Customer insights are crucial for any business. Even, in a fast-paced competitive business world, customer feedback surveys are vital to a robust marketing strategy.
However, due to the difficulty of extracting meaningful information from a text on a big scale, most companies turn a blind eye to customer insights. But, the good part is that you can address this challenge with AI-powered sentiment analysis. You can better analyze the survey results in a fast, precise, and easy manner.
Here, we will see how survey result analysis is done semantically, and what benefits it brings for your business.
The Major Challenges in Survey Data Analysis
The big challenges start with incorrect goals and an inaccurate target audience. Moreover, human prejudice, non-conducive interview methods, incorrect sample size, and the concern of verbose responses to open-ended questions also pose a roadblock in inefficient survey result analysis.
Let’s have a detailed look below:
Goal Setting
It’s important to outline accurate goals.
Ensure that your goals are aligned across teams and are in tune with the final objective of your marketing team. Goals can vary from searching a potential market for a new product to getting a perspective of the company’s brand image.
Therefore, defining goals is crucially the first step before you decide on how to analyze survey data.
Right Target Audience
Determining the right target audience is important for correct survey data analysis. For example, if the survey is about employee satisfaction, the target audience is obvious. But, when it’s all about establishing the right audience for a new product or service, the TA is less obvious, and interviewing the right demographic is crucial to meet your goals.
Sample Size and Quota
It’s essential that the survey represents a large sample size, including sub-groups in the target audience. Besides, it establishes the right quotas because the accuracy of the survey significantly depends on this.
So, to concisely reflect the general population’s opinions, the survey needs to ensure that the percentage of all relevant subgroups mirrors their percentage in the current market share of the region the survey is being conducted in.
Human Bias
It’s crucial to address human bias when establishing how to analyze survey data. Bias can creep in when human intervention is used for survey result analysis.
For example, the surveyor may be biased against ex-employees and candidates. So, can they be biased against ex-customers or even people who phone in or send survey results voluntarily, thinking that they are heavily invested and thus, have stronger opinions that may be correct.
Therefore, an AI-based automated model reads and interprets data analysis of survey results and overcomes this challenge.
Benefits Of Survey Analytics With Semantic Analysis
Survey analytics with sentiment analysis provides a detailed and holistic view of the reasons behind consumer behavior. Through sentiment scoring and data analysis of survey results, a company can take corrective and focused measures for products and services enhancement, as well as operational efficiency.
Precisely, sentiment analysis in survey analytics helps businesses with:
- Identify semantic similarity
- Understand open-ended questions
- Find aspect co-occurrence
- Extract sentiment for each aspects/feature/service
- Analyze different types of media formats (audio/video/text)
Let’s find out how!
To find out how, it’s imperative for surveys to have open-ended questions, whatever format they may be in. They can be easily clutched through video interviews, chats, online forms, telephonic conversations, survey emails, etc.
But, to be precise, an ideal survey software ensures that all open-ended answers are categorized and analyzed for accurate results using rigorous text and audio content analysis. Also, it will decipher data collected from video channels that are used in candidate interviews, employee satisfaction surveys or patient voice through video content analysis.
The answers collected are then run through an NLP algorithm to be analyzed for recurring topics, aspect co-occurrence, themes, etc so they can be eventually processed for sentiment analysis. The model will identify semantic similarities amongst latent variables and gauge sentiment behind open-ended responses; collected and aggregated to help companies understand qualitative measurements by allowing the respondents to elaborate on the “why” behind their answers.
To be precise, responses like these are very valuable to businesses, especially when collected and analyzed at scale, as they can easily find solutions to declining customer appeal.
Survey Result Analysis Done With Semantic Analysis – Let’s See How!
When done with semantics, survey results analysis is based on survey response patterns that an AI-powered natural language processing algorithm can predict. The model does so by using available and predetermined information before conducting the survey.
This pre-existing or pre-collected data may be in phrases, topics, texts, aspects, and features that have already been collected with the subject of the survey. This information can be collected in different languages, depending on how many languages the survey will take place.
Use Latent Variables To Dig Deeper
In surveys, a latent variable is an unmeasurable variable that can influence the responses to several other aspects of the subject in the questionnaire.
An unmeasured variable is where there are no simple quantitative measures of a “yes” or a “no”. The degree of accuracy of arriving at a deeper level of the answer depends on how many related questions one can ask about the subject. That’s why there is more than one question related to the same topic in a survey.
Quantitative datasets are relevant but equally are the qualitative ones. It may sound vague to the respondent, but for data scientists, all the questions are equally important to arrive at a satisfactory conclusion.
Semantic Overlaps of Latent Variables
Data scientists understand how these variables are connected and that survey scores can contradict each other in many different ways. Other times, the answers to latent variables may have patterns emerging.
This often illustrates when a rating methodology is used in survey data analysis. Let’s explain this in a little elaborated manner.
The Rensis Likert Method:
Mostly, surveys use a rating method introduced by Rensis Likert where respondents rate a statement on a scale varying from “strongly approve” to “strongly disapprove”. In this, an AI algorithm uses the rating method to semantically score survey data and predict answers depending on what answers the survey respondent has chosen.
Read the complete article at: How Is Survey Result Analysis Done With Semantics?
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