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How to Prepare a User Interview Questionnaire?

5 mins 10 secs read

The User Interview is a qualitative, non-binary, open-ended process that is a crucial contextual inquiry research method. And creating a good questionnaire is like sharpening your weapon to make the best impact.

A well-prepared, well-rehearsed user questionnaire is a must for an insightful interview.

Why make a questionnaire before a User Interview?

To avoid Response Biases*!

Seven-eight response biases influence user interviews greatly; understanding the user persona objectively is crucial for good design decisions. A thorough understanding of the response biases will help you use them or avoid them to get functional responses from the user.

*Note: There will be an upcoming blog on Response Biases and how to use or avoid them in research. Keep following Mantra Labs’s blog posts.

To make interviews more relevant to the research goals:

Planning an interview questionnaire keeps the facilitator on track during interviews. What does this mean? Multiple probing questions are asked during any user interview to gain more information and a deeper understanding of the participant’s behavior. Due to the probing, three situations occur.

1. The facilitator gets carried away in probing and deviates from the central questions under the influence of the participant’s response.

2. The facilitator asks questions spontaneously under the influence of their biases that might not be relevant to the research goals.

3. There is a chance of losing track of interview goals and shifting off-topic. Sometimes it’s a good idea to check all the possibilities that could influence the user’s behavior. However, there should be a clear line at which to stop and come back to the main questions.

To make sure questions are open-ended and revised/reframed before interviews:

“Details are in the story,” and Open-ended questions allow participants to tell the story of their experience. Since participants’ responses should not be influenced or bound to respond in a certain way, it is crucial to plan open-ended questions.

E.g., a pet product company is researching the possibility of their product entering the market and developing some pet grooming products for millennial pet owners. Let’s look at some 

questions to understand how open-ended they are.

Q 1: Would you use shampoo to make your dog smell better?

Q 1.1: What shampoo would you use to make your dog smell better?

Problem with these questions: 

Q 1: It’s a Yes/No question that will not give any qualitative insights.

       This question has little scope to know all the products the participant uses for grooming the dog.

Q 1.1: This is better than Q.1 because it has the scope of answering about shampoo products, but more is needed to know every detail of the pet cleaning-care habits of the user.

This can be a follow-up question to dig deeper, but not the main question.

What would be an excellent open-ended question?

Q. 1: What does your pet grooming process look like, and what products do you use while grooming?

Now this question has scope for the participant to tell the story of their pet’s grooming, including their process, the product they use, and how everything impacts their pet’s grooming pattern.

To avoid repetitive/similar questions:

If the questions still need to be pre-prepared and revised, the facilitator often asks questions similar to prior questions. This scenario causes a waste of time, sometimes irritating the participants as well.

To limit the number of questions and make them effective per the interview time limit.

  1. The time taken in an interview significantly impacts the qualitative information received from the participants. 40-45 minutes is the sweet spot for an interview. More than that, it starts becoming too much for the participant. If the facilitator continues to engage, the participant might get disengaged and be in a hurry to finish the interview.
  2. If it takes less than 40 minutes, you might not get a deep understanding of the question due to needing more probing in the response received from the participant.

How to make a good User Interview questionnaire?

Now that we know why, a good questionnaire is crucial before the research interview. There are some parameters and ways to write a good questionnaire.

Defining the business goal and user goal:

Keep stakeholder analysis as the first priority before writing the questionnaire. It’s crucial to know the business goals of the critical stakeholders to lay the foundation for your research interview. Also, try to understand and write the User Goal according to key stakeholders and their target personas.

Doing secondary research and gaining more information about the products, services, and business: 

It helps the facilitator make questions more relevant to the topic. The planning of the questions would be about the product and the service, which would help to find impactful insights. Here are some matters to focus on while doing secondary research:

  •  Analytics of the website (whatever possible, Google Analytics, Hotjar, Similarweb), target personas, user journey, pain points, what are your most trafficked pages, which site pages rank high in SERPs, visits from organic sources, traffic referrals from other sites and channels, Traffic from direct URL into the search bar, Devices used by the traffic ETC.)
  • Top performing keywords
  • How long do people typically spend on your website?
  • Page load time of the site?
  • Competitors analysis
  • User Personas
  • Geolocations of major user bases
  • Industry trends
  • The existing research paper regards the context
  • Existing customer journey map (If any)

Write the User Interview objective and key result properly:

This is the foundation of any research. Defining the OKR of the interview will keep your approach more constructive. The researcher should define why they are interviewing and what they want to achieve. Later, the questions should be formed in order to get the relevant information with regard to the interview objective.

The questions need to be framed in four categories that go in order one after another during the interview:

Intro Question and Ice Breakers

* Hi, How does your day/week go in Mantra Labs?

* Could you tell us about your role, followed by what your team does at Mantra Labs?

* How do you plan for your certain work to achieve your ‘X’ target?

Topic Specific Questions

* How do clients approach you and vice-versa? 

* Could you describe a couple of scenarios where you failed to perform the task and how it happened?

* How do you overcome obstacles when performing such tasks?

Opportunity specific questions

* What is making a good impact on existing clients during the project?

Opinionated questions

* In your opinion, where would you suggest this service should improve?

Conclusion

User Interview Research in UX is crucial to making informed decisions to solve complicated problems for any product or service. As it is essential, it needs to be understood. 

What does it mean? How should it be done? Also, when should it be done? But above all, the biggest problem with interviewers is a need for more technical and experiential knowledge of how to prepare Research Interview Questionnaires.

To do that, Interviews need to understand fundamental response biases, which can destroy the creation of the questionnaire and interview insights. Do thorough secondary research about the matter. Next, review the business goals and write the interview research goals and OKRs. And finally, have a structured questionnaire covering all types of questions. 

About Author,

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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