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AI Survey

AI surveys are revolutionizing how data is collected, analyzed, and utilized, offering new dimensions of efficiency and insight. These advanced tools use AI technologies, such as machine learning and natural language processing, to streamline the survey process, enhance the accuracy of data analysis, and generate more nuanced understandings of respondent behaviors and preferences.

AI surveys can adapt in real-time, personalizing questions based on previous answers to gather more relevant and deeper insights. Organizations across sectors are increasingly relying on AI surveys to make informed decisions, understand customer satisfaction, gauge employee engagement, and much more.

What are AI surveys?  

AI surveys are a new breed of surveys that leverage the power of Artificial Intelligence (AI) and machine learning to enhance the traditional survey process. These surveys leverage AI algorithms and techniques to improve data collection, analysis, and interpretation.

Listen, recognize, award, and retain your employees with our Employee engagement software  

How do AI surveys work?

Here's a breakdown of how they work:

1. Advanced question design

AI can analyze your survey goals and suggest the most suitable question types (multiple choice, open-ended, ranking) and answer choices. This helps reduce bias in the way questions are phrased and ensures answer options accurately capture the range of respondent experiences.

2. Dynamic questioning

Unlike traditional surveys with a linear flow, AI surveys can adapt based on a respondent's previous answers. This allows for more nuanced questioning. For example, if someone expresses dissatisfaction with a product feature in an early question, AI can then ask follow-up questions specific to that feature, gathering deeper insights.

3. Automated analysis

AI excels at analyzing large amounts of data, especially open-ended responses that can be challenging for traditional methods. AI can extract sentiment, keywords, and themes from open-ended responses, transforming qualitative data into quantifiable insights. This allows you to understand not just what people say, but also how they feel about it.

4. Predictive analytics

AI goes beyond simply analyzing past data. It can identify patterns and correlations within survey data to predict future trends and customer behavior. This allows you to proactively address customer needs and make data-driven decisions.

5. Visualization and reporting

AI tools can generate visualizations such as charts, graphs, and dashboards to present survey findings in a clear and understandable format. These visualizations help stakeholders quickly grasp key insights and make informed decisions based on the survey results.

6. Iterative improvement

AI surveys often incorporate feedback loops to continuously improve the survey process. This may involve analyzing response data to identify areas for optimization, refining survey questions based on respondent feedback, or adjusting survey distribution strategies to reach target audiences more effectively.

7. Ethical considerations

Throughout the survey process, ethical considerations such as data privacy, transparency, and fairness must be considered. AI surveys should adhere to relevant regulations and guidelines to ensure the responsible use of data and the protection of respondents' rights.

Why use AI surveys?  

The reasons to choose AI survey compared to traditional surveys are

1. Deeper insights  

AI helps uncover hidden patterns and extract valuable insights, particularly from open-ended questions.  Imagine a survey question about customer satisfaction with a new product.

Traditional analysis might just tell you the percentage of satisfied customers. AI can analyze the open-ended responses to understand the reasons behind satisfaction (or dissatisfaction), providing more actionable insights.


2. Improved accuracy

Dynamic questioning reduces bias by ensuring respondents answer questions most relevant to their experience. Traditional surveys with fixed question flows might ask everyone the same questions, regardless of their prior answers. This can lead to inaccurate data if someone is forced to choose an answer that doesn't quite fit their experience.

3. Increased engagement

Personalized surveys that adapt to individual responses are more engaging for participants. This can lead to higher completion rates and more reliable data.  Imagine a survey that feels like a conversation rather than a monotonous questionnaire. People are more likely to stay engaged and provide thoughtful answers.

4. Faster analysis  

AI automates data analysis tasks like sentiment analysis and identifying keywords, saving you time and effort.  This allows you to focus on interpreting the insights and taking action.

5. Predictive power  

AI can analyze survey data to identify trends and predict future customer behavior. This allows you to be more proactive in addressing customer needs and making data-driven decisions.


For instance, an AI survey might identify potential customer churn based on specific response patterns. You can then use this information to develop targeted interventions and prevent customer loss.

Who can benefit from AI surveys?

AI surveys offer a wide range of applications across various sectors. Here's a more detailed breakdown of how different industries can leverage AI surveys:

1. Marketing and advertising

  • Develop targeted marketing campaigns based on customer preferences and sentiment analysis.
  • Measure the effectiveness of marketing campaigns and advertising messages.
  • Gain insights into brand perception and identify areas for improvement.

2. Sales

  • Predict customer churn and identify at-risk customers for proactive interventions.
  • Personalize sales pitches based on customer needs and purchase history.
  • Measure salesperson performance and identify areas for coaching and development.

3. Human resources (HR)

  • Improve employee onboarding experiences by gathering feedback and personalizing the process.
  • Identify potential areas of employee dissatisfaction and proactively address concerns.
  • Measure the effectiveness of training programs and development initiatives.

What factors determine the cost of AI surveys?  

The cost of AI surveys can vary depending on several factors:

  • Survey complexity: More complex surveys with advanced features like sentiment analysis or branching logic might cost more.
  • Number of respondents: Larger surveys with a higher volume of responses will typically incur higher fees.
  • AI survey platform provider: Different AI survey platforms have varying pricing models, so it's important to compare options

Here's a breakdown of potential cost considerations:

  • Platform fees: Some platforms charge a monthly subscription fee for access to their AI features and functionalities.
  • Per-survey fees: Some platforms might charge a fee based on the number of surveys you conduct or the number of respondents.
  • Data analysis fees: While AI automates some analysis tasks, complex projects might require additional data analysis services, which could incur an extra cost.

What are some examples of successful AI survey implementation?

Companies across industries are using AI surveys to gain valuable insights and improve their operations. Here are some real-world examples:


1. Netflix

Leverages AI-powered surveys to personalize learning and development recommendations for employees. The company analyzes survey data on employee skills, interests, and career goals.

Based on this information, AI suggests personalized learning paths and training programs, ensuring employees are equipped with the skills they need to succeed.

2 Hilton

Uses AI chatbots powered by survey data to answer employee questions about benefits, payroll, and company policies 24/7. This improves access to information for employees and frees up HR professionals to handle more complex issues.

3. Walmart

Analyzes employee sentiment through AI surveys to identify potential areas of dissatisfaction and proactively address concerns. This helps Walmart maintain a positive work environment and reduce employee turnover.

4. Adobe

Implements AI-powered microlearning modules based on employee survey feedback on preferred learning styles. By understanding how employees learn best, Adobe can create more engaging and effective training programs.

5. EY (Ernst & Young)

Uses AI to analyze survey data and identify high-potential employees. Based on this data, EY can recommend these individuals for promotions or leadership opportunities and provide them with personalized development plans to help them reach their full potential.

Employee pulse surveys:

These are short surveys that can be sent frequently to check what your employees think about an issue quickly. The survey comprises fewer questions (not more than 10) to get the information quickly. These can be administered at regular intervals (monthly/weekly/quarterly).

One-on-one meetings:

Having periodic, hour-long meetings for an informal chat with every team member is an excellent way to get a true sense of what’s happening with them. Since it is a safe and private conversation, it helps you get better details about an issue.


eNPS (employee Net Promoter score) is one of the simplest yet effective ways to assess your employee's opinion of your company. It includes one intriguing question that gauges loyalty. An example of eNPS questions include: How likely are you to recommend our company to others? Employees respond to the eNPS survey on a scale of 1-10, where 10 denotes they are ‘highly likely’ to recommend the company and 1 signifies they are ‘highly unlikely’ to recommend it.

Based on the responses, employees can be placed in three different categories:

  • Promoters
    Employees who have responded positively or agreed.
  • Detractors
    Employees who have reacted negatively or disagreed.
  • Passives
    Employees who have stayed neutral with their responses.

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