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.
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, it’s time to put sentiment analysis into practice. After reading this section, you’ll have every necessary tool to gather and download NPS responses and analyze them with your own sentiment analysis model. And the best part; you don’t need to have any previous machine learning or coding knowledge. With no-code software, like MonkeyLearn, sentiment analysis is accessible to everyone.
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. With other survey tools, such as Delighted, you can select the data range you want to analyze and export it as a CSV file. 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 Wootric, Medallia, and Qualtrics. Once you’ve gathered all the data from NPS responses you want to perform sentiment analysis, it’s time to execute.
Whether you’re aiming to perform sentiment analysis on your NPS surveys through Retently, SatisMeter, Wootric, or a plain and simple CSV file, MonkeyLearn offers a super easy-to-use platform to train and use your own sentiment analysis models.
We’re going to go through two step-by-step tutorials that will provide you with all the information you need to perform an aspect-based sentiment analysis on your NPS responses.
Aspect-based sentiment analysis not only detects how people are talking about your products or services, but also determines which aspects or topics they are talking about.
For instance, aspect-based sentiment analysis allows you to focus on NPS responses related to specific topics, like customer support, quality, and pricing, and discover the overall sentiment for each of these topics.
First, you’ll need to create a sentiment analysis model:
1. Create your model:
2. Choose ‘Sentiment Analysis’ as the classification type for your model:
3. Upload your NPS responses:
Now, you’ll need to upload the data you want to train your new sentiment analysis model. MonkeyLearn lets you choose Excel or CSV files, as well as data from apps like Promoter.io, Zendesk, Front and many others:
4. Train your NPS Sentiment Analysis model:
Next, you’ll start training your new sentiment analysis model for NPS answers. Sentiment models are preset with three basic tags for categorizing text data as Positive, Negative and Neutral.
To train your sentiment analysis model, simply tag each example from your uploaded NPS data according to the expected sentiment:
After you tag the first examples, you’ll start seeing auto-tagged responses thanks to the power of machine learning. Keep in mind that your new model will make some mistakes at this early stage. As you provide it with more examples, its accuracy will begin to improve significantly:
5. Testing your sentiment model:
Once a minimum amount of samples have been successfully tagged, MonkeyLearn will ask you to name your new model and give you the option to test it or keep training it:
Once you’ve named your model, you can choose to start testing how much the model has learned from the examples you’ve provided it with, or keep training it for improved accuracy:
6. Put your sentiment model to work
Once you are happy with the results of your model, you can use it to analyze new NPS responses in bulk. You can easily upload a file with new NPS responses by going to the “Batch” option on the “Run” tab of your model. There you will be able to choose an Excel or CSV file to analyze its contents to your sentiment analysis model:
Otherwise, and provided you have a team with coding expertise, you can use the MonkeyLearn API to tag data programmatically using coding languages like Python, Ruby, Java and more:
Now, let's train the other half of aspect-based sentiment analysis! Creating an aspect classifier is a very similar process to creating a sentiment classifier:
Now, it’s time to type out the tags we want our aspect classifier to work with. In this case, we’ll use the same text examples as in the sentiment analysis model tutorial we just saw, so we’ll choose Pricing, Performance, and Support:
Next, we’ll use the NPS answers we’ve uploaded to train our aspect classifier model, and tag them correctly. This helps the algorithm understand the defined tags:
Finally, we’ll test our new classifier model by feeding it new text to tag. You can try copying some NPS responses, or just make something up to see how accurate your model is. If you detect mistakes in the criteria, you can always go back to the “Build” tab and keep manually tagging more samples:
Once you are happy with the results, you can upload fresh batches of NPS responses, with an Excel or CSV file, for your aspect classifier to analyze and tag. Just go to Run > Batch.
And that’s it! Try building your own sentiment analysis and aspect classifying models. Just head over to MonkeyLearn, sign up for free, and begin enjoying the wonders of machine learning.
Now that you have everything you need in order to start building your own sentiment analysis and aspect classifying models, streamlining the whole process with a tool like Zapier can absolutely change your workflow forever.
For instance, incoming NPS responses can be automatically sent over for analysis, and the results added to a Google Sheet spreadsheet without a single, team member getting involved in this otherwise tedious process.
Zapier offers more than 1000 app integrations that you can use with MonkeyLearn, and tons of preset Zaps (workflows that connect your apps, so they can work together), based on apps like Retently, Promoter.io, Delighted, Satismeter and Wootric.
For example, you could automate the whole process of sentiment analysis and aspect classification of NPS responses with Zapier using Retently by setting a Zap that:
You can set this Zap very easily, without a single line of code!
Would you rather scan a big spreadsheet showing the results obtained from aspect-based sentiment analysis or, instead, see the results beautifully illustrated as charts and simple graphs?
Fortunately, there are several data visualization apps that help you interpret results quickly, and in a way that the wider team can understand.
With the following data visualization tools, you can 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. This is very convenient if you need to present all the data gathered by these models quickly, and in an appealing way for an upcoming presentation.
MonkeyLearn not only provides the machine learning tools to automatically analyze your data but also visualizes your insights in a striking dashboard. Discover MonkeyLearn Studio and request a demo to see how you can go from analysis to visualization all in one place.
Google Data Studio
Google Data Studio is one of the most popular data visualization tools for a number of reasons, but mainly because it’s free and easy to use. For instance, you can connect hundreds of different data sources and upload them in no time.
After that, Google’s tool allows you to choose from a wide range of charts, graphs and other ways of visually showcasing all of your data. Sharing, embedding and presenting your latest open-ended NPS answers is also a pretty handy function.
Here’s a tutorial on how to get you started with Google Data Studio if you’re not familiar with it.
Looker allows companies to display huge amounts of data, visually and analytically, and with a large number of connected apps. One of the things that makes Looker the go-to data visualization platform for countless businesses is the fact that it focuses on showing “the bigger picture”, providing a deep view of metrics and KPIs stemming from huge databases, which are impossible to analyze manually.
Imagine you have to process a year’s worth of open-ended NPS answers that have already been run through an aspect-based sentiment analysis model. Finding a way to visually represent all of that data is going to take a while, without the help of data visualization tools. With Looker, you can easily feed the system your analyzed data and transform it into engaging presentations.
You can check out this video about how to create and configure a new project with Looker.
This tool offers different functionalities for different company sizes. Individual analysts, teams, organizations, and corporations can satisfy their specific data visualization needs with Tableau. Data prep, analytics and cloud storage are some of the features offered by Tableau, which can be used by anyone, regardless of their knowledge base.
But developers can also find what they need in Tableau’s service, which provides dev tools for an in-depth approach of data analytics. You can try the platform out with a free trial, or select one of its diverse plans, starting at $12 for a basic viewer.
Take a look at some very handy tutorials for Tableau right here.
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