I envy the statistical information available to baseball managers. Elias Sports Bureau provides major league teams with clean and consistent data for every single pitch: who’s pitching, who’s hitting, pitch thrown, inning, count, temperature and much more.
Not so with marketing analytics statistics. The main limitation Web marketers face today is that the analysis data set available in aggregate form. I’ll leave it to others to debate merits of Internet privacy protection and marketing analytics. My focus is getting the most out of the available data.
The antidote to aggregate data sets is to build profiles of target buyers, perform drill-down analyses on the profile’s data segment and disproportionally attract users to your site that are within your profile.
Profiles in Success
Consumer marketers have managed to profiles for years focusing on statistics like gender, age, income and geography. These profiles remain critical today for print and television advertising efficiency. They are less useful on the Web and with B2B purchases. As Peter Steiner’s New Yorker cartoon famously states, “On the Internet, no one knows you’re a dog.” In other words, you need to create profiles based on the data you have, not the data you choose.
When defining profiles, remember the goals of your analytics efforts:
- Ensure you are attracting a sufficient number of visitors who match your profile to your web site in a given period
- Measure visitor satisfaction with the information on your web site.
- Excel at persuading visitors in your profile to provide you with contact information. In marketing parlance: convert.
For B2B enterprise software purchases, sales and executive teams have learned to appreciate profiles based on the following web statistics:
- Geography/locale
- Visit Source
- New or Returning
- Conversion
Work up-front to get consensus on your profile from the executive and sales teams. This should be an exercise in strategic success, not an internal effort by and for the marketing team. Executives in particular will have greater confidence in funding marketing programs that continue to drive measurable improvements.
Drilling into Trends
Web statistics fall into one of two categories. First are magnitude statistics. Magnitude statistics provide an accounting of outcomes. Examples include number of visitors, number of conversions, and duration of visits (pageviews or time on site).
Second are productivity statistics. Productivity statistics measure, among other things, whether you are improving at your goals. Among the most important productivity statistics are % of visitors who fit a profile and conversion rate.
Both types of statistics are important. It’s a rare company that grows through a singular focus on one statistic. Good marketers, like good baseball players are (at least) five-tool players.
