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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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Lake, Lakehouse, or Warehouse? Picking the Perfect Data Playground

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In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes 

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

FeatureData WarehouseData LakeData Lakehouse
Data TypeStructuredStructured, Semi-Structured, UnstructuredBoth
Schema ApproachSchema-on-WriteSchema-on-ReadBoth
Query PerformanceOptimized for BISlower; requires specialized toolsHigh performance for both BI and AI
AccessibilityEasy for analysts with SQL toolsRequires technical expertiseAccessible to both analysts and data scientists
Cost EfficiencyHighLowModerate
ScalabilityLimitedHighHigh
GovernanceStrongWeakStrong
Use CasesBI, ComplianceAI/ML, Data ExplorationReal-Time Analytics, Unified Workloads
Best Fit ForFinance, HealthcareMedia, IoT, ResearchRetail, E-commerce, Multi-Industry
Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

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