, the world’s leading meal-kit delivery provider, is on a mission to change the way people eat. Founded in Berlin in 2011, the company delivers high-quality meal kits through a flexible, customizable subscription program. Its combination of affordability and convenience has turned it into a global powerhouse delivering 1 billion meals to more than 7 million active customers in 2021.
The Problem: Overwhelmed By Manual Processes to Analyze Growing Customer Feedback From Disparate Sources
Running a successful subscription-based business relies on keeping customers happy, especially when it comes to dinner. For HelloFresh customers, having access to over 50 meal options each week satiates their hunger for meal customization — yet also brings more potential for a user experience glitch to drive them away. The company’s digital and physical product teams looked to user feedback for insights on delivery issues, cancellations, and user sentiment, but the process was entirely manual. With roughly 80,000 points of unique customer feedback flowing in each month from chat and phone support, surveys, social media channels, and app store reviews, HelloFresh was overwhelmed by so much customer feedback. HelloFresh also didn’t have the time or resources to manually turn that flow of user feedback into data-driven directions or prioritizations.“We really care about what makes our customers happy, or unhappy, from the moment they convert to ongoing delivery, meal prep, loyalty rewards, and more. But our process for gathering user feedback relied on manual effort so we couldn’t quickly see sentiment, recognize trending issues, or track quality issues over time. That’s why we embraced unitQ.”
HelloFresh needed a better way to quickly and effectively capture, categorize, and analyze user feedback from across internal and external channels. Internally, it managed customer support via chat and phone, and ran customer surveys within the HelloFresh smartphone app and on its website. Externally, it monitored Twitter, Facebook, Google Play, Reddit, Trustpilot, the Apple App Store, YouTube, and other social channels for user feedback, sentiment, and product-related issues. Plus, because HelloFresh serves customers across the globe, user feedback was provided in English, German, Dutch, French, Japanese, Swedish and other languages. It was a lot of data from too many sources to manage with manual processes. This prevented HelloFresh product teams from digging into this valuable user feedback data to identify issues and gauge sentiment on problems ranging from delays or wrong orders being delivered, customer login errors, missing menus, payment mishaps to a host of other customer-facing issues.
The Solution: unitQ Streams User Feedback From Public and Private Sources, Categorizes Issues and Alerts
So HelloFresh turned tounitQ Monitor
, an AI-enabled product quality monitoring platform that provides access to all user feedback in one centralized place. It captures user feedback from public and private sources, intelligently understands the sentiment, categorizes issues, and provides alerts, reports, and integrations to internal tools. HelloFresh uses unitQ to capture user feedback in several languages from internal and external channels, translate it into English, and automatically categorize the data based on issue, sentiment, and more. And, using unitQ’s integration with Slack, alerts are sent instantly to the right teams to triage issues and create data-driven prioritizations for engineering fixes. Not only is it convenient to get alerts on tools like Slack, HelloFresh is taking advantage of unitQ’s integration with Jira, another tool the HelloFresh team uses internally. With thisJira integration
, HelloFresh developers are creating Jira tickets right inside the unitQ Monitor, without having to go into Jira. These tickets notify HelloFresh product teams to fix issues that unitQ has surfaced.“unitQ’s Jira integration allows HelloFresh to save so much time getting issues fixed, and because of this, our developers can spend more time enhancing our platform.”
To better understand and mitigate issues before they become widespread, product teams within HelloFresh also use unitQ to uncover previously hidden issues using the context contained within user feedback. These product teams can also see how new product features are impacting users and run ad hoc analyses to understand user feedback across regions, app versions, operating systems, and more. They can even pull user empathy data into their product planning and design efforts. For HelloFresh, the UX Design teams use this analysis, paired with UX Research, to create even stronger customer insights to help guide the product strategy process.
HelloFresh also uses its unitQ Score, a product quality metric, as a leading indicator to predict changes to its Net Promoter Score (NPS). The unitQ Score lets HelloFresh quickly, efficiently, and effectively understand and act on user f eedback while giving HelloFresh developers an easy-to-understand score for tracking product quality and user experience trends. While the NPS simply provides HelloFresh a metric, unitQ Score provides HelloFresh with the intelligence to improve its NPS and, most importantly, provides a detailed roadmap to build a better user experience both immediately and into HelloFresh’s future.“We need user feedback to be actionable so we can make decisions and prioritizations based on current data. unitQ does that, automatically. So, instead of our teams spending days collecting and manipulating data, they can act on the breadth of user feedback that’s captured and categorized in unitQ.”
The results: Improved Product Quality, Increased Customer Support and Sentiment
Now with unitQ Monitor, HelloFresh can rely on timely and comprehensive user feedback data to prioritize issues, guide support decisions, understand emerging product impacts, and inform product teams as they work to improve the user experience around the world.
Other unitQ Monitor benefits for HelloFresh include:
“Ultimately, we want to get unhappy customers back to being happy customers as quickly as possible. unitQ gives us the data to do just that, quickly.”
- A single repository for comprehensive user feedback from internal support channels and public feedback sources.
- Faster, easier analysis of user feedback data to uncover issues, add context from users, search on keywords and sentiment, track issue resolution times, and provide data-driven justifications for projects, priorities, and plans.
- Intelligent topic categorizations help product teams quickly identify and fix emerging and high-impact issues.
- More granular insights to continuously improve customer support, speed issue resolution, add empathy data to decision making, and keep customers happy and returning for more meals.