Postmodern shopper

In the Dec. 19th edition “The Economist” writes about how today’s shopper follows the postmodern paradigm of leading postmodern philosopher’s like Derrida, Lyotard, or Foucault. The “postmodern” shopper deconstructs any pattern of old shopping behavior and erases any strict boundaries of price, image, brand, and location. One shopper might “perform” the following activities during one day: Buy cheap socks at Wal-Mart, and an expensive iPod at the Apple store; Eat lunch at McDonalds, and dinner at a five start restaurant; browse online for the best flat panel TV at Circuit City’s peer review site, and buy it at Best Buy at the nearest store, eat cheap white “Wonderbread” and drink a $100 bottle of red wine to go with it.

We marketers need to much better understand the foundation of a postmodern society and its inhabitants to do our job. There are three key elements for a postmodern shopper society:

• It’s not just a fragmentation of our society into numerous groups and segments; it’s about the fragmentation of one consumer into a multitude of personalities. These personalities are more and more difficult to cluster and segment into marketable groups. They segment themselves into one person units. It’s the potential nightmare of 1:1 marketing that can not be executed due to the inconsistencies of the “1” element.
• It’s not just about more diverse value and belief systems, it’s about the ever shifting values and criteria for a purchase and consumption decision of one consumer. Any traditional research to understand the long obsolete “purchase funnel” will have less and less value. Consumers don’t move down logically the purchase funnel but are jumping back and forth without regards for our nicely designed funnel programs.
• Its not just about the loss of control of brands for us marketers, it’s about the lack of understanding how a brand interacts and can influence any consumer. It is not a marketing strategy to just accept the loss of control and hand over the brand to consumers. It’s more about enabling the consumer to experience the brand in his or her way, and to enhance this experience by any form of participation.

I have seen glimpses of postmodern marketing but our marketing community is far away of setting a standard in postmodern marketing. But it might be that the pure lack of this standard is one of the enduring sides of today’s marketing discipline in a postmodern world. And still, we have to understand how to build the underlying marketing science and derived marketing strategies in a post modern world.

Shadow Data

Last week Simon Dumenco wrote in Advertising Adge about a fascinating project by Jim Hellriegel (http://dialidol.com/) to predict very accurately the survivors of every week’s American Idol. The offered free, downloadable DialIdol software turns your computer into a personal assistant that can dial in as many times as you want it to – and it will keep trying even if it gets busy signals. Hellriegel used the correlation of busy signals with singers popularity (the more busy signals, the more people are voting for a candidate) to predict very accurately the survivors of each American Idol round.

Dumenco calls the phenomena of transforming data holes into data points the creation of “shadow data”. “The DialIdol phenomenon, when you really think about it, is more than a bit unnerving, because what’s being tallied and analyzed here is not data per se, but lack of data – shadow data. Not data about votes, but data about attempted votes.”

We might not see the data point we were looking for but its absence creates a shadow data point that can be used as a proxy for the desired data point. Sometimes we are just so busy and focused on identifying the right data points that we are forgetting to utilize shadow data that is as insightful as any other data family. As you can imagine, I am much more excited about adding another family of data points into our discipline than being “unnerved” about it. Here are some other data shadow phenomena:

• The whole medial science defines health as the absence of sickness. Doctors routinely look for data points of sickness and get confused when they have some data points (sickness symptoms) but can’t find the causal data that explains the sickness. Health functions as a shadow data point for any physical problem. If there is no “sickness” data point, then you are healthy
• The march to this year Super Bowl puts again a lot of attention on team’s or individual athlete’s statistics. Bill Belicheck from the New England Patriots defensive scheme seem to focus as much on what the opponent does and what the opponent does not do. Every non-action represents a shadow data point to decipher the strategy of the opponent’s offense. This enables Belicheck to design an even more intricate game strategy for his defense.
• In our marketing world, the white space for a brand (= a market opportunity that is not occupied by competitive brands) is nothing else as a space where the non-existence of brands and thereby lack of brand data points creates a data vacuum. This creates an opportunity for another brand. Data Shadows are here used to portray a business and marketing opportunity.

Over the next years, we will see an increased focus on this group of shadow data, especially since storage and analysis of more data points becomes cheaper, more commoditized, and automatic.

Reducing Risk

Most of us marketers embrace risk. We love to say that most companies are too risk averse, are too careful in trying out exciting ideas, new channels, or just any kind of different communication. We love to advocate that only risk takers succeed long-term, that every brand needs to evolve in bold moves to new (naturally higher) ground, and that the discipline of marketing is too void of risk-loving personalities. Let me proclaim just the opposite (at least for one day): we marketers are not focused enough on reducing risk; we should be the best risk managers of any corporate function.

Why? A deep data-centric understanding of the consumer and its behavior is the most repeatable and scalable way of reducing risks. We marketers own this, not the CFO! There are three significant factors for risk mitigation through data-centric understanding of the consumer:

• Sales for a particular company is nothing else than the sum of individual purchase decisions of many consumers. The more one can understand the behavior underlying consumer purchase (or attrition) decisions, the more one can predict future sales. M&A modelers follow the right track when they model out Revenue/Sales numbers based on historical data, and external forwardgoing factors, all scored with a particular risk pattern. They are just missing the consumer dimension in most modeling aspects. The combination of marketing intelligence and financial modeling would provide a significant improvement in how we can deal with risks
• Marketing Investments can function as a buffer against risk. If a holistic and smartly designed brand communication ensures the steady flow of awareness, leads, and sales, then marketing provides a risk mitigation factor. We just have to better understand how our marketing efforts contribute to truly incremental sales and revenue beyond the baseline of non-marketing driven activities
• Our marketing activities and plans should include much more a risk and probability score. If we set marketing goals and recommended a plan to achieve these goals, then we should be able to project these two strategically important metrics. If not, we have just written the marketing goals and plans in an intuitive and hasty manner but definitely without the necessary input that is required for such an exercise.

Most marketers just love to focus too much on the image of the risk-taking and rule-breaking warrior. But there are moments in a business day or in a life of a brand, where we have to behave more like reliable risk mitigators, deeply rooted in the understanding of consumer behavior. This is not a call for a research dictatorship but for a scientific approach to manage risk for the brands that we are involved in.

Media Intelligence

Fortunately for our marketing discipline data analytics has proliferated significantly over the last years. It’s not just that some of the most popular economists (e.g. Stephen Levitt) have used statistical means to understand phenomena but that almost every marketing discipline has been penetrated and heavily influenced by the scientific approach of analytics. But one of the most important disciplines for any marketer today has shown mixed willingness to be “enlightened”: Media Planning and Buying.

Looking at Wikipedia’s definition of Media Planning…: ”Media expertise and media buying consists of studies and techniques aimed at optimizing the efficiency of advertisements by determining the media best suited for reaching the largest number of readers, listeners, viewers or clickers who are part of the target market. Media expertise has become increasingly important with the decline of traditional television advertising, the complexity of TV viewer data, dramatically changing consumer media habits, and the depth/breadth of alternative communication platforms.”, …it becomes clear how instrumental and complex Media is in making any smart marketing decision in today’s world.

Therefore, Media Planning and Buying need to embrace a significantly deeper utilization of data centric intelligence and rely less on the genius of individual planners. Consider these three enormous challenges:

• Media owns the understanding of the “Moment” dimension of any communication. When is the right moment of intersecting/interacting with a consumer? This question becomes more and more difficult due to the increasing behavioral fragmentation of consumers (Groups with same behavior patterns are getting smaller and smaller, therefore the number of groups with same behavior patterns are growing) and the multi-media interaction patterns of most consumers (Consumers rarely interact with only one medium in one moment).
• Media owns the smartest distribution of marketing investment dollars, not just across channels but also across geographies, consumer segments, times, and product categories. The intrinsic complexity of this challenge requires an approach that combines analytical data intelligence and technology based planning solutions. One brilliant mind alone won’t be able to solve it anymore, despite all attempts to capture it in a multitude of excel sheets
• Media owns the creative opportunity of designing unique “events” that attract the consumer’s attention in todays crowded and over stimulated world. But the creative genius of inventing these events needs to be inspired and driven by data intelligence. If not they become just self serving advertising events.

Hopefully we will see more Marketing Geeks entering the Media Planning and Buying field as one of the most central marketing disciplines, always with the mission of fundamentally changing the process of creating media solutions. We might not know everything yet of what and how it has to change but it will and needs to change.

IBM's visualization practice

Over the last years a small team at IBM’s information visualization practice has done some extraordinary work that they are starting to share with a broader public (http://www.research.ibm.com/visual). Their work centers primarily around “Democratization of Visualization” for which they are building small visual software applications that can present any kind of data in an appealing manner (e.g. email communication, baby names). Its mission seems to focus on changing the paradigm of our analytical discourse: From an “Analytical Expert to Analytical Expert” exclusive sphere to a “Broad audience to broad audience” conversation, dialog, and community. It’s not just about changing the consumption side of the analytical work but the creation side, too. A broader audience should not just be able to read intuitively the results but participate in the creation of the analytical solution, too. The group calls it communication-minded visualization.

It’s interesting enough that IBM funds these kinds of projects without any immediate applicability to any of their businesses. I can only think of Google as the other key player that has realized the importance of this paradigm change and therefore actively invest in similar projects. Most other firms continue to ignore its importance and are definitely not willing to share any progress or projects with a broader public.

But before I get lost in the corporate politics and strategies of most Fortune 500 companies, let me try to decipher some learnings from the group’s public available publications, presentations, and interviews:

• “Democratization of Visualization” is only successful if the borders of analytical work product and art object is vanishing. Especially Fernanda Viegas seems to stress the beauty of the created objects
• If one creates an easy to use and intuitive graphic software application on top of public available data set, then one unleashes the curiosity of a large suddenly interested community. One can never predict which questions members of this community will ask or try to answer with the new application. The visual software application functions as an inspiring translator between an expanding group of users and the raw data.
• Its concept of Communication minded stresses the three steps and related benefits of analytical visualization: Creation of the Visual Solution, Interpreting & Deciphering Insights, Sharing with Others. Only the successful integration of all three steps will ultimately lead to a democratized visual.

One can only hope that this IBM team continues to not only create outstanding projects but to share the underlying logic and applications with us. I am sure we will see much more over the next months and years.

Kandinsky

Last week I went to an amazing exhibition at the Kunstmuesum in Basel, Switzerland, that tracks Kandinsky’s evolution from more concrete paintings in his early age to his famous abstract painting style (http://www.kunstmuseumbasel.ch) . It’s an extraordinary visual education of how an artist attempts to reduce his visual work to the bare essentials of what he considers the true nature of objects.

In one of his outstanding written explanations of his art he writes (“Line and Fish”, 1935, published in Londoner magazine Axis):” The environment (in which a visual object is presented) is the composition. The composition is the organized sum of the internal expressions of all parts of a painting.” Kandinsky explains further that the composition of which objects within a painting are arranged, are positioned against or with each other, enables that one sees the hidden truth and forces of any object. The combination of the right composition and the reduction to the absolute essential unveils the essence of the objects.

Why was I so impressed with Kandinsky’s paintings and his evolution to one of the early masters of abstract paintings? Here are my thoughts:

• We often underestimate the importance of composition within our work of presenting data insights. In some of my previous blog postings I wrote about how close our work can be to art objects. Therefore we have to think more intense and smart about how we are arranging the different elements of our work results. We should think of them as very client customized and unique compositions, similar to Kandinsky’s compositions of different objects in the right environment.
• Kandinsky’s drive to create abstract art is grounded in the attempt to reduce objects to its truest nature. His paintings try to eliminate anything that is not relevant, anything that distracts from a truthful representation, anything that hinders the viewer’s access to the object. The best data analyst does exactly the same. Our work is less and less about adding something, and more and more about reducing to the minimum.
• My often proclaimed need to develop real data visualization expertise could be linked to the birth of abstract art. The abstract artists were always attempting to find new truths in pushing visual representations to an extremely new form but always with the intent of unveiling something that no one saw before. Isn’t this what we are trying to do every day?

Maybe we marketing geeks should spend more time in understanding the techniques of painters to further the impact of our discipline. And we should definitely spend more in museums and study the genius of creative artists.