Insights · 6 min read

How DXI-ready teams connect the dots between metrics and revenue

Gardner Rordam
Posted March 01, 2022
How DXI-ready teams connect the dots between metrics and revenue

In my previous article, I detailed three common traits of companies who are well-prepared for success with Digital Experience Intelligence (DXI). Those three traits are:

  • Cross-functional and collaborative team organization

  • Customer-focused decision-making

  • Revenue-based metric tracking

In this article, I’ll dive deeper into the last trait. In other words, let's follow the money trail of DXI insights.

From merely interesting to impactful

Many times, we see our customers get caught up in mere “interesting” findings from FullStory. And while it may feel like enough to have learned a new tidbit about your users, I always encourage my customers to dig deeper into these “interesting” insights.

“Interesting” won’t land on an executive’s desk. And if it does, it won't spur action. More likely, it will be interesting today and forgotten tomorrow.

Here are examples of a few of those kinds of findings:

  • Only one-third of our customers aren’t going through the sign-up flow in the order we expected.

  • Half of the customers who try to add a specific product to their bundle get an error message.

  • 10% of all checkouts result in Dead Clicks or error messages.

So what’s the next step after unearthing one of these pieces of information?

Organizations that provide the most effective digital experiences are able to consistently connect the dots between the “interesting” findings and business impact.

Too often, we think of our companies’ websites as simply enabling transactions—but that’s a gross oversimplification. Findings like the examples above represent new and complex obstacles your customers face while spending money with your company, and may even prompt them to seek out your competitors for a better experience.

FullStory provides numerous ways to go beyond these "interesting" findings and connect the dots to real value—but three of the most effective strategies are:

  1. Revenue events

  2. Friction events and unhappy paths

  3. Before/after feature release analysis

Quantifying the connection between data and dollars with FullStory

Revenue Events

Though it requires a little technical set-up, once implemented, Revenue Events transforms these “interesting” events into definitive value without much additional thought for your team. Establishing this API takes the guesswork out of understanding lost revenue from digital experience (DX) issues.

Additionally, teams can use FullStory Segments to identify big spenders (whatever that threshold is for your company), and understand how their behavior differs from the general customer base. This segmentation lets you identify places where large transactions are thwarted by errors or confusing user flows, and quantify lost revenue to inspire quick action to implement a fix—and regain customer trust and value.

Perhaps most powerfully, you can also link Revenue Events to Conversion Analyses. This connection means you’re not just reporting on lost conversions, but you’re able to quickly reference dollar values.

Tying opportunities to a real revenue value is critical for large companies with competing priorities. Every company’s engineering department has too much work to do and too little time.

What’s more, weighing priorities based on other departments’ KPIs (errors, conversion rates, bug findings, user studies, etc.) is exceedingly difficult. Conversely, it’s pretty easy to prioritize a checkout error directly linked to $1,000,000 in lost revenue opportunities.

Friction events and unhappy paths

One of the first things most FullStory users do is to map out expected user flows. For example, a simple ecommerce journey is Product → Bag → Checkout → Confirmation. It’s fascinating to watch your customers go through this flow and gratifying to see that it works.

After this, many teams will turn to analyzing Rage Clicks or Dead Clicks… but they’re forgetting one crucial thing: These are customers who made it through! Digital experience friction may have been present, but it wasn't detrimental to the end transaction.

To make this analysis more effective, bring the confirmation step down into the exclusion criteria. Now, you’re looking at customers who did everything in the flow except get an order confirmation.

Whatever friction they encountered on the last step is now much more meaningful, as it caused them to abandon their transaction. Even if you don’t have revenue events instrumented, you can estimate Average Cart Value and have a very clear picture of the cost of the friction—and a case for why your company should act to fix it.

Before/after feature release analysis

Depending on the maturity of your product organization, new features can sometimes also fall prey to the “interesting” fallacy. Without DXI capabilities, it’s hard to identify the biggest opportunities to improve your site or SaaS product, making prioritization of new features and expected outcomes difficult as well.

Of course, we’re all looking to make products “better,” but how is that defined and measured?

Once again, it should all come back to dollars. Are we looking to make a customer’s life easier? Offer more products? Reduce time in checkout flows? Build in a new capability for a SaaS product? All of these ultimately lead to higher revenue.

Ideally, you’ve got A/B experiments set up and integrated with FullStory so that you can get a preview of changes, test hypotheses, and only launch the features that are proven to achieve your end goals with relevant customer samples.

With or without A/B experiments, it’s still critical to know measurable performance of the new feature after release. Is your company actually getting increased revenue after the release of the new feature?

One FullStory user tested a new checkout feature extensively with A/B tests, but didn’t stop there. He had two Conversions Analyses set-up: one for the week before the feature went live, and one for the day after.

Immediately after, they saw completed checkouts nosedive. With FullStory, he found the error, escalated it to the engineering team, and got it fixed within a day, saving the company hundreds of thousands of dollars and realizing the expected gains from the new feature.

Moving beyond interesting

These strategies are your guide for taking your “interesting” findings and converting them into actionable priorities for your company. The beauty of this approach is that one doesn’t need to be an established executive to create change. As an analyst just starting in their career, or someone relatively new to a company, you can identify these opportunities and then advocate for them in a meaningful way.

Circling back to our “interesting” findings above, we can rephrase the exact same findings with a little more data and focused on the metric that matters:

  • One-third of our 750,000 customers are going through sign-up on an unexpected path, missing an upsell opportunity worth $100. We’re missing out on a potential $25 million.

  • Half of the 100,000 customers who try to add this $50 product to their bundle get an error message. That’s $2.5 million of lost revenue.

  • 10% of our daily 10,000 checkouts result in Dead Clicks or error messages. At an average cart value of $200, we have an opportunity to recoup $200,000 per day—or around $6 million per month!

Curious how a Digital Experience Intelligence solution can increase conversion rates?

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Gardner RordamEnterprise Customer Success Director

About the author

Gardner is an Enterprise Customer Success Director at FullStory. He is based in Atlanta, GA.

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