Utilizing Surveys for Real-Time Customer Feedback

Here is the final post on adaptive surveys where I cover technical integration and implementation steps. Interested in your thoughts! Technical Integration... 1. NLP & NLU: Utilize NLP and NLU capabilities of LLMs to interpret open-ended responses accurately. This includes sentiment analysis, keyword extraction, and contextual understanding. 2. Real-Time Processing Framework: Implement a robust real-time processing framework capable of handling the computational demands of LLMs, ensuring that the adaptive logic can operate without noticeable delays to the respondent. 3. Data Privacy and Security: Ensure all integrations adhere to the highest standards of data privacy and security, especially when handling sensitive respondent information and when using LLMs to process responses. Implementation Steps... 1. Objective Setting and Mapping: Define the survey based on your business objectives and map out potential adaptive pathways. This stage should involve a multidisciplinary team including survey designers, data scientists, and subject matter experts. 2. Question Bank Development: Develop an extensive question bank, categorized by themes, objectives, and potential follow-up pathways. This bank should be dynamic, allowing for updates based on learnings from existing survey responses. 3. Algorithm Design: Design the adaptive algorithm that will decide the next question based on previous answers. This algorithm should incorporate machine learning to improve its predictions over time. 4. Platform Integration: Integrate the adaptive survey logic with the chosen survey platform, ensuring that the platform can support the real-time computational needs and that it can seamlessly present and record adaptive questions and responses. 5. Testing and Iteration: Conduct thorough testing with a controlled group to ensure the adaptive logic operates as intended. Use this phase to collect data and refine the algorithm, question pathways, and overall survey flow. 6. Deployment and Monitoring: Deploy the survey to the target audience, closely monitoring performance for issues in real-time adaptation, respondent engagement, and data collection quality. 7. Analysis and Learning: Use insights and respondent feedback to continuously improve the question bank, adaptive logic, and overall survey design. This should be an ongoing process, leveraging the power of LLMs to refine and enhance the adaptive survey experience over time. I would be curious to hear your thoughts on: 1. Is this something you could see being successful in your company? 2. Is this something you think your company is ready for? 3. Who do you think would own implementation? DM me if you want to talk more about this. I don't pretend to have all of the answers, but I'm confident that, collectively, we can figure this out. #customerexperience #surveys #llm #ai #technology #surveys #nps

User experience surveys are often underestimated. Too many teams reduce them to a checkbox exercise - a few questions thrown in post-launch, a quick look at average scores, and then back to development. But that approach leaves immense value on the table. A UX survey is not just a feedback form; it’s a structured method for learning what users think, feel, and need at scale- a design artifact in its own right. Designing an effective UX survey starts with a deeper commitment to methodology. Every question must serve a specific purpose aligned with research and product objectives. This means writing questions with cognitive clarity and neutrality, minimizing effort while maximizing insight. Whether you’re measuring satisfaction, engagement, feature prioritization, or behavioral intent, the wording, order, and format of your questions matter. Even small design choices, like using semantic differential scales instead of Likert items, can significantly reduce bias and enhance the authenticity of user responses. When we ask users, "How satisfied are you with this feature?" we might assume we're getting a clear answer. But subtle framing, mode of delivery, and even time of day can skew responses. Research shows that midweek deployment, especially on Wednesdays and Thursdays, significantly boosts both response rate and data quality. In-app micro-surveys work best for contextual feedback after specific actions, while email campaigns are better for longer, reflective questions-if properly timed and personalized. Sampling and segmentation are not just statistical details-they’re strategy. Voluntary surveys often over-represent highly engaged users, so proactively reaching less vocal segments is crucial. Carefully designed incentive structures (that don't distort motivation) and multi-modal distribution (like combining in-product, email, and social channels) offer more balanced and complete data. Survey analysis should also go beyond averages. Tracking distributions over time, comparing segments, and integrating open-ended insights lets you uncover both patterns and outliers that drive deeper understanding. One-off surveys are helpful, but longitudinal tracking and transactional pulse surveys provide trend data that allows teams to act on real user sentiment changes over time. The richest insights emerge when we synthesize qualitative and quantitative data. An open comment field that surfaces friction points, layered with behavioral analytics and sentiment analysis, can highlight not just what users feel, but why. Done well, UX surveys are not a support function - they are core to user-centered design. They can help prioritize features, flag usability breakdowns, and measure engagement in a way that's scalable and repeatable. But this only works when we elevate surveys from a technical task to a strategic discipline.

🎯 "The best salespeople ask good questions." But what if AI could help you ask the PERFECT questions at scale? Tomorrow on the GTM AI Podcast, I have Lihong Hicken joining me, the CEO of TheySaid | World's 1st AI Survey shared a brilliant framework for using AI surveys to both book meetings AND deeply understand your customers: Here's her fascinating approach: 1. Start with the Money Question "I ask sales teams: What's the ONE question that tells you if this is a good lead or not?" Real example she shared: ❌ Don't ask: "Are you interested in outsource development?" ✅ Instead ask: "How satisfied are you with your current outsource development agency?" 2. Let AI Go Deeper The AI then: - Explores pain points - Uncovers specific challenges - Discusses potential solutions - Books meetings with qualified prospects 3. Train AI Like Your Best SDR "You don't want a general AI chatting with your customer. You want it to be YOUR employee." She programs the AI to: - Use company messaging - Focus on specific value props - Route enterprise vs. SMB leads differently - Book directly into the right rep's calendar 🎯 The brilliant part? While booking meetings, you're simultaneously gathering intelligence about: - Customer satisfaction - Common pain points - Buying triggers - Product gaps She is thinking outside the box and leverage her own AI survey tech to do things that could not have been done before, like: Pipeline Generation & Sales: - Using AI surveys as an outbound prospecting tool - Converting research requests into sales opportunities - Qualifying leads through AI-driven conversations Customer Intelligence: - Getting honest feedback through AI conversations vs. human interactions - Capturing customer sentiment at different journey stages Win/Loss Analysis: - Moving from annual to continuous feedback collection - Reducing analysis costs (from $20-50K to $150) Upsell Strategy: - Moving from "sitting duck" to proactive upsell approach - Identifying expansion opportunities through AI conversations Customer Experience: - Creating engaging survey experiences vs. traditional methods - Using AI for continuous customer pulse checks Product Development: - Using customer feedback for feature prioritization - Understanding pricing expectations Market Research: - Reducing research costs and timeline - Getting both quantitative and qualitative insights Operations Efficiency: - Automating feedback collection - Reducing need for large BDR teams So excited to show this to you all and I may or may not have an example survey for you to test the tech out on yourself! Drops tomorrow ;)