Understanding Artificial Intelligence Surveys
Artificial intelligence surveys improve the process by automating the eligible tasks and accelerating several other aspects. I have experience in both manual and AI-based surveys.
As a technology that is designed to think and work like humans, it works together with machine learning to identify patterns and make predictions based on the collected data.
I have seen first-hand the speed and accuracy of AI in collecting and analyzing data, which gives us the gist of everything to make quick decisions. Depending on the requirements, the AI tool you are using will;
- Suggest the right questions to ask.
- Helps establish a balance between answer choices and reduce bias.
- Collect precise data related to the product, service, or feature.
An AI tool for survey can create skip logic that can dynamically adjust the survey based on previous answers to ensure a smooth experience. At Qwary, we have built AI surveys by taking design guidance, leading to an optimized layout and format for better accessibility.
What’s even more amazing is that an AI survey will let you personalize the surveys at scale. So, you can collect specific customer data from a large group at once. Taking your requirements into account, an AI survey analyzes past data to recommend clear and unbiased questions while adapting to the flow according to the demographics.
Advantages of Completing a Survey with Artificial Intelligence
By leveraging AI, brands can achieve greater precision and higher accuracy in generating customer responses and identifying patterns. Using insights from the results, you can tailor offerings to match the customer’s requirements.
Automation and Efficiency
Eliminating repetitive tasks, AI automates data entry, clearing, and some basic analysis using the power of machine learning. This frees up human resources for a higher level of data interpretation, making you strategically strong in decision making.
Working with AI tools, I have seen how they can automate survey creation, distribution, and analysis, helping my team launch the product quickly.
Accurate Sentiment Analysis
Natural language processing (NLP) - enabled AI surveys can effectively analyze open-ended responses, interpreting the user sentiments behind them. You will know about the respondent’s emotions, sarcastic responses, and the subtle nuances of the language used.
With this, you will get a holistic view of the respondents, along with the scores arranged according to sentiment intensity and emotional variation. Use this information to tailor your interactions with the customers.
Fast Question Generation
A detailed analysis of your past surveys and results will allow the machine learning algorithms to streamline the question generation process. Not only will it automate the process, but you can also benefit from dynamic question adaptation.
This is something we have added to Qwary’s Converse AI, where the next question in the survey is set according to the responses. This lets you curate a personalized survey for your users and get even more accurate results.
Predictive Survey Targeting
AI can successfully analyze respondent data, including demographics and past responses, to predict the likelihood of individuals participating in the survey. Based on this data and customer segmentation, it can orchestrate survey distribution for maximum impact, effectively reducing survey fatigue.
As you will know the likelihood of receiving responses, you can make adjustments and provide incentives to improve data collection.
Easy Respondent Recruitment
Prediction-based incentivization is one of the ways to improve survey participation. Along with this, you will get social media integration tools to identify potential participants on social media. Based on pre-defined criteria, AI can help you reach a wider audience and upscale the recruitment process.
Real-Time Feedback
No more waiting for survey results, as AI can analyze the results and provide insights immediately. This means you can address the customer’s concerns quickly, increasing brand loyalty.
In addition to this, the immediate analysis also helps curate personalized offers and adapt the campaigns accordingly. It creates a dynamic continuous feedback loop and ensures rapid improvement.
Bias Mitigation
Identifying biased responses or flagging potentially biased wording is something better trusted when done by a human. However, we now have advanced AI models and algorithms that can detect biased wording and sentiments to provide a fairer data collection.
Moreover, when setting the questions and their response choices, an AI can minimize social desirability bias, which has proven effective for us when building Qwary. This has led us to create surveys that are more socially acceptable and attract better responses.
Data Visualization Tools
It's easier for an AI tool to create interactive dashboards based on the results since everything is automated. Compared to checking the numbers on your screen and some graphics pie charts, don’t you think the latter is much better?
You can even ask the AI tool to generate data summaries and highlight the most important findings. I know it’s not easy to find a tool that provides everything you need. I spent months in search of a tool to help me speed up the survey and analysis process when building Qwary.
How Come We Started Using AI for Surveys?
At Qwary, my team adopted artificial intelligence, but we never discussed how we got into it. Using AI for survey creation, analysis, and everything else came naturally to us.
It was only when I was writing this article that I started thinking about how we actually started using AI, and here are a few reasons I can think of…
- Lengthy Surveys and Low Completion Rates: The traditional survey creation process was long and tedious. I spent the entire day analyzing the results. Moreover, when creating one, we ended up adding more questions than required, which led to respondent fatigue and eventually dropouts.
So, it naturally reduced the sample size, potentially leading to biased results and a product that the target audience may not want.
- Inability to Account for Bias and Social Desirability: With the right degree and the ability to read between the lines like a machine does, my team may have been able to detect bias and extract accurate data.
A respondent’s urge to select the answer which is more socially desirable leads to biased results. For someone like me, it was not possible to detect this. However, we were able to identify a pattern because no respondents gave hard answers.
- Several Data Blind Spots: Open-ended questions form the basis for extracting valuable insights, but manual analysis of the same was time-consuming. Given the subjective nature of the responses, it was not easy for anyone to capture the respondent’s full sentiment and context.
Then, How Did AI Help My Team Address These Challenges?
I can think of three subsets of AI-based technologies that are allowing us to address these challenges;
- Big Data: AI can process vast amounts of data from past surveys to identify patterns, effectively removing survey analysis fatigue and completion rates.
Using the analysis, we can proactively identify the respondent's concerns and make changes to the survey for a better outcome.
- Machine Learning: ML algorithms analyze past survey data and respondent details to identify patterns of bias and social desirability. This has allowed us to refine the survey questions and answer choices to mitigate bias effectively.
- Natural Language Processing: NLP-enabled AI programs can analyze open-ended questions with greater accuracy. We can identify sentiments, emotions, and sarcastic responses in the surveys. Hence, we now have a better understanding of our respondents and their opinions.
AI doesn’t stop here. It continues to add new capabilities and innovations. You can check out our Converse AI tool to explore how we are using this advanced technology to create better surveys.
Applications of AI in Survey Analysis
Coupled with my personal experiences and industry knowledge, executing a survey with artificial intelligence, I can cite several examples of how AI has helped us conduct better surveys.
Here’s an overview of what you can do with an artificial intelligence survey;
Higher Customer Satisfaction
As you expedite survey collection with the help of AI tools, it is easier to leverage insights to achieve higher customer satisfaction. Utilizing AI, you can streamline the survey process, personalizing every bit of it to make it more customer-centric and efficient.
As you can now target every customer with dynamic survey questions, it increases engagement and improves the overall experience.
Case Study: Stitch Fix, an online fashion retailer, used AI and algorithms to analyze customer feedback. The feedback provides insights into customer style and preferences, allowing the company to recommend relevant products. This led the company to record higher customer engagement and satisfaction.
Build Innovative Products
Innovation is at the core of a business's progress, and AI surveys play a pivotal role in helping businesses innovate. This is not plain innovation, but with the right execution, you can innovate in the same direction as your customers expect.
Using AI surveys, you can gather insights and grasp the opportunity to optimize the development process while making informed customer-centric decisions.
Optimize Marketing Campaigns
In-depth survey insights will help optimize marketing campaigns by;
- Listening to customer insights
- Personalizing the campaigns
- Making data-driven decisions
In an MIT report, 14% of the respondents agree that AI will be used in Media and Marketing business process.
Using an AI survey solution, you can segment the questionnaire according to the demographics, past interactions, customer history, etc. This means you can tailor the marketing messages and offerings according to maximum impact.
Better Market Research
AI can effectively improve marketing data collection in two ways;
- It allows for a dynamic survey creation and execution process, giving you in-depth insights into the market and enough time to customize the surveys.
- Since you can automate repetitive tasks, you can collect higher volumes of data in the same amount of time.
AI surveys can help you adapt the marketing material and insights according to the requirements.
Decode Customer Sentiments
As we have already discussed before, AI can identify human sentiments and emotions. Harness this capability to know more about your customer’s frustrations and sentiments to create surveys that go beyond simple rating scales.
The image above gives an overview of how AI models process customer sentiments leading to effective results.
You can collect qualitative feedback from open-ended questions and uncover the emotions behind making a choice. By doing so, any business using AI can gain a more accurate understanding of customer satisfaction levels and identify areas of improvement.
Qualitative and Quantitative Feedback Analysis
I have already shared how NLP can improve qualitative analysis. But what you should also know is that it can help you categorize the survey responses by different categories. With this, you can identify recurring issues and flag potential areas of concern.
Moreover, an AI survey tool can create a link between qualitative and quantitative data. This helps you identify areas where customer sentiment aligns with the numerical responses, providing more detailed insights.
Best Practices of AI Surveys
A big part of conducting a survey today is to understand AI. This implies, you need to know its limitations, potential, and the correct way to use the advanced technology.
Even with AI at your side, conducting high-quality and high-impact surveys is easier said than done. As someone who has learned from A/B testing and making mistakes, I can quote a better way to conduct AI surveys.
- Define the Objective Clearly
Before building and launching the survey, focus on your goals. Clearly define the information you wish to extract from the data. Align this information with the key objectives to ensure you gather the right information and make informed decisions. By doing so, you can harness the right AI functionality for specific goals and achieve maximum impact.
- Data Quality and Integrity
Any AI model relies on clean and accurate high-quality data and the same applies to the applications of the technology. I had to redo a survey multiple times with a new dataset to get good results.
Moreover, choose well-structured and easy-to-understand questions, avoiding any form of ambiguity and bias. Use AI to detect biases and address them to improve the survey design and structure.
- Give Priority to Ethical Considerations
The respondents sharing information with you must know everything about how their data will be used, stored, and collected. It's better to obtain consent from them before conducting the survey.
In addition to this, employ strong data protection and security practices to protect customer privacy. For this, check out techniques like anonymization and differential privacy.
- Work with Updated AI Models
AI models are dynamic as they evolve. Hence, chose the AI tool that uses updated models with new data sets and advancements. This ensures you can capitalize on the latest AI capabilities and ensure optimal performance.
- Use Mixed Question Types
Even when using the latest technologies to conduct a survey, remember the impact and importance of going traditional. Use different types of questions, such as MCQs, Likert-scale, etc., to obtain a well-rounded data set for in-depth analysis.
- Personalize the User Experience
Qwary’s Converse AI helps you conduct dynamic conversations by changing the questions based on the respondent's answers. This keeps them engaged in the process, effectively reducing survey fatigue.
While we are onto something great with Converse AI, we are working to make it better. A dynamic survey process also means that the AI tool can skip irrelevant questions based on the answers.
- Provide Clear Survey Instructions
With every survey, clarity is the key to extracting honest feedback from the respondents. Giving clear instructions on what the survey is about and how it will help the respondents will make them agree quickly.
What Does the Future of AI Survey Hold?
We live in a customer-centric world where businesses compete to gain a larger market share. Understanding customers is essential for any business to succeed.
Personally, I think that as customer perceptions, expectations, and customer behavior change, the AI tools we are using today might not be effective in the future. Hence, an artificial intelligence survey system will also need to be better.
- Conversational AI to Conduct Survey with Chatbots
I came across a study conducted in 2020 to measure the impact of conversational AI chatbots on conducting surveys. The results here were amazing, where conversational AI-based chatbots delivered higher engagement levels and better quality of responses.
Using this as inspiration, the Qwary team built Converse AI. Our tool is as dynamic as it can get, effectively changing the questions based on the responses. Through this, businesses using Converse AI can engage in an interactive dialogue, leading to higher completion rates.
- Real-time feedback with AI Survey
In the future, real-time survey dashboards will be implemented, allowing you to provide instant feedback to the respondents. This means you will identify trends almost instantly and make changes or send follow-up questions on the fly.
This means a company conducting a survey can analyze the responses in real time to identify areas of confusion and dissatisfaction immediately.
- Sensor Data Integration in Surveys
Requesting customers to share their feedback is self-reported data. However, with sensor integration into survey responses, we will get a holistic picture of customer experience.
The applications of this method can extend beyond our imagination now. The survey data you get from users combined with the sensor data can authenticate emotional responses and provide better insights.
Conclusion
There’s no need to conduct standardized surveys as the one-size-fits-all approach doesn’t work now. AI surveys are changing the way you collect and analyze survey data to improve customer experience in all the ways possible.
At Qwary, we are pioneering in the survey world, letting our clients build better products and solutions. Now that we are using artificial intelligence to improve our product and offerings, you will get an even better understanding of your customers, and that, too, quickly.
Want to check out how Qwary can help? Book a product demo today to learn how we are helping change the survey game.
FAQs
- Will AI replace humans to conduct surveys?
Artificial intelligence can automate various tasks related to conducting a survey. It can capture and analyze data while automating repetitive tasks, freeing up humans from these tasks. While AI can effectuate shifts in roles and responsibilities, it’s not yet that advanced to replace humans.
- Can AI models like ChatGPT answer surveys?
AI models can effectively analyze survey data, enabling us to focus on the investigation and processing. However, AI models cannot answer surveys on a human’s behalf as there is still a lack of human emotion. Hence, with AI models answering surveys, the data you get may be biased, incomplete, or even disoriented.
- Can I use AI to create a Survey?
Several tools are available today to help you create and deploy surveys. Qwary has built Converse AI to help you build dynamic surveys that evolve according to the responses. As a result, you will have updated and customer-centric data, benefitting from higher customer satisfaction.
- How safe is using AI for surveys?
Safety considerations related to using AI for surveys relate to data privacy and bias. Hence, it’s essential to have human oversight when using AI to create surveys and process the results. Since AI lacks a contextual understanding, it is susceptible to mistakes, which can be avoided with human oversight.
- What methods are used to analyze survey results?
Some of the most popular methods are descriptive statistics, cross-tabulation, regression analysis, factor analysis, and cluster analysis. In addition to these, there are T-Test, analysis of variance, and text analysis methods employed for the purpose.
