These days, consumers have more choices, more incentives and more reasons to comparison shop for the best deals out there. But 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 question is, are they?
If caught early enough, these issues can be easily connected. To do so, marketers must identify which types of data patterns to pay attention to and use that data to inform their next steps.
1. Homepage Bounce Rates of 55% or More
If more than 55% of visitors are turning around as soon as they get to your site, it’s a major red flag that something is terribly wrong. It’s likely that visitors aren’t finding what they’re looking for. (BTW: you 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%.)
So what gives when this issue arises? It’s usually due to issues with layout, design, navigation, site elements, functionality, content, or messaging. By A/B and multivariate testing these 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.
2. High Average Shopping Cart Abandonment RatesMany online shoppers initiate a purchase, only to leave the items behind in their cart. The
Baymard Institute found that the average cart abandonment rate is about 65%. Luckily, there are a number of options you can test to bring this number down. 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.
3. Low Search Engagement
The importance of search on visitor engagement and purchases is often overlooked. By encouraging consumers to explore the site and streamlining the shopping process, the chances you’ll turn more visitors into customers, increases. 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
4. Unsatisfactory Average Order Values
What about those customers that just 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.
This is where personalization can really help. By 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, you’ll be able to customize their recommended items. Product recommendations and behavioral targeting are two common ways to combat this problem.
5. One-Time Buyers
66% of Amazon.com’s sales are attributed to repeat buyers. Remarkably, only 7% of the entire ecommerce industry can say the same. But it’s going to be tough to match this success without employing automated personalization with behavioral targeting solutions.
Using data such as previous purchases, searches, page views, geography, demographics, type of button click, transactions, etc., is crucial to keeping customers loyal. Behavioral targeting tailors content and offers to individuals based on both their past behaviors and their unique “buyer personas”.
Placing customers at the heart of online content decisions and giving them unique, personalized experiences is an important part of faithful consumer relationships.
.