Let me manage your expectations. This brief blog will not take a political position for any candidate or political party.
Let me manage your expectations. This brief blog will not take a political position for any candidate or political party. That would be a fast way for me to alienate any reader if I did. What stimulated me to write this piece is the opening sentence of a recent news article that stated “The first phase of the 2012 Republican presidential campaign, ending with the 10 states that vote this week on Super Tuesday, has been about money and message. The next several months will be about maps and math.”
It was those four M’s that caught my attention: money, message, maps and math.
Any candidate for any major office inevitably needs to concentrate on that last M – the math. This is another example of the role that analytics plays in the pursuit of any objective for an organization or person. It is now an optimization game.
The first M – money – establishes the initial conditions for a candidate. They obviously need financial resources to start their campaign. But just like any organization, the rest of the pursuit, similar to a financial budget, is about how to most wisely spend that money to maximize its yield in the context of an objective. For example, commercial companies first ask which type of customer micro-segment is attractive to retain, grow, win-back, and acquire. Once answered, the next question is how much to optimally spend on each micro-segment with offers, deals, advertisements, discounts, and so on. One can over-spend on loyal customers and therefore destroy shareholder wealth. In contrast, one can under-spend on marginally loyal customers and risk their defection to a competitor. It is optimization math to maximize shareholder wealth.
For the presidential candidates the last two M’s – maps and math – is where the smartest team will win. I will spare you a primer on how analytical today’s campaigns are. You obviously know that political races now involve sophisticated data mining of voters by residence location, past voting history, income, and so on. The point is that without applying deep analytics, candidates and organizations are at risk to lose to competitors that have embraced analytics and have the competencies to use them.