Online Dating



Quality Challenge
Manual process, Zendesk, in-app

  • 42% improvement in the unitQ Score
  • 28% improvement in App Store and Google Play ratings
  • Increased session length and average sessions per day
  • 40% reduction in support ticket volumes
LOVOO helps match millions of people with that someone special, be it for a quick icebreaker or a lifetime commitment. Their service differentiates by going well beyond the simple swiping of photos to provide an authentic, high quality experience. But, since every connection is sparked on the LOVOO smartphone app, nothing derails a budding love connection faster than a frustrating glitch.

Frustrated with Friction

App quality issues usually prompted annoyed users to stop their dating ambitions and let someone know. Those quality issues ranged from trouble sending chats and disappearing matches, to revenue-impacting issues, like the inability to upgrade to a premium account. Their primary outlet was either a disappointing app store review, an angry social post, or a message to LOVOO support. But finding those omnichannel complaints, getting them sorted, and making sure they were quickly routed to LOVOO product and engineering teams was a friction-filled process. And, with millions of global users interacting in 15 different languages, even a minor quality issue could balloon into a disruptive product crisis.

Struggling with a Manual Process

At the heart of their pain was an entirely manual process to capture, parse, and prioritize incoming quality issues. Making matters worse, their visibility was limited to only those issues arriving via their Zendesk support system and in-app chatbot, so they missed out-of-band issues and frequently underestimated the impact. But, even through their monitored channels, incoming tickets reached close to 40,000 per month, which meant their Quality Assurance (QA) team had to manage over 1,000 tickets per day.

But even when QA did spot a potential issue, they had trouble making sense of it; uncovering its scope across operating systems, versions, and devices; quantifying its impact and prioritizing it against other, in-process improvements; and then getting it into the Engineering queue.

This manual QA process was not only slow and expensive, it lacked many of the background details required for a quick and effective fix. Furthermore, LOVOO missed quality-related comments made in app store reviews and on social media. That is, until they connected with unitQ.

Getting Quality Insights with unitQ

LOVOO chose unitQ Monitor for constant, cross-channel monitoring and analysis that identified quality issues and alerted the right teams, all while reducing the cost and effort, and freeing up QA to work with Engineering on fixes. unitQ integrated with Zendesk in just 10 minutes, and unitQ APIs completes the quality picture with data from LOVOO’s in-app chatbot and from Apple App Store and Google Play reviews. unitQ also added device, language, and platform metadata to pinpoint the source of quality issues. These complete and granular details helped accelerate prioritization, and also helped Engineering find and fix issues faster.

“We love unitQ Monitor! It used to take us so much time to identify and quantify issues,” said the head of QA at LOVOO. “unitQ Monitor has really improved the workflow between our QA and Product teams, because we now have all the data we need to make decisions in real time. Plus, it’s great that we can now search everything from one place!”
— Anja Richter, Head of QA, LOVOO

Better Quality for a Better Experience

Users noticed the improvements, raising LOVOO’s unitQ Score - The Quality Metric by 42%, from 65 up to 92/100. And, in just a few months, their App Store ratings increased from 3.7 to 4.6 and Play Store ratings rose from 3.2 to 4.1. This combination of channel data, quantified insights, and noise reduction makes unitQ the single source of truth for quality at LOVOO.

Lovoo growth chart

In a space where quality definitely matters, LOVOO now has a cost effective process for spotting issues faster. unitQ quantifies potential issues in real-time to offer indisputable reporting to hone in on issues and prioritize fixes with Engineering. And unitQ uses advanced machine learning to filter out the noise of other user feedback unrelated to quality issues, allowing LOVOO to quickly route feedback to the support team, saving everyone even more time.

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