Tecovas is a direct-to-consumer online retailer of cowboy boots, Western-style apparel, leather accessories, and denim products. With a dedication to online sales, Tecovas relies on the performance, functionality, and ease of use of their website for success. Many of their company- and team-wide goals tie back to successful customer conversions.
Eric Jones, Frontend Engineering Manager at Tecovas, noticed that his engineering team struggled to accurately diagnose the severity of reported issues, especially during the holiday season when traffic is highest.
LogRocket helped Tecovas develop a product-focused mindset centered on providing the highest quality user experience. They executed on this by unifying the company’s understanding of how their technical issues impact conversion rates. With this shared understanding, Tecovas significantly reduced customer issue reports, and then surfaced and resolved a previously undetected issue that had cost them hundreds of thousands of dollars in lost revenue opportunities.
Ahead of their busy holiday season, Eric noticed that their issue and error monitoring info was solely based on customer issue reports. This was a problem because the holiday season often brought a wave of bug fixes, error alerts, and customer issue reports — along with a dip in their conversion rate.
He recognized this was an issue for other teams as well: declines in conversion rates often drew a lot of attention and each team deemed separate issues as high priority. As each team triaged separately, they ended up with disparate ideas to resolve problems that overlapped, leading to duplicate work and misalignment of priorities.
“I knew we needed to be able to see things that are really critical to our core user journey leading up to conversion,” Eric said. “We want to be as proactive about fixing issues along that journey as possible, and know where those vulnerabilities and risks are at important times.”
Boosting site performance and customer conversion all at once
LogRocket was first able to help Eric and his team with a widely reported slow checkout page that was decreasing conversion. Engineering and customer service had been working to resolve the issue with Shopify, whose template powers their checkout page, but they hadn’t been able to reproduce the issue. To make matters worse, the issue didn’t seem to occur consistently.
Using LogRocket, Eric created performance dashboards for Tecovas’s checkout pages to record key performance indicators, such as average load time and time to first byte, to narrow down the possible areas of concern.
“I wanted to validate whether this was happening in real life — you know, ‘works on my machine’ and all that,” Eric explained. “I wanted to see if — across different browsers, across different operating systems, across different geographical regions, all these different conditions — was there something to this?”
Eric quickly identified the issue after creating a dashboard to view a multi-day data set: over the course of a day, something in Shopify’s template drove up the time it took to load the checkout page to over 15 seconds — well over the fraction of a second users typically expect.
Eric shared these reports with his Shopify support team to bolster their previous reports and prove out that something was happening. With this new information, Shopify was able to pinpoint the checkout page issue to a CSS file that invalidated the cache.
“Custom styling on our checkout page doesn’t change, so it doesn’t need to be revalidated so often,” Eric explained. “Fixing just this reduced the page load average by 50%.”
Connecting conversion-related technical issues to lost revenue opportunities with machine learning
Tecovas looked to build on their initial success with page load times by using LogRocket to examine other areas of their app.
“I started looking into LogRocket’s Recommended Issues and what it recommends as the highest impact,” Eric explained, referring to one of LogRocket’s powerful machine learning features that determines the highest-priority issues affecting users in a given project.
Recommended Issues are a part of Galileo, LogRocket’s larger machine learning layer, which proactively scans applications to surface the most critical issues impacting your users. It cuts through the noise of traditional monitoring and analytics tools, which gather thousands of errors without providing any indication of which ones matter.
Recommended Issues delivers a short list of the highest severity issues, such as those that include user interactions that would normally be cause for concern. This is how Eric used LogRocket to discover a much bigger impact on customer usability metrics: dead clicks on their “Shop Now” and “Checkout” buttons that could have cost Tecovas hundreds of thousands of dollars.
He quickly realized that the dead click issue, which left users unable to navigate to product or checkout pages, was affecting over 2,000 users.
Without LogRocket, Tecovas might not have been able to identify this as an issue, let alone address it. As these dead clicks happened during their holiday season, it was estimated that the bug had resulted in hundreds of thousands of dollars in lost revenue opportunities.
“Figuring out where users are running into problems and what we can do to improve became a big priority.”
With LogRocket, Eric was able to demonstrate the importance of the issue across the board with session replays, then make the case to reprioritize work to fix it.
“LogRocket gives us the leverage to say, like, ‘Hey, there's issues here. Holiday season’s coming up and while we may have wanted a new landing page or a new enhancement to this feature, here's why we should prioritize resolving these issues,” Eric said.
With this insight, Tecovas as a whole became better positioned to identify the issues that were highest-priority and highest-impact, particularly to their bottom line.
Bringing teams together with a product-focused mindset
Sensing there were other teams with more to learn about their website, Eric began using LogRocket session replays to provide leadership with better insight into their user issues and build awareness around the company about how user behavior and technical issues affected conversion rates.
“If decisions are made when you're not all looking at the same data and not looking at the same roadmap, you create a lot of inefficiency, churn, and rework,” Eric said. “We’d been putting a lot of work into features or initiatives that we couldn’t fully confirm would meet our users’ needs.”
To bring a product-focused — and user-focused — mindset to Tecovas, Eric considered how other teams used their product and behavior analytics tools to track user journeys and conversion rates. The silos they created were hard to break and collaborate through.
“When I'm evaluating tools, it’s nice to know, like, does this isolate teams or does this bring teams together? And LogRocket was a tool that brought teams together.”
Integrating LogRocket with these other tools allowed Tecovas to create a dedicated space for engineering, ecommerce, and product team members to unite multiple points of view toward achieving a single goal.
Taking LogRocket to new sites
With this product-minded approach coming into focus, Eric and his team plan to deploy LogRocket on their new site as soon as it’s ready for production.
“The big push is going to be getting our analytics pipeline in place in LogRocket so that we can track the events that need to be integrated,” Eric said.
The hope after that is to use LogRocket to build further product analytics assessment into their workflows. “We want to dig into how the site performs as soon as it’s up, and what optimizations we might be able to make, based on the LogRocket insights we see.”