Making a sale online isn’t easy. In fact, approximately 96% of visitors that come to your

website are not ready to buy. That’s why you can’t afford to lose even half a percent of your customers that show interest.

However, the reality is that there are hundreds of factors that go into transforming your interested visitors into paying customers. It’s a revenue funnel, and it doesn’t always function perfectly. In fact, sometimes it completely breaks down—leading some of those customers who are ready to buy to abandon your site altogether.

 

<< Click Here to See UserReplay in Action: How You Can Discover and Monetize Your Customers’ Online Struggles >>
 

Why should you be concerned about this hidden and unrealized revenue?

  • You’ve already earned it. You don’t need to offer incentives to close this business because customers are already motivated to buy.
  • You don’t need to hypothesize or run experiments—you’ve already done them.
  • This low-hanging opportunity is often financially significant.

You could be missing out on thousands of dollars in revenue on your digital channel because, for one reason or another, your customers are having trouble converting. Why does this happen?

Reasons for Hidden Revenue

There are many reasons that your customers could struggle to put money in your pocket. A few of the most common are:

  • Technical Issues: Sometimes websites don’t function as expected and there’s no clear explanation. In some cases, the issue can’t be replicated in testing. For example, a major fast food retailer experienced an issue where customers going through the checkout process were automatically redirected to a postcode entry page. It didn’t happen every time, but it happened often enough that the company was missing out on $1.8 million in annual revenue.
  • Timing: A one-second delay in your site speed can result in a 7% reduction in your conversions. A slow website on mobile or desktop can make your customers give up before they reach the end, even if they want to make a purchase.
  • Third-Party Compatibility: Many times customers are lost not because of something that goes wrong with your software or website, but because of something that went wrong with a trusted third-party technology.
  • Customer Service Disconnect: Trying to match your customer’s online experience to your customer service team can be frustrating. When things go wrong for your customer, or if they’re required to go through a lengthy process to get the issue resolved, most of the time they’ll give up instead and leave their purchase behind. Pizza Hut UK had exactly this problem. They had a difficult time tracking their customers online, so, when an issue occurred, their customer service team took too long to fix the issue. Overall, it cost them almost £7 million in lost annual revenue.

How to Obtain this Digital Channel Hidden Revenue

To make sure you don’t continue to miss out on your hidden revenue potential, you need a customer experience analytics (CX analytics) solution integrated into your website, that tells you precisely what’s going on.  This CX analytics solution should have the ability to provide:

  • Analytics: You don’t have time to sift through hundreds of customer journeys on your website. Instead, you need to be able to discover the most relevant events within your customers’ journeys so you can review only the data you need.
  • Alerts: Instead of having to review your data and analytics on a daily or hourly basis, you should be able to set “alerts” on critical issues so that your team can react in seconds instead of hours or days.
  • Reports: Having the right data and analytics are keys to discovering where things go wrong. Reporting that helps you quantify and monetize the customer struggle is crucial.
  • Usability Testing: Customers want to make a purchase on a digital channel that is easy to use. You need an at-a-glance summary of the customer experiences on your site, so you know where customers are having potential issues before you lose significant revenue.
  • High-Fidelity Replay: You need real-time, high-fidelity replay capabilities so you can watch your website as orders are being placed and review the entire customer transaction process to uncover the unrecognized revenue opportunities.

By using a solution like UserReplay’s customer experience analytics, you can uncover hidden revenue that you have already earned. Our solution lets you record, re-run, and analyze every visitor’s journey on your website. This invaluable information will help you improve your conversation rates, monetize the online customer struggles, fix technical issues, resolve customer disputes, and recover abandoned baskets.





New Call-to-action




 

In this digital age, “business as usual” is not sufficient; customers have come to expect their digital experience to be tuned to their needs. New techniques for meeting customer needs and expectations are required to leverage the digital channel more effectively.

Machine learning is emerging as a digital disruptor and an essential piece of technology to take advantage of the enormous amounts of customer data now available to Marketing and eCommerce managers—especially regarding their customers’ digital experiences.

Research shows that Marketing and eCommerce managers are struggling with how to identify, quantify, and prioritize customer experience issues in their digital channels. And while there is nothing simple about machine learning it’s important to understand machine learning enough to see how it applies in the world of digital customer experience.

 

What Is Machine Learning?

Machine learning is the science and engineering of making machines “learn.” That said, intelligent machines need to do more than just learn—they need to plan, act, understand, and reason. Collecting, organizing, cleansing, synthesizing, and even generating insights from large volumes of structured and unstructured data are now typically machine learning tasks. At its core, machine learning is predicated on interwoven algorithms that can manage massive volumes of complex data more effectively than individuals.

Using machine learning enables us to process deep volumes of data and adapt to new categories in real time. This scalable programmability is complex, but offers businesses a number of ways to improve the customer experience. For Marketing and eCommerce managers, machine learning should be viewed as a strategic tool for gleaning actionable insights out of big data.

 

How Machine Learning Applies to eCommerce Retailers

eCommerce companies are beginning to use machine learning to improve search results on their websites. In the same way that Google uses machine learning to suggest the most relevant results to searches, eCommerce sites are implementing this technology to improve product browsing experiences.

However, use cases are expanding beyond search experiences like these to identifying the myriad of opportunities for eliminating customer churn and optimizing opportunities to improve the customer experience more effectively, without the need for hard-to-find resources like data scientists.

While these are just a couple of different ways machine learning can impact online businesses, the potential use cases are virtually unlimited. According to McKinsey, having the right people in place to translate machine learning insights into actual business decisions is the key.

 

Machine Learning and Customer Experience: How Marketing and eCommerce Managers Can Make the Two Meet

In a recent study we conducted, we found that 85% of organizations have trouble understanding why customers may not be converting on their website. The promise of machine learning is that we can now identify previously undiscovered revenue opportunities and mitigate friction in the digital channel much more efficiently.

At UserReplay, we have recognized the need to help Marketing and eCommerce managers capitalize on machine learning and we have made significant inroads into using machine learning for customer experience opportunity improvement. Stay tuned for more information about how the next generation of our customer experience analytics solutions are using machine learning.



New Call-to-action