Last week, my good friend Avinash Kaushik blogged about Web analytics 2.0, generating lots of discussion on the Web and in analytics groups.
At first, one could say we don’t need anymore 2.0 buzz. A quick search on Google for “2.0” returns nearly 500 million results. There’s nothing new about people slapping 2.0 onto the end of something and saying everything’s changed. In this case, though, Kaushik nails the difference between the way companies use first- and second-generation Web analytics:
Web Analytics 2.0 is:
- (1) the analysis of qualitative and quantitative data from your website and the competition,
- (2) to drive a continual improvement of the online experience that your customers, and potential customers have,
- (3) which translates into your desired outcomes.
I won’t review all of Kaushik’s points here; I encourage you to check them out if you’re interested in more detail. But I did talk to him about what Web analytics 2.0 means to me.
We’re often tasked with helping to bring our clients to the next level to understand and identify ways to act on visitor behaviors. As Shane Atchison and I outlined in our recent book, “Actionable Web Analytics,” it often takes a complete organization shift in the Web team. To climb to the next level of Web site success, you can no longer redesign the site every 12 to 18 months and push out new campaigns four times a year. You must constantly optimize, (Kaushik’s second point), continually improving site performance. I’ve written about this a lot in the past. It can range from tuning your site through A/B or multivariate testing using tools like Offermatica and Optimost to using behavioral targeting (e.g., TouchClarity) to tune the experience on the fly.
In the Web analytics 1.0 world, which still encompasses more than 95 percent of all enterprise-size organizations, it’s all about creating reports. How did we do last week? How did we do last month? It’s like looking in the rearview mirror. You have all sorts of people putting high-level reports together that don’t offer any insight whatsoever. They just tell you high-level information about traffic last month. These reports aren’t actionable, and they’re often very one-sided — in most cases only behavioral Web analytics. They aren’t based on specific business goals. And they aren’t segmented, typically only looking at aggregate information.
What do companies practicing next-generation Web analytics, a mere 5 percent of enterprise-size organizations, have in common? Some top traits of our most successful clients in terms of understanding their customers and prospects:
Web analytics, whether 1.0 or 2.0, represents just one small piece of the puzzle. It shouldn’t be seen as the be all, end all, but as a tool in your toolbox. To be truly successful online, companies must comprehend their customers’ needs; what they’re doing online; how the experience makes them feel about the company, product, or brand; and how the site meets clients and prospects needs. Web analytics, while an important aspect of this understanding, is only a small part of that. To succeed with Web analytics, and with online in general, you must break through the normal corporate barriers, share information, and change the way decisions are made involving the Web.
Kudos to Kaushik for putting a line in the sand, but as his definition points out: Web Analytics 2.0 isn’t just about Web analytics.
ClickZ’s recent webinar on Mastering the Art of Data-Driven Attribution was a great reminder of the opportunities available for companies to make strides in this rapidly-evolving area of marketing.
We all need data on the users that matter to us most. In many cases, to get this data, we need to have data forms to collect and capture information directly on our websites.
“You cannot succeed in analytics and marketing unless they are central to business operations and are helping business answer the questions that will drive dollars to the top or bottom line,” says Kerem Tomak, Sears Chief Digital Marketing & Analytics Officer.
The use of psychology in marketing and sales is not new, but it may be more useful than ever in an attention economy where time is precious and focus is rare. How can you tap into a demanding consumer to check whether there is an actual interest in your product?