It’s late in the day and your customer experience team receives a batch of the latest NPS (Net Promoter Score) surveys. Everyone’s eager to see if last quarter’s customer satisfaction scores have improved, and what customers are saying about your company’s products and services.
So, they calculate the latest average NPS score, and compare it to the one from the previous period. Easy, right.
Now, for the second step. Your team has to manually read every open-ended response and tag ‘what’ customers talked about and, most importantly, ‘why’ or ‘how’. This is the only way to understand why customers left the scores they did. For example:
Sending NPS surveys is arguably the best way to discover how customers perceive your business, in just two simple steps.
But manually sorting through thousands of open-ended answers creates a long list of problems and setbacks.
Fortunately, there’s a solution. You can automate survey analysis on open-ended responses using sentiment analysis.
Sentiment analysis is a natural language processing technique that uses machine learning to automatically understand opinions in the form of written or spoken language.
Sentiment analysis can automatically sort through huge amounts of NPS responses and tag them for you, helping you save time and money, and freeing customer support teams to focus on more fulfilling tasks. You'll also gain up-to-the-minute actionable insights, so you can make data-based decisions on the go.
Humans are also biased, which means that when your team dives into hundreds or thousands of NPS responses, each member of staff will apply slightly different criteria when tagging – simply because they’ll have different views of what’s positive, negative, and neutral.
By handing over the tedious task of manually sorting and tagging open-ended NPS responses to sentiment analysis models, you’ll:
Long-term benefits include:
Now that you have your NPS responses in a spreadsheet, the next step is to analyze them for sentiment.
With MonkeyLearn's Templates, you can upload your spreadsheet data and our sentiment machine learning models will do the rest. In the tutorial, below, we’re going to show you how to perform aspect-based sentiment analysis. This analysis will detect how people are talking about your products or services and which aspects or topics they are talking about.
For instance, you might discover the overall sentiment for specific topics, like customer support, quality, and pricing.
Templates are super simple to use and perfect for anyone who’s new to machine learning.
Here’s how MonkeyLearn’s NPS Survey Template works:
The first thing we’re going to do is to gather the NPS responses we want to analyze with a sentiment analysis model. Almost every NPS tool available today provides fast and easy ways to do this by exporting and downloading either a CSV or Excel file to your computer. Other popular tools such as Zapier and Google Sheets provide useful integrations that’ll allow you to easily gather Net Promoter Score responses for later analysis.
For instance, Retently offers a versatile export feature with alternatives for filtered and complete data. Their platform also lets you choose which aspects of your customer’s responses to export for a later NPS sentiment analysis. You can select to export just your customers’ responses, and Net Promoter Scores in order to cut down the data flow and keep the key indicators you’re looking for.
Promoter.io has also developed a pretty intuitive interface for exporting both survey and feedback data. For people with experience managing data using code, SatisMeter offers a handy API to export NPS responses in either CSV or JSON formats.
Similar benefits can be obtained from other customer support software providers like Medallia. Once you’ve gathered all the data from NPS responses you want to perform sentiment analysis, it’s time to execute.
Now that you have your NPS responses in a spreadsheet, the next step is to analyze them for sentiment.
With MonkeyLearn's Templates, you can upload your spreadsheet data and our sentiment machine learning models will do the rest. In the tutorial, below, we’re going to show you how to perform aspect-based sentiment analysis. This analysis will detect how people are talking about your products or services and which aspects or topics they are talking about.
For instance, you might discover the overall sentiment for specific topics, like customer support, quality, and pricing.
Templates are super simple to use and perfect for anyone who’s new to machine learning.
Here’s how MonkeyLearn’s NPS Survey Template works:
Choose the NPS template to create your sentiment analysis workflow. As mentioned earlier, this template also combines topic analysis and keyword extraction to get even more granular insights.
If you don't have a CSV file:
Here are the field you’ll need to match up:
Our data visualization dashboard allows you to display your results in convincing, striking detail. Quickly identify critical changes to your company’s NPS trends, determine what aspect has the most impact on your score and understand what aspect of your product or service needs to be urgently taken care of.
Machine models that have been trained to detect opinion units are much more precise when it comes to analyzing data. Why? Well, it’s a lot easier for a machine to understand a sentence with one sentiment, than it is to understand a sentence containing multiple sentiments.
Nowadays, machine learning and the power of automated language processing has become an accessible tool for anyone wanting to increase productivity and reduce the time spent by manually going through tons of Net Promoter Score responses, without the need of a programming or machine learning expert.
If you’re interested in building your own aspect-based sentiment analysis models for NPS surveys, sign up to MonkeyLearn for free.
Have any questions? Feel free to request a demo and we’ll reach out right away!
March 31st, 2019