In the Web analytics industry, there has been an ongoing debate for some time over whether analytics is easy, hard, complex, or both hard and complex. Certain thought leaders advocate for each
categorization. My take on it is "it depends" on the tools you are using, the site you are analyzing, your company's requirements, your team's skill set, and the processes you
define for analytics. Let's dig a little deeper into each of these concepts:
Yourtools. While Web analytics tools all share common functions,
like counting, at a minimum, views, visits, referrers, time-based metrics, and allowing you to view the data across various time ranges, it's clear that not all analytics tools were created
equal. Some tools allow for advanced drill-down and slicing and dicing of data from directly within the interface with little to no backend customization or advanced tagging required to do
so. Other tools are simple static dumps of canned reports that have little to no ability to become dynamic or enable interactivity with the tool. They just report pre-programmed
numbers. Some tools allow for customization of data directly in the tool, while others require changes to tags. Still other tools collect data differently - some sniff packets, some
use javascript tags, others use log files, while others allow you write directly to a database and expose the data in reporting.
As you might imagine, the functionality afforded in
your tool will define your judgment of Web analytics difficulty. When you have the ability to collect data the way you want and report data the way you want, Web analytics becomes easier.
When you can't, it's hard. When you want to integrate data from internal systems, third parties, or qualitative studies, it can get complex.
Yoursite. Some sites are harder to instrument and more complex to analyze. Sites within the same company can vary in their user experience, backend architecture, information
architecture, functionality, and goals. Sites, such as craigslist, still focus around the paradigm of a page view, whereas other sites have moved to rich internet applications (RIA) and AJAX
functionality where the page view is largely irrelevant. Some sites make use of widgets, video, and provide mobile-only functionality. Certain more "rich" and "web 2.0"
sites make data collection and tagging implementation and instrumentation hard and complex, while more static page-view driven sites are easier to instrument.
Your
company's requirements. How your company decides to use Web analytics and their goals for the data impact an analyst's assessment of whether doing analytics is hard. All
companies have different analytics requirements. Some companies regulate analytics to the discipline of marketing and emphasize the importance of using the data for campaign tracking and
optimization, reach, frequency, attribution, and landing page optimization. Other companies seat analytics in product development and emphasize the data be used for functionality assessment, error
detection, and overall usability analysis. Still other companies use analytics for ecommerce, and look at shopping cart performance, funnel analysis, and product performance. Other
companies want to understand how specific visitors behave on the site and identify their propensity to convert to leads. Analytics can even be nested in a research group. Some larger
companies have all these requirements and more.
Your team's skill sets. Not all teams were created equal. Some companies choose to homegrow Web analysts and
give them training. Other companies choose to homegrow and say RTFM. Certain companies hire skilled analysts with years of experience at other companies. People with backgrounds in
database marketing, business intelligence, financial analysis, business analysis, systems analysis, or even no analysis (entry-level) can be on an analytics team. Some analytical teams require
only skills with Web analytics tools. Other teams require people to have skills with Customer Experience and Voice-of-Customer data. While certain teams need people who can create custom,
proprietary tools around web data residing in internal databases. The often varied and sometimes motley skill sets of web analysts dictate the how difficult or not it is to do the job required
by the company.
Yourprocesses. Successful Web analytics teams have defined processes for performing critical activities, such as collecting data,
requesting reporting and analysis, and investigating data anomalies and discrepancies. Regardless of process, there is always a certain amount of chaos that revolves around Web analytics: the
site is always changing; the reporting requirements can constantly shift; yesterday's site goals have largely become irrelevant; your tags have been pulled off the pages. By placing process
around the chaos, the job of a Web analyst can become easier.
At the end of the day, is Web analytics easy, hard, or complex? I would argue that it depends on a combination of the
factors I've presented above. There is no simple one-size-fits-all answer. So what do you think, good reader?