WalMart’s milk jug: great design or flop design?

The New York Times is reporting that WalMart’s new fangled milk jug is getting mixed reviews.

What’s not to like? Plenty, as it turns out.

The jugs have no real spout, and their unorthodox shape makes consumers feel like novices at the simple task of pouring a glass of milk.

The design of the milk jug is so bad that WalMart has taken to doing in-store demonstrations of “how to pour” with this new jug.

This jug is a design flop! Right?

Well not so fast. It seems that the designers of the milk jug created it for a specific purpose: to save money. The new jugs are stackable, saving shipping costs and space. The company saves up to 70% of labour costs using these new jugs. The milk arrives at the store fresher, sometimes even the same day. This jug is a great design! Right?

The truth is somewhere in the middle. If business requirements trump user needs, this product is a winner! It saves time, energy, and most of all, money. It’s easier to ship, easier to manage, and much more efficient.

But if user needs trump business requirements, then this jug is a total flop. No  one knows how to use it. They spill it. Their children can’t pour it themselves, forcing parents to spend more time to use the jug. They feel stupid when they can’t pour it correctly.  Talk about crying over spilled milk! WalMart’s new milk jug off-loads all its design failings onto its users, keeping all the benefits of the new design for itself.

WalMart is famous for putting its business needs ahead of its workers and its communities. Off-loading the negative effects of this milk jug onto its consumers? That’s another in a long line of WalMart putting itself and its shareholders first.

Great design aligns business and user. There are trade-offs in every phase of product design. But not knowing what your users before making a design change makes it impossible to do this. The verdict? Not a total flop, but clearly a business-driven design. Truly great design balances the user’s needs with the business’s needs.

The Myth of The “Average”

We bandy about the word “average” all the time. What exactly IS an average, and how does it help design research?

Use the average to quickly summarize something that is already a number: minutes, ages, heights, visits, etc. Don’t use the average to explain something that needs more detail. And keep in mind, the average gets “dragged up” or “dragged down” by extreme values. Sometimes it doesn’t tell you much of anything.

An example design research project might be about how people use their stoves in their kitchens. How can we use “the average” to help us design a new stove?

The average, in statistical language, is actually called “the mean,” which is a measure of “central tendency.” Researchers use central tendency to describe all their results quickly. Other measures of central tendency include the mode (the most common response) or the median (50% of responses are higher than this; 50% are lower).The mean describes the “typical” or average result.

But here’s the big myth: there is no such thing as “the average” in your data. If you ask 500 people to rate your new stove design on a scale of 1 to 10, and the average is 4, there is no guarantee that any single person actually said 4! In fact, the majority of responses could be higher than 7, but some 1s or 2s could “drag down the average.”

Worse, it makes no sense to use the “average” or “typical” in qualitative research. If you do interviews or observations, there is no way to calculate “the average.” So when you say, “the typical person has a four-element stove,” you’re actually doing a calculation. This may be actually quite false. What you may mean to say is “most people in our study have a four-element stove” (which is the mode).

Qualitative research does not accept the “typical.” It actually looks at each case individually and in enough detail to allow for exceptions or outliers. There is no “typical” case in qualitative research because you do not do calculations. You do not summarize your data in that reductionist way.

That said, how could you use “the average” in your kitchen stove study?  You can do a back-of-the-envelope calculation to summarize your data. The “average age” of your respondents, for example, will tell you about how old people are. The “average number of minutes spent cooking” will give you a snapshot of how long people spend in their kitchens. The “average purchase price of a stove” will also give you a quick snapshot. Using “the average” is to quickly summarize something that is already a number.

But the “average use of the stove”? That doesn’t make sense. Nor does the “typical grocery shopping process,” or the “average complaint of stove use.” These cannot be summarized in “the average.”

What product designers don’t get about gender

Newsflash from the Obvious File: The New York Times tells us that apparently, women like smart phones! This simplistic understanding of gendered experiences with technology is what makes poor technology. The journalist cites a marketer from AT&T, a supposed “expert” on gender-based design:

David Christopher, the marketing chief of AT&T’s wireless division, said women were less likely to be wowed by fancy gadgets. Instead, as smartphones have become sleeker, smaller and cheaper, they have become more appealing to them.

“Now they are small enough to be in your purse or pocket,” Mr. Christopher said. “Design does matter.”

Thank goodness Mr. Christopher is involved in product design! What might happen if my smart phone did not fit in my purse? Why, I may have to choose between lipstick and my smart phone! Quel horreur! This kind of simplistic approach to gender continues with a representative from RIM:

“We picked a shade of pink that fit in all kinds of settings — not too flashy,” said Mark Guibert, vice president for corporate marketing at RIM. “It was the only color that was purely driven by the female audience. Years ago the market was much more focused solely on function. Now there is more focus on lifestyle.”

Well at least Mr. Guibert is standing on guard for my right to have the right shade of pink. Does anyone else see what’s wrong with this picture? No? Allow me to enumerate the ways.

1. Gender-based features reinforce steretoypes: Product designers tend to reinforce systemic patterns when they create “women focused” features that speak only to the wife or mother role. Case in point is the notion that “juggling children’s schedules” is a women’s feature in smart phone design. It is more accurately known as a parent’s feature, but when it is only portrayed as one that suits mothers, we actually create a self-fulfilling prophecy. Women’s features become parent’s features.
2. Gender-based design is about recognizing systemic patterns and alleviating the burden of them: Women may indeed be more responsible for “juggling children’s schedules.” But instead of foisting this “women’s feature” in smart phone design, instead make it possible to literally share “children’s schedules” among all smart phones. This would allow parents to better share the burden of child rearing, without painting it as a “women’s feature.”
3. Not all women like pink: This seems pretty obvious, but it’s not. Some women do like pink, that is true. I personally don’t like my technology to look “girly” (or really anything else for that matter), nor do many of my female friends. To create a “women’s colour” of smart phone is to imply that that every other colour is a “men’s colour.” I can’t have red if I’m a woman? Is that what your product is telling me? How off-putting.
4. Gender is fluid and constructed: Let us not forget what Simone de Beauvoir told us: “One is not born a woman but becomes one.” By this, she means that we are taught what it means to be feminine. Likewise, we are taught what it means to be a man, to be gay, to be Black, to be an immigrant. Product designers must remember that not all women will accept what they have been taught. Moreover, product designers must also recognize their role in constructing and reconstructing gender.

Product designers who take their cue from the tenor of the NY Times’ piece will design staid, gender-constricting products that alternatively confine women to status quo roles, or alienate women who openly reject them.

The promise (and failure) of Brandtags.net

I loved it when I first saw it. Brandtags.net invites users to look at a logo and type in the first thing that enters their minds. I found it fascinating — until I realized it’s yet another example of poor research perpetuating negative stereotypes of women.

Type in “Oprah” and see what happens. The top three most entered words? Fat. Black. Bitch. Yes, that’s right, Oprah, the maven of women’s media landscape is nothing more than a fat black bitch. How valid a representation of Oprah is this?

Oprah’s media universe is worth a fortune. She earned $260 million in 2007 and is worth $2.5 billion. Her daily talk show alone gets 7.3 million viewers (that’s compared to 2.9 million viewers for Grey’s Anatomy).

So I got to thinking. How is Brand Tags so wrong? So nasty? So racist? (Type in NBA or Citibank and you’ll see what I mean). Researchers are Harvard have shown how stereotypes work. We know that people rely on implicit stereotypes when they make snap judgments. This is the downside of Malcolm Gladwell’s Blink.

We live is a complex social world. We try to make sense out of it by looking for patterns. Theorists Berger and Luckman call these “typifications” or roles that we take for granted. Typifications help us because they allow us to know what to do in social situations without really thinking about it, or, as Berger and Luckman explain it, they alleviate us from making “all those decisions.”

All Brand Tags really does is tell us what those typifications are for the people who visit their site. Who is visiting their site? We don’t really know. The first rule of sampling is to ask yourself, are the people who participate systematically different from the people who don’t?

People who participate in Brand Tags are obviously Web savvy. Someone forwarded them a link and they filled it out. Perhaps they read business media because Brand Tags has gotten some press. They have the time to enter text. They are also anonymous.

Is this what you would consider a “representative sample”?

Brand Tags has promise (I myself have used it to gain insight about a few things). But it mostly has the worst of our stereotypes. Is that insight? Perhaps. But it’s not insight about Oprah — it tells us a lot about the people who are talking ABOUT Oprah.

Online Surveys 101

Folks,

Below is a (very!) brief overview of online surveys. This slideshow, via slideshare, is intended for people in the Web design industry. IAs, designers, media planners, strategists, usability researchers, and producers will learn if they should, in fact, do a survey.

MESH 08 Presentation: Reputation Monitoring and Management

For those of you interested in my presentation from Toronto’s MESH 08 conference, here is the presentation via slideshare. Part of this presentation was inspired by my thoughts on the brand as a self.

A great summary of the talk by Mark Blevis, and another by Connie Crosby.

What is design anthropology?

Dori Tunstall has written a fantastic post that details how a simple card sort can become a deeper exercise in analysis. Dr. Tunstall is a PhD anthropologist and the University of Illinois at Chicago. She explains how anthropology takes design research to a deeper level in card sorting, a common technique for information architecture:

“In addition to the information architecture, I delivered statements about the continued meaning of gender classifications. In the course of conducting the card sort, I learned that men and women continued to classify domestic products based on stereotypical gendered spaces of male equals outside/garage, and female equals inside.”

Dr. Tunstall clearly lays out some of the deeper implications of design anthropology later in the post. This is a must read for anyone looking to deepen their design or research practice.

Getting meaningful insights from qualitative research

The output from qualitative research is often overwhelming. Unlike quant research, qual findings are often messy and hard to decipher. Here are some techniques to manage the voluminous data of qualitative studies.

1. Start with clear research questions: in an earlier post I explained how to set up a design research project, step by step. One of the most important steps is to create a clear and answerable research question. This seems like an obvious point, but often it isn’t. Qualitative research often appears to be “just talking to people,” which gives us all the mistaken impression that it is entirely unstructured. It isn’t. Take the time to define research questions.
2. Summarize frequently: Let’s say you’ve chosen to do in-depth interviews. After each interview, take 20 minutes to write out a brief summary of what you remember being the most important points of the interview (note that this is not a substitute to taking notes during the interview). These notes are the first step toward analysis. You are reducing “clutter” and irrelevant information. You are also exploring connections with previous interviews.
3. Reduce, reduce, reduce: You will always have more data (e.g., videos, photos, transcriptions) than you can use. Be ruthless by reducing what’s important. Edit down your videos to only the clips that are most important (keep the raw data for another time). Reduce your transcriptions down to select quotes that speak to your research question (and again, save the entire transcription for another time). The goal is to have a workable set of artifacts.
4. Visualize the results: Many qualitative researchers make use of summary tables and diagrams to further summarize results. My favourite visualization method is the mental model, which can convey a huge amount of information in a synthetic way, quickly. Other tools include mind maps and even the simple bulleted list.
5. Hunt for connections: There is no science to this process. It is iterative and intuitive. But there are approaches you can use to find connections. I frequently use the “open sort” technique, with nothing more than a blank wall and post-it notes. Scribble themes onto post-it notes. Sort them into categories. Name the categories. Collapse as many categories as you can until you only have 4 or 5 “buckets” that explain your findings. If you’re researching children’s commutes to school, for example, you may have a category called “independence” which would talk about kids’ desires to be grown up, to have their own transportation method, and the knowledge to get to school. They are related only through the higher-order notion of “independence” and not the lower-order ideas of “transportation” or “age.”
6. Ask “So what?” often: When I was in journalism school, I had a professor who tirelessly quizzed us with his version of so what: “What does it mean to metro?” he would demand, meaning, why should the people of this city care? Why should your design team care about these results? What does it mean for their process? Why should the users of this product care about your results? How might it make their lives easier or more pleasant? And of course, why should bean counters of all sorts care? How much money will it actually save?

These general guidelines will help you in your journey to deciphering meaning. But no qualitative project can be save from poor research design. Make sure you’re using the right approach.

Why music on mobile phones is not music

The music industry is in a pickle. CD sales are falling, big-name artists are signing with touring companies. Independent artists are having a go on their own. The solution, the industry thinks, is to sell its music through mobile phones.

They are dead wrong. Here’s why.

Mobile phone users don’t use music in the same way that music listeners do. Music listeners — whether at home or on the go with their iPod — are listening to what the artist has created. Even “digital” forms of the music are still relatively analogue because the listener cannot slice and dice the music into a new mashup. The best she can do is skip a track (which she has done since the times of vinyl).

Music on mobile phones has both “listening” and impression management behaviors. Mobile phone users use music more often to present a version of themselves to the public world than they do to actually “listen.”

Music on mobile phones has truly become what Nicholas Negroponte calls “co-mingling bits.” Mobile phone operators have already sliced and diced the music into snippets for its users to use in various ways. The ringtone is one version. The “ringback,” which a caller hears when he’s waiting for his friend to answer her phone, is another version.

Now mobile phone users can download all sorts of “co-mingling bits” off the Web. Some of these bits happen to be musical. Some of them are unrecognizable from what the artist originally intended.

This kind of behavior is not listening to music; it is impression management. What is the effect, for example, when your friend hears Paranoid while he waits for you to pick up the phone? What is the effect if it were I Can Hear You Breathe?

Music companies think  this new form of music consumption can save the industry. They hope that album sales will be replaced with mobile phone downloads of full tracks. They are wrong.

Consider the following numbers from eMarketer.

Full track downloads as percentage of ringback and ringtone downloads:
2006: 23%
2007: 33%
2008: 47%

While the share is growing, it is certainly not replacing album sales. Artists should recognize that mobile phone music is not “music” but the public adornment of their art. And music companies should recognize that mobile phones will not save a bloated and dying industry.

Why do ethnography?

Ethnographic research is mandatory for all design. Why? Because the role of design is to improve people’s lives. This you cannot do unless you know what people’s lives really are like — and not what charts and graphs and tables are like. Why do ethnography? Here are some clear reasons.

1. People don’t know what they actually want: Would anybody ask for a translucent mirror? That’s what they now get at Prada. The dressing room’s at Prada ’s flagship New York store allow you to do something you normally do — but better. Shoppers can first view outfits on themselves, then can invite their friend’s to view their outfit — but turning the mirror into a window. Instead of coming out of the dressing room, leaving your handbag behind, you can instead simply click a button with your foot, and show your new outfit to a friend.This innovation did not come from asking people what they want, but by thinking about the process of buying and trying on clothes.
   
2. Context matters: Most people who design mobile phones don’t think that electricity has anything to do with their product. But they are wrong. Researchers in Africa have learned that when the power goes out, people can’t charge their mobile phones. The solution? Various forms solar and wind-powered chargers.Designers must know where their product will be used. Deep insight into that context can only come from knowing the context.
3. People lie: A well known example of urban ethnography finds a contradiction. People say they want a quiet space to eat lunch, but when you watch lunchtime routines in urban spaces, people do anything but seek out quiet spaces. Now are they lying to be naughty? To be elusive? No, they lie because they believe the “normative” or “should do” practice of eating lunch is a quiet experience.Actual experience plays out much differently.
4. Designers design symbols — which can’t be understood through numbers: The reason why people love quantitative research so much is because it is short and easy to communicate. You know the “average” household income, instead of having to think of all the possible household incomes. You know how many people answered “yes” to a pre-defined question.Designers are designing or adapting symbols. They cannot do so without knowing what they represent. But you can’t summarize symbols. Symbols *are* summaries already — and not numerical summaries.A national flag conveys many ideas for people within that nation state (and many more for those outside it). Likewise, a kitchen stove is a symbol that conveys much about the household, gender relations, and family life. This cannot be conveyed in the “average” number of kitchen stoves.

Many designers will take numbers or focus group research or even usability test results and design their products. They may even improve people’s lives that way. But short observational research provides “thick description” that all designers need.