I recently wrote a paper on Math Marketing and Ogilvy was kind enough to make it available on their website.  I will posts pieces of this paper on our blog over the next couple of days.  The paper starts with a history of Math Marketing, which I posted earlier.  The second chapter describes some of the Math Marketing challenges we are all facing today.

Although Math Marketing’s possibilities seem endless, there are some substantial challenges ahead. They come in the form of fragmentation, myopia, data deluge and the talent crunch.

Fragmentation

It is interesting to note that all of the techniques developed during the various stages in the history of Math Marketing are still being used. Mass marketing techniques such as econometric modeling are still around, as are CRM techniques which, arguably, have become even more important now that they can be applied to digital data. Digital mathematical tools are also evolving every day, and because of their incredible growth in marketing, an extremely high level of specialization is required to master them. This has caused a high degree of fragmentation in the Math Marketing world. Nobody is offering a complete range of analytical services and tools and, as a consequence, nobody provides the full 360 Degree picture.

Myopia

In our digital world, we can receive feedback on campaign performance almost instantaneously. We no longer need to wait weeks or months to attain sufficient data points to make a judgment on whether something worked or not. This opens up a number of opportunities for the real-time optimization of campaigns. But it also means that the focus of analysis has shifted to the short term. In the days of mass marketing, econometricians often had to look at the impact of marketing over multiple years in order to get sufficient data points. This meant that they analyzed both short-term and long-term effects. Today’s focus on short-term results is reinforced by the pressure most CMOs are under these days to demonstrate short-term ROI. Their performance is increasingly being measured by the same KPIs that are used by CFOs and CEOs. These tend to be short-term financial metrics, which are forcing many CMOs to shift their emphasis from the long term to the short term.

Data Deluge

While the volumes of data have increased exponentially and the Math Marketing tools have grown considerably, the biggest challenge remains the translation of mathematical insights into recommendations and actions that can make a direct impact. The math can be extremely complicated and it can rely on millions of data points, but ultimately the recommendation will have to help marketers improve their everyday decision making. Marketers need to contact the people most likely to buy, determine how much to invest in them, find them through the appropriate channel, serve them with the right offer, and make sure creative and content are enticing so they stand out from the glut. All too often math marketers lose sight of this. They are driven by what is technically possible rather than by what will make a real difference.

Talent Crunch

Good Math Marketing talent is extremely hard to find. First of all, math marketers need math skills, and math talent is scarce in the U.S. According to the OECD PISA test, which tests 15-year-old students in different countries, the U.S. ranks 35th in the world in terms of math literacy. In The World Is Flat, Thomas Friedman outlined how the lack of engineering and math skills among the U.S. population is having detrimental effects on U.S. competitiveness in the global marketplace. John Kao describes in Innovation Nation how the U.S. math education system lags behind that of most other developed countries, most notably China. However, math skills alone are not enough. Math marketers also need to have an affinity for marketing and a desire to work in what can be highly creative “right brain” marketing environments where very few of their colleagues may understand, let alone know how to apply, math. Hence, math marketers must be able to use advanced mathematical techniques and explain their findings in a marketing context to a nontechnical audience, an extremely rare combination of skills.

It’s official, it was on the front page of the NY Times yesterday : Statisticians are hot.  Google claims they have the sexiest jobs in the world and IBM is planning on hiring 4,000 of them.  They will have the pleasure to mine the enormous amounts of data that are being generated every day in our digital world.  We can no longer express the volumes in Giga, Tera or Peta bytes.  We’re up to the Exabytes now. 5 Exabytes can hold all words ever spoken by human beings.  We’ll probably need about 1,000 Exabytes to store all forms of data that will exist in 2010 – that’s a lot of data!!  And that’s an unbelievable playground for our mathematicians and statisticians.  They will trawl through it all to find the insights that will drive the next innovations in medicine, new product development, finance and marketing.

So where do we find these suddenly hot number crunchers?  Good question.  Here’s the problem.  Math and Stats have not exactly been the favorite topic of our students over the past couple of decades.  This is especially true in the US.  According to the OECD PISA test, which tests 15-year-old students in different countries, the US ranks 35^th in the world in terms of math literacy. In The World Is Flat, Thomas Friedman outlined how the lack of engineering and math skills among the US population is having detrimental effects on US competitiveness in a global marketplace.  John Kao describes in Innovation Nation how the US math education system lags behind that of most other developed countries, most notably China.  And my own personal experience tells a similar story.  Most of the resumes I get for positions in the Ogilvy Analytics team are from mathematical talent who learned their skills outside the US (mostly in China and India).  Stricter Visa regulations have made it harder to hire this talent.  And foreign mathematicians are also less eager to leave the new prosperity of their countries of origin. 

The NY Times article will certainly make a handful of mathematicians and statisticians feel good about themselves.  But I really hope it will make math and stats cool again in the classrooms as well.