5 Problems AI Surveys Solve That Traditional Surveys Can't

In a rapidly evolving world, understanding the pulse of your audience is more crucial than ever. Companies have invested heavily in products or campaigns only to realize later that the market wasn't receptive. Such pitfalls can be expensive, both financially and reputationally. Surveys have traditionally acted as the bridge between companies and their customers, offering insights and feedback before major decisions are made.

However, the digital age, with its relentless pace and vast data, demands more than what traditional surveys can offer. With the advent of AI, a new horizon in surveying has been unveiled, introducing capabilities that were once thought impossible. As you embark on your journey of gathering insights, it's crucial to evaluate the pros and cons of both AI and traditional surveys. In this blog, we'll delve deep into how AI surveys are revolutionizing the realm of feedback collection, offering solutions to challenges that traditional surveys often grapple with.

What are AI Surveys?

AI Surveys refer to surveys that utilize artificial intelligence technologies, such as natural language processing (NLP), to generate questions and analyze the data collected from users. These surveys differ from traditional surveys in that they are able to process a wide variety of survey questions and generate high-quality responses with improved accuracy and efficiency.

AI surveys address several limitations of traditional surveys, ranging from low response rates and biased responses to the inability to understand complex questions and gauge user sentiment accurately. In the end, the intention of both traditional and AI surveys is to get valuable insights from the end user, but how accurate and more reliable data is collected from the whole process is what makes the difference.

Problems with Traditional Surveys

Traditional surveys can be conducted using various methods, such as paper and pencil surveys, online forms, telephone interviews, or face-to-face interviews. These surveys have been used for decades by researchers, businesses, and organizations to gather insights. But it has its own pros and cons, with the evolving AI technology, we can solve what traditional surveys are struggling to address.

1. Time consumption

Design Phase: Designing a traditional survey can be a lengthy and exhaustive process. Every question needs careful crafting, and pilot tests may be required to ensure clarity and comprehension. Rewording and revising questions for clarity and objectivity takes more time.

Once the survey is crafted, determining the length of the survey, the order of questions or choosing the right components will be a time-consuming process.

Analysis Phase: Raw data needs to be cleaned and sorted. Responses to open-ended questions have to be manually categorized and coded.

How AI solves it

• AI surveys drastically reduce the design time by leveraging machine learning algorithms. These algorithms can automatically generate relevant questions based on the survey's goals, ensuring clarity and pertinence.
• AI can analyze the objectives of the survey to generate relevant questions. By leveraging extensive databases and linguistic models, AI can auto-generate questions that are clear and pertinent.
• Machine learning models can predict which questions are likely to yield the most valuable insights, optimizing the questionnaire for length and relevance.
• AI-driven surveys like BlockSurvey often come with built-in analytics tools. As soon as a respondent submits their answers, the data is processed and visualized.

2. Bias & Human error

Design phase: The manner in which questions are framed, the order in which they're presented, or even the choice of response options can inadvertently lead respondents towards certain answers. For instance, leading questions can influence participants to answer in a particular way, thus not truly reflecting their feelings or opinions.

Human error is another challenge, human involvement leaves room for inadvertent errors that can affect the accuracy of survey results.

How AI solves it

• AI can utilize vast datasets to understand the subtle implications and biases of language.
• By analyzing previous survey questions and their outcomes, AI can generate questions that are more neutral and less leading.
• AI can design surveys that adapt based on previous responses. If a respondent appears to misunderstand or misinterpret a question, the AI can rephrase it or provide clarifying prompts.
• Natural Language Processing (NLP) can be employed for open-ended questions, categorizing responses accurately and efficiently without human biases.
• One of the strengths of AI is its ability to learn and adapt. With every survey it administers, an AI can learn more about potential biases and errors, refining its processes for the next one.

3. Adaptability & Personalization

Traditional surveys typically have a static set of questions. Each respondent gets the same set, irrespective of their previous answers. This can lead to irrelevant questions, causing respondent fatigue or a lack of engagement.

Because the survey structure is predetermined, it doesn't adjust to the unique perspectives and experiences of individual respondents. This one-size-fits-all approach might result in missing out on nuanced data or insights from certain groups of respondents.

How AI solves it

• Dynamic Questioning: AI-powered tools design surveys in a way to adapt in real-time based on a respondent's previous answers. If a respondent indicates that they don't use a particular product, for instance, the survey can automatically skip questions related to that product's specifics. This ensures that each question is relevant to the respondent, leading to better engagement and more accurate results.
• Personalized Survey Paths: Beyond just adapting to answers, AI surveys can be tailored to the individual even before they start. If the survey platform has access to certain demographic or behavioral data (ethically sourced and used, of course), the AI can craft a unique survey path that's most relevant to the intention of the survey.

4. Actionable Insights

Traditional surveys are often constrained by the analysis techniques known or available to the survey designers. This means that the data is analyzed using a limited set of methods, which can potentially overlook nuanced insights or deeper patterns.

Manual analysis can lead to surface-level insights where broader patterns are noticed, but deeper, more intricate relationships may be missed.

How AI solves it

• AI surveys utilize deep learning to sift through data. These models can detect patterns in large datasets that may be imperceptible to human analysts.
• Open-ended questions, which traditionally are a challenge to analyze, become much more manageable with AI. NLP techniques can analyze text responses, categorize sentiments, and even cluster similar responses together.
• Beyond just interpreting current data, AI can make predictions about future trends or behaviors based on survey results. This predictive analytics can guide decision-makers in anticipating future challenges or opportunities.

5. Low response rates

Traditional surveys often rely on static questions that might not engage the respondent effectively. A lack of personalization can result in respondents feeling that the survey isn't relevant to them, leading to early drop-offs or non-completion.

How AI solves it

• AI can design surveys in a way where respondents might not lose interest and adjust the survey format, and question type, or even offer encouragement to re-engage the participant.
• Instead of a fixed set of questions, AI can adapt the next question based on previous answers. This can make the respondents feel the survey is more conversational and tailored to them.
• Transforming surveys into a game-like experience can make them more engaging. AI can adjust the "game" in real-time based on the respondent's interaction, ensuring they remain interested.
• AI can weave the survey questions into a narrative or story, making the process feel less like a survey and more like an interactive story.

To wrap it up

As we've explored, AI not only solves many of the challenges associated with traditional surveys — such as time consumption, bias, and low response rates — but also introduces innovative methods that redefine the survey experience for respondents.

These advancements offer richer insights, greater personalization, and increased engagement, allowing businesses, researchers, and decision-makers to obtain more accurate and actionable feedback from their target audiences. Embracing this change not only elevates the quality of data we gather but also deepens our understanding of the ever-evolving human experience. If you're keen on experiencing the next-gen survey platform, give BlockSurvey's AI survey creation a try and see the difference for yourself.