The new breed of web site analytics digs into problemsthat hide deep below the surface.
Mattress manufacturer Select Comfort was getting results from its paid search listings on Google and Overture; its selection of paid keyword was driving web site sales. Yet the company wondered: Given its investment, was the paid search campaign as effective as it could be? And were online sales the only way to measure that?
The application of new and more sophisticated web analytics provided the answers to Select Comfort`s questions: no and no. Given the high-consideration nature of a mattress purchase, Select Comfort suspected that after finding the site through search and studying it, many consumers were then using a toll-free number to complete the transaction at the call center. But site managers had no proof. The company decided to deploy a system that would provide dynamically generated toll-free numbers for different keyword listings, allowing it to track the keywords that lead to the most calls.
Not just clicks
Factoring in the offline sales driven by keyword buys overall shot Select Comfort`s ROI on its keyword investment up from 460% to a whopping 1165%, providing a more accurate picture of just what it was getting for what it was spending on paid search. And despite ROI that jumped from the triple into the quadruple digits, the findings suggested Select Comfort could do even better. Analytics showed that 58% of Overture keywords and 35% of Google keywords generated fewer than 10 clicks and no sales in either channel.
Apart from how the individual keywords performed on the different engines, Select Comfort`s cost per order on Google was 47% less than on Overture and 25% less than on MSN`s search engine. As a result, Select Comfort is now reducing spending on more poorly-performing keywords and boosting keyword spending on MSN.
Retailers believe they`re doing well when they see sales add up online, but web analytics that illuminate customer behavior in ever-increasing detail now show that sales numbers are only half the picture. The kind of analytics-driven intelligence that wasn`t available even a few years ago today is capturing where customers arrive at the site from, their pathway on the site, their exit points and where they go after they leave a site--even blending that with data drawn from other sources.
Web analytics` new sophistication highlights missed opportunities and points toward improvements in ways that sales figures alone don`t. Online retailers are finding, as did Select Comfort, that problems may lurk underneath even the seemingly satisfactory performance of a site. Digging deeper into customer data with a powerful new generation of analytics, they`re learning several lessons.
They`re optimizing the wrong pages or links. Web real estate may be infinite, but shopper`s page views are anything but. "Retailers really have a very limited amount of real estate to optimize for buyers," says John Mellor, vice president of marketing at web analytics provide Omniture Inc. In a study it commissioned of Jupiter Research Inc., Omniture found that no matter how many pages a retailer put up for its site, the average number of pages viewed on the site per visitor per month was fewer than 20 for more than 50% of sites studied.
"56% of your traffic interacts with your site through fewer than 20 pages over the course of a month," Mellor says. "With three to five of them being checkout pages, you`re down to about 15 pages that you need to optimize so shoppers make a purchase. It becomes very important to understand which pages on your site are producing the most revenue."
That means going beyond using analytics to determine the number of clicks on page links to using analytics to figure out revenue per click. One of Mellor`s e-retailer clients, for example, placed its gift card offer on the lower third of its home page, until analysis revealed that at an average $14, the gift card link was generating higher revenue per click than anything else on the site. After placing the feature more prominently, the retailer saw overall revenue from the gift card link increase dramatically.
Some retailers who`ve done this analysis have discovered that individual product category pages account for as much as 30% of revenue on the site, Mellor says. If so, those pages should get the lion`s share of testing and optimization resources, he adds. "A 2% to 3% gain in efficiency on that page is worth much more than it is on a page that only participates in 5% of your site`s revenue," he says.
Their keywords are off target. "It`s almost impossible for a retailer to know all the different combinations of keywords that match its business or product with what`s in the customer`s mind," says Olivier Silvestre, senior e-business analyst at web analytics provider WebSideStory Inc. For many e-retailers, analytics have shown just how big that gap can be.
One WebSideStory client provides an international directory of information on spas. It focused much of its keyword program around terms containing the word "spa," but analytics suggested it was missing out by not expanding keyword buys to phrases that described the spa concept without using the word itself.
"People may search for `relaxing vacation ideas.` If a spa happens to come up as an alternative in that search, they may realize that while they didn`t think to search for `spa` in the first place, a spa is what they are looking for. By understanding this, you can fine-tune your keyword strategy and get a lot of customers that way," says Silvestre.
Analytics can also show when a keyword buying strategy has become outdated, and when to move into or out of a broad-match keyword buy, he adds. Under a broad-match buy of the term "car," for example, a retailer`s listing would appear in any search for which a user has typed in any keyword phrase that contains that word. A phrase match buy for "red car" would bring the listing up in any search that contains the phrase with the words in that order, even if part of a larger keyword phrase. An exact match buy would bring the listing up only for searches in which the user types in the phrase "red car."