part I think we bring it on ourselves, and it's within our power to overcome it. Over the next year I aim to meet people throughout my company who call themselves analysts; we may work in completely separate departments, but our common interest in data unites us.

3) Windshield, not rear-view

A common theme among analytics maturity models is the idea that the advanced stages rely heavily on predictive modeling rather than simply reporting on events that have already occurred. The motorist analogy sums it up nicely: as you drive a car, you look in the rear-view mirror to see where you've been (reporting); you look through the windshield to decide where to go next (predicting).

I'm not ashamed to say that, in my current work environment, we still need to work through some of the fundamentals before we realize the full potential of the predictive stage - but it helps to have the end goal in mind as we lay the foundation.

4) Share

Last March I made the switch from analytics consultant to analytics practitioner. It's been an incredible learning experience, but I haven't written or spoken too much about what I've been doing lately. Although my role does require a certain level of confidentiality, I would like to share more over the course of the next year. It's not just shop talk. I believe that, when we compare notes with our peers, we are driving innovation and carrying our industry forward as a whole.

Here's to a happy, healthy and productive 2012.

Notes from the Tableau Customer Conference

Last week I attended the Tableau Customer Conference in Las Vegas. I'm not going to tell you what I did in Vegas (because, as they say, what happens in Vegas ....). Instead, I thought I'd share my conference notes. Fascinating.

If you fancy yourself a data analyst, you probably know and love Tableau. It's like Excel on steroids. I've used the product off and on since 2006; although I'm not a power user at the moment, there are some pockets of great expertise within my company.

Given my background, I attended the conference with two main objectives: 1) meet analysts who are doing "cool stuff" with data, and 2) figure out where Tableau fits in the broader business intelligence ecosystem. Here's what I found out.

Analysts doing "cool stuff" with data
The show featured case studies from diverse industries, including:

• Intel, HR/staffing group
• Electronic Arts gaming product, operational efficiency and product development
• General Motors, supply chain
• DePaul University, admissions and enrollment
• Securities, real-time decision support for stock traders

Although the subject matter varied quite a bit from group to group, the common thread was a "do it yourself" approach to each initiative and an emphasis on self-service. Often the Tableau administrator was also the subject matter expert for the data at hand, and the output served their business group directly - no heavy process, no data warehousing middleman.

Tableau's position in the business intelligence ecosystem

• Grassroots. Tableau usage definitely seems to be more "grassroots" than "institutional" - analysts love to get quick access to their data, but it does lack the structure that many of us are used to seeing within traditional data warehousing.
• Decentralized. Deployment at some institutions is totally decentralized – groups will take it and use it when they have a need, as opposed to big, centrally-managed BI initiatives.
• Versus other BI tools. Tableau has not totally replaced tools like Microstrategy in most institutions. There's room for both types of tools. Some consider Tableau as a prototyping tool for dashboards that are later built in Microstrategy. I ran into a former colleague who serves as a Microstrategy administrator at a very large company. And yet, there he was at the Tableau conference. No irony. He sees them as complimentary products.
• Presentation. Some practitioners say that Tableau is actually changing the way that they interact with their executives – less static Powerpoint, more exploratory data sharing sessions.
• Beauty and the Beast. Tableau can produce some beautiful output, but there's no accounting for taste. If you create heinously ugly Excel charts, you will probably create only slightly less ugly Tableau output. Believe me, I saw it.
• Again, grassroots. I saw numerous examples of individual contributors leading the way, rather than centralized BI/DW groups. Broader corporate socialization about Tableau often bubbled up from groups that demonstrated success.

Keynote notes

• Tableau leadership and tech teams – showcased new features in Tableau 7. Collectively thumbed nose at traditional BI/DW companies (by, for example, eschewing bloated mission statements).
• Guy Kawasaki – broadly appealing, though his content was certainly not tailored to the subject of the conference. Made a number of references to "Mac vs PC" debate, but perhaps not realizing that Tableau runs on PC only.
• Cory Doctorow – spoke on data privacy. Philosophy a bit at odds with analysts in the audience, but worth surfacing as valid concern.
• Stephen Few - whose talk I missed, but whose contributions to the industry I respect a great deal.

Some technical tidbits

• Hadoop support – new in Tableau 7. As I see it, it's just another data source, but connection allows for some control over data modeling
• Google Analytics support – no direct connection available, must use an intermediate layer like Analytics Canvas

A Big Change

Throughout my career I’ve always enjoyed counseling fellow web analysts on professional development. Everyone wrestles with questions like: How can I get my start? What can I do to ensure positive momentum? Which opportunity would suit me best? Although I’ve helped so many of my peers navigate job changes, it’s always a humbling experience when I go through it for myself.

I’ve left Semphonic

Earlier this month I resigned from my position on the leadership team at Semphonic. Looking back over the past 3 ½ years, I feel very fortunate to have worked with such wonderful colleagues, clients and partners. I leave with a wealth of experience and good memories. Thank you, everyone.

I’ve joined Apollo Group

After 7 years in consulting, I’m excited to say that I’m returning to the client side. I’ve accepted a position as Director of Web Analytics and Customer Insight at Apollo Group, the parent company of University of Phoenix. My work will support the online learning platform team, which is based downtown San Francisco.

In this new role I look forward to all of the challenges that come along with building and managing a thriving web analytics practice.  I also get the satisfaction of knowing that my work will improve the educational experience for hundreds of thousands of students, alumni and faculty members. I’m thrilled.

I will remain involved in the community

Several people have asked if I’ll continue to serve on the Board of Directors of the Web Analytics Association after my job transition. The answer is absolutely yes, I’ll stay active in the WAA. I will also keep blogging and tweeting about the work I do, and of course you can count on seeing me at Web Analytics Wednesday.

Here’s to career growth, yours and mine.

Web Analytics Conferences Worth Your Time

Here's my ranked list of the best North American web analytics conferences:

Top tier

• eMetrics SF and DC
• Semphonic X Change
• Omniture Summit
• Webtrends Engage

Rising stars

• OMMA Metrics SF and NYC
• WAA Symposiums
• O'Reilly Strata

Somewhere in the middle

• Coremetrics and Unica user conferences
• Google Analytics Certified Partner Summit (closed admission)
• OMS San Diego (this is their flagship; they also have smaller, regional ones)
• eMetrics elsewhere (they're not as large as the SF and DC events)

Tangential but still interesting

• Predictive Analytics World
• Conversion Conference
• DMA
• Ad:Tech
• SMX
• SES
• OMMA Global
• Vertical-specific ones like Shop.org Summit

There are probably others, but these are the ones that are top-of-mind for me. If you've got a suggestion, please post a comment.

Thanks, Chris Brinkworth, for suggesting that I publish this list.

10 Must-Have Analytics Tools for Small Business: June's Picks

I was recently one of several people interviewed for an Inc.com story about Web analytics tools for small business.  My fellow contributors included industry thought leaders such as Bryan Eisenberg and Eric Peterson. The resulting article provided an excellent summary of our collective opinion. I’ve chosen to publish my personal top 10 list here so you can see the full range of my own recommendations.

Taken together, the tools on my personal list cover the full palette of Web analytics: quantitative, qualitative, optimization and competitive analysis. I've also thrown in a couple of tools you can use to measure emerging media such as social and mobile.

Every tool on my list is either low-cost or free.  Think of them as starter tools for each type of measurement category. 

If you’re having trouble getting funding for expensive-but-important tools, try this approach: use low-cost tools to showcase some early successes with limited budget, then win over management and use that as leverage to get funding.  (Credit goes to Avinash Kaushik on this clever solution to the budget issue.)

Without further ado, here are my top picks for broad-coverage, low-cost, high-impact analytics tools for small business.

Category: Quantitative

1) Google Analytics
See where your visitors come from, what they do on your site, and how often they come back. Setting up goals allows you to perform the essential analysis function of tying behavior to outcomes.

2) Crazy Egg
Generate heatmaps that show you exactly where your visitors are clicking.

Category: Qualitative

3) 4Q Survey
A simple 4 question survey that allows you to monitor online task completion.

Category: Optimization

4) Google Website Optimizer
Multivariate and A/B testing tool. Run different versions of content live on your site to determine which one produces the highest conversion rate.

Category:  Competitive

5) Compete
Compare your own site traffic alongside your competitors.

6) Google Insights for Search
Compare search volume trends within your industry.

Category: Emerging Media

7) Bitly
Shorten and share URLs through social media platforms, then track traffic volume over time. Adding a "+" to the end of any bitly URL will allow you to see stats for that link - so you can use it as a competitive tool, too.

8) Facebook Insights
Dashboard that shows usage and demographic info for your Facebook Page.

9) Percent Mobile
Simple tool that allows you to see the percent of visitors to your Web site who use mobile devices.

Bonus Addition

10) Brain Power
You won't get anywhere with these tools unless you actually have someone to use them and interpret results - so make sure you have a dedicated analyst who can feed recommendations back to your business.

Tracking Social Media Links as Campaigns

It’s not difficult to track social media marketing efforts as campaigns, but I haven’t seen too many companies actually doing it yet. Before I lay out step-by-step instructions, here’s a story that gives me hope for the future:

At last month’s X Change conference I sat in on a very popular social media analytics discussion; there were about 20 Web analytics practitioners in the room from a variety of large enterprises. At a certain point our conversation turned toward measuring ROI. The room grew quiet except for one voice.

“I can tell you exactly how well social media is working for us,” said an individual - who shall remain nameless - representing a major consumer brand. He pulled up a Web analytics report on his smartphone and stated, “Here’s a Twitter campaign we ran last month that generated $23,000 in revenue.”  

He was able to make this claim precisely because his company tracks their social media links as campaigns. If you want similar bragging rights for your own company, just follow this 4-step process:

1. Tag

First, append campaign codes to the URL you plan to post on social media platforms. Follow your company’s campaign coding standards if an established policy exists. Treat social media just as you would more traditional channels like email and banners.

If you’re planning to post on multiple platforms, I recommend creating one campaign code for each platform. For instance, make a unique code for Twitter, a unique code for Facebook, a unique code for LinkedIn, and so on for each intended destination.

2. Shrink

Short links are easier to share, so pass each of your tagged links through a URL shortener like bit.ly or goo.gl. There’s also an opportunity to collect stats at this step. See my related blog post on URL shorteners with analytics.

3. Post

Once you have a set of tagged short links, go out and post them on Twitter, Facebook, LinkedIn, etc.

Although these first 3 steps may seem tedious, there are some opportunities for automation. For instance, if you use Hootsuite you can tag, shrink and post within a single interface, since campaign coding and URL shortening are built directly into the tool. I’ve also seen companies build simple tools from scratch that allow them to automate tagging and shrinking their URLs, they then grab the links and post them manually. The choice is up to you; just find a process your team can live with.

4. Analyze

Now sit back and wait for data. By default your Web analytics tool will give you visit volume for each of the campaign codes that you’ve used. Beyond that, assuming you’re tracking your site’s goal behaviors - purchases, downloads, form submissions, video views – you’ll be able to see the downstream impact of your social campaigns.

In the story I told at the beginning of this post, the company uses their Web analytics tool to track purchases on their commerce site, so they’re able to connect the dots between campaigns to revenue. 

If campaign tracking is new territory for you, I recommend that you read your Web analytics vendor’s documentation on this topic. Although social media may not be mentioned specifically, think of it as just another campaign channel. Also, here’s a great step-by-step Google Analytics example and a related post from the Omniture blog.

A final note on URL shorteners: Since it’s possible to get basic clickthrough stats from popular URL shorteners like bit.ly, some people may question whether it’s necessary to append campaign codes at all. Here’s the clincher: URL shorteners track clicks but they do not tie to downstream goal behaviors. If you’re serious about connecting social media efforts to outcomes, you must track links as campaigns.

A New Web Analytics Salary Guide

The phrase "web analytics salary" hasn't been uttered on this blog in nearly 3 years, and yet my post on that topic remains one of the all-time most popular pieces of content I've written. I see it as a testament to just how hungry Web analytics professionals are for information that will help us determine fair compensation for ourselves and the people we hire.

If you are on a quest for salary information, you must read this great new piece of research from Corry Prohens at IQ Workforce: Web Analytics Salary Guide.

Corry's explanations and disclaimers are required reading; you cannot simply grab a cell value and treat it as the gospel. Instead, think about what makes your situation unique and develop a realistic range for yourself based on broader research: other salary studies, job board info, personal conversations, etc.

Thank you, IQ Workforce, for releasing this into the public.

Photo credit: Dystopos 

How to Get Bounce Rate for Google Website Optimizer A/B Test Variations

Suppose you’re planning to run a simple A/B test on a Web page and you’d like to use bounce rate as your measure of success. Can you do it with Google Website Optimizer (GWO)?

I’ve had this issue crop up a number of times recently, so I decided to research it. Here’s what I found out.

Can I set bounce rate as a conversion goal within GWO?

No. GWO itself does not report bounce rate, nor does it allow bounce rate to be set as a conversion goal. It’s possible to approximate bounce rate as a goal by coding every single link click on every single experiment page as a conversion event, but that gives you exit rate, not bounce rate - plus the setup can take a lot of effort.

Can I get bounce rate for GWO A/B test variations from Google Analytics or Omniture?

Yes. Although bounce rate isn’t reported within GWO, you can get it from Web analytics tools such as Google Analytics and Omniture. Integrating the data turns out to be very easy, and in fact requires zero coding as long as all experiment pages contain your standard Web analytics page tags.  

Here’s why it’s easy: As GWO serves variations in an A/B test, visitors are redirected to unique URLs (indexA.html vs. indexB.html vs. indexC.html, for example). This behavior was contrary to my initial assumption that all visitors got the same URL, as is the case for GWO multivariate tests. Since URLs are unique in an A/B test, you can simply view the pages report within your Web analytics tool and filter on (for example) index*.html to see one row per variation.

Do I need to pass any custom variables from GWO to my Web analytics tool?

No. You don’t need to pass any custom variables to get reporting for an A/B test.

If you're running a multivariate test, on the other hand, you must pass the GWO variation ID to your Web analytics tool as a custom variable, since the URL remains the same for all variations. If you’re interested in this technique, there are good discussion threads on this topic in the GWO support forum.

If I optimize for bounce rate outside of GWO, do I still need to set up a goal within GWO?

Yes, but it can be a simple placeholder. When you create a GWO A/B test you’re required to provide a conversion goal before you can launch. However, if you're planning to optimize for bounce rate or some other metric in your standard Web analytics tool, you can spoof a GWO conversion goal by specifying a dummy page somewhere on your site. In effect, you're simply using GWO to manage the serving of page variations.

To prove that it can be done, here are some screen shots from a test I set up:

Now it's your turn. Got any tips, tricks, admonishments?

How to Compare 2 Lists of URLs Using Excel

Sometimes I need to compare 2 lists of URLs and find out how much they overlap. Which URLs are only in list #1? Which URLs are in list #2? Which URLs are in both lists?

Here’s an example: say I’ve got one list of URLs from my Web analytics tool (like Content>Top Content in Google Analytics) and another list of URLs from a sitemap-generating spider. Comparing the 2 lists lets me see which pages are actually getting traffic on my site versus which pages are reachable by spider. Although most URLs that get traffic are also spiderable, it’s also likely that some URLs get traffic but aren’t spiderable (like thank you pages) and some URLs are spiderable but get zero traffic.

I've developed a quick and easy way to do this kind of comparison using Excel. You can download it here. I’ve included step-by-step instructions for you to follow. There are 8 steps:

1. Paste in your first list of URLs
2. Standardize them
3. Pivot to remove duplicates
4. Paste in your second list of URLs
5. Standardize them
6. Pivot to remove duplicates
7. Concatenate the standardized, deduped lists and add a "magic" code
8. Pivot again to get results

All the details are in my example spreadsheet.

If you're a programmer I’m sure you have a fancier way to compare lists. However, the beauty of my method is that anyone can do it as long as they have a copy of Excel and a basic understanding of pivot tables.

Speaking of pivot tables, if you've never created one I encourage you to learn right this minute. It's something that every single Web analyst should know how to do. If I ever interview you for an analyst job, even an entry-level one, I will quiz you on pivot tables.

What do you think? Do you have an even easier method? And, as a Web analyst, why have you needed to compare lists of URLs?

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