If you want to register, hurry.  Registration closes in two days (2/12).  To find me at other events, subscribe to my Plancast feed.

On the upcoming panel, I’ll be talking about how you can better track search results in Google’s Universal Search results page, allowing you to understand not only what search terms drive traffic to your site and/or or value for your business, but which type of result (blog, image, etc.) is working best for you so you can optimize for the specific words and result types that best benefit your business.

Join me and the other panelists from PSU’s Digital Marketing Strategies certificate program for a lively and informative discussion about Google Universal Search.

Update:

If you were at the panel discussion and didn’t get a chance to jot down the blog posts I mentioned here they are…

This post, by Martijn Beijk at SearchCowboys.com explains how to set up Google Analytics to track which type of search result was clicked on the Google search engine results page.

This post, by Mike Belasco at seOverflow explains how to set up Google Analytics to track clicks on local search results on the Google search engine results page.

Many companies have invested hundreds of thousands of dollars in web analytics tools and talent, and still find themselves frustrated by a lack of demonstrable value — a lack of real, calculable return on that investment.

It’s not a good situation to be in.  It’s bad personally for the managers and executives who have overseen the investment.  It’s bad for the vendors who take the blame for providing no value.  And it’s bad for the business which, unless corrective action is taken, will continue to throw good money after bad.

What to Do?

Typically, the response to this situation is to blame the vendor and put out an RFP.  It’s a natural response.  But is it the right response?  Most of the time, it isn’t.  The problems preventing you from getting value with your current vendor likely stem from unclear roles and responsibilities in the organization, a lack of defined and well-understood processes, and out-of-date or missing business rules for data collection and reporting.

These problems will still exist if you simply go to RFP, and will cause you not to get value from a new vendor.  Even if one of your problems is a poor implementation or a poor fit of vendor to business requirements, invariably issues of process, roles and responsibilities, and business rules (governance, essentially) lie at the heart of how you get to the point where your implementation fails to suit your need.

What, then, is the right response?  When a business is underperforming, do the owners demand that you rip out your accounting technology and replace it?  No.  They usually initiate a turnaround process — a process that involves bringing in experts to assess the current situation and develop a plan to get the operation back on track and providing value to the owners.

Turn it Around

That’s also the appropriate response to an underperforming web analytics investment — a web analytics turnaround.  And that’s why Greater Returns is now offering Web Analytics Turnaround as a service offering.  It makes much more sense to evaluate your overall strengths and weaknesses with web analytics before deciding on a course of action.  Are your problems with process?  People? The technology?  If your problems are the technology, is it a shortcoming of the technology or a shortcoming of your process for governing management of that technology?

Once you understand where your true problems with web analytics lie it becomes easy to develop a prioritized plan for turning around your web analytics operation.  You may well decide that you need to rip out your vendor and start over — but you should never take that course of action based merely on an assumption.  You’ll likely find yourself in the same place 12 months from now.

Check out the Web Analytics Turnaround offering here.

The power of the reporting and ad-hoc segmentation is as I wrote about here, but this is even more interesting because rather than segmenting and constructing reports only from data collected through page tagging, we’ll be leveraging the power of web services and OLAP style reporting to integrate data about a single visitor from multiple databases and 3rd party systems.

I’ll post more as the project moves forward.

Dainow demonstrates a near complete lack of understanding of web analytics and the web analytics marketplace. Google Analytics already dominates the market in terms of total domains coded, but dominance isn’t defined by the breadth of your coding, it’s defined by the success your customers have using your application!

I’ll go one further. What Dainow fails to see is the difference between a product and a solution, where a solution is a product and a set of services combined to solve a business problem. While the market well served by Google doesn’t really require a “solution” as much as they simply need a tool to do a job, the customers served by the big players (my employer included) tend to need (and have the money for) services to ensure successful solution design, implementation, deployment, and adoption of the tool set and the business processes required to make good use of the tool set.

The farther you go up the market, out of mid-market and into true enterprise class solutions, the more this is true. In fact, I would argue that in true enterprise-class solution deployments (the area of consulting I specialize in) the services are more important than the tool set. The greatest tool in the world isn’t worth anything if it can’t be successfully deployed across a global enterprise with a standards-based approach. Google, neat tool that it is, is nowhere near displacing the few vendors who can play at this level.

If you’re not familiar with OLAP, or multi-dimensional reporting tools, the first page and a half of this article are worth a read. It’s a good introduction to what’s coming.

Essentially, OLAP tools, and WebTrends Visitor Intelligence is no exception, allow you to do deep, ad-hoc drilling and re-arranging of data, on the fly while maintaining the proper relationships and correlations between the dimensional data. While there are pre-configured reports, called Starting Points, in Visitor Intelligence that will meet the needs of “just give me the data” end-users, curious analysts will find themselves in a playground of possibilities.

Don’t like like the order of the dimensions in the report you’re looking at? Rearrange them, and the relationships stay in tact (just like in a pivot table). Don’t like the measures that are in this report? Grab any available measure and drop it in the report. No longer are you confined to defining your report view, and being stuck with that. Nor are you working in a cumbersome environment where the relationships between data are not clearly represented and easily manipulated. The real power is that with OLAP reporting, all you need to know is which dimensions you want to report on, and which measures you want to report. From there, you can construct whatever report you want, on-the-fly, or you can set up starting points that are essentially pre-built reports. Also, the ability to create custom measures on-the-fly is absolutely awesome. No processing time, no analyzing. It’s just there.

Here’s a real-life example from a customer I’ve been working with to develop a robust reporting solution using Visitor Intelligence. This customer has a globally distributed and decentralized online business, which is organized roughly by regions of the world (each country is a division), brands operated by each division, and customer groups serviced on each site. Of particular importance to the customer is understanding how much of each division’s business comes from a country other than where that division operates, and what services those “out-of-country” customers are consuming. This insight will help the business better understand who their customers are, and how to market to them.

In this case, the customer has tagged each of their web sites with a single, universal meta-data model that describes each and every web page in the world, and how it fits into the global organization. The model passes values for the region, country, and division, in addition to descriptive data about product lines and the divisional business units offering the product lines. The result is that we collect a rich set of data easily turned into dimensions in an OLAP environment. The icing on the cake is visit and visitor geo-location data built in to WebTrends that allows us to determine who is “out-of-country” in a particular visit, and who is not.

Upon launch the business will have both default report views tailored to their specific needs, and the flexibility build exactly the right views of the available data. User A, a global business manager who wants to see Unique Visitors by Country, Division, Brand, and Product can easily create that view. User B, a product manager, can build a view that shows Visits and Unique Visitors by Product and Brand. And User C, an analyst, can build a view showing Visitors, Visits, and Visits per Visitor for “out-of-country” visits only broken down by Division, then Country of Visitor, then Product usage broken down to Business Units offering that product.

I’ve never before worked with a web analytics tool that is this powerful, and opens up so many possibilities — and I’ve worked at three different analytics vendors. It still comes down to business results, though, and what a full-featured OLAP solution brings to the table is the ability to easily explore and manipulate the data to discover the insight needed to make business improvements with measurable impact.