Identifying Black Swans

Prior to starting Predictful, I spent a good deal of time delving into the world of predictive analytics. I had spent significant time the previous 7 years working with marketing and sales analytics, identifying behavioral patterns of customers and prospects that helped companies understand where an individual generally was in the buying process. Recent technology has made it much easier for marketers and business development professionals to target these people based on their place in the journey. Predictive analytics was the logical next tool that organizations could utilize with these patterns to create better forecasts, and in turn make better decisions. After all, as Nate Silver writes, we make decisions each day based on a forecast of an outcome based on that decision. Even simple choices such as how to get to work today (I want to get there in the most efficient way), what to eat (I want to be healthy) and where to shop (I want to get a good deal) are all done based on what you think will happen if you make a particular decision…at least they should!

Anyway, I dug in and researched what many startups and established predictive analytics companies were doing (there are 764 companies alone at this writing on AngelList under “predictive analytics”) and I didn’t see anything that really caught my eye. The one thing I did notice was that the companies were mainly focused on historical information, crunching massive amounts of data to find patterns that could be used to forecast future outcomes. Brilliant stuff, don’t get me wrong, but to me it did not feel complete. What was missing was bringing into the equation any sort of changes in environment and behaviors by clients, prospects or the organization itself that could drastically alter analytics forecasting. There needed to be another element added into the modeling and forecasting. But then again, how can you model technology to forecast for something that has never occurred previously? The answer is you cannot without the advent of human intelligence. Hence came about the concept for Predictful.


So What About Black Swans?

Black swans. Yes that’s what this post is supposed to be about, so let’s talk about them. The black swan theory, as it goes, is the introduction of a surprise event or outcome that has a major effect. The term comes from an old world belief that all swans were white and there was no such things as a black swan…until they were discovered. Therefore all studies and writings on swans had to be rewritten. There have been a number of notorious black swan events in history; the stock market crash of 1929, the dissolution of the Soviet Union, the rise of the internet, and 9/11 to name a few. Heck, we even have one now in our U.S. election process! If you look at your organization I am sure you can name several that have occurred over the past several years as well. One of the goals of organizational leadership is identifying them before they occur. If you can, you can pivot out of a potential disastrous situation or accelerate into a great outcome. Either way threats and opportunities, especially those that are currently unknown, should always be on the radar.

So how does an organization stay on top of their potential black swans? If traditional predictive analytics cannot forecast them, how do you track them? This is the problem that I wanted to tackle with Predictful. What we have done is created a SaaS tool that allows you to gather the insight from every stakeholder in your organization, be it a customer, an employee, an investor, or a supplier. Anyone who has any knowledge about a future outcome of your company can be brought in to share their knowledge, and our sophisticated algorithm can provide probable outcomes based on that insight. We even have an early warning system that triggers a message when someone shares an outlier insight. It’s a very simple-to-use tool that provides a comprehensive look at future events. I highly recommend you take a look at it. Of course I do. What else would you expect me to say?

It’s not perfect by any means. Black swans will still continue to occur both in and outside of organizations. But the more insight you receive the better off you are, right? Isn’t that why you’re spending so much money on predictive analytics platforms?

I’d love to hear what people are using internally in their organization to help identify black swans. Feel free to share below in the comments.

Comments Please!