Leo Burnett - Cultural Fuel

I recently had the pleasure of being a panelist at Kellstadt Marketing Group’s 2011 symposium: Click: Emerging Media and the Empowered Consumer. My panel covered Business Intelligence, and how managers can get CRM working in their enterprise. One of the questions the audience put forward was about how to start, and what to expect as you start to bring actionable data in.

In our discussion we compared the stages of the enterprise to the evolution of a new country or civilization - where some brave souls have to enter the wild frontier, and establish outposts to grow from. As cities develop and expand, the industry and economy evolve into new and more powerful forms, eventually becoming information-based.

The first stage is The Frontier - a scary wilderness where the enterprise functions without any structured data at all.  Some businesses have huge areas of activity that don’t collect or manage any data at all. Up until recently, many consumer packaged goods manufacturers were in this phase.  Just like the frontiers of old, this creates vast unknown areas, brimming with opportunity and ready for exploration. And it only takes one brave soul (or a group of them) to get to the next step.

Colonization - the stage where companies start to use data to make suggestions to users, or personalize experiences.  Companies in this stage are testing the waters, so just like the first colonies, there are only a few outposts - not a real support network. They are looking to provide a solid case before investing any real infrastructure money. That said, if you’re in this situation, then make sure that your operation is properly set up to prove ROI, because your ’supply ships’ will stop coming if you can’t prove that using data is a winning proposition. Many services companies get caught in this phase, with their management waiting to see proof that it makes sense to invest in anything more than a basic customer database. Once a case is made and accepted by management, then expansion into the next phase is possible.

Industrial Revolution - the stage where things start getting automated at scale. More and more of the company becomes dedicated to getting customer personal and behavioral data pulled into the stream, and infrastructure gets built on a massive scale to expand the reach of data both in the company and out to users. Some banks are good examples of this kind of enterprise - with each product group in the company depending on their customer data to excel, but tending to work in silos, without a centralized view of the customer. Just like in the actual industrial revolution, there are stories of both winners and losers. Some companies will not adjust fast enough. Some areas of the company will hang onto ‘how things were done in the past’.  Winners get to move to the next step, which is also a revolution.

Information Revolution - this is where data becomes so central to how the business operates, that the company can say that information drives the business forward.  In this economy, if you’re out of the data stream, you’re out of the picture. Due to technology advances in handling large structured and unstructured data sets, massive amounts of internal and external data get joined together to predict behavior, needs and barriers. Models are built to ensure correct customer handling, even with imperfect data, and the enterprise continues to search for what new metadata could be added to make better decisions and offers. Things change quickly in this economy (five years ago who would have predicted that social media would need to be tracked in consumer databases?), and companies like Amazon and Netflix are good examples of leaders that know that continual reinvention and innovation is crucial in order to stay ahead.

With this path to sophistication in mind, we can recommend some steps to get you from where you are to the next level in these Evolutionary Stages:

1. Know where you are. You have to be honest about ‘Where you stand Evolutionarily’.  And there are likely to be multiple, conflicting opinions in the company about how data is used. Talk to stakeholders, and avoid proclamations too early in the process. It will take time and persistence to develop a clear picture that everyone can accept.

2. Start at the top. Does your CEO know where the company stands? Does he or she agree that data can help the company evolve?  Educate through examples - showing how adding data into company operations can avoid costly mistakes, angry customers, and ineffective campaigns.

3. Make the case to evolve. Investment in infrastructure and process change must be earned. If tests and pilots are not planned to give evidence of ROI, then all the logic in the world won’t convince management to take action on data, so keep the end in mind when agreeing to how experiments will be structured, run, and measured.

4. Think big, but start small. Develop a vision of how your company could be operating if you had perfect knowledge of your consumer, and use this to guide an long-range plan. But since you know you can’t get there overnight, keep the steps in the plan small and manageable. Many successful companies value failures for what you can learn, but it’s also true that a CEO’s favorite ‘learning’ project is small, fails quickly, and delivers information that lasts.

So, are you an Information Revolution company? How are you planning to get there (or stay there)?

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Leo Burnett Worldwide CCO Mark Tutssel sent us this inspiring video today. Jay Denhart also blogged about this from a semantic point of view here a few weeks ago, but I felt like talking about its relevance to the study of human behavior and brand management.

In this TED talk, research Deb Roy talks about an amazing project in which he recorded every word and image in his house, as his newborn son grew to learn how to talk and walk. Every bit of human behavior recorded, tracked. He has also found ways of visualizing this data in interesting new ways, unveiling patterns that may not have been apparent before.

Taking this approach the connected mass media world, he has used the tools available to him to show how people, mass media, content and contexts can be interlinked in 3D models, so that we can observe human behavior in the form of new social and interaction structures.

As a creative agency that has declared people and their behavior as the starting point of all our work (and with it behavioral planning), the sheer amount of MIT Media Lab computing power, long-term research vision and prowess to study human behavior makes me drool in envy. But also, as we move away from the brand era of mass media messaging to the people era of connected experiences, the work of Deb Roy reconfirms that continuous and deep study of human behavior - and the endeavor to create tools that help us understand it - is a worthwhile cause. Simply finding out about people’s attitudes and values, and inferring their preferences, just doesn’t cut it anymore. Rather, not only does behavioral planning unveil new patterns and types of insights that we wouldn’t have seen before, it also inspires us in ways to help brands make a qualitative difference in people’s lives that the tools of the TV and Brand era could never have.

While unfathomably complex to unravel and to look at, behavioral insights are much more substantive than traditional “consumer” insights, as they do not express an inferred interpretation about what people think or say about a brand (and how we then may be able to manipulate their perception) but rather, behavioral insights are building blocks to people’s journey through different product categories that paint a much more complete picture of how they actually live, and what they actually do. In other words, finding out what people say or think isn’t nearly as interesting or inspiring as what they do. Not only because those two things are rarely the same, but, more importantly, because today brand management and creating brand engagement isn’t so much about saying something to people but doing something with or for people along their whole customer life cycle. Observing behavior and understanding the drivers of behavior (as beautifully visualized by Deb Roy) therefore leads to not only to a completely different way of creating communications, but also to more purposeful interactions and experiences that allow brands to play a meaningful role in people’s lives.

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David McCandless made a name for himself recently by being able to predict when people break-up based on Facebook status updates. The original presentation he held at a TED conference is worth watching in toto, though. David talks about various aspects of data visualization, how we need it more and more, how little of it we can process and how data requires relativity.

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On their blog, GE’s Brian Lawrence explains their new breakthrough in storing 500GB of data on a DVD sized disk. The goal is to alleviate the data burden on people. Sounds good, but we’ll have the same problem again in a few years. Is there any future in physically contained stored data?

Read the whole article here
via Ralph Schremper

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