Maybe I’m overly obsessed with marketing, but I can’t shake the thought that the presidential elections are a lot like an A/B test. This is a test in which the population is trying to choose a president, and either A or B is going to be the winner. As we have seen during the past two weeks, this system forces us to pay the price of generalization in a very big way: even a small tilt in favor of A or B compels the complete population to concede to the winner.
As we know, Trump didn’t win all the states – some states actually preferred a different alternative. Think about California, for example. California is a Democratic state, and the people of California chose Clinton as their winner, but they are paying the price of a general election. If we drill deeper to the county level, in some states certain counties differ from the generalized outcome. For instance, although Florida chose Trump, there are some areas in Florida who would have preferred Clinton as president. So the price of generalization is also being paid on a more granular level.
General Election: Even a Small Tilt in Favor of the Winner Binds the Entire Population
In a utopic world, we’d want every state, or rather, every county, to have its own winner – some will get Clinton as president, and others will get Trump. Imagine how delighted everyone will be if all the red areas had Trump as president, and all the blue areas had Clinton. But as the elections go, there is only one winner for the entire country, and this winner is chosen according to the aggregated results from all the different states.
Letting every county – and even every citizen – have its own winner is, essentially, what AI based customer marketing does. AI based customer marketing provides each micro-segment of customers with the offer they prefer out of all the available offers. It is built exactly to avoid paying the price of generalization.
Of course, this might not be the best solution for Western democracies. But it does wonders for communicating with customers. One of the biggest problems marketers face is designating the optimal offers for selected customer groups (called segments or micro-segments). How can a marketer know which offer will elicit a more favorable response? This is what A/B tests are for – but these tests are labor intensive and require ongoing measurements, recalibrations, more tests, more measurements, ad infinitum. For marketers deploying numerous concurrent campaigns for tens and sometimes hundreds of customer segments, this is simply impractical.
Consider that the US population would have been faced not with a choice between Clinton and Trump, but with two different marketing offers, sent weekly. Let’s call these offers “Trump” and “Clinton.” Based on the election results, a marketer wouldn’t want to send a Trump offer to Californians, or a Clinton offer to the people of Florida. However, within these states there are some segments who would receive the opposing offer.
The Self-Optimizing Marketing Campaign: Every Subgroup Gets Exactly what it Wants
Algorithms that continuously scan the preferences of micro segments and the way they react to previous offers can adjust the offers on an ongoing basis, making sure that every sub-segment receives the most appealing offer, which will generate the optimal satisfaction or reaction.
This is exactly what self-optimizing campaigns do. Based on collected data they are able to identify the wants and needs of customer subgroups, even when those change overtime, and match them with the most relevant offer.
Pini co-founded Optimove in 2012 and has led the company, as its CEO, since its inception. With two decades of experience in analytics-driven customer marketing, business consulting and sales, he is the driving force behind Optimove. His passion for innovative and empowering technologies is what keeps Optimove ahead of the curve. He holds an MSc in Industrial Engineering and Management from Tel Aviv University.
