The Displayr Guide to Customer Feedback Survey Analysis
Welcome to our complete guide to customer feedback surveys!
If you’re looking to better understand your customers and make data-driven decisions based on their needs, then you’ve come to the right place. We’ll take you through the entire process, from designing your survey to interpreting the results.
An Overview of Customer Feedback Surveys
Let’s start with a simple question: what is customer feedback?
Customer feedback can come in many different forms. Broadly defined, it is any information provided directly by customers about their thoughts on your company, brand, product, or service. A retail company could prompt customers for a rating after their product is purchased, and also scan review aggregators for more detailed feedback. A website could ask for a simple thumbs up/thumbs down reaction from users, and follow-up with a longer survey. These are all useful forms of customer feedback.
While there are many ways to gather customer feedback, this guide will focus specifically on customer feedback surveys.
What is a customer feedback survey?
A customer feedback survey can take many forms. It can be as simple as a Yes/No question, and as complex as a multi-level questionnaire. The size and structure of your survey should depend on your product, your user-base, and the kinds of insights you’re seeking from your customers.
Customer feedback questions can be divided into two broad categories:
- Open-ended questions: those that allow users to answer in their own words. This usually produces more detailed responses but can be difficult to analyze.
- Closed-ended questions: those that have a restricted response format, like multiple choice or a 0-10 scale. The most common types of closed-ended questions are Net Promoter Score (NPS) and Customer Satisfaction (CSAT) questions.
Why customer feedback surveys are important
Customer feedback can inform your decision making in many ways. It can identify groups that are vulnerable to churn, pinpoint parts of your product that require improvement, and even unearth some fresh ideas you would not otherwise consider.
Acquiring a new customer is almost always more expensive than retaining an existing one, so it makes sense for every business to make customer retention a key priority. By carefully and regularly analyzing your customer feedback data, you can better understand the needs of your customers and increase brand loyalty and customer retention.
Before diving in, you need to ask yourself two questions:
- How many questions should I ask?
- What questions should I be asking?
There are no definitive answers to these questions, but here are a few tips and pointers to get you started.
Asking the right questions
Right out of the gate, there are two closed-ended questions you should consider:
- Net Promoter Score: How likely are you to recommend this product/service/company?
- Customer Satisfaction: How satisfied are you with this product/service/company?
These two basic questions have a long history in market research, and they can form the backbone of your customer-relationship strategy. Other closed-ended questions regarding specific attributes of your products and comparisons to your competitors can enrich the overall feedback you receive.
Open-ended questions can allow users to elaborate on a response they gave to a closed-ended question. For example, you can start with a closed-ended question like, “On a scale of 1-10, how would you rate this product?" and follow up with an open-ended question like, “What did you like about the product”. The combination of closed-ended and open-ended questions allows the customer to paint a complete picture of how they feel about your brand, product, or service.
Limit the number of questions in your survey
It is tempting to ask as many questions as possible in a survey. After all, more feedback means more information. But asking too many questions in a survey often frustrates respondents and can drastically lowers response rates. As a general rule, you should aim for under 10 questions in a feedback survey. If a question isn’t absolutely essential, get rid of it!
Ask well-defined questions
Your questions should not be ambiguous or open to interpretation. Customers should immediately understand what is being asked and should be able to easily respond. Related questions should be grouped together to avoid bouncing from topic to topic. If you ask how satisfied a user is with a product, you can continue by asking questions about specific components of that product. This technique is often referred to as "drilling down."
Common Do’s and Don’ts
|Do Ask...||Don't Ask...|
|The same questions regularly||Leading questions|
|Both closed and open questions||Non-essential questions|
|Demographic questions||Too many questions|
Sending out your survey
Once you’ve designed your survey, it’s time to release it into the wild!
Here are some questions to consider:
- Who should you survey?
- When should you survey?
- How often should you survey?
How to collect feedback from customers
The first thing you need is a contact list of users and customers to survey. Hopefully, you already have a collection of email addresses from users and customers that you can use. There are many ways to contact your customers. Using a Customer-Relationship-Management (CRM) system to issue your survey is a popular approach, and there are also many third-party options to choose from. Many companies also have a built-in pop-up survey that prompts users on their website.
When to survey
A survey can be issued directly after a transaction is made, or sent out to users at regular intervals. A survey sent after a transaction should try to gauge a customer’s first impression and perhaps ask how they found out about the product. A regular survey should ask questions that can be used to track customer feedback metrics over a longer period of time and measure their overall sentiments.
How frequently you should survey depends on your product and your customers. If your product is constantly changing, then you want to continuously track the response to each major change. And if your customer-base is expanding, then you should be eager to find out more about your new users.
Who to survey
Though the terms are often used interchangeably, there is an important distinction between a “user” and a “customer.” A customer is already paying for your product, while a user may be undergoing a free trial or using a freemium option. It is vital to survey both groups. Retaining paying customers and convincing non-paying users to become paying users are both crucial to your success.
Survey sample sizes
Major studies usually require a sample size of at least 100 participants. But of course, that is not always possible in the real world. When it comes to surveys, more participants is always better. A larger sample size delivers stronger conclusions and usually produces more statistically significant results. You can maximize your sample sizes by making sure you are always collecting email addresses from users, customers, and potential customers. In the meantime, try to design a survey that maximizes response rates.
Increasing response rates
It can be notoriously difficult to convince a customer to fill out a survey. They can be time-consuming, confusing, and complicated. You can improve response rates by making your survey as convenient as possible for your customers. Restrict the number of questions, remove any ambiguity from survey questions, make sure it's functional on all devices, and make the survey feel intuitive.
Preparing your survey data
Consider yourself extremely fortunate if your data is ready to analyze without any data preparation. Chances are there are a few issues that need to be taken care of before diving into the analysis process.
Dealing with missing data
Missing data is a common problem in real-world data analysis. Perhaps a question doesn’t apply to a subset of customers (how old is your child?), or maybe a question makes some customers uncomfortable (What is your annual income?). Before determining how to deal with missing values, we must first determine why they are missing in the first place.
A common approach is to simply exclude missing values from the analysis. A more sophisticated approach is to use statistical methods to predict missing values based on other available responses in the dataset. Whatever you decide on, Displayr can do the heavy lifting with its built-in functions to deal with missing data.
Cleaning messy data
There’s a good chance your data set will contain some formatting issues. Perhaps a numerical score is stored as text, and maybe your text contains HTML tags. Displayr can make informed decisions on how your variables should be formatted and will take care of many issues behind the scenes, but it’s always good to always make sure your data is correctly formatted before you start analyzing your data.
Displayr can recode variables into formats that are specific to market research, like Net Promoter Scores and Top Box Scores. It also automatically detects the names of states, countries, and geographic regions.
Analyzing your survey data
Finally, it’s time to analyze! This is where the magic happens. It’s where data is transformed into information, and information into insight.
Calculating NPS and Customer Satisfaction
Customer Satisfaction and Net Promoter Score are two of the easiest metrics to calculate. The Top 2 Box score is the commonly used measurement for customer satisfaction, though a simple mean or median can also be used.
The Net Promoter Score follows a more rigid formula:
Percentage of Promoters – Percentage of Detractors
- Detractors: respondents who gave a score between 0-6
- Passives: respondents who gave a score between 7-8
- Promoters: respondents who gave a score between 9-10
Customer Satisfaction and NPS are great starting points for your customer feedback analysis.
Before rushing to judgment, take a moment to research the benchmarks for your industry. You can gauge where you stand by comparing your survey results to industry averages.
The best way to place your results in context is through interactive data visualizations. Click on the button below to learn how to visualize customer satisfaction scores with a column chart.
Data modeling and algorithms
Let’s take a step beyond NPS and Customer Satisfaction Scores. There is a whole world of data modeling techniques and algorithms that can be used to analyze survey data. Regression modeling can be used to identify the drivers of customer satisfaction, and cluster analysis can show how feedback varies across different market segments.
You can harness the power of cutting-edge machine learning algorithms without writing a line of code. Displayr has built-in functions to run the models for you. Click the button below to learn how to perform Driver Analysis using a regression model.
Sentiment analysis and Topic Modeling
When it comes to open-ended questions, conventional data algorithms can only do so much. When it is no longer feasible to manually curate and code responses, there are text processing algorithms you can use to help categorize feedback. Sentiment analysis can measure the tone of your survey responses and topic modeling techniques can identify common groups of words and topics.
These algorithms have their limitations and can rarely replace human judgment, but they are extremely useful when used alongside traditional coding methods. Click the button below to learn how to calculate sentiment scores with Displayr.
Data visualizations do two things: they present your findings in a visually appealing way and help you better understand your own data.
There are many ways to visualize your results in Displayr. A time-series line chart is a great way to demonstrate how satisfaction scores have evolved over time, and a clustered column chart can be used to compare those scores across different age groups. A correlation chart can illuminate the main drivers of satisfaction, and a donut chart can show the make-up of satisfied customers.
To find out what works with your data, you’ll need to get your hands dirty and try them out for yourself. Click the button below to learn how to visualize geographic data with Displayr.
Monitoring customer satisfaction over time
Customer feedback data is especially useful when tracked at regular intervals over a prolonged period of time. By monitoring your NPS and Customer Satisfaction scores, you can guage reactions to specific changes in your product, company, or brand. It also gives you more data to work with. A single survey result could turn out to be an anomaly, but the aggregate of results over a lengthy period should be fairly accurate.
Click the button below to learn how to visualize satisfaction scores over time with Displayr.
Insights and Actions
So you’ve made sense of your customer feedback data. Now it’s time to communicate your results and take actions based on your findings.
Sharing your results
The adage “Show, don’t tell” holds true in market research. It’s always better to show how satisfaction scores have changed over time than to simply state the changes. Make full use of model outputs and data visualizations to present your findings.
Be mindful of your target audience. A report for customers and the general public is intended to showcase your results and let customers know that their voice is being heard. A report for management should be tailored to driving future success.
The insights you gather from your customer feedback surveys should guide the way you move forward. You should identify groups within your customer-base that are vulnerable to churn and find the sources of their dissatisfaction. Find groups with high rates of satisfaction and search for potential customers with similar traits. With the knowledge and insights gathered from your survey results, you will be able to make informed, data-driven decisions to guide you into the future.