Approaching your questionnaire with the right principles in mind and tools in hand will produce easily-understood results packed with actionable insights.
In this guide you'll be led through the basics behind questionnaire data, then move on to a step-by-step approach for analyzing your responses.
Survey data, aka questionnaire data, is data collected during a survey campaign. This data can be analyzed and broken down, yielding statistics and insights that can be used to boost business.
The end all be all of customer feedback collection, whether questionnaires, online reviews, or other data, should always be the improvement of your overall customer experience for the benefit of existing and future customers.
The modern market has shown customer experience (CX) to be the number one differentiator between competitors. A large amount of this is by virtue of active customer experience management's attentiveness to existing customers -- companies who are able to convert existing customers into 'Promoters' (on the NPS scale) improve their lifetime value by 6 to 14 times according to Bain & Co.
This is especially relevant when it comes to customer surveys as surveys are invariably distributed to existing and/or past users. The data they collect and the insights they derive apply directly to the customer journey.
By actively listening to the voices of your customers and analyzing survey data you are getting strategic tips from the best, and most honest, possible source.
Close-ended data is what people think of first when they imagine a survey result. It is data that translates directly into numbers. The 'big three' feedback questions (NPS, CSAT and CES surveys) all start with a close-ended question. They vary in format, with CSAT being a yes/no binary, NPS a 1-10 scale and CES a 1-5, but the responses can be tabulated in a straightforward manner and analyzed using basic software such as Excel.
From there, close-ended data can be interpreted using basic statistics to derive clear insights. This is basic survey analysis, and there are a ton of tools out there to help you quickly and effectively break down, cross tabulate, and display your results.
Open-ended data is the 'why' behind your close-ended metrics, and for this reason it is key to excellent questionnaire analysis.
You know those additional written comments at the end of surveys? Those are open-ended questions. Throwing out these responses means missing out on the context behind whatever rating the customer is giving you.
The next logical question is, 'How can I measure text-based responses?'.
Until a few years ago, each dataset's answers would require manual tabulation, which is both tedious and inaccurate. Now, with the power of machine learning and utilizing techniques such as sentiment analysis and keyword extraction you can interpret your open-ended responses right alongside your close-ended metrics at scale, and in real time.
Having the right customer feedback analysis tools at your disposal can help make sure your survey analysis approaches, both close and open ended, are properly paired and integrated. This is crucial as losing which open-ended comment is tied to which close-ended score can mean losing the depth behind that data, making accurate analysis impossible.
That in mind, let's move on to the main course, our step-by-step approach to survey data analysis.
We landed on these particular steps because they convey a clear journey from the inception of your survey campaign to the implementation of your survey's insights.
An easy first mistake some businesses make is not knowing what they are looking for out of their survey. This of course directly affects the question(s) you are going to ask within your survey.
So, to form the best possible question and get clear answers, interrogate what you are looking for. Are you curious as to customer opinion of your price point? Or is it something else entirely.
Deciding on the main goal or goals of your survey before distributing it ensures that you will, at bare minimum, answer your main concerns. That is not to say drilling down on what you are asking limits the possibilities of your survey. With additional comment or thought bubbles for customers to fill out, yielding open-ended response data, you are sure to uncover other, related but hidden, trends. But clarity as to purpose makes sure you don't confuse yourself, or worse, your customers with your survey.
Cross tabulate is just a fancy word for filtering your survey so that you can compare customer groups aka subgroups. Think of it as the process of sorting your data by demographic so that you can unearth trends.
Take a look at this table for instance which reflects the answers to whether attendees of a conference think they will attend again next year, breaking the answers into three sub groups (Administrators, Teachers, and Students):
What at first might have remained hidden if you only looked at the total percentage that wanted to return now becomes clear.
Administrators, as reflected in their 40% 'No' responses and their 46% 'Yes' responses (compared to 86% Students and 80% Teachers) clearly didn't get what they were looking for out of the conference.
Curious questionnaire/survey analysis is good practice -- by taking a deeper look at the data, in this survey's case, uncovered a hidden trend. However, referencing our first step, this wouldn't be possible without asking the right question and keeping track of the three distinct demographic groups.
With this discovery in hand, it would be wise to continue to compare and contrast your data. This could also be a form of benchmarking -- meaning viewing your data in contrast to other surveys. You could compare the number of attendees this year to those in the ten years previous, and, if possible, isolate the subgroups from those years (if they were surveyed). Doing so would let you know which years were most popular with each subgroup.
Now it's one thing to know the Administrators, in this year's case, were the least likely to come back, and quite another to know what made them feel this way. Here's where those pesky open-ended questions come in, and why they are so critical to obtain and dissect.
While this is third in our list it really needs to be a priority from the jump. Taking every step possible to solicit written feedback will truly take your questionnaire/survey campaigns to the next level.
Attaching open-ended questionnaires to your survey campaigns will add depth to your data and inform you of the 'why' behind your scores.
Luckily, it's easier than ever with advancements in artificial intelligence. Which brings us to our next step, accurately and effectively analyzing your data.
Machine learning-backed software, such as Monkeylearn takes heaps of text data and transforms it into objective insights.
These, and other open-ended analysis techniques such as topic analysis make sure you get the absolute most of your data, deepening and adding context to your extant quantitative data. These include plug-and-play templates, designed for no-code users to be able to access and mold questionnaire data - Monkeylearn even offers a ready-made survey data template - book your demo today and try it out for free.
Insights are worthless if they cannot be conveyed to the appropriate decision-makers. Look no further than complete visualization suites to get the graphs, stats, and charts that keep modern businesses ahead of the curve.
Monkeylearn's all-in-one dataviz suite, as seen below, embraces the ideal that best-practice visualization means having up-to-the-minute data visualization at your fingertips at all times.
If you have all the right graphs, and the ability to transform them at all times, you are able to deliver whatever graphs you need to your strategy times, rest assured that they are up to date and accurate.
Here is where we double down on the difference between insight and market data. Insights are the end product of any well-run questionnaire/survey campaign. But they require diligence in regards to what kind of questions you are asking and how deep you are digging to get actionable answers.
Great survey analysis/questionnaire campaigns ensure the applicability of their end data by maintaining a clear idea from the start of what kind of consumer insights they are looking for, while taking care to find the reasons behind their data via open-ended analysis along the way.
Just like that, if applied with care, you have an effective methodology for questionnaire analysis.\ Monkeylearn is here to help with the most powerful survey analysis software. Sign up for a free demo with one of our data analysis experts to get a custom model built for your business, or jump right in with a free trial today.
March 24th, 2022