Practical Ecommerce

Web Analytics: Translating the Wall of Data

Assume an ecommerce operator launches a paid-search advertising campaign. That is, assume the operator purchases several keywords on Yahoo! Search, Google, MSN and others, and then assembles text ads on those search engines to attract visitors to his site. Assume that the campaign starts and the website traffic increases. The campaign, we’ll assume, appears to be working.

“When monitoring a paid search campaign,” says John Marshall, President of ClickTracks, a company that provides software to analyze the online behavior of website visitors, “It’s essential to segment visitors to a website. You need to know if the visitors are coming from paid-search listings or free organic-search listings. We’ve seen sites that generate the most visitors from paid-search listings, but generate the most sales from free organic-search listings. It’s the sales increase that’s important, of course, and not merely visitors.”

Segmenting visitors to a website is a basic feature of web analytics software, says Marshall. It’s “translating the wall of data”, according to Marshall, and it’s crucial for ecommerce operators to understand.

Andrew Hazen, President of Prime Visibility, a firm that consults and manages paid search campaigns, agrees with Marshall. “It’s necessary to determine the important visitorsegmentation variables to a website, and then establish dashboards to continually monitor them,” says Hazen. “The better analytics packages will do this.”

The “dashboards” to which Hazen refers are simply quick-reference sections within a web analytics program that can be easily monitored and checked by an ecommerce operator. The operator can determine the important segmentation variables for his site (such as keyword tracking between paid and free search listings) and then set-up a “dashboard” that automatically reports on the variables.

The trick is to determine which variables are important for the success of a website. ClickTracks’ Marshall, “We start with time-on-site. We like to monitor which keywords produce the longest visitor time on the site. And then we segment those keywords between paid search listings and free listings. If, for example, a keyword-phrase is generating the longest visitor time-on-site from a free organic search listing, we’ll focus on that keywordphrase and optimize the website around it. This can save an ecommerce operator a lot of money in search advertising fees.”

So, using Marshall’s example, an ecommerce operator would first establish logical keywords that he believes would generate traffic to his website. That’s the first variable, logical keywords. Next, the operator would track the amount of time that visitors spend on his site after having typed-in those keywords in a search engine and clicked on a paid or free search listing. That’s the second variable, time-on-site. Finally, the operator would monitor whether the greatest time-on-site came from a paid-search listing or a free organic listing. That’s the third variable, segmenting between paid listings and free listings.

And that process, parsing and segmenting visitor behavior on a website, is the purpose of web analytics software. The end result is that ecommerce operators who use analytics software and understand it will make more money. Large ecommerce firms have departments to do this. It’s that important.

Prime Visibility’s Hazen, “We have a client that sells moving boxes. We use our analytics software, Omniture, to carefully track the keywords that visitors search for to find the site. We noticed that many of the visits from free organic search listings were using the keyword phrase ‘discount moving boxes’. Until we saw that, we had not built those keywords into the site, but we have now. We now track the visitors to the site who have searched on ‘discount moving boxes’. This has improved our client’s business, and we would not have known about the phrase ‘discount moving boxes’ without our analytics package.”

In other words, Hazen has (a) established an important keyword phrase, “discount moving boxes”, (b) determined that visitors who search on this phrase have come from free organic listings, and (c) changed the website so that the “discount moving boxes” phrase will appear higher in the free search listings and that the visitors to the site from this phrase will more-likely turn into paying customers.

ClickTracks’ Marshall also likes to establish a conversion goal for a site (perhaps a productpurchase or a newsletter sign-up), and then determine the most popular pages on a site for the visitors who successfully “converted”.

“The most popular pages on a site are not necessarily the pages that produce the best conversion results,” says Marshall. “Just because more visitors go to those pages doesn’t mean the visitors buy something or sign-up for something. Our software will segment between the pages that lead to the most conversions and we can then reorient the site so that those pages are relatively more prominent.”

So, in that example, Marshall has segmented the visitors who convert and he’s then determined the web pages that those visitors went to prior to the conversion. He might establish a “dashboard” to track the pages that lead to the conversion and then change or eliminate the pages where visitors have exited the site, for example.

“Web pages within a site are not linear,” says Marshall. “The conversion funnel varies from visitor to visitor and we track that. We’ll segment the web pages that most likely lead to successful conversions and emphasize those pages.”

Marshall continues, “Whether it’s segmenting visitors from paid or free listings or segmenting the pages that lead to the most conversions, you have to look beyond the wall of data to determine what’s making you the most money.”

Kerry Murdock

Kerry Murdock

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