Case Study

How Freshly Transformed Survey Data into Valuable Customer Insights

About Freshly

Freshly is a major force in the hyper-competitive meal delivery space. They differentiate themselves from their competition with meals prepared by chefs that can be served quickly with no cooking required.

The Challenge: Simplify The Customer Feedback Loop

By the time Freshly came to re-evaluate their customer feedback approach they were already ahead of the game in two significant ways.

First, they had succeeded in getting executives interested in improving their NPS scores. Second, volume wasn’t Freshly’s problem – their CX team was already gathering a wide variety of written and survey feedback.

But they had a convoluted approach to customer feedback that needed fine-tuning. The problems Freshly faced when it came to their feedback were three-fold:

  • Receiving data from Multiple sources. Freshly’s reviews were coming from four sources: Freshly Core NPS, Freshly Fit NPS, Freshly Fit Goals, and Freshly Cancellation surveys.

  • Non-standardized data. Further complicating things, these reviews were being gathered by a number of different software programs and uploaded to Slack before being plugged into Zendesk – cancellations were handled by Brightback, NPS surveys by Delighted, and Freshly Fit Goals reviews were uploaded manually.

  • Non-actionable feedback. While Freshly understood the value of actionable feedback and how it could improve overall CX, they were hampered due to a lack of ability to accurately, comprehensively, and efficiently quantify their customer feedback.

The Solution: Make Data Accessible & Actionable

To handle their data, Freshly brought in CX savvy Megan Merrick to head their NPS and Voice of Customer (VoC) program. Understanding the problems they faced, Megan developed a strategy that involved using AI tools.

Looking to enhance Zendesk’s capabilities, Megan researched compatible AI tools and turned to MonkeyLearn – a no-code AI text analysis solution that offers full Zendesk-integration, is easy to use, and could address Freshly’s feedback at scale.

Linking the two programs, Megan sought to standardize and analyze her data using automated text analysis.

  • Using Zapier and MonkeyLearn’s API, Freshly’s customer feedback data was automatically connected to MonkeyLearn’s text analysis workflows and data analytics dashboard, allowing Freshly to streamline feedback analysis and visualize valuable insights in one central location.

“Being able to look at feedback in one central location has been huge for us, as well as the fact that you don't have to be a data analyst in order to utilize the tool and understand the data/numbers shown.”

- Megan Merrick, Associate Director, Innovation and Brand Experience at Freshly

The Results: Actionable Insights & Team Hours Saved

Megan emphasized the value of the actionable insights that MonkeyLearn unlocked for Freshly.

“The most noticeable change for us at Freshly has been the ability for us to snapshot feedback instantaneously and to build a fleshed-out business case to improve the user experience”.

For example, recently Megan and her team decided to dive into NPS commentary about the food delivery experience.

Before, Megan’s team would have had to create a filter in their NPS tool, for topics like "delivery", and read each comment to understand the more granular issues impacting food delivery scores.

Now, with MonkeyLearn, this task is much easier.

Megan explains: “It’s as simple as searching ‘delivery’ over a specific timeframe in MonkeyLearn’s dashboard.”

“From there we’re able to understand exactly how delivery impacts our NPS scores, the overall sentiment breakdown, the frequency and sentiment by granular topic, as well as being able to read the individual commentary.”

Additionally, if Megan’s team spots an incorrectly categorized comment, they can retrain MonkeyLearn’s no-code text analysis tools to make them smarter – without reaching out to their data science team.

Megan explains: “MonkeyLearn essentially allowed me to analyze data without bothering our data team.”

Ultimately, automated feedback analysis has empowered Freshly’s CX team by:

  • Delivering accurate and actionable insights.

  • Eliminating 5+ hours of manual survey analysis per team member a week.

  • Reducing CX demand for data scientists, freeing them to do essential work elsewhere.

The Future: Honing AI Feedback Analysis & Delivering Proactive Customer Support

Megan and her team are now seeking to put Monkeylearn’s software suite to further use, attempting to perfect their feedback loop, and using NPS score insights to spur greater proactive communication with customers.


“Almost instantaneously we had a central source of truth for survey data and I'm forever grateful!”

Megan Merrick

Associate Director, Innovation and Brand Experience @ Freshly


MonkeyLearn Inc. All rights reserved 2024