Package it, slap a label on it and sell it for $4.99 a pound. It’s as simple as that when you’re selling groceries, right? Hardly. Food, meat in particular, is tied to cultural sensibilities about production, cleanliness, family values and a host of other topics.
Meat, like Norman Rockwell images of the American farm, is myth. We’ve been conditioned to turn away from the origins of our food and respond to blood and death with repulsion. Or have we?
With wealth comes the desire to learn about where our food comes from, how it’s produced and what exactly is in it. The point is that shopping for food is an increasingly complex process as has less to do with securing calories than it does with symbols and meaning.


Defining Context

Planners, researchers and marketers increasingly think about consumer in complex ways. We understand that in a changing digital landscape, where people are dialed in 27/7, the context in which they learn and shop is incredibly important and influences what messages we deliver and how we deliver them.  So increasingly, we are thinking about what situations govern behavior and designing to fit that complexity. 

We spend a great deal of time talking about context, but rarely use models to define elements of it.  This particularly true when talking about mobile devices and accounts for the hit-and-miss quality of  most apps available on the market.  It is one thing to design a usable app that conforms to human factors and cognitive requirements, but it is quite another to design a stage in an environment, or an environment itself, when there are innumerable semi-autonomous devices mediating an swirl of information.  Consequently, it makes sense for us to think about how we structure context so that we can determine what exactly we can affect.

Physical Context

From the computational side of things, physical context refers to the notion of imbuing devices with a sense of “place.”  In other words, devices can distinguish the environments in which they “live” at any given moment and react to them. But this is much more difficult than it at first appears. Mapping out longitude and latitude is one thing, but reacting to socio-cultural features (political, natural, social, etc.) is much more problematic. Getting beyond demarcation of identifiable borders and structures, means coming to grips with place (as opposed to space).  That in turns having to be “aware” on some level. 

Think of a mall.  Within that mall are hundreds of stores, each with hundreds of devices and/or nodes of information. The device now has to decode what information is most relevant to itself, what information is most relevant to the user and how it will deliver that information.  Returning to the mall example, we have to think about a host of things in order to make any app relevant.  What competing retailer apps get precedence over others? When you receive an offer from one store, will the device “tell” other retailers in order to generate real-time counter offers?  When someone else is holding your device for you (say, while trying on clothing but needing to set the iPad aside or while your child plays Angry Birds on the couch in the evening), how will the device know what incoming content is private and what is public?  How will the device communicate with a location or with other devices as it moves throughout the mall? Is it even necessary? The point is simply this; we increasingly have access to the digital landscape at all points throughout the day and getting design right means understanding the systems in which people operate.

Device Context

Just as various kinds of sensory apparatus (GPS-receivers, proximity sensors, etc.) are the means by which mobile devices will become geographically aware, another class of sensors makes it possible for devices to become aware of each other. There is a fundamental difference between the ability to transmit data between devices and the ability (and desire) of devices to discover each other. And this presents a series of problems that are different in nature than those of physical context. Because this deals with choices of communication.

We are on the verge of existing in a world with zero-infrastructure networks that can spring up anywhere, anytime. That means that devices are in a potentially constant state of discovery.  Returning to the mall for a moment, imagine that you are with a friend whose device is communicating with yours.  In there mall are a couple of thousand devices, all of which are discovering each other.  What happens now?  Assuming we’ve dealt with the problem of my mobile phone communicating with my friend’s phone while blocking out the other 2000 devices, we still have several thousand potentially “identities” that may have useful information for us.  How do we select how to manage that without devoting a ridiculous amount of time to setting up the hundreds of variables that shape what we do and don’t want at any given time? Perhaps more importantly, how do we develop a process to manage it that mimics, or at least compliments, the human brain and cultural patterns of behavior? All this is couched in a neat little world defined within a single, bounded  geographical unit.  So understanding device context is as important as understanding physical context.

Information Context

This is the realm of information architecture, plain and simple.  But with the advent of pervasive mobile, this topic is becoming even more complex.  Specifically, data no longer resides, literally or figuratively, “in” our computers.  Our devices are extensions of the cloud and exist as something akin to perceptual prostheses.  They exist to manipulate data in the same way a joy stick allows us to handle the arms of robot in a factory.  And this is important because it reflects a shift in how we think about and use information because all information (and the aps that carry that information) is transitory and by and large, public. 

 This changes the nature of what the device has to actually be. Storage issues are essentially removed from the equation.  Content can leap from place to place and device to device in an instant. All content will be customizable and reflect the human-application interaction rather than shaping it. This leads to the point that devices, and the people who use them, will find themselves in the 4th kind of context of social interaction, with all its peculiarities and contingencies. Just as our behavior and worldview shapes and is shaped by the moment in which we find ourselves, so too will our apps and information need to adapt to the moment.  In other words, devices will need to be more human.

Socio-Cultural Context

The whole humankind is riven with contrasting practices, cultures, tongues, traditions and world views. A cultural context may exist on levels as diverse as a workplace, a family, a building, a city, a county, a state, a nation, a continent, a hemisphere etc. A cultural context provides a shared understanding of meaning provides a framework for what “works” in the world. It is what helps you recognize “your kind” in all senses of the word.

And it is at the point of socio-cultural understanding where we gain a better perspective on what will and will not be accepted in the mobile universe.  We need to understand the essence behind the veil of design and usage to uncover meaning.  Take the beer pouring app as an example.  Here we have a simple app that mimics the pouring of a beer when you tilt your device.  On the surface it has little relevance to our daily lives.  It serves no direct function and yet it has been tremendously successful because of the cultural needs it to which it speaks – workplace breaks from the mundane, the ability to show off the newest thing, male-to-male bonding, etc.  Its absurdity is precisely what makes it relevant.  But in another context, say Saudi Arabia, the context shifts and meaning must change to fit that particular milieu.

The nature of our successes lies in understanding the reasons behind our beliefs and actions, in the symbolic exchanges we are part of and our abilities to code and decode those symbolic exchanges.  The nature of our mistakes essentially lies in a lack of comprehension. It leads to UI and app development that speak to a minority of the population even as they try to sell to the masses. Without understand the underlying epistemological constructs of a group (or more accurately, a mix of often associated groups at different points of interaction and interpretation) then we miss opportunities.

So What?

So why does any of this matter?  It matters because good design and messaging are increasingly difficult to master.  Our great technological leaps forward have also produced more complexity, which in turn leads to a greater need to make sense of what is “going on” in the broadest sense of the term when it comes to gathering insights and translating them into design and business applications. Without a means by which to categorize context, we can’t isolate those things that matter most and we miss enormous opportunities. So how do we get at underlying contexts? To be perfectly blunt, there is no perfect system because contexts change if we’ve done our jobs well (cause and effect), but there are ways to come close. Depending on the project, questions may be very tactical and specific or very strategic and broad. In either case, the first step is to clearly articulate what the overarching goal is.

First, rethink the problem. Frequently, what we see as the problem is in fact a facet of something else. For example, when researching something like an eBook the problem to be solved isn’t technology, it may be understanding why people read different material in different contexts. It may be about displaying books for colleagues and friends as a means of gaining status. The point is that the problem we see may not be the problem at all and we need to think about possibilities before we enter the field.

Second, begin defining the contexts.
Where does an activity or practice take place? Defining the contexts we want to examine helps articulate the range of possibilities for observation. For example, if we’re studying beer drinking, we need to articulate all the possible contexts in which beer is purchased and consumed.

Third, think through the complexity of the sample.
Who are the people we want to talk with? What are the social and cultural circles that will shape the event? It isn’t enough to define a demographic sample, you need to think in terms of cultural, social, professional and environmental systems, determining not only who will be the primary participants, but also the actors that shape the context.

Fourth, make a game plan that involves direct experiential information gathering, don’t just dig into statistics. Put together a guide to help navigate the data collection and a method for managing the data (remember, everything is data and it is easy to become overwhelmed without a plan). Having a series of key questions and observational points to explore is the first component. But don’t just think about the questions you will ask, but also include opportunities for observation, mapping, and participation.

Fifth, head into the field.
This is the heart of the process. Meaningful insights and moments of “truth” are slow to get at. Low-hanging fruit will be easy to spot, but the goal should be to find those deeper practices and meanings. Because everything is data, from attitudes to mannerisms to artifacts, it is important to capture as much as possible. Take notes, draw maps and sketches, take photographs, shoot video, and collect audio – the smallest piece of information may have the greatest impact

Sixth, do the analysis. Hands down, analysis is the most difficult, but also the most rewarding part of research. A trained ethnographer, for example, will do more than report anecdotes. A trained ethnographer will bring a deep understanding of cultural understanding and social theory to the analysis process. This goes beyond casual observation and starts to pull together the web of significances and practices that get to the underlying structures of why people do what they do. Analysis should always work within a framework grounded in the social sciences. Analysis takes time, but the results will include modes of behavior, models of practice, experience frameworks, design principles, and cultural patterns. Once the data has been analyzed and crafted into something meaningful, the research team should be able to provide a rich story with a clear set of “aha” findings.

Finally, it isn’t enough to simply hand off results. As compelling as we may find our insights, that doesn’t always translate into someone seeing immediately how to apply them. Once insights and findings are shared, you need to work with others to craft those findings into action plans, product ideas, etc.

The end result is that you create greater value for the client and for yourself. The process is, admittedly, more time consuming than traditional approaches, but it ultimately yields greater insight and reduces time and costs on the back end. It also yields better work that will impact the customer or end user more significantly.