When “Mad Men” and Mathematics Collide

Insights on the Benefit from Text Analytics

  • Understanding humans by understanding text. Data volume on the Internet and elsewhere is growing exponentially, and most of it is human-generated text. This is why text analytics is necessary. If you want to understand people, especially your customers and how they use your product and engage with your organization, then you have to be able to possess a strong capability to analyze text.
  • Context matters. Marketers ( notionally the ” Mad Men of Madison Avenue”) spend a lot of time and money understanding the touch points customers reach on their pre purchase journey. Attribution, in that sense, equals context, and text analytics is a key ingredient in establishing that context. The concept is important not only in marketing but in government, research and development, finance, and elsewhere. Attribution is in effect a form of predictive modeling. It is a way to predict, based on past behaviors, what a customer likely will do. For that we have to combine human sentiment with the trace customers leave on the Internet for example. To understand sentiment we need quite complex text analytics.
  • Live not by text alone. If not for combining text and statistical data, it is unlikely he would have made the breakthroughs he did for an aeronautics client. You will see, at best, half of the picture. After all, combining the two, he states, is essentially what the human brain does.  You have to combine semantics with statistics, I am very deeply convinced of that.

Let’s considers the television show “Mad Men” a snapshot of marketing in transition, one that underscores his point. By the end of the show’s run, its cynical ad men were conceding, if grudgingly, that there was business value in the primitive computer that had been installed in their agency. Of course, the insights gleaned from a computer in 1970 would have been limited, but today, he says, those limits have all but disappeared. In fact nowadays compute power and the plethora of available and capturable data provide tremendous insights. In today’s world, and tomorrow’s too, it is an absolute necessity to take advantage of the ability of machines to predict a customer’s behavior, he remarks.

We have been transitioning from these guys who are a little bit chauvinistic, who infer or assume behavior while they smoke the cigars and drink the booze—and then make a commercial. Gut feeling is being replaced by actual insights derived by analyzing big data.

The exploding field of marketing attributions is at a crossroads where text data and statistics merge to generate deep insights that Madison Avenue could scarcely have dreamed about five decades ago. Customers create a recording and story by leaving digital markers along their unique  engagement with vendors and products. Today, we have fine-tuned tools to read  and analyze those markers. The ad man’s intuition is supplanted by machines that learn.

Before those guys on Madison Avenue had it all in their head and they had to figure it out by inference and lengthy validation cycles. Today, we can really analyze and predict people to understand the feelings and intentions they have about a certain product to enable the marketer to speak directly buyers and consumers.

I had a big revelation some years back while doing a job for a major aerospace defense contractor when I combined statistics along with text analytics and achieved an otherwise unobtainable outcome.

The client wanted to institute a system of preventive aircraft maintenance. The idea was to predict the failure parts before they would actually break. The traditional method involved replacing parts on a timetable—often well before they were worn out. When we do that, we leave money on the table. Applying natural language processing and machine learning, I discovered several critical, undiagnosed problems. Some sensors, for example, consistently burned out early because undetected engines failures generated more electricity than the sensors could handle, but the newly replaced sensors showed no record of this problem. Text in the written maintenance logs were the crucial pointer to the undetected engine failures; this is how combining text analysis and statistics results in insights and value.

It requires no great logical leap to transfer the lesson of that story into the marketing realm. When Mad Men Collide with Mathematics is the holy grail of marketing. We all want to predict why and when a customer will buy, and when the part will fail before it actually breaks, and to do that, text analytics is a critical necessity.

About Paul Hofmann

Paul Hofmann, Ph. D. Vice President Research, SAP Labs at Palo Alto Before joining SAP Research Paul worked for the SAP Corporate Venturing Group. Paul joined SAP 2001 as Director Global Strategic Supply Chain Management Initiative EMEA. Prior to joining SAP, he was Senior Plant Manager at BASF’s Global Catalysts Business Unit in Ludwigshafen, Germany. After joining BASF 1989 Paul headed the development of object-oriented production planning and scheduling software for BASF's plant; one of the first big object oriented software projects in German industry. Paul was Researcher and Assistant Professor at top German and US Universities, like Northwestern University in Evanston/Chicago, Illinois, USA and at Technical University in Munich, Germany. At Northwestern he did computer simulations to explain molecular beam reactions collaborating with Sir John Pople (Nobel Prize Laureate). Paul graduated with a Ph.D. in Physics from Darmstadt University of Technology, Germany, a MS in Chemistry and a BS in Biotechnology from University of Vienna, Austria. About Paul Hofmann
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