J. Walker Smith writes: Digital technologies have turned marketing upside down, but not by revolutionizing the fundamentals. The core principles of marketing remain the same. Instead, marketing research has moved closer to the action than ever before.

The traditional cycle of research and marketing is one of “study, act, study.” Data precedes decisions, then the impact of decisions is assessed before the next decisions are made. Each step of the way, research is the gate through which decisions must pass.

With the rise of digital technologies, cycle times have been compressed. The old starting point of “study” has become a luxury few marketers can indulge. The new digital cycle is one of “act and react.” “Act” not “study” is now the point on which everything else pivots.  And so research in the digital age has shifted from “study” to “act,” thereby moving closer to the action.

Learning on the go

So, research is changing to match today’s fluid purchase process. Answers are needed in real time. “Good enough” is, well, good enough. Marketers learn on the go, testing alternatives by doing not by asking, in the marketplace. The spotlight is more on how consumers behave than on what they think. The premium is on the analytics and storytelling that make sense of the flood of data.

This shift in marketing is being intensified by a parallel pivot to passive in consumer and commercial digital technologies. Whereas previous advances in digital technologies usually required active consumer engagement, the coming wave of passive digital is different. Sensors, not screens, will be the interface by which marketers connect with consumers.

Wearable technologies might be catching the headlines, but passive digital runs deeper: think of Amazon’s anticipatory shopping software, or Apple’s iBeacon that mines data from and pushes alerts to nearby iPhones, or Foursquare’s shift from active check-in to passive location awareness.

Predicted fit

In this passive digital world, the new consumer value proposition is less about the customized fit that consumers actively demand, and more the predicted fit that marketers calculate from passive digital data streams. Eventually, this will be accomplished by algorithms embedded in the data flow of a self-learning system that runs itself. Marketing agency will reside not in brand managers, but in algorithms that analyze continuously collected passive data streams to make real-time adjustments in marketplace execution.

If this sounds futuristic, it’s here already: the real-time pricing of ad insertions for content about to go viral, or in-progress updates to customer service scripts based on real-time analytics of caller language and tone of voice, or dynamic retail pricing based on real-time demand and environmental conditions like temperature.

Insights and big ideas aren’t less important, but they are no longer enough. Behaviors, it’s said, are more honest, so big ideas must find their payoff in what consumers do. The future of research is about a new metric of success rooted in a new digital cycle, closer to the action.

J. Walker Smith is Executive Chairman of The Futures Company. He tweets at  @jwalkersmith. A longer version of this article can be found in the June edition of Marketing News. The image at the top of this post is from digital camera HQ, and is used with thanks.  

One thought on “Closer to the action

  1. Lynd Bacon says:

    A very interesting set of observations, Walker. Thanks! The increase in the availability of continuous streams of marketing-relevant data we’re seeing is clearly enabling a more fluid, continuous approach to learning about customers. It’s somewhat analogous the the kind of hypothesizing/testing lean product development process that has become popular in some enterprises. I do wonder whether a good portion of what’s strategically focused marketing research will continue to be in the “study-act-study” mode because broad-reaching management decisions tend to be made episodically, rather than continuously. At least they haven been, traditionally.

    It’s definitely the case that fairly sophisticated, effective algorithms are already in use in support closed-loop, real-time marketing tactics. Some of them are adaptive and they learn from streams of data points. Sequential Bayes applications are examples. They are based on what’s sometimes called the diachronic interpretation of Bayes Theorem.

    It’s believed that marketing is the major use of “big data,” and the technologies for doing scalable, real time analytics are developing rapidly. Relatively new languages and platforms like Storm, Esper, and S4 are being used by a number of organizations to process large volumes of sensor data in applications like nuclear power plants, jet engines, locomotives, wind farms, and facial recognition. These languages are open source and so are available for marketing and other kinds of applications. “Big data” search and social media firms have been developing their own proprietary tools for real-time advertising and related marketing activities. Some have been disclosed at recent science and technology conferences.

    On a somewhat related point, it’s clear to those of us who are involved in teaching marketing analytics that going forward quantitative marketing professionals will need to have good understanding of the data management and analysis technologies being used to support digital marketing, if not facility in the use of them.

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