About MonkeyLearn (now Medallia)
I recently put MonkeyLearn to the test, and it's quite an interesting tool for anyone dealing with customer feedback, surveys, or support tickets. The no-code aspect is a big draw; you can build custom text classifiers and extractors without having to touch a line of code. This means you can create models to categorise feedback by topic, detect sentiment, or tag support tickets based on urgency—all from a user-friendly visual interface. The pre-built models for sentiment analysis and topic detection are particularly handy for those who want quick results without diving deep into the nitty-gritty of machine learning.
One of the standout features is the dashboard, which visualises trends over time. This is a game-changer for product teams analysing NPS surveys or marketing teams tracking brand mentions. You can easily spot emerging issues and get a real sense of customer sentiment. Plus, the integration with popular tools like Google Sheets, Zendesk, Freshdesk, and Zapier means you can automate your text analysis within existing workflows, saving time and effort.
However, let's talk pricing because that’s where things can get a bit hairy. While there is a free trial, the Team plan starts at a hefty $299 per month. This might make it a tough sell for smaller businesses or freelancers who don’t have that kind of budget. In my experience, that pricing is justified if you're processing a large volume of data consistently, but for sporadic use, it can feel like overkill. Overall, MonkeyLearn is a solid choice for teams that need to make sense of large amounts of text data but may not be the best fit for everyone.
In summary, MonkeyLearn is a powerful no-code tool for text analytics that can simplify the process of extracting valuable insights from customer interactions. It's especially suited for teams that regularly handle large volumes of data and need a straightforward solution to classify and analyse that information. However, smaller teams or those with occasional needs might find the pricing to be a barrier, so it's essential to consider your specific use case before diving in.
Our Review
Verified 11 May 2026Reviewed by Delv Editorial, Delv Team
So, here’s the deal: I took MonkeyLearn for a spin, and I have to say, it’s quite a handy platform for anyone drowning in customer feedback or support tickets. The no-code aspect is a standout feature; I’m not a coder by any stretch, and I was able to set up custom text classifiers and extractors without breaking a sweat. The visual interface is genuinely user-friendly, which is a breath of fresh air in the often convoluted world of data analytics.
What really impressed me were the pre-built models for sentiment analysis and topic detection. I was able to get insights almost immediately, which is perfect for teams that need quick data to drive decisions. The dashboard offers clear visualisations that make it easy to spot trends over time. I found it particularly useful when analysing NPS surveys—seeing how customer sentiment shifted week by week was incredibly eye-opening.
However, let’s talk pricing because this is where things start to get a bit murky. Sure, there’s a free trial, but once you hit the Team plan at $299 per month, it can feel like a punch in the gut, especially for smaller businesses or solo freelancers. If you’re not processing a large volume of data regularly, that price tag can feel excessive. I’ve been in situations where I needed to analyse feedback but couldn’t justify that kind of expense.
When comparing it to alternatives, I found that while MonkeyLearn is great for quick, no-code solutions, tools like Qualtrics offer more comprehensive survey capabilities, and Google Cloud Natural Language is ideal for those who want to dive deeper into API integrations. However, for teams that regularly deal with large amounts of text data and need a straightforward solution, MonkeyLearn shines through.
In conclusion, I’d recommend MonkeyLearn to product teams, marketing teams, and support teams that need to extract insights from text data without the hassle of coding. It’s a solid choice if you’re committed to using it regularly, but if you’re just dipping your toes into text analytics or operate on a tight budget, you might want to explore some alternatives first. It’s an excellent tool, but just make sure it aligns with your actual needs before signing on the dotted line.
Getting started with MonkeyLearn
With MonkeyLearn, you'll be able to classify and extract valuable insights from customer feedback, surveys, and support tickets without writing any code. This guide will help you set up your account and create your first text analysis model quickly.
Step 1: Sign up and set up
Step 2: Your first text classifier
Step 3: Get better results
Pro tip
After creating your model, use the Auto-Train feature to automatically retrain your model with new data. This saves you time and ensures your model stays relevant.
Common mistake to avoid
Avoid skipping the step of testing your model with real data after training. Failing to test can lead to inaccurate classifications, affecting your insights and decision-making. Always validate your model's performance before deploying it.
The Verdict
MonkeyLearn is a solid choice for teams needing a no-code solution for text analytics, especially if you're regularly processing customer feedback. However, the steep pricing could deter smaller businesses or occasional users. If you're committed to extracting valuable insights and have the budget, it’s worth a look; otherwise, consider cheaper alternatives.
Best For
- Product teams needing to analyse customer feedback consistently.
- Marketing teams monitoring brand sentiment across various platforms.
- Support teams wanting to efficiently route tickets based on urgency.
- Data analysts looking for quick setup of text classifiers without coding.
- Businesses that regularly handle large volumes of text data.
At a Glance
MonkeyLearn is a no-code text analytics platform that allows users to classify and extract insights from customer feedback effortlessly. Its intuitive interface and pre-built models make it a breeze for teams to analyse sentiment and trends, but the steep pricing could be a hurdle for smaller businesses.
Strengths
- +The no-code interface is a significant advantage, allowing users without coding skills to set up custom classifiers and extractors quickly and efficiently.
- +Pre-built models for sentiment analysis and topic detection save time and provide immediate insights, making it easy for teams to get started without extensive training.
- +The dashboard visualisation helps identify trends over time, which is crucial for product teams looking to understand customer sentiment and emerging issues.
- +Integration with popular tools like Google Sheets and Zendesk allows for automation within existing workflows, making it easier to incorporate text analytics into daily operations.
- +The ability to train custom models means that organisations can tailor the tool to their specific needs, providing more relevant insights compared to one-size-fits-all solutions.
Limitations
- -The pricing structure, starting at $299 per month for the Team plan, can be prohibitive for smaller businesses or freelancers who may not need such extensive features.
- -The learning curve for more complex custom model training might be steep for users without a strong background in text analytics, despite the no-code promise.
- -While there are pre-built models, they may not cover every niche or specific need, requiring additional time to build custom solutions for unique scenarios.
- -The platform can feel overwhelming with its numerous features, which might deter users who are looking for a straightforward, simple solution.
- -Customer support can be slow at times, leading to frustration when trying to resolve issues or get assistance with more complex tasks.
Use Cases
- -Product teams analysing NPS surveys to identify customer satisfaction trends and areas for improvement without needing deep analytical skills.
- -Marketing teams monitoring brand mentions across social media and online platforms to gauge public sentiment and react quickly to potential issues.
- -Support teams routing tickets based on urgency and topic, ensuring that high-priority issues are addressed promptly and effectively.
- -Customer experience teams extracting keywords from feedback forms to better understand customer pain points and areas for enhancement.
- -Data analysts who need to set up text classifiers quickly for ad-hoc analysis, without the need for extensive coding or machine learning knowledge.
