Survey analysis helps businesses identify gaps in their customer experience strategies and fix them in time. Let’s explore more about the concept with a simple example.
Imagine that a multi-million dollar company launches new software, carefully researching the market. However, the results are far from satisfactory. So they decide to survey their customers to identify the shortcomings in the software.
This is an example of how businesses do survey analysis to understand the problem areas before their customers start churning out.
Figure out these statistics. A customer service fact states that the service provider will get complaints from 4% of its dissatisfied customers. They are more than enough to bring a bad name to your brand. Another B2B customer experience report states that as many as 62% of businesses are ready to invest to meet the expectations of their customers.
There are plenty of reasons why knowing customer preferences through survey analysis can be so critical. In this post, we will be talking about survey analysis, how to analyze the results, and the ways to present the findings to your team.
Table of Contents
What is Survey Analysis?
Survey analysis is a process that involves analyzing the results from customer-specific questionnaires. You gather these by surveying your customers. You can further conclude from findings, which could be qualitative statements, numbers, or percentages. The results can give you plenty of takeaways to alter your strategy. You can find ways to ensure customer satisfaction levels remain high.
Survey analysis can include metrics like Customer Effort Score (CES), Customer Satisfaction Score (CSAT), Net Promoter Score (NPS), etc. If you run a customer feedback survey for a website and find out complaints about the loading speed, you will understand why the bounce rate is high.
Read Now: Check out our Ultimate Net Promoter Score Guide to know what is NPS, how to calculate it, and different NPS surveys.
Types of Survey Data
Companies can ask one or more questions to their customers in the form of open-ended and close-ended questionnaires. Take a look at seven different types of survey data with a detailed explanation.
Open-Ended Questionnaire
This questionnaire contains open-ended questions where respondents can answer in open text format. Their answers will be holistic and based on a decent knowledge of the subject. You can get rich insights based on the answers of your respondents. This data will be handy if you want information on a particular topic for your survey analysis.
Close-Ended Questionnaire
Respondents have to choose predefined options like agree/disagree, yes/no, right/wrong. This questionnaire helps gather quantitative data that you can use to arrive at numbers and statistics. As there are various close-ended questions, it is essential to select the appropriate ones. It should align with the needs of your survey analysis.
Ordinal Data
It is a form of categorical data that comes with a scale or set order. An example is when respondents show their liking of a product, say software, by rating it on a scale between 1 and 10. The differences in scores do not get measured on a standard scale. A new user might rank the software high without using it well. A long-time user might do the opposite being aware of the pros and cons of the software. So personal factors and bias comes into the picture here.
Nominal Data
It classifies categorical variables that do not offer any quantitative value. The data can be either quantitative or qualitative in some cases. Common examples include data like marital status and birthplace. It differs from ordinal data where there can be options for a question. It can include “likely” or “less likely” to recommend a software to peers. Furthermore, it is possible to classify the data based on the mode instead of the mean.
Scalar Data
It is just like ordinal data and involves relative quantity and quality. There will be items that rank higher than others. Common examples include the age of people in years, time taken in minutes or hours, exam scores out of 50 or 100. The data is available from a sliding scale question format or a drop-down.
Interval Data
The data follows an order with a meaningful distance between values. An example would be the monthly spending capacity of a business on marketing efforts. There can be options like USD 10, 20, and 30k.
This information can segregate the respondents and give them only relevant questions. If a respondent answers 20k, you can further ask them questions that relate to your objectives. It is essential to use intervals with equal sizes to get average and summarize data well.
Natural Language Data
It includes speech recognition, where spoken words get converted to text for additional analysis. There is machine translation in cases where spoken words are not English. Furthermore, there is language modeling and text grading. The datasets here have a unique analysis that provides for the complete needs of natural language.
How to Analyze Survey Data?
The process from collection to analysis of survey data requires dedicated customer interaction from the CX department in companies. Customer retention strategies in SaaS companies include exploring insights from survey data about customers’ requirements and feedback. Take a look at how you can do survey analysis through the following methods.
Define Questions and Collect Data
When you understand how survey questions get analyzed, you can figure out the questions for your survey. An example here includes “how well do our customers perceive our brand?” You can then look at questions like “how likely will they recommend our software to their peers?” Segmenting the questions is crucial. It is essential if you want data that relates to the survey analysis objectives.
Analyze Quantitative Data
Quantitative data can help you arrive at results quickly. It is essential to note that the information can be subjective, making it tough to analyze. No doubt, you get exciting insights about a subject. You can convert the data received from close-ended questions into numeric values. It can help you compare and find trends in the survey analysis.
Quantitative data can help you comprehend your qualitative data better. It makes the former a better option to get started. If there is positive feedback, you can move to questions where respondents have rated well about their experience. It will help you identify what is working for the business.
Choose a Tool to Perform Survey Data Analysis
There are various tools to do survey data analysis. The selection of a particular tool depends on the amount of survey data you have. If the survey data is less, say only a few rows and columns, you can analyze it in excel or spreadsheet. However, if the data is large, say in GBs with thousands of rows and columns, you would need advanced Machine Learning to analyze survey data. Take a look at the list of tools you can use for survey analysis.
How to Analyze Survey Data in MS Excel
You can start your survey analysis by exporting the file. It needs to be in a .xls or .csv format and you will have to open it in Excel. This survey analysis methodology is the most popular.
The first step is to clear blank data rows as, in some cases, respondents may skip some questions. Empty rows scattered everywhere on Excel aren’t a good sight.
Make sure you delete only empty rows and not the ones that might look empty at first. You can do that by identifying blanks column-wise.
You would like to know the count of blanks in your file. It will help you understand the number of answers that went unanswered. Use COUNTBLANK to describe and check a range against empty cells.
It is advisable to avoid deleting all blank cells as it can affect the data structure. If the feedback collection plan does not have an option to handle respondent anonymity, it can cause issues. The data might get shifted from one row to another if you remove blank cells.
Do VLOOKUP to sift answers based on numerical values in selected columns. It will further help you analyze the data. You can get an idea of what the number in your file means. Use VLOOKUP to get various insights such as the amount of time spent by respondents on a software, their subscription value, validity, and more.
Furthermore, convert two-choice answers into a numerical value like 1/0. It is possible to represent these 1/0 answers graphically with CORREL. You can use the Compare Files tool to correlate two different columns, sheets, or files.
How to do Survey Data Analysis With Machine Learning
It can be tough to analyze qualitative data. You cannot turn long answers into numerical values with Excel. Open-ended questions are a vital part of any survey as they help you understand a respondent’s sentiments. AI and ML tools can come in handy in any sort of customer experience analytics, be it survey analysis, churn analysis, or customer VOC analytics.
Read Now: Know what leads to customer churn and how advanced churn analysis techniques can help you retain them.
Survey analysis with machine learning can help you turn open-ended survey answers into useful insights. You can upload the Excel file, perform various analyses, and get the output back into the file. Sentiment analysis is a vital component of text analysis. It uses natural language processing (NLP) to sift data into neutral, positive, and negative information.
You can furthermore get options to categorize responses into emotions and opinions. However, it is possible to have more than one opinion in some open-ended survey answers. In such cases, it is essential to separate them into individual opinion units. It bodes well for an accurate analysis. If you don’t do this, the responses will get flagged as neutral because opposite sentiments will negate each other.
You can go further deep into data analysis when you have separate opinion units. When it comes to aspect-based sentiment analysis, it breaks texts into aspects and assigns sentiment to each. You can use aspect-based sentiment analysis to analyze open-ended questions well.
Customer Experience Solution: Check out our solution on NPS Analytics and how we use customer sentiment analysis and behavior analysis techniques to improve Net Promoter Score.
How to Represent Insights From Survey Data Analysis?
Finding insights from survey analysis can help customer experience managers quantify the ROI for CX efforts. Based on the insights they can request their seniors to allot a budget to improve customer satisfaction scores. But first, the insights from survey analysis reports should be clear enough for decision making. There are several ways to represent insights gathered from survey analysis. Take a look at a detailed overview of four popular methods.
Create Automated Survey Analysis Results Report
Presentations look appealing as they help you explain everything through appealing charts and numbers. You might need those long reports at times to meet the needs of stakeholders. In some cases, when you present to clients or senior executives, a report with all the findings will prove handy.
With automated plugins, it requires little to no human intervention to build insightful PowerPoint reports. The in-built templates and plugins can visualize the insights in various formats from simple automated charts to complex data visualizations.
Check out SlideSense – A PowerPoint Automation Plugin that helps business users create data-driven presentations and automated reports.
When you are presenting, you would not want to refer to it. You can share it with your audience once you get done with the meeting. Your audience can read the report later.
Show Survey Analysis in a Data Table
Tables are an excellent option when you want to present numerical data. Make use of tools like Excel or SPSS Statistics to display data. It will also be appealing to project numbers in the form of a data table. You can identify patterns that are not visible. The capacity of a human brain to process information like images or tables is higher than text. Your audience will understand the findings if you represent the statistical analysis of survey data visually.
Instead of a long list of numbers stacked with each other, rich visuals will be easy to understand. The data table can help your audience identify the trends. It is essential to understand that statistics need good portrayal. If not, badly represented statistics can be misleading. Design is critical, so visuals with complex portrayals fail to convey the message to the audience.
Show Results With Data Dashboards and Visualizations
Gramener specializes in creating visual analytics applications and different types of data visualizations to represent any data. We can help you turn your survey results into intuitive dashboards with insightful reports that update on a real-time basis. Customer experience managers and executives can start by filtering the data and selecting the questions they want to present.
Furthermore, they can fix charts and edit the layout by adding images, texts, logos, videos, etc. CX executives can even add widgets to graphs and tables to enhance the visual appeal further. There is ample scope for a workaround based on your project requirements.
Create Survey Analysis Infographics
Infographics present another option to captivate the minds of your audience with business statistics. You can transform the survey results into charts and graphs, making it an ideal setting for infographics. They can make your survey analysis information look impactful and appealing.
A rich infographic has the potential to get shared through social media. It will make a better impact on the minds of your audience. You do not need to have special skills to create infographics as they are simple to create.
Conclusion
Surveys are no mean feat. You need to have the right set of questions to derive rich customer insights. It is in addition to an effective distribution strategy. All of that will get reduced to nothing if you cannot find patterns and trends in data. Create surveys that can yield rich insights for your team.
With survey analysis, you have the options of Excel, Google Sheet, and AI-ML tools (recommended). As we all know, it is tough to quantify open-ended questions. AI-ML tools can help you translate them into insights. This makes it our favorite method of analyzing survey results.
It can be helpful for you to plan by identifying the opinions and emotions behind the responses. There is a story behind numbers, and you need to dig deep to find it.
Interested in knowing more about how we analyze hard data to create numbers and tell a story through them? Contact us today, and we will guide you through the survey analysis process in a no-commitment consultation call.
