Just 4 Things a Web Analyst Can Do Today to Increase His Salary Tomorrow

I've been in the field of Behavioral Analytics for nearly 10 years. In that time, I've seen demand for web analytics and experienced professionals increase dramatically. But after doing this for so long, what information can I provide to analysts that may help them to increase their perceived value to the organization and put a few extra dollars in their pockets?

So, let's get it:

Stop reporting
Surprising, huh? Take this one with a grain of salt. If you find yourself managing to a myriad of requests for 'hits' or page views and few inquiries for analysis or optimization, then start re-evaluating your status. I am not saying reporting is bad. Quite the contrary, but reporting should be used to help you understand how far you are from reaching predetermined goals.

Think run-rate over targets!

Compare and contrast your efforts to similar tactics, previous goals, and/or competitors, but find a way to make the data relevant to the average marketer. Feed the marketer within you. I forget who said this to me, but take your least favorite marketer out to lunch and work hard to make them a star!

Above all things, automate your reports. If you can't do that, then 'stop reporting'!

Start Testing
Fifty percent of web analytics is really just best practice, albeit even that is often ignored. The other 50% is testing and optimization. Stop leaving money on the table and put it in your pocket! Some of the largest web sites in the world never test a single thing.

If you can help it, seek at least a couple of optimization projects per quarter. If you really want to show your value to the rest of the organization -- show them the money! You don't need to purchase any multivariate testing software to start off. In fact, Google Optimizer is a very attractive solution for anyone. You say you don't have time to learn new software, then use A/B testing to your benefit. You can use 'time' and 'dates' as your control/test group. I would imagine that nearly every analytics tool supports that most basic level of testing.

Align Your Efforts to the Profits
Eat the data, daily! Cut your teeth on it and always align your efforts to profits. I recognize that going that far back into the financial side of your organization can be difficult, but it shouldn't stop you from trying. Start to play with basic financial models. James Lenskold wrote a book titled, "Marketing ROI". I always found it useful. So check it out.

Promote Yourself
I should have actually started with this one, but it's a top four, in no particular order. Many years ago I read a book from Tom Peters titled, "Brand You". I was forced to write a boring paper describing what it meant to me. Luckily I did. I think it was the most meaningful book I've read in a long time. My mentors were people who really didn't engage me formally, but I watched them. I asked questions. Make friends with one or two people senior to you in the field of web analytics. Don't be afraid to start a conversation -- .

Promoting yourself isn't shameful.

• Start a blog.
• Practice your craft when you aren't at work.
• Write a white paper. Contribute to the analytics field and bring an opinion!
• Public speaking works and do it as often as you can.

Profitting from a Web Measurement Strategy

Measuring your site's effectiveness seems logical, but how do you determine if your site is a candidate for a web analytics?

As a marketer, I would suggest that if you are in the business of maintaining a web site, then you'll need the proper management tools to be successful. Regardless of the web analytics application, improving your site's ROI is often a combination of web site visitor tracking, strong performance management tools, consistent web reporting and timely site changes.

But don't take my word for it. Just to be sure, I've provided a proper staging ground for making that final determination: So, to Measure or not to Measure?

Your site is a likely candidate for web analytics tracking and process measurement:

• When visitors on your site complete a process and your business makes money as a result of that success.
  
• If your site metrics decreased by more than 15-20%, would your business loose money? Would you loose your job?
• When understanding customer intent or online behavior might help you improve your business or help you better budget your resources.
  
• If you'd like to better understand if customers are experiencing problems with a process you have created or a message you have delivered.
  
• When you have spent money to enable a process and now you need some sort of return on your investment.
  
• When your customers can complete similar processes on your competitor's web site.
  
• When you typically make changes to your site based on hunches, the HIPPO or because your web designer suggested that you do?

If you've answered yes, to one or more of these questions, then perhaps its time you began to measure your site for success. You say you have analytics in place, but are you still managing your site blindly?

Web analytics can be an astounding listening post for online marketers. A solid analytics implementation, should enable you to consistently leverage the experience and knowledge that is being captured from your site daily.

The framework for web site measurement is easy, but it's the execution that is most often problematic. If you need help with this execution, hire an agency that specializes in analytics or seek to hire top notch analytic talent. Caution, the last may be the most difficult of all.

The Problem with Web Reporting

We've all certainly run reports about online activities. For a select few, we've spent time running reports about offline activities. But of all of the hours spent in generating these mission critical reports, how many were used to drive organizational change?

The web analyst really does have an embarrassment of riches when it comes to web reporting. It was my friend and mentor Matt Berk that said this in 2002.

Let's take a look at the standard reporting palette:

• Page Views
• Visitors
• Referrer Traffic
• Commerce (cart views, cart abandonment, checkouts, orders)
• Organic Searches (branded, categorical, local, etc)
• Internal Site Search (failed searches, null searches)
  

Now to complicate things, if you are fortunate enough or unfortunate, depending on your heartache, you have an application that allows the cross referencing of one reporting metric against another. Under this scenario, you may have access to thousands of reports.

How do you manage that??!

With so many reports and so much literature surrounding Web Analytics, there is one thing that still persists!

Read on.

I suspect that many analyst struggle with helping organizations define business success. This simple but necessary exercise is the reason the field is so disproportionately split and why conversion laggards, if lucky, ineptly drive success just 2% of the time.

One of my favorite analysts, "Ashish Braganza' astutely said, "Ross, most organizations do a great job at spending money, but a horrible job at conversion".

Why do conversion funnels consistently look like Martini glasses and yet, we keep on reporting and we keep on spending!

There are two camps:

We have organizations that report (99.5%) and the compelling few that seek change (.5%).

The fact that few organizations seek change means that most online managed channels simply lack strategy! Now just imagine that! Here's an industry that generates billions in spending, but lacks any fundamental strategic principles?

To frame it all up, how do you define metrics and KPIs without understanding what your business goals are? If you aren't aligning your projects towards these goals, what are you really doing besides wasting resources and maligning talent?

Reporting is not analytics and analytics is not a substitute for strategy.

I want to leave you with this and hopefully you will learn from the mistakes of the many.

• Draft a business objective (even one puts you well ahead of your competition).
• Establish the metrics to support the measurement of those objectives.
• Define your KPIs.
• Generate expected values (Run forecasting models with a few years of data).
• Define your business goals (10% increase in Q3 subscriptions, 15% increase in online bill pay adoption, or a 40% increase in yearly online sales).
• Seek change (A/B split, multivariate testing, behavioral segmentation)
• Monetize your efforts (not every task put before you deserves your full attention. You need prioritization)
  

Marketing to the 'Anonymous': Using Cluster Analysis to Fuel Behavioral Segments

The Value of Clickstream Analysis
Clickstream is the traditional strength of web analytics. Analyst new and experienced understand the ubiquity of page views and visits. Arguably all three are the core components of any analytic package from Coremetrics to Clicktracks. We've all made a living off of this kind of data, as it was the very first piece of informaton that could be mined from logfiles. But that was a very long time ago. How useful is clickstream analysis today? Do these historic models still stand the test of time? Can you reasonably market to anonymous users? Or does our insistence on doing so explain the anemic conversion rates we routinely record?

Graph 1.1 (Traditional Visitor Bar Chart from Omniture's Discover 2.5)

A New Perspective on Unique Web Visitors

The mature web analyst understands that in order to market to consumers we must 'personify' our web visitors. The familiar edge of bar charts across spreadsheets may be enough to appease a pedestrian use of analytics, but it's a very mild distraction from the real question.

What can we learn from online behaviors? How can we profit from these visitors? How do we derive a communication strategy around visits? What vehicles may we use to profit from these customer paths? Are there patterns in the data that help us to segment millions of visitors into meaningful structures beyond the web analytic framework?

Graph 1.2: Typical Process Funnel based on a step through process of a shopping cart/Discover 2.5

A Slightly Different Take on Web Profiling
Personifying your web visitors as unique is the first step in creating behavioral profiles. One may develop meaningful customer profiles by leveraging attributes such as keywords, time of day, entry page, recency, clicks, orders and the like.

True, web analytics does a fair job of creating buckets that can be analyzed and applied to process models, but the problem is that web analytics was not meant to maintain complex historical attributes at a visitor level. That kind of data quickly becomes indiscernible. But if you can identify a unique key for each visitor you have a formula for communicating with a customer, rather than continuing to measure and market to anonymous visitors.


6d2b2bb62df2739dc791b0ccd64611bc = unique visitor ID x 10,000,000 visitors/10x web analysts.

Its absolutely not a leap of faith to take a unique visitor ID and append every piece of available data to it, but clearly after a few hundred records over a 3 - 6 month period, this could become taxing to even the best web analytic tool. Besides, web analytics can not discern this level of detail to identify the level of customer information we ultimately require.

Moving Beyond One Size Fits All Marketing
So how do we go from marketing to technographic segments (segments defined by entry pages/paths, etc) to influencing customer segments? Fortunately for mere mortals, machines (SPSS or SAS) can perform much of the invasive surgery required to make sense of this data. All of that precious clickstream information can be gleaned from our unique visitor and organized into clusters. Now we can take a look at customer reach by applying the COMM (creative, offer, messaging, monetization) principle for marketing optimization. The question then becomes which messages are appropriate to each unique customer segment and how do we measure the lift between theses discrete groups.

Using Cluster Analysis in Web Analytics
The primary goal in cluster analysis is to find meaningful structure in exploratory data and yes...this would be particularly true in web data.

Cluster analysis is an exploratory, analytic technique used to classify observations into finite and, ideally, small numbers of groups.

So this: (illustration pulled from SPSS/Visualizing Clusters/cluster analysis demonstrating distance measures based on response pattern similarity)

Can be used for this: (illustration from Omniture Discover 2.5/Segment Builder/Claritas Prizm)

In Summary
From linear behavioral segmentation to cluster analysis through SPSS and then back again.

There are all new techniques to be used in Web Analytics. Our industry is still so very new, but we must continue to borrow over techniques and approaches from other, well established industry processes.