The Data Dilemma - Quantum Marketing Book -- Read more

Once the marketer has the right mission, the next area of focus is on how to bring it to life and be able to execute on it. To be able to successfully thrive in the Fifth Paradigm, marketers need to gain a good grasp of and expertise in many areas. I will address them in this chapter and the chapters that follow, covering areas ranging from technology to the sciences behind marketing. But first things first. Marketers need to understand and gain command of data, data analytics, and AI. Data is the precious commodity in the Fifth Paradigm. Let’s start exploring it.

I worked at Citibank from 1994 to 2009. Very early on, in 1995, I established the first data analytics unit in the UAE. The analysis was mostly used for the newly launched credit cards business then. And the results were immediately evident. From being one of the last entrants to the market, we quickly grew to be the market leader in that geography within just one year and established a robust, profitable business. Mind you, that market was predominantly cash driven and here we showed up with credit cards and drove both the category growth and our own brand growth. Since then, I have discovered the immense power data can bring to marketing. It has always been and always will be a powerful and indispensable tool in my marketing toolbox. In every industry I have been in since, I found data analytics to be one of the most significant drivers of sound strategy formulation and its efficient execution.

Given how much more vital data’s role is going to be in the Fifth Paradigm, and given that most marketers come from the nonquantitative side of the house, I will cover in this chapter most aspects of data that a marketer needs to be aware of, in simple terms.

Data analytics in marketing came of age with credit card companies in the United States, which would send billions of direct mail pieces every year. For every million pieces they sent, hardly four thousand people would respond. That is just 0.40 percent. Put another way, 99.6 percent of their mail went straight to the trash bin. In some ways it was the ultimate example of “spray and pray” versus precise and efficient targeting.

So they began looking at new ways to identify prospects who were more likely to respond to direct mail on the one hand, but also, on the other, be most profitable to the company over the lifetime of the relationship. This required powerful data analytics, necessitating that acquisition marketers understand and leverage the power of data effectively. Those card companies and banks that knew how to leverage data had a substantial competitive advantage. Data was becoming the new currency to differentiate oneself and excel in the marketplace.

With the advent of enterprise-wide databases, marketers were able to compute lifetime values of their customers across all the relationships they had with the company. This enabled them to create relationship-based strategies, as opposed to product-based strategies.

With the arrival of Google and the various ad platforms, marketers who were leveraging the digital channels started genuinely appreciating the power of data, and they realized the opportunity to get to precise, targeted, and actionable insights as never before. It helped them refine their messages and create highly optimized promotions that would motivate consumers to choose their brand over the others. Marketers also had suddenly gained the ability to serve consumers with ads in a contextually relevant manner. They could measure return on marketing investment (ROMI), quite accurately. It became evident that it’s not the raw data that provides competitive advantage, but the ability to play with, analyze, and act on data that provides competitive advantage.

All this required constant gathering of data, updating the database, cleaning the database, analyzing in as near real time as possible, and enabling both reactive and proactive actions. When marketers married their own data (first-party data) with third-party data bought from others, they significantly enriched the quality and depth of their insights further. That, in turn, made marketing significantly more effective.

Rohit Chauhan, executive vice president of artificial intelligence at Mastercard, summed it up very well: “Data is literally an ocean. You need a way to get your head around it and make sense out of it. If you boil everything down, you have three buckets of data: descriptive (what happened), predictive (what will happen), and prescriptive (what the dimensions of consumer data are). I can give a simple analogy that will demonstrate the difference among these. The companies that rely mostly on descriptive data are akin to driving their cars while looking in the rearview mirror. It is useful, but only up to a point. The companies which are using predictive data are forecasting the future and preparing for it. They are driving their car looking through the windshield and looking ahead at the road. Which is good. But what can be better than that? Driving with the help of a GPS! That is prescriptive data. It tells you where to take a right turn, how far you are from your destination, is there an accident or a pitfall ahead, etc. It takes your driving efficiency and effectiveness to a different level altogether.”

That is indeed a brilliant way of capturing the gist! Prescriptive data is about not only looking back and looking forward but also looking at what is not visible to you at the moment.

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