Web Metrics | Search Marketing
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"In a nutshell, Jason created our marketing analytics capability. He was able to figure out what data we collect, where it is, what was missing, and hook it all up so we canget meaningful, actionable data. Our marketing efforts have improved leads and conversions in some cases by an order of magnitude. He knows his stuff."
Chris Foleen, Marketing Project Coordinator, TransCore, Inc.


Monday, November 20, 2006

Review of "The New Age of Web Analytics 2.0"

Last week I attended a pretty cool webcast on Web 2.0 marketing and measurement. I've just been too busy to post my summary of it yet. But here it is, for anyone who is interested.

This webcast focused on two topics: what Web 2.0 marketing vehicles are there and how to measure in a Web 2.0 environment.


Shar VanBoskirk talked for most of the hour about marketing in a Web 2.0 way. I say "way" because it was more about how Web 2.0 represents a different philosophy rather than truely new methods. In this respect, the presenter focused on web efforts as facilitating people finding what they want and web tools as encouraging collaboration, especially collaboration. This
second point was taken to not be just collaboration between people, but also as collaboration between people and institutions (companies).

Both of these motivators were wrapped up in the concept of social computing. Shar defined social computing as "A social structure using technology to empower individuals and communities instead of institutions." Her talk seamed to be how to make institutions fit into this model. The Web 2.0 marketing efforts seemed to be that in-road.

Most of the Web 2.0 efforts revolve around the growth of socialization on the internet, including a move away from standard interfaces to custom interfaces. A more "personal" web, if you will. Most Web 2.0 innovations (and new web innovation in general) seems geared toward pulling different pieces together in completely individualized ways. Often geared toward fun rather than work.

And while this use of the web is still small, it is growing fast enough that marketers cannot ignore it.

All Web 2.0 efforts seem to all qualify as "experimental". Some commonalities:
  1. Don't expect normal ROI
  2. Devote 10% of the marketing budget to experimental efforts
  3. Approach each as a sort of branding experiment rather than a sales effort.
  4. Try to use each to build relationships with consumers and make friends with them.
  5. Use Web 2.0 tools as a sort of bizdev tool
  6. Measure to find responses


Akin Arikan talked for the last 15 minutes of the 60 minute webcast about measuring. Nothing surprising here. Web 2.0 needs an event based measurement model, as different from the current pageview based model. Because of this, more planning is needed in the project from the beginning. A brief overview:

Web 2.0 measurement should use cookies and logins to identify users. Cookies should be persistent so repeat visitors can be recognized (this concept of repeat visitors seems central to Web 2.0 efforts since it is used to denote a relationship).

Segment & measure in these ways:
  1. Use unique URLs
  2. Drive people to unique micro sites
  3. Offer unique telephone numbers
  4. Use unique buzzwords. then look for these buzzwords in tools such as Google Trends to see if buzz is developing around your slogans/unique phrases.
Additionally, KPIs were recommended to be divided up in these ways:
  1. Application level KPIs--overall usage. Includes traditional analytics and event-based analytics.
  2. Market level KPIs--meaning segment visitors into groups. Especially look for responses to messaging.
  3. Customer level KPIs--use individual customer behavior to drive individualized offers (permission based offers, etc.) and services then measure usage/adoption rate.

Thursday, November 02, 2006

Freshbooks Interview with John Marshall

Freshbooks had a great interview with John Marshall. They talked about ROI, internal search and Funnel Optimization. Some interesting points came out and some unexpected insights too! Here are my (somewhat raw) notes from the call. Good stuff.


Only 20 to 30 % of sales (conversions) can be meaningfully tracked back to the originating campaigns.

Holes in ROI data:

1. There is an implicit trust in the data that creates false hopes
2. Cookie deletion and blocking
3. Product recommended by one person and purchased by another

Beyond ROI

Try to find how successful a campaign is, regardless of campaign without depending on ROI.

"Average Time on Site" is the preferred new solution.

Not connected directly to dollars, but has almost no holes if done correctly.

Successful campaigns consistently produce more time on site.

Does usability effect this?
Yes, so some rules:

1. Only compare numbers against campaigns on your OWN website because every site is different.
2. Website redesign requires new benchmarks.

Internal Search

External search terms are polluted because people may not have wanted to find what you have.

Internal search represents people who have at least some interest in what you have, so what they type into that internal search box is much more pure and more valuable than external search terms.

Make sure internal search does not negatively effect usability.

Use internal search to bring forward what people are looking for.

If internal search hurts usability, then just use if for a little while to get some insights, then pull it.

Analysis of "no results" returns is very important and experience should be handled with alternate offers.

About Path Analysis:
Path analysis can be misleading and/or just plain wrong, especially "Top Paths" report.
The obvious paths are expected.
The long tail of random paths that constitute the vast bulk of paths is so convoluted that it is impossible to make any meaning out of it.

However, looking one page forward or back is reliable. Path analysis can be done only one click at a time.

Exit page is important because there is no follow-up page--ask what is wrong?

User segmentation is the most important feature of a web analytics tool.

Create a user segment based on people who get to a specific goal then see what the top exit page for that segment is and learn from it.

Funnel Optimization

Segmentation and goal definition is key.

Funnels suffer from some of the same problems of path analysis.

Funnel Background:
Based on traditional sales funnel like a inverted pyramid and is a linear process.

In online this does not make sense, because your website can handle as many leads as possible and does not need to weed out the weak leads.

The online sales funnel should not be funnel shaped because it does not have the same constraints.

The online analytics point of view of the sales funnel should expect to convert 100%--pipe shaped, not cone shaped.

Also, in traditional funnel, people either progress down the sales path or they exit. But this is not accurate for online sales process because they meander around and the sales process is non-linear, even in the checkout process.

Some very valuable data:
You can find what pages are influential at getting people to the final conversion page. By breaking the website up into stages of conversion, you can find what are the most influential pages at moving people to the next stage closer to conversion.