Customer Satisfaction Survey Tool, CSAT & NPS (Net Promoter Score) Software (2024)

Customer feedback data is all the information you collect from customers about their experiences with your company. The more data points you have, the closer you come to understanding your customers’ complex, shifting needs and opinions.

Remember: 42% of companies don’t even bother to collect feedback, and many more ignore the full potential hidden in the data. By crunching the numbers and having clear goals in mind, you can stay ahead of your competitors.

Ordinal data is used to rank or order experiences based on a specific attribute.

For example, job application candidates might be asked to rate their experience on a three-point scale: entry-level, mid-level, or senior-level. Age brackets and income brackets count as ordinal data too.

In the world of customer satisfaction, we often work with ordinal data that is expressed numerically. For example, we may ask customers to rank their experiences from “very unsatisfied” (1) to “very satisfied” (5).

You collect ordinal data through surveys with close-ended questions, which means you list all the possible answers and there is an obvious ranking between them. For example, a survey question where the possible responses are 😊, 😐, and 😟 yields ordinal data, and you can assign numerical values to the responses when analyzing them.

How do customer satisfaction metrics interpret ordinal data?

We use customer satisfaction metrics to interpret survey data about customer perception; these metrics are expressed numerically, which means they can be used to:

• Benchmark performance against industry standards or competitors
• Measure the impact of new initiatives on customer satisfaction
• Track progress over time, etc.

Customer satisfaction scores prioritize certain segments of the customer base while ignoring others.

For example, CSAT is calculated by dividing the number of satisfied customers by the total number of survey respondents.
It doesn’t draw a distinction between dissatisfied and neutral customers. By only looking at the overall score, you might miss out on a sudden drop in satisfaction. That could lead to you overlooking a problem employee or a malfunctioning product.

Takeaways:
👍 Always analyze the totality of responses instead of just looking at the overall score.
👍 Use customer satisfaction survey responses to identify unsatisfied customers you need to follow up with.

Low response rates may skew the overall score.

NPS is an extremely valuable metric that measures customer loyalty. However, it is inflexible.

The main question of NPS surveys is “On a scale of 0 to 10, how likely are you to recommend us?” Based on their responses, customers are classified into three categories: Promoters (score 9-10), Passives (score 7-8), and Detractors (score 0-6). The NPS score is calculated by subtracting the percentage of Detractors from the percentage of Promoters.

Flexibility means that the metric doesn’t account for minute changes in a customer’s mood, or for the different cultural contexts customers bring to a survey.

Plainly: the difference between a 9/10 score and an 8/10 score isn’t obvious to customers. The real difference between the sentiments of a Passive and a Promoter may be very small or non-existent. (Similarly, a customer may go from a Detractor to a Passive or back depending on their current mood).
If you have a large enough customer base, these variations even out. But with a limited pool of respondents, you might see big changes in the overall NPS score that don’t necessarily reflect customer sentiment.

Takeaways:
👍 Don’t jump to conclusions if your NPS score changes dramatically. Take a closer look at the responses.
👍 Part of the reason why this metric is structured this way is that it helps you find customers to reach out to. Promoters are likelier to leave reviews, recommend you on social media, agree to a case study, etc. This is true for companies of all sizes! Use the full potential in the data instead of just focusing on beating your previous NPS score or reaching industry benchmarks.

Customers will always be biased.

Ordinal data helps shine a light on what customers prioritize and how they feel about different aspects of their customer experience. However, there are various biases that impact their responses.

For example, the central tendency bias means they’re likelier to give ‘moderate’ scores than very high or very low ones. The length of your survey and the exact language you use can impact the responses you get too. The longer a customer has been with you, the likelier they are to be overly generous with their ratings, etc.

Takeaways:
👍 It takes a while to find the best ways to ask questions from customers, and it’s important to remain flexible.
👍 Ordinal data alone isn’t enough to explain what the customers care about.

You can give the respondents a set of options to choose from, and there is no innate hierarchy between the options.

Example question:
Please indicate the main factor that contributed to your satisfaction rating:

• Quality of the product/service provided
• Quickness of service delivery
• Helpfulness and friendliness of the staff members
• Price-to-value ratio
• Something else (please specify)

When working with the responses, you can internally assign numbers to the responses for ease/clarity, but they don’t carry any numerical meaning.

• “When humans read text, we see mood, sarcasm, innuendo, nuance, and meaning. […] Computers can only “see” and “read” numbers. The mass of unstructured text data must first be converted to numbers and the structured datasets you’re familiar with in order to be analyzed. This process—converting unstructured and potentially messy text with misspellings, slang, emojis, or acronyms into a tidy, structured dataset with rows and columns—can be a subjective and time-consuming process.” –Becoming a Data Head by Alex J. Gutman and Jordan Goldmeier

How do you convert descriptive data into numbers?

• Sentiment analysis means you categorize feedback as positive, negative, or neutral. After that, you can treat it as ordinal data.
• Word frequency analysis requires counting the frequency of specific words or phrases in customer feedback to identify the most commonly used terms. This can help you pinpoint the key issues that customers are talking about. You can use the most frequent words as nominal data.
• Thematic analysis is a way of categorizing customer feedback into themes or topics based on content.

Each of these can be performed manually (by reviewing and categorizing feedback) or automatically (using machine learning algorithms). Automated sentiment analysis is faster and more efficient, but it doesn’t always capture the nuances of customer sentiment.

It’s a good idea to have at least one qualified team member read all responses, so they can notice important observations and share them with the team when appropriate.

Categorization involves grouping feedback data into specific categories based on common themes or topics. These categories could be predefined by you and your team, or they can be created later (based on the data you have).

With Simplesat, you can add tags to survey responses to improve organization. Types of categories you could use:

• Bug mention – you can narrow it down to the specific bug if it’s a recurring problem
• For review – what does your team need to do about a response? How does it enter your workflow?
• Why did the customer leave this feedback?
  Example tags:
  • product quality
  • support
  • professionalism
  • turnaround time
  • pricing website
  • usability
  • request for update/new feature/new product
• Junk – sometimes, users respond to open-ended questions with nonsense text like keysmashes; this could skew your data if you don’t categorize it and remove it from the data set.

It’s important to be flexible and thorough when categorizing feedback data. Be prepared to create sub-categories if necessary, and make sure every team member is on the same page about the category labels being used.

Filtration and segmentation are used in processing customer feedback data. They help you identify trends and patterns that might not be visible in the overall data.

While they are related, they serve slightly different goals. They’re often used together to achieve higher accuracy.

Filtration involves narrowing down the data by applying filters based on specific criteria. For example, if you want to analyze customer satisfaction data for one particular product, you can use filtration to isolate responses related to it. This helps to eliminate noise and focus on the most relevant data. Filtration can also be used to exclude data that is irrelevant, such as responses from customers who are outside your target demographic.

Segmentation, on the other hand, involves dividing the data into meaningful groups (segments) based on shared characteristics. For example, you could segment customer feedback data by demographic information, such as age, gender, or location. You could also group customers based on their behavior, such as frequent purchasers or first-time buyers. You can tailor your strategies as you learn more about the priorities and pain points of each segment.

⚠️ A common pitfall with filtration and segmentation is over-reliance on a single criterion. This can lead to a biased interpretation of the data. Plus, too much segmentation can lead to small sample sizes and unreliable conclusions. It’s important to strike a balance between meaningful segmentation and maintaining an adequate sample size.

Once the data is filtered and segmented, it’s helpful to visualize the results using tables, charts, or graphs. This helps you highlight key insights and trends in customer feedback.

For example, a line chart might show how a specific customer satisfaction metric has changed over time. A bar chart can show which products or services are most commonly associated with positive or negative feedback, while a pie chart shows the distribution of responses for a particular question.

Leaderboards are another quick and easy way to parse customer feedback. They show the top performers or results within a specific category. For example, leaderboards can be used to identify the highest-rated products or services based on customer feedback. You could also use them to highlight the most positively reviewed members of your team within a particular time period.

Descriptive data (e.g. comments) can also be represented through visualization! Word clouds are a striking way to draw attention to recurring keywords and they can give you a quick general impression of customer priorities. You can use bar charts to represent which words are likely to appear in positive reviews and which ones are common in negative feedback.

Crosstabulation (also known as contingency tables or cross-tab) is an important method of analyzing and comparing the relationships between two or more variables.

It can be used to analyze the relationship between different customer segments and their satisfaction levels with specific products or services. For example, you could use crosstabulation to analyze the relationship between customer age and satisfaction with a specific product. The resulting matrix can help identify trends and patterns – and you might discover a blind spot in your product development or marketing.

Longitudinal analysis is a method used to track changes over time in a specific variable. By identifying trends and patterns in customer feedback, you can make informed decisions about how to improve your product/service.

For example, you could track changes in your NPS score across several years. Compare changes in the score every month/quarter/year, look for trends, and compare the results with industry benchmarks or internal goals.

You can use segmentation along with longitudinal analysis, in order to identify which demographics are most satisfied or dissatisfied with your product and how satisfaction levels keep changing over time.

Longitudinal analysis doesn’t always require deep knowledge of statistics: you are simply tracking a metric over regular time intervals. But there is a significant pitfall to keep in mind.

You’ve heard the warning that correlation doesn’t imply causation. That is true in all aspects of customer feedback interpretation. But it’s especially easy to fall into this trap when you’re looking at changes over months or years.

If customer satisfaction scores increase after a new product is released, it’s tempting to attribute the increase only to the new product. But the change may be attributed to other factors, such as changes in your marketing strategy or external factors like the economy.

This is a technique used to examine the relationship between one dependent variable and one (or more) independent variables.

By identifying the factors that have the greatest impact on customer satisfaction, your business can develop targeted solutions to improve the customer experience. You can use regression analysis to make data-based predictions for future changes in customer satisfaction.

However, this method is complex and requires a higher degree of statistical knowledge. It works best in conjunction with visualization, crosstabulation, etc.

Reporting customer feedback data involves summarizing the data and presenting it in a way that’s easy to understand. Suggesting actionable goals is a crucial part of reporting. The goal is to provide insights into the customer experience and to identify areas where improvements can be made.

Here are some key elements to consider when reporting customer feedback data:

• Summary: begin with a brief executive summary that provides an overview of the key findings and recommendations in your report. Highlight the most important insights.
• Methodology: describe the methodology used to collect the data, such as the questions you used, the sample size of your survey, and the rate of customer responses. This will help your audience understand the context of the data you’re presenting – as well as the limitations of your analysis.
• Key findings: summarize your key findings based on customer feedback data. These may include common themes in customer feedback, areas of high or low satisfaction, and trends over time. Use charts, graphs, and tables to visualize the data.
• Recommendations: based on the key findings, provide recommendations for how the company can improve customer satisfaction rates. These recommendations should be specific, actionable, and tied to the data.
  Conclusion: conclude the report with a brief summary of the key takeaways and immediate steps to take.

When reporting customer feedback data, it’s important to keep your audience in mind. Naturally, a report for customers should focus on how their feedback is being used to improve the customer experience. A report for investors is more likely to focus on how the company is using feedback data to drive business growth.

In conjunction with operational data (such as customer demographics and churn rates), you can use customer feedback for many different forms of improvement, including:

• Root Cause Analysis (RCA): finding the source of customer dissatisfaction.
• Tailoring products, services, and marketing strategies to different customer segments.
• Setting goals and targets based on clear data.
• Predictive analytics: analyzing historical customer satisfaction data to uncover patterns and trends that help predict future customer behavior and satisfaction levels.

In addition to more formal periodic reports, you want to be as transparent as possible about the customer feedback you receive.

At Simplesat, we offer a number of customizable feedback widgets that let our users share customer feedback as soon as it comes in. We encourage sharing CSAT and NPS scores with teams and customers alike.

In our opinion, this is the most important use of customer feedback.

Customer satisfaction data can be a powerful tool for internal improvement. Here are some ways to use it to increase team cohesion and improve your product/service:

🔄 Automate sharing customer feedback with the team
Research by Gallup has found that frequent feedback boosts employee performance, and this is true for positive and negative feedback alike. While customer feedback data can’t and shouldn’t replace guidance from managers, it can complement it. Automatically sharing feedback data (especially comments) can give your employees the boost that they need to excel.

🎯 Make customer feedback a part of team goals
Incorporate customer feedback data into all your team goals and performance metrics. For example, you could set a goal for improving your team’s NPS score by the end of the upcoming quarter. Use customer feedback data as a regular agenda item in team meetings. This will keep customer satisfaction top of mind, and it encourages team members to think about how their work impacts the overall customer experience.

🎉 Celebrate successes
When the team receives positive customer feedback, celebrate it! A good score is often the result of teamwork behind the scenes, and every team member deserves to bask in the praise. If your office culture encourages healthy competition, you could also share leaderboards of the highest-scoring team members.

🤔 Identify areas for improvement
Use customer feedback data to identify areas for improvement. In the process, you will build better customer relationships with the customer base. Customer feedback data can tell you what you need to rethink, as well as which features customers want to see more of.

While your customers can’t always pinpoint the root of a problem, they’re happy to tell you the symptoms. On the other hand, if something you did made a lasting positive impression on multiple customers, it’s worth exploring its potential to the fullest.

🧪 Experiment boldly
Using customer feedback to experiment allows you to validate assumptions and test hypotheses before committing significant resources to a new initiative.

Based on feedback data, you can develop clear hypotheses about what changes you can make to improve your service/product. Then you can conduct A/B tests, pilot new features or use other testing methodologies to evaluate the impact of potential changes. Customer satisfaction surveys are integral in collecting and evaluating the results of these tests.

🎓 Use customer feedback as a learning/teaching opportunity
Customer satisfaction data lets you identify areas where the team can improve – that means you should consider providing training/coaching sessions for team members. At the same time, some customer feedback points to areas of confusion among the customer base (especially in the case of software companies whose products require some technical knowledge). That could mean you need to offer better help documentation or a free webinar about your product.

We’ve known for a while that customer satisfaction is impacted by employee satisfaction. But there is also a growing awareness of the fact that frontline employees have insight into customer satisfaction beyond what you can gain from surveys.

When you have a thorough understanding of customer satisfaction data, it becomes easy to create a dialogue with team members from every department. Your employees can help you discover the full context behind the numbers.

The feedback data above shows you which questions get low response rates. When that is the case, it’s possible that the question is confusingly phrased or boring; there’s also a chance the survey is too long. Make sure to eliminate any redundant questions, and make your questions skippable when possible. That makes customers less likely to abandon the survey halfway through.

If a survey question tends to receive only positive responses, you might be asking a leading question. Experiment with phrasing and track the changes in the responses you get.

Analyzing data from open-ended survey questions and social media can help you identify the phrases and themes that resonate most with your customer base. This is a good foundation for asking more targeted questions. By using the same expressions your customers use, you build more authentic customer relationships.

As you filter and segment your customer feedback data, patterns begin to emerge. Different parts of your customer base have different priorities and you can personalize future survey questions accordingly.

Data collection is a never-ending process, and there is no such thing as having enough data. With each new data point, you gain more insight into your customer base, and you can make better decisions accordingly.

This McKinsey article is a must-read for decision-makers who want to make the most out of customer feedback. We want to emphasize this quote:

• “Businesses often agonize over whether they have the right metric. But our research shows that whether a company is using a net promoter score, customer-satisfaction score, customer-effort score, or another popular metric of the day, it matters less which score customer-experience managers choose than what they do with it.”