CSAT surveys are the standard way to gauge the happiness of the customer with your product or service. They give customers a voice. CSAT data can be qualitative and/or quantitative. There are customer satisfaction survey designs that gauge the emotional CSAT on a figurative scale, as well as those that calculate definite numeric scores. But what kind of CSAT survey gets you reliable responses and data?
Qualitative Data: The Great Lost Metric For SaaS CompaniesJune 24, 2019
This post originally appeared on UsabilityGeek.
Data doesn’t lie. That’s the general thinking in marketing and product development departments. That adage may be true, but it isn’t quite the whole truth. Data-driven decision-making is a huge part of working at SaaS companies. What gets lost in this narrative, though, is that qualitative data is an extremely valuable and underutilized form of feedback.
We all love traditional quantitative data for its purity: the numbers can tell you about general trends in usage, which features are most useful or which campaign is converting. But what many SaaS companies miss altogether, or worse, take it upon themselves to guess without much external input, is why something isn’t resonating and-even more importantly-how we can create a more engaging experience for users.
Determining the “why” and “how” isn’t relevant only to content or ad campaigns but to the entire customer feedback loop. To help teams get these answers, qualitative data is invaluable.
First: Why the Blind Spot for Qualitative Data?
If qualitative data is such a game changer, why don’t more teams use it? In my experience, it comes down to a lack of resources and not having the right tools. Historically, qualitative data has been much more difficult to interpret because it is so open-ended, and it’s time-consuming to sift through all that written or verbal feedback to find actionable ways to improve your company.
However, using the right tools and approaching qualitative data with the correct mindset can help SaaS companies take their products and services to a new level. Below are three ways that Qualaroo benefits by integrating qualitative data into our feedback stack.
Utilize Sentiment Analysis
When people in the SaaS world think of qualitative data, “inefficient” may be the first word that comes to mind. This is one of the most common arguments in the qualitative data discussion. And it is true that a small team with limited resources may not have the bandwidth to parse through hundreds of qualitative responses on their own.
To help with the heavy lifting of analyzing those responses, Qualaroo relies on IBM’s Watson for help. Watson can interpret all of those individual responses and deliver emotional scores and keyword clouds that provide powerful insight. With this type of real-time feedback, a team could, for example, implement a qualitative survey on a page with a high bounce rate to quickly discern whether customers are leaving because they aren’t interested in this type of product or because they need more information.
Sentiment analysis paired with quantitative data allows teams to be surgical with their actions.
Customize The Experience
Cloud communications company Twilio uses Qualaroo to help improve and customize their clients’ onboarding processes. Because the SaaS company has a diverse portfolio of clients, it is important for Twilio to find ways to customize the onboarding experience to fit the customers’ unique needs.
This customization can be achieved by segmenting customers and adjusting content to provide information at different points in the funnel, delivering a tailor-made experience.
Twilio’s extensive use of qualitative surveys gives them the information necessary to make “data-driven” decisions about segmentation and content. Even something as simple as asking “What brought you to our site today?” can yield data about the goals of the customer and which parts of the product they are most focused on.
Create Systems And Go Deeper
One of the most powerful tools within Qualaroo is its branching logic system, which offers different questions to users based on user research and feedback they’ve already given. It’s almost like a product interview, giving teams the power to isolate audiences and get more immediately actionable feedback.
Let me paint you a picture of branching systems in action with this real-world example:
- Step 1 – Use the numbers: Start with the quantitative data. Let’s say the numbers are showing a 40% bounce rate on a particular product page.
- Step 2 – Ask why: Rather than simply go back to the drawing board and try to guess why the page isn’t getting conversions, a team could instead implement a qualitative exit survey. From the responses, the team could discover that a large percentage of those leaving the product page are leaving because they were unable to find what they were looking for.
- Step 3 – Branch out: Thanks to differentiated logic, this is where branching gets interesting. When a respondent on that exit survey selects “Couldn’t find what I was looking for,” Qualaroo can automatically follow up with another question asking, “What are you looking for?” After gathering just that one additional layer of feedback, you may find that 60% of those respondents were looking for a free trial.
That information distills a once-complex problem down to an actionable item. By simply displaying the free trial option more prominently on this page, a marketing team could generate new leads and new customers almost immediately. Gathering feedback in this way enables you to get to the core of how the customer feels and what they really need from a product or service.
Context Is Key
Qualitative data is an amazing tool to have in your software stack because of its ability to provide context. The numbers tell a story, but the sentiment behind those numbers makes it compelling. Stay curious and ask questions: it could help your team to more efficiently and effectively delight customers every day.
Many people, including some professional researchers, use the words survey and questionnaire synonymously. But, there is a difference between the two terms.
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