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El poder de la innovación se encuentra en resolver problemas reales para las personas, de forma creativa y reveladora. Encuentra aquí contenido sobre este tema seleccionando para ti.

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👩‍🔬 METODOLOGÍA: ¿Qué es el Diseño Centrado en las personas?

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👩‍🔬 METODOLOGÍA: ¿Qué es el Diseño Centrado en las personas?

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Whitemyer (2006) Anthropology in Design.pdf

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¿Cómo dirigir una entrevista increíble? No es un secreto pero tiene su chiste.

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How To Conduct User Interviews Like A Pro (UX Design)

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📖 LECTURA: Solo necesitas evaluar tu idea con 5 usuarios

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📖 LECTURA: Solo necesitas evaluar tu idea con 5 usuarios

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Las elaboradas pruebas de usabilidad son un desperdicio de recursos. Los mejores resultados provienen de probar con no más de 5 usuarios y ejecutar tantas pruebas pequeñas como puedas permitirse.

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Some people think that usability is very costly and complex and that user tests should be reserved for the rare web design project with a huge budget and a lavish time schedule. Not true. Elaborate usability tests are a waste of resources. The best results come from testing no more than 5 users and running as many small tests as you can afford.

In earlier research, Tom Landauer and I showed that the number of usability problems found in a usability test with n users is:

where N is the total number of usability problems in the design and L is the proportion of usability problems discovered while testing a single user. The typical value of L is 31%, averaged across a large number of projects we studied. Plotting the curve for L =31% gives the following result:

The most striking truth of the curve is that zero users give zero insights.

As soon as you collect data from a single test user, your insights shoot up and you have already learned almost a third of all there is to know about the usability of the design. The difference between zero and even a little bit of data is astounding.

When you test the second user, you will discover that this person does some of the same things as the first user, so there is some overlap in what you learn. People are definitely different, so there will also be something new that the second user does that you did not observe with the first user. So the second user adds some amount of new insight, but not nearly as much as the first user did.

The third user will do many things that you already observed with the first user or with the second user and even some things that you have already seen twice. Plus, of course, the third user will generate a small amount of new data, even if not as much as the first and the second user did.

As you add more and more users, you learn less and less because you will keep seeing the same things again and again. There is no real need to keep observing the same thing multiple times, and you will be very motivated to go back to the drawing board and redesign the site to eliminate the usability problems.

After the fifth user, you are wasting your time by observing the same findings repeatedly but not learning much new.

Iterative Design

The curve clearly shows that you need to test with at least 15 users to discover all the usability problems in the design. So why do I recommend testing with a much smaller number of users?

The main reason is that it is better to distribute your budget for user testing across many small tests instead of blowing everything on a single, elaborate study. Let us say that you do have the funding to recruit 15 representative customers and have them test your design. Great. Spend this budget on 3 studies with 5 users each!

You want to run multiple tests because the real goal of usability engineering is to improve the design and not just to document its weaknesses. After the first study with five participants has found 85% of the usability problems, you will want to fix these problems in a redesign.

After creating the new design, you need to test again. Even though I said that the redesign should "fix" the problems found in the first study, the truth is that you think that the new design overcomes the problems. But since nobody can design the perfect user interface, there is no guarantee that the new design does in fact fix the problems. A second test will discover whether the fixes worked or whether they didn't. Also, in introducing a new design, there is always the risk of introducing a new usability problem, even if the old one did get fixed.

Also, the second study with 5 users will discover most of the remaining 15% of the original usability problems that were not found in the first round of testing. (There will still be 2% of the original problems left - they will have to wait until the third study to be identified.)

Finally, the second study will be able to probe deeper into the usability of the fundamental structure of the site, assessing issues like information architecture, task flow, and match with user needs. These important issues are often obscured in initial studies where the users are stumped by stupid surface-level usability problems that prevent them from really digging into the site.

So the second study will both serve as quality assurance of the outcome of the first study and help provide deeper insights as well. The second study will always lead to a new (but smaller) list of usability problems to fix in a redesign. And the same insight applies to this redesign: not all the fixes will work; some deeper issues will be uncovered after cleaning up the interface. Thus, a third study is needed as well.

The ultimate user experience is improved much more by 3 studies with 5 users each than by a single monster study with 15 users.

Why Not Test With a Single User?

You might think that 15 studies with a single user would be even better than 3 studies with 5 users. The curve does show that we learn much more from the first user than from any subsequent users, so why keep going? Two reasons:

There is always a risk of being misled by the spurious behavior of a single person who may perform certain actions by accident or in an unrepresentative manner. Even 3 users are enough to get an idea of the diversity in user behavior and insight into what's unique and what can be generalized.

The cost-benefit analysis of user testing provides the optimal ratio around 3 or 5 users, depending on the style of testing. There is always a fixed initial cost associated with planning and running a study: it is better to depreciate this start-up cost across the findings from multiple users.

When To Test More Users

You need to test additional users when a website has several highly distinct groups of users. The formula only holds for comparable users who will be using the site in fairly similar ways.

If, for example, you have a site that will be used by both children and parents, then the two groups of users will have sufficiently different behavior that it becomes necessary to test with people from both groups. The same would be true for a system aimed at connecting purchasing agents with sales staff.

Even when the groups of users are very different, there will still be great similarities between the observations from the two groups. All the users are human, after all. Also, many of the usability problems are related to the fundamental way people interact with the Web and the influence from other sites on user behavior.

In testing multiple groups of disparate users, you don't need to include as many members of each group as you would in a single test of a single group of users. The overlap between observations will ensure a better outcome from testing a smaller number of people in each group. I recommend:

3-4 users from each category if testing two groups of users

3 users from each category if testing three or more groups of users (you always want at least 3 users to ensure that you have covered the diversity of behavior within the group)

Reference

Nielsen, Jakob, and Landauer, Thomas K.: "A mathematical model of the finding of usability problems," Proceedings of ACM INTERCHI'93 Conference (Amsterdam, The Netherlands, 24-29 April 1993), pp. 206-213.

Follow-Up Articles

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mathematical model of finding usability problems

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Elaborate usability tests are a waste of resources. The best results come from testing no more than 5 users and running as many small tests as you can afford.

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Guía práctica para aplicar herramientas de empatía en tu proyecto de campo.

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Aprendamos nuestro usuario y a capturemos los aprendizajes de nuestros clientes, después de conocerlos.

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Sirve para definir el público al cual tu solución esta tratando de resolver un problema.

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Make My Persona - Free Buyer Persona Template Generator (2023)

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Create professional, customizable buyer personas in minutes with the help of our intuitive free generator and share the templates with your team.

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📖 LECTURA: Desbloqueando el potencial de la etnografía digital

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📖 LECTURA: Desbloqueando el potencial de la etnografía digital

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¿Cómo empatizar cuando no puedes salir de casa?

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By Anne Lacey, antedote

The first time I drove across the United States, I couldn't help but be struck by how different each state and region feels. Even so, I saw that there were clear commonalities that tie together to make the U.S. a single nation. The closest I've come to gaining a grasp of what makes that nation tick has come from examining both the commonalities and differences between all its people and place. It's been the project of a lifetime to develop anything resembling a coherent and comprehensive of picture of just this one country.

Over time, this personal journey to understand the United States led me to examine my professional career, identifying consumer insights for innovation-how can I do qualitative research that enables me to spend time to understand what really motivates people and that gives a more representative picture of the country?

Over the last couple of decades, ethnography and qualitative research have become distilled into short dips of a couple of hours into peoples' lives, often in popular research cities such as Chicago. This type of research relies primarily on self-report by the consumer rather than observation or behavioral analysis. This has made it increasingly difficult to ascertain the kinds of new insights about fundamental shifts in people's needs and motivations that make the work truly valuable.

To gain to new insights and opportunities, we need to think and approach research differently. Digital ethnography can fuel new ideas and research approaches, as my colleagues at antedote and I have seen in the years since we designed and built a mobile and online tool for studies from the ground up. Although digital ethnography has become an umbrella term for a great many online qualitative research tools, we use it specifically to mean a lengthy study (a week or two to several months) with consumers via computer and/or mobile phone, comprised of a blend of observation, live experience-alongs, interviews and user-generated content. Though these elements are common to it, each study has custom elements to it, premised on one big idea: using cutting-edge technology to restore some of the original intent and benefits of ethnography.

Interestingly, the longer we've worked with it, the more we've identified practices that enable digital ethnography to provide an ideal complement and enhancement to other qualitative and quantitative methods. One of the key guiding rules we've found is to gather research based on the strengths of the technology rather than trying to directly translate other research methods to digital.

These are some of the guiding principles we have identified and apply: Observe real behavior in its natural setting to get to deeper learning

Seeing life as it happens in the real world is the promise of ethnographic research. In commercial practice, it's often a brief two-hour interaction, most of it spent chatting in the respondent's living room. The question is, how can we get a true picture of respondents' lives and dig into their conscious and subconscious motivators in two hours? A digital ethnography, by contrast, makes it possible to accompany people when and where they live their lives: at the office, the bar, curled up on the couch with a glass of wine or on a hike. You can see where they really go and witness in-context interactions and even communicate with them in the moment and gather insights that are not available after the fact.
Create space for reflection and dialogue over time to enable greater involvement
Face-to-face research is entirely of the moment. While experienced researchers leave room for respondents to think and consider before they respond, there still isn't time for a respondent to really weigh a question, sleep on it, and come up with something profound. Digital tools allow both respondents and researchers to reflect on their dialogue, get beyond the most immediate answers, and head to a deeper level of understanding. This allows for the development of a relationship between researcher and respondent-the kind of relationship that encourages deeper, richer conversation and sharing.
Capture content that can be quickly shared globally
One challenge to getting qualitative research and insights to affect the parts of the business they are meant to is that the people who need to act on that work often don't get directly involved. It's enormously valuable to get decision-makers in the field-and can also be impractical or impossible. But if a leader can have access to digital ethnography to read and watch videos from participants all over the planet and even interact with them, many of the same benefits that come from meeting consumers face to face can be realized. There is also no limit to the number of people who can observe digital ethnography, while packing twenty-some execs into a twenty-something's apartment is absurd on its face. Giving access to the entire team also creates space for all of them to have input on the study and evolve it as we learn, rather than waiting for the output to raise their concerns. Everyone can move from passive to active involvement fast and without a lot of travel and conference calls.
Capture actual behavior by connecting with consumers' existing apps.
If Apple's recent World Wide Developers Conference is any indication, apps are now prevalent and increasingly connected to each other. Consumers voluntarily share large amounts of their lives, be it where they are going for a drink or eating dinner to their sleep and fitness patterns. It's becoming possible to understand and analyze social and behavioral data from tracking devices and apps within the context of observation and consumer interactions, creating a richer and more detailed picture than would otherwise be possible.
Make both quantitative and qualitative studies richer
In spite of all the benefits digital ethnography can deliver, it should be viewed not as a substitute for existing methods but as a new approach that can complement and enhance what has come before. In-home interviews deliver information that digital ethnography cannot-for example, it's often hard to find the one artifact in the house that encompasses an insight through consumer-shot videos alone.

Digital ethnography can, however, help you both profile 75 participants in a week and then select the best ten to study in person the next week, as we did for a media study earlier this year. You can then create ten individual conversation plans in order to probe deeper on topics and activities that each participant can tell you the most about. When you actually show up in person, you'll have two hours of incredibly rich conversation and observation, rather than trying to cover every conceivable topic in a short window of time. Similarly, digital ethnography can help to bring to life the people found in segments from quant studies in a more involving way than summaries alone. Based on topics that rose to significance over the course of a digital study, it can also set the stage for quant by legitimately blending qualitative and quantitative methods to create something richer.

An important new research method in its infancy
Though it was first promoted in the design and innovation research community years ago, we are only now starting to realize the full potential of digital ethnography. It would be a shame if it didn't continue to evolve to enable research activities that would have been impossible without it. Like any technology, it needs time to grow beyond its initial use cases. The mobile phone was invented to enable voice calls outside the office and home-and voice is now at most a small fraction of what we use phones for.

What's most exciting is that there is room for similar transformation within the digital ethnography. It's already become a dramatically more useful technique since smartphones became ubiquitous in the U.S. and Europe. Now, with developing markets doing likewise, it's exciting to think of the possibilities there. Just imagine what will be possible as we become able to link these studies to fitness bands, blend it with big data or even ride along with respondents for a day through their eyes via a wearable like Google Glass! I can't pretend to know all of the uses creative researchers will find for it in the coming years, but I also can't wait to see what we collectively discover, so long as we remember to treat digital ethnography as its own practice-and not just as ethnographies conducted online.
Anne Lacey is a founding partner at antedote, a strategic innovation and insight consultancy based in San Francisco. You can reach her at anne[at]antedote.com.

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Unlocking the Potential of Digital Ethnography - Core77

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By Anne Lacey, antedoteThe first time I drove across the United States, I couldn't help but be struck by how different each state and region feels. Even so, I saw that there were clear commonalities that tie together to make the U.S. a single nation. The closest I've come to...

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📖 LECTURA: 10 maneras de integrar la etnografía en corporativos...

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📖 LECTURA: 10 maneras de integrar la etnografía en corporativos...

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...para lograr descubrimientos profundos de nuestros clientes y descubrir necesidades reveladoras.

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📖 LECTURA: ¿Cómo transformar una buena investigación en grandes innovaciones? 

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📖 LECTURA: ¿Cómo transformar una buena investigación en grandes innovaciones? 

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This is the first essay in a three-part series by Jon Kolko, author of the new book Exposing the Magic of Design, on how to embrace design synthesis in your organization.
You've just completed design research in the field, capturing hours of video, thousands of photos, artifacts, papers, and documents. And now, you're stumped and overwhelmed. What should you do next? How do you do it? And how do you know when you've done a good job?

Design synthesis - the process of translating data and research into knowledge - is the most critical part of the design process. Yet in our popular discussions of design and innovation, we've largely ignored this fundamental role. We engage in debates and discussions about process methodologies (waterfall vs. agile, user-centered design vs. technology-driven design) and management techniques (topgrading, negotiation), yet we rarely engage in conversation about incubation and translation: making meaning out of the data we've gathered from research, as we strive for innovation. It's as if this part of design is magical, and for us to formalize our techniques would somehow call attention to our sleight of hand.

We rarely engage in conversation about making meaning out of data.

Without a formal strategy and approach to synthesis, experienced designers rely on their intuition, built up over years of trial and error. "Just trust me," says the old-guard, and the discipline of design loses credibility. Worse, younger designers flail and waste precious time, becoming frustrated and ultimately rejecting the ethnographic research methods themselves. Yet it isn't the research methods that are at fault, as research itself does not produce new ideas. The approaches to incubation and translation can be formalized, and to do so offers a great service to designers who are struggling to work through increasingly complicated problems in business and culture. During design synthesis, truly revolutionary innovations emerge.
Focusing on Activity and Action
Most of us engaged in large-scale design efforts agree that ethnographic research is required to build empathy and understanding. Yet immediately after research has been conducted, a period of complacent inaction emerges. The team is overwhelmed, and without a clear indication of what to do next, they sit, idle, sifting through the transcripts and video with no rhyme or reason to their method. In my experience, clients - reacting to this inaction - dismiss the entire value of research at all. Similarly, creative directors - frustrated with a lack of forward movement - often circumvent process and rely on their personal experiences, doing whatever worked well last time, again. Precious time is lost; ideas become flat and tired; the team tends towards inertia. Here are three tactics to overcome this inertia.

1. Get out of the laptop. The data created or gathered from contextual research will often take many forms: photographs, video clips, transcripts, requirements lists, magazine clippings, and other artifacts related to the problem. In an effort to maintain some sense of coherence, we'll frequently horde the data in our laptops. The digital format lets us easily organize ourselves around files, folders, wikis and databases. But the file structure also arbitrarily limits the ability to manipulate individual pieces of data freely across file types, to form connections between pieces of data, and to manipulate the data quickly. The physical limitation of the laptop (the size), combined with the digital limitations of the software (the organizational schema), dramatically limits our ability to understand the research in totality. Externalize the data through a process of spatialization. This is the quintessential (and unfortunately overplayed) image of the designer moving Post-it notes around (typically on a glass wall, and usually photographed through frosted glass with a dramatic depth of field). Each Post-it contains a piece of data. A week of research will generate thousands of data points. Take the time to rigorously move from a digital medium to the physical world.

2. Identify and celebrate patterns and anomalies. As your data is now quite literally all around you, methodically identify relationships between customer utterances, feature requests, technical constraints, and other data points. These relationships are hidden in the data, and it's up to you and your team to identify them; the unique disciplinary makeup of the team will determine the lens through which you examine each data point. Quite literally, compare note 1 and note 2, and if they are related, place them next to each other. Repeat this, through several passes, over the entire room. Time-box the exercise based on your schedule; when you are done, look carefully at patterns you've discovered, as these can begin to articulate opportunities for "low-hanging fruit" or ?quick hits?. But don't discard the extreme outliers, either - as these articulate opportunities for dramatic and radical innovation.

If the data isn't modeled, it never happened.

3. Build a model of something, anything. A model is a visual representation of an idea; we use models to understand, and to communicate, and to simplify. Build a model of your data. Start with circles and arrows, and connect noun and verb pairs. Identify existing or potential work flow changes, and map them in a process flow. Extract the power dynamics implicit in any human system (literally, the people who hold the power, and whom they hold it over), and make a chart of this data. Because these are thinking tools, tools for synthesis, there's only one wrong way to do this: not doing it at all. Looking at the data and talking about the data doesn't count. If it isn't modeled, written, drawn, and otherwise solidified in an artifact, it never happened.

These tactics are fundamental for making sense of gathered research data, in order to move from data to information. This is the beginning of synthesis: Making meaning of data in order to produce new, compelling, and appropriate innovations. These tactics can be applied to the design of new products, both physical and digital, and to business process problems, systems and services, and even to complicated issues of channel optimization, go-to-market strategies, and other complication core business issues.

In Part II of this series, I'll describe the culture that's necessary to support these types of activities. See you next week.

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How Do You Transform Good Research Into Great Innovations?

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This is the first essay in a three-part series by Jon Kolko, author of the new book Exposing the Magic of Design , on how to embrace design synthesis in your organization. You've just completed design research in the field, capturing hours of video, thousands of photos, artifacts, papers, and documents.

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Una herramienta para ayudarte a diseñar tu entrevista.

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TIPS: 5 pasos para crear una buena entrevista

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TIPS: 5 pasos para crear una buena entrevista

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Entrada de blog con consejos para armar una buena entrevista

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Coleman Yee is an experienced UX practitioner, and over a month ago I've had the chance of personally learning from him. This post is a crystallisation of some of the lessons that I've picked up from Coleman.

User interviews are an exceptionally useful tool for user research, because it allows you to speak directly to users, and get responses to specific questions that you have. If done well, it could also uncover nuances or directions that were previously unknown or unthought of.

Like most research, however, user interviews are prone to bias, and interpreting responses can be tricky. But with a good guide in hand - and lots of practice - we can avoid the most common pitfalls and get the most out of user interviews.

The first step in conducting user interviews is the creation of interview questions. Badly scripted interview questions can not only nullify the benefits of user research, but also lead product development down the wrong path.

Here are 5 comprehensive steps that will help you create a list of good user interview questions.

This might sound obvious, but it's important that you know why you're conducting user interviews. In other words, you need to know what questions you want answered. These are the problem statements - write them down in a doc (or on a piece of paper).

Examples of Problems Statements: "Why do people make purchases online?" "How do people make purchases online?" "What's the difference between online and offline purchasing for the consumer?"

Now, think of different perspectives to express the same problem statements. This is more than an exercise in paraphrasing.

You'd have to imagine each statement as a piece of gem: your job is to turn it in your hand and look at it from different angles.

Here's an example:

As you can see, each of these reframes provide a different perspective to the same topic: the original statement is more logic/rationale-driven, while reframe 1 made it more emotion/desire-driven, and the last 2 reframes focus a bit more on the product instead of the consumers.
The benefits of reframing problem statements are two-fold:

1. You stumble upon questions/perspectives that you might not have considered at the beginning, that might help you with your research.

2. You lay a base on which to build your interview questions.

If you did step 2 above properly, you'd already have laid a good foundation to build your questions. In other words, you kinda know what questions you want to ask. The tricky bit, then, is how you should ask them.

Here are some general guidelines to help you phrase your questions properly:

A) Don't ask leading questions.

Leading questions are questions that are framed (wittingly or unwittingly) in a way that influences the answers given by respondents.

Leading Question: "How angry do you usually feel when an online transaction fails to go through successfully?"

The example above is leading because it focuses the respondent's mind to a single emotion (anger), and makes it unlikely that he/she will talk about other emotions, like anxiety, or shock.

The purpose of conducting an interview is to get real, truthful answers to questions that you have. Asking leading questions defeats that purpose, so don't.

Non-leading Question: "Recall a time when an online transaction failed to go through successfully. How did you feel?"

Unlike the first example, this non-leading question opens up the whole range of emotions to the respondent.

Note, however, that the question still frames the respondent to only think about the emotions that he/she associates with failed online transactions.

Unless the research that you're doing is concerned primarily about the emotions of online shoppers, you might be better off asking this:

Even More Non-leading Question: "Recall a time when an online transaction failed to go through successfully. What did you think or feel?"

Now, the question isn't restricted only to emotions, but also includes the respondent's thoughts. Accordingly, you should probe further about their emotions if they focus too much on their thoughts, and vice versa, to get both aspects of the answer.

All in all, it's easy to ask leading questions, but also easy to avoid them. All it takes is a little more thought.

B) Ask about specific incidents in the past, when possible.

The more speculative a question is, the less you can rely on it. That's because you're effectively asking your respondents to create scenarios in their heads, and then answer questions based on those imagined circumstances.

In other words, the rigour - and truthfulness - of their answers become dependent on the rigour and truthfulness of their imagination; you're increasing your chances of getting bad data.

Using the same when-an-online-transaction-fails question, here's an example of a speculative, versus a context-based, variation:

Speculative Question Based on Imagination: "Tell me what goes on in your head when an online transaction fails to go through successfully." Context-based Question Grounded in Reality: "Tell me what went on in your head the last time an online transaction fails to go through successfully."

The second question gives respondents a specific context to hold on to when answering your question, which in turn increases your chance of getting genuine, insightful data.

Of course, speculative questions have their advantages, but as much as possible, they shouldn't be used to uncover current or past behaviours.

C) Ask more open-ended questions.

Sometimes, you might want to cast your net a little wider, because the respondent might have something relevant to say that you didn't think of asking.

Specific Question: "What was the last thing you bought online?" Open-ended Question: "Tell me more about the last time you purchased something online."

The first question asks specifically about the product being bought, while the latter asks about the experience in general. If there were any part of the purchase that stood out to the respondent, it would've been missed by the first question, but captured by the second.

And if the respondent didn't mention anything about their purchase that you think is important, you can always follow up and ask.

D) You can ask more than one question to find out the same thing.

Doing so allows you to have more than one data point to verify that you've understood the respondent correctly, and to check for contradictions in the respondent's answers. The reframing exercise in Step 2 should've already yielded you multiple questions for the same topic.

Here's an example:

Question 1: "What are some reasons why you shop online?" Question 2: "What are some things that will make you decide not to buy from an online store?"

Do note that these 2 questions don't necessarily have to come one after the other; pick a flow of questions that make the most sense to the respondent (more on that in Step 5C).

(Extra reading: if you're interested, you can look up a similar research method called " data triangulation ", which uses multiple data, theories, or methods to inform research findings.)

E) Don't ask if they'd buy your product.

As tempting as it is, don't.

As I mentioned in this blogpost, asking a respondent if they'd buy your product is akin to asking a mother of two if she loves one of her kids more - it'll take a brutally honest person to say no.

What you should do, instead, is to ask questions that reveal your respondents' intent to purchase.

The point of this step is to have a list of paraphrases, in case the respondent doesn't understand what you mean the first time you ask them.

You can skip this step for super-obvious questions, like what the respondent usually shops for online.

A user interview is usually made up of 4 parts: an introduction, some warm up questions, the main body questions, and a wrap up.

A) Introduction

The point of this section is to give the respondent some idea of what's going on, so that they don't feel too confused throughout the interview. But be sure not to tell them too much, because you don't want to overly frame their minds to think in a specific way.

For instance, let's say you're working on an app that secures online transactions, and you want to find out how important online transaction security is to online shoppers.

If you let your respondents know exactly what you're working on, you would've already preconditioned them to think about online security. This makes it hard for you to determine if online security is really something that they think about when they shop online.

In general, it's better to keep the introduction slightly vague, but let your respondents know that you'll address their queries at the end of the interview.

This is also a good point to let the respondent sign any release form that you have, and ask for permission to record the audio/video of the interview.

Here's an example of an introduction that I often use:

"Hi Alice, thanks for coming down. My name is ______, and I'll be conducting the interview today. We're doing some market research, so some of the questions might be a little personal, and will concern your lifestyle. If you feel uncomfortable answering any of the questions, just let me know and we can skip it. I can't tell you much about what we do or the exact reason we're doing this research now, because it might affect the way you answer the questions. But I can definitely tell you after the interview is over. Do you have any questions so far?
[pause for respondent's reply] If you don't mind, I would record the audio for this interview; it will only be used internally, for the purposes of this research."

B) Warm up questions

At this point, you want to start getting your respondents comfortable talking. Ask them 3-5 generic questions that are related to the topic of your interview.

Examples of warm up questions: "What's your occupation / what do you do all day?" "Can you tell me more about your hobbies?" "How often do you use the internet? etc."

C) Main body questions

This is the meat of the interview, and pretty much the whole point of conducting one to begin with.

You should already have a full list of questions that you want to ask your respondents, and the last thing you'll have to do now is to order them.

As much as possible:

1. Start with questions relating to specific incidents in the past, before moving on to speculative ones.

2. Follow a flow that makes sense to the respondents. For example, ask about their general buying behaviours before asking specific questions about their thought process behind different stages of the buying process.

D) Wrap up

At the end of the interview, you should give your respondent a sense of closure.

Don't forget to ask them if they have anything they'd like to add or ask - more than once, I've received insightful data from respondents at the end of their interview.

Finally, thank them for their time and contribution towards your research.

5 steps to create good user interview questions:

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5 Steps to Create Good User Interview Questions By @Metacole - A Comprehensive Guide

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Coleman Yee is an experienced UX practitioner, and over a month ago I've had the chance of personally learning from him. This post is a crystallisation of some of the lessons that I've picked up from Coleman.

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Mr. Nichols, who heads up a Communications Program at the University of Minnesota, is nationally known for his many articles and lectures on communication problems. In 1951-1952 he served as President of the National Society for the Study of Communication, an organization chartered to make a continuing and systematic study of communication as a means to building more harmonious relations within groups and among individuals. He has also served on the editorial boards of two national publications, has taught in a wide variety of adult education programs, and has been President of the State Speech Teachers Associations in Iowa and Minnesota.

Mr. Stevens is a free-lance writer and a consultant on oral presentation to a number of leading companies and also is affiliated with Management Development Associates of New York. For two years he served as News Editor and Newscaster for educational radio station WSUI in Iowa City. He was doing research on the comprehension of news writing and broadcasting at the University of Iowa when he met Mr. Nichols. They collaborated on some short articles on listening and now have a book coming out on the subject-Are You Listening? (McGraw-Hill Book Company, Inc., 1957).

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Recently the top executives of a major manufacturing plant in the Chicago area were asked to survey the role that listening plays in their work. Later, an executive seminar on listening was held. Here are three typical comments made by participants: "Frankly, I had never thought of listening as an important subject by itself.