Shades of Blue: Marrying Art and Science

When chemists at Oregon State University4.jpeg discovered a brilliant new blue pigment serendipitously, they were not thinking about
creating art. But in a true art meets science moment, an applied visual arts major bean using the blue pigments in her artwork as part of an internship in Subramanian’s laboratory. This was also her first foray into the world of chemistry. Human history is filled with examples of innovation that occurred at the juncture of art and science, whether it’s as profound as Leonardo da Vinci’s explorations of anatomy or as mundane as liquid nitrogen ice cream. The point is simple – creative inspiration, whether in product development, advertising, or any other activity, is a matter of rethinking how we look at a problem.

Driven by CEOs that want to see ROI and engagement for every cent spent versus the equally valuable but often nebulous idea of “brand impact,” campaign and branding initiatives can be particularly challenging for CMOs today. Seemingly competing world views clash in large part because we take a binary position – it’s an either/or mentality where art and science are somehow in conflict. But is that fair or is it a modern construct? Are art and science so divergent or have we slipped into a lazy pattern of thinking.

Brands that want to take advantage of the intersection of art and science can start by simply acknowledging the fact that creative and metrics are not mutually exclusive concepts. By blending these two components of the creative process (and yes, science is a creative enterprise) and giving them a common goal to work tow
ards, we see focused innovation. We see new expressions of a common undercurrent.

Blending art and science is about collaborating in ideas generation: the inter-relationship is critical, you can’t have one thing without the other. Code or data are
just a bunch of numbers without the art. A visual masterpiece that produces no action is inspired but not inspiring. Science enables us to be more creative, and creativity allows us to get the most out of our data. But consider “the multiplier effect”. If either the data or creative are bad, the idea will fail. Or worse yet, if they work alone, without the cross-pollination that happens when different ways of experiencing the world come together, then the result can be flat out detrimental. It’s not one or the other that we need, it’s both. It’s not science plus art equals results, it’s more science times art, so a zero for either means failure.

That is where the interesting ideas are – at the intersection of exploration. The future is all about ideas connecting. Those who can bridge art and science will be in demand, will be powerful. If our ideas are going to change hearts and minds, then we need to find expression that can move freely between the boundaries of art and science.

 

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Big Data vs. Insights

Over the last 20 years and the emergence of digital as a central element behind marketing and advertising, the industry had gotten smarter and smarter, creating an expanded set of new metrics: dynamic segmentation modeling, click-through rate, impression share, engagement rates, share of voice, bounce rate, etc. Even with these, it is still very hard to measure success in a clear, distinct way. With technology and consumer behaviors evolving as fast as they do, we face new issues every day, from different attributions models to cross-device measurements to connecting online activities to offline sales.

Out of this barrage of metrics grew the messianic promise of Big Data. Add to that the rise of business intelligence tools, and suddenly every agency, no matter the size, needs to have a data scientist. Don’t get me wrong, talented data researchers and masters of analytics have helped shape since the earliest days of advertising data scientists have revolutionized the advertising industry. However, the work has also left many in a situation where they are unable see the forest for the trees, let alone align metrics with creativity and business objectives.

As much as I love data, and I do love it, the whole Big Data movement has come with a hefty price tag. We have lost the ability to tell meaningful stories or insights in favor of huge reports filled with analyses and pivot tables. We have all the data can’t make sense of it in a new, dynamic, enlightened way that makes for advertising and marketing that make brands sing and become part of the broader social fabric. We can target the living hell out of people, but that doesn’t mean what we tell them resonates.

The data we use should help us to create the story, answer questions, and find moments of inspiration. Furthermore, the data should be a tool rather than an object we roll out in lieu of light-bulb moment. Too many agencies have fallen under the data spell and have forgotten to turn those results into stories that align to a client’s objectives and strategies. It’s like talking to a customer about product features (empty of emotion) without selling them on the benefits (the emotional hook).

Quite simply, we need to get back to delivering meaningful consumer insights instead of only data. Delivering insights means telling the brand what is going to happen in their industry, how something we did had an impact on their bottom line, or how we discovered something that will change the way they do business. Simpler still, an insight produces positive change, regardless of whether it comes from data, an interview, or a poem for that matter.

 

Finding Balance: Data, FIeldwork, and Creativity

There is perhaps nothing new about the ongoing battle between data and qualitative work, and the influence they have on creativity and design. Data is everything, creativity is dead vs. the argument that creativity is paramount and data is a distraction. Neither position is true, though there is some truth in each argument. The goal is to deliver insight that inspires creativity, regardless of the methods by which we gain those insights. The central need is to determine how data and inspiration work together to drive change.

As advertising, marketing, and design come to rely more on technology, we are forced to reconsider what constitutes creative quality. It also means being honest with ourselves and recognizing that data is not a panacea. It, like qualitative work, is part of a thinking process that helps identify the underlying story we need to divine and craft tools that inspire action. At times that can be found in the data alone, but more often it’s found among outliers. Without the two sides working hand in hand, we get half truths.

For marketers, nothing could better define both the essence and preeminence of creativity than empathy. We all recognize the pace of technological change and changing customer behaviors. And we all recognize there is tremendous opportunity in being able to derive greater targeting from the data we collect. But behavioral measurement shouldn’t lull us away from using the creative process to intuit what customers will experience, whether we’re trying to convince them to take an action or building a tool to meet a need. Data underpins everything, but meaningful success will come to those who can augment data with a deeper understanding of the audience. What role does symbolism play? What metaphors connect? How does the object we create make sense in their lives? These are the sorts of things we come to understand through deep immersion.

As an example, some years ago I did work on a medication used in treating schizophrenia. Based on the success rates and data collected about patient behavior, it should have been an easy product to market. However, the sales were flat. It wasn’t until we began examining the process of schizophrenia that we were able to tease out where the problems were. Access to transportation, difficulties with case management, distrust of the psychiatric community, and the role of friends and family all had a significant impact on how the medication was understood. This wasn’t the sort of thing you could get at via data analysis. And yet, using the two methods together allowed the team to develop creative work that resonated deeply and was targeted at the right place and the right time.

What we need to be doing is rebooting brand planning as a qualitative and a quantitative art. What designer, strategist, etc. tasked with building a tool or developing an engaging brand experience wouldn’t want to know a bit about how the audience for their art behaves? How they engage with content? How they engage with a device? But the trick is not getting caught up in the numbers at the expense of the human being behind them.

Data, Advertising, and the Death of Forests

In every aspect of business right now, companies collect data until they see a pattern that appears statistically significant, and then they use that tightly selected data to drive decisions. The problem is, we assume that the data has merit, that it is objective, and that it holds the answers that will change the way business is done. Data is anything but objective because there are always humans involved. Critics have come to call the problem p-hacking and the practice uses a quiver of little methodological tricks that can inflate the statistical significance of a finding:

  • Conducting analyses midway through experiments to decide whether to continue collecting data
  • Recording many response variables and deciding which to report post analysis
  • Deciding whether to include or drop outliers post analyses
  • Excluding, combining, or splitting treatment groups post analysis
  • Including or excluding covariates post analysis
  • Stopping data exploration if an analysis yields a significant p-value

Add it all up, and you have a significant problem in the way our society produces knowledge. Increasingly, we desperately try to reduce the vast complexity of the world into a series of statistics that we can use to try to comprehend what’s happening. As if staring at the numbers long enough will give us the secrets of the universe. We divest brands of meaning, devalue the art of marketing, and fixate on sample size. But the world is a bit more complex than that. And when we get it wrong, it can be a disaster.

In the second half of the 18th century, Prussian rulers wanted to know how many natural resources they had in the forests of the country. So, they started counting. And they came up with these huge tables that would let them calculate how many board-feet of wood they could pull from a given plot of forest. All the rest of the forest, everything it did for the people and the animals and general ecology of the place was discarded from the analysis.

But the world proved too unruly. Their data wasn’t perfect. So they started creating new forests, the Normalbaum, planting all the trees at the same time, and monoculturing them so that there were no trees in the forest that couldn’t be monetized for wood. Based on the data at hand they began to transform the real, diverse, and chaotic old-growth forest into a new, more uniform forest that could be controlled.

And for the first hundred years or so, the scheme worked. But then the forests started dying. The complex ecosystem that underpinned the growth of these trees through generations were torn apart by the rigor of the Normalbaum. The nutrient cycles were broken. Resilience was lost. The hidden underpinnings of the world were revealed only when they were gone.

Now, take the ad-supported digital media ecosystem. The idea is brilliant: capture data on people all over the web and then use what you know to show them relevant ads, ads they want to see. Not only that, but because it’s all tracked, an advertiser can measure what they’re getting more precisely. And the spreadsheet makes an awful lot of sense at first. Unfortunately, looking at data alone overlooks the peculiarities and complexities of the human experience. Because data is very good at answering how and what, we assume it can also answer why. This is in fact rarely the case.

Advertisers and ad-tech firms want to capture user data to show them relevant ads. They want to measure their ads more effectively. But placed into the real-world, the system that grew up around these desires has reshaped the media landscape in unpredictable ways.

We’ve deceived ourselves into thinking data is a camera, but in fact, it is an engine. Capturing data about something changes the way that something works. Even the mere collection of stats is not a neutral act, but a way of reshaping the thing itself.

There are numerous quotes about how important data is, and how decisions should always be backed by data. Data is one perspective. What your users are saying is another perspective. What you internally want to do is another. What makes financial sense is another. To make a decision you gather the perspectives that matter to you, weight them according to your judgment and then make your call. Data is a false god. You can tag every link, generate every metric, and run split tests for every decision, but no matter how deep you go, no matter how many hours you invest, you’re only looking at one piece of the puzzle.

 

 

 

Doing Microethnography

Microethnography is a powerful method of research for studying practices in dynamic social systems where interactions reproduce unexplored or poorly understood conditions. It is a powerful intervention for discovering, making visible, or getting at what is happening as it happens in the interactions. Analyzing moment-to-moment interactions enables a better understanding of practices and expectations in order to create spaces to transform meaning and activities that maintain the status quo. But what is it and how does it differ from traditional ethnography?

First, microethnography is NOT simply a small group of in-depth interviews. While the sample is generally small and the timelines compressed, there are process behind doing it well and producing something useful for the client,. Microethnography is the study of a smaller experience or a slice of everyday reality. Microethnography is the process of data collection, content analysis, and comparative analysis of everyday situations for the purpose of formulating insights. It is tight, focused and targeted.

Like traditional ethnography, microethnographic research that attends to big social issues through careful examination of “small” communicative behaviors, tying them back to specific business and design needs. The research and/or research teams study the audible and visible details of human interaction and activity, as these occur naturally within specific contexts or institutions. Microanalysis may be coupled with statistical data to form a more complete understanding of the question at hand, but microethnography always employs ethnographic methods such as informant interviews and participant observations, all in an effort to better understand practices and problems.

Microethnographic methods provide qualitative, observational, cross-cultural, and ethnographic data, giving researchers the potential to 1) examine consumers, users, etc. across their community contexts, explicitly addressing class, power, and cultural structures of that community and 2) explain disproportional uses and buying patterns among subgroups.

While it also takes observation and environment in to account, microethnography focuses largely on how people use language and other forms of communication to negotiate consent with attention given to social, cultural, and political processes. Informed by critical discourse analysis, it emphasizes how the uses of language simultaneously shape local social interactions and reproduce patterns of social relations in society. The central difference between microethnography and in-depth interviewing ultimately is the analytical process and the phases that make up the research itself.

Data collection and analysis for microethnography typically takes place in six stages:

  • Stage One: Data Collection for the Primary Record – This consists largely of passive observation in the settings/contexts in which an activity occurs. It is meant to give a grounding in the activities occurring with objects, people and brands to create not only data points, but the right questions.
  • Stage Two: Reconstructive Data Analysis of the Primary Record – This consists of rough, unstructured, brief interviews and information gleaned for intercepted conversation. Initial meaning reconstruction, horizon analysis, and validity reconstruction take place at this stage through the review of transcripts and videotape.
  • Stage Three: Dialogical Data Generation – During this phase the research relies on a mix of in-depth interviews and feedback interviews with participants. A series of hypotheses are in place and pinpointed concepts are addressed with the participants.
  • Stage Four: Reconstructive Data Analysis of the Interviews – Once interviews are conducted, a second phase of meaning reconstruction and stage horizon analysis are conducted to uncover contradictions and pattern of practice and meaning. Out of this process, specific design and business needs are aligned.
  • Stage Five: High-level Coding – At this stage linguistic and behavioral matches are made. Out of this analysis, the multidisciplinary team begins to create new product or branding concepts and build out how they would actually function and gain traction with customers or users.
  • Stage Six: Final Reconstructive Analysis – This is the stage when we put new concepts and old to use. During this phase, new design or branding ideas are presented to participants, who work directly with the research and design team to generate co-created ideas and concepts.