Market segmentation is a key technique in targeting marketing messages - and maximizing ROI.
And, the use of Web analytics tools, such as Google Analytics, is making segmentation more specific and more valuable.
I've written a feature article that covers the use as Web analytics tools in combination with traditional market segmentation techniques. Take a look at Segmenting Your Market with Web Analytics.
Have you wondered which Web analytics products are most popular? I was, so the August survey on my main Web site asked marketers which Web analytics product they use.
Google Analytics was, of course, the most used product (69%) because it's hard to compete with "free." Google's 69% is similar to other research I've conducted on professionally managed Web sites. So, it appears that Google Analytics has captured about two-thirds of the Web analytics market .
Even though this wasn't a scientific survey, it represents a wide range of marketing professionals. Most participants answered the question while viewing an article on a marketing topic unrelated to Web analytics.
Here are the complete results for the 235 people who choose to participate:
Which Web analytics software or service do you use?
| Google Analytics | 69% |
| ClickTracks | 7% |
| Unica | 6% |
| WebSideStory/Visual Sciences HBX | 5% |
| Digital River Fireclick | 5% |
| Omniture SiteCatalyst | 3% |
| Nedstat Sitestat | 3% |
| WebTrends | 3% |
| Coremetrics Online Analytics | 0% |
For the commercial products it appears that, in general, the lower priced products are more popular than the large, expensive systems. Nothing new here - there are always many more small and mid-sized companies than large companies with big budgets.
The size of Google's share of this market is probably making life tough for the commercial product vendors. Free products create a "floor" that keeps commercial vendors from selling their new, less expensive products to smaller companies.
It'll be interesting to repeat this survey in a year to see how the landscape changes.
The process of converting interested Web visitors into a paying customers is a path of several steps, and it's up to us to guide prospective customers along that path.
Web analytics consultants frequently talk about using the conversion funnel to measure the last few steps of going through the shopping cart payment process. However, there are additional steps in the purchase process where the conversion funnel tool can be used.
For many years marketers have used various models to describe the changes that occur as a prospect becomes more likely to buy a product. One model is AIDA:
Each of these states requires the prospect to accept additional information and update their attitude toward your product. These changes represent a "conversion" to a new purchase belief.
You can increase overall performance by monitoring and improving how well these individual conversion points affect sales.
First, identify the pages where you want visitors to change their attitude from skeptical visitor to interested prospect. Then, use conversion tracking to measure the effect they have on close rates, order size, and other key performance indicators.
If these pages help increase sales, guide more visitors through them. If they don't help, have your best direct marketing copywriter update them until they do increase your site's performance.
When customers are making the decision to purchase, make sure you have whetted their interest and peaked their desire so they're ready for action.
One of the challenges of using Web analytics techniques to improve a site's performance is that Web analytics can only measure what's there - not what's missing.
In working with two new clients I found that the most of their key indicators in Google Analytics were fine, but revenue was below their potential.
Both sites needed to make adjustments that put their Web sites on the path that consumers normally follow when making purchases. Some or these changes dealt with what customers do before going on the Web to make a purchase. Other changes dealt with matching products to customer personalities.
Joshua Porter, a user interface designer for social Web applications, pointed out that consumers frequently use off-site techniques to make purchase decisions:
For example, we did a huge user testing study where we tested over a dozen e-commerce web sites. We had 70 or so people actually buy products from these web sites and part of our research was to find out how they made purchasing decisions. In more cases than I can count people said things like “Well, I knew I wanted a digital camera but I didn’t know what kind. My friend really likes Canon cameras and recommended them to me”. People who don’t know something rely on their social network to find it out.This is a piece of the marketing puzzle that no Web analytics system could measure. It takes combining an understanding of how customers make purchase decisions with the Web experience you provide to create a compelling reason to buy from you.
This requires constantly testing ways to help customers evaluate and purchase your products and measuring the performance of each part of your customer experience.
