U.S. consumers spent $194.3 billion online in 2011, yet Forrester Research notes that for every $80 companies spent driving traffic to their sites, they spent only $1 trying to convert those visitors into customers. Given how much revenue is up for grabs, and how little companies are spending to stake their claim to it, there are huge opportunities for marketers willing to commit to rewarding, incentivizing and personalizing their visitors' experiences.
Consumers have more choices, more incentives and more reasons to comparison shop for the best deals out there. Marketers can use online behavior and web analytics to reveal patterns and warning signs indicative of the type of customer retention issues that lead to "online cheating."
The good news is that these issues can be corrected if caught early enough. To do so, marketers must identify which types of data patterns to pay attention to and use those data to inform their next steps.
Here are five common predictors of online customers who either aren't interested or not committed, and tips on how to use that data that to solve the problem.
1. Homepage Bounce Rates of 55% or More
As far as homepage bounce rates go, anything over 55% should be a red flag that something is terribly wrong. If more than half of a site's visitors are leaving without so much as a click-through, they're not finding what they're looking for and aren't convinced there's any value in waiting around to be proven wrong.
But before contacting a web analytics team to determine why this is happening, marketers should know that the average industry home page bounce rate is around 50%, and that a well-performing homepage has a bounce rate of between 0% and 25%.
Using data to solve the problem:
Marketers can make some basic assumptions when they observe high homepage bounce rates. The two most common are that there's a lack of aesthetic appeal and/or the customer isn't immediately finding the right information. Both of these problems are typically a function of some combination of layout, design, navigation, site elements, functionality, content, and messaging. Changes can be tested with actual visitors, using A/B and multivariate testing.
By testing individual homepage elements in various combinations, marketers can discern which elements are contributing to a higher conversion rate, and which are contributing to the high bounce rates.
Knowing which elements to replace is often all that's necessary for drastic improvements. Consider UK retail giant ASDA Wal-Mart, which was suffering abnormally high home page bounce rates and used A/B testing to compare two completely different layouts of their homepage alone, allowing it to identify a layout that reduced its bounce rate by a whopping 19%.
2. High Average Shopping Cart Abandonment Rates
Many online shoppers initiate a purchase, only to leave the items behind in their cart. According to Forrester, the top six reasons for abandoning a cart before making a purchase are: excessively high shipping & handling costs (44%); insufficient preparedness to make the purchase (41%); a desire to compare prices on other sites (27%); a higher product price than a buyer is willing to pay (25%); saving products in the cart for lower comparison (24%); and listing the shipping costs too late in the checkout process (22%).
Using data to solve the problem:
The Baymard Institute found that the average cart abandonment rate is about 65%. Marketers need to make sure the basics are taken care of, and there are a number of options they can test. These include estimating shipping costs at an earlier point in the buying process, allowing guest checkouts, highlighting in-stock versus out-of-stock status, providing auto-fill forms based on cookie tags for repeat visitors, and using shipping discounts or specials.
Thorough testing will help determine which of these updates are working best for the target audience. For example, by multivariate testing a number of elements in their checkout process with live visitors, National Express Coaches & Buses was able to lower itscart abandonment rates and produce a 14% increase in cart conversions and purchases.
3. Low Search Engagement
Ecommerce marketers need to think about the importance of search on visitor engagement and even purchases. By encouraging consumers to explore the site and streamlining the shopping process, the chances of turning visitors into customers increase.
Using data to solve the problem:
Every single component of the search feature—placement, layout, default search box text and even the color, size and design of the graphic elements—affects engagement with this important tool. Multivariate testing can help marketers discover which combinations work best for their target audience.
Gift retailer Harry & David noticed a low search engagement on their site and decided to use multivariate testing on its search feature. Over a period of weeks, the firm tested 2,500 varying elements and combinations. The winning combination produced a double-digit increase in conversions and sales.
4. Unsatisfactory Average Order Values
Then there are those customers who aren't abandoning the checkout, but also aren't buying as much as they could be. Chances are they have a very specific product in mind, and aren't being persuaded to add more items to their cart.
Using data to solve the problem:
Marketers can use personalization techniques such as inserting and/or customizing information that's relevant to a specific user based on implicit behaviors (items purchased, pages viewed) as well as explicit details (location, age, gender) provided by that particular user to customize their recommended items suggestions.
"Wisdom of the crowd" endorsements can be particularly useful for up-selling and cross-selling relevant products. One approach is called item affinity, which selects recommendations based on the past habits of visitors with similar interests. Those most commonly come in the form of "Visitors who viewed this, also viewed that." To ensure these endorsements reach the customer in the best possible way, it's smart to also test recommendations in terms of design, placement and content.
5. One-Time Buyers
Sixty six percent of Amazon.com's sales are attributed to repeat buyers. Remarkably, only 7% of the entire ecommerce industry can say the same. To get past the initial visit with these customers, marketers should consider moving toward the world of automated personalization with behavioral targeting solutions.
Using data to solve the problem:
There is more to capturing customers and keeping them coming back for more than just throwing money at affiliates and SEO. Behavioral targeting uses individual online behavior and CRM data to create defined audience segments based upon visitors who behave and react similarly when visiting a particular site. These techniques tailor content and offers to individuals based on both their past behaviors and their unique "buyer persona" (audience segment).
Buyer persona attributes include previous purchases, searches, page views, geography, demographics, type of button click, transaction and other interaction data…the list goes on and on. Providing customers with this sort of singular attention is crucial to keeping them loyal. European airline bmibaby recently used this strategy to target travelers with a "Feature Destination" based on their unique buyer persona. The result? A 41% increase on people booking their featured destination.
Customers provide marketers with an immense amount of data throughout the shopping process. Placing customers at the heart of online content decisions and giving them unique, personalized experiences is a major step forward in rounding out the online experience, and one that ensures visitors will convert into customers.
Mark Simpson is founder and president of Maxymiser.
Network
