With all the time we spend talking about the best ways to measure, the best metrics to measure and how to measure, it’s important to occasionally raise our heads to look at what is on the horizon. Then we can rise above some of the minutia that fills our brains today and get a glimpse of where this is all headed. As technology allows us to process more data faster the next logical step is to start to actually predict the impact a few key decisions will have on sales, revenue and costs.
If you have an extra $30k in your budget, we all want to know where we should put it to get the most impact. Should we put it into more content marketing or should we increase our online ad spend? What if you could log onto a measurement dashboard and answer exactly that question? What if it even projected the difference in revenue based on which triggers you move? Would you want that?
I think most of us would absolutely say yes. The good news is that the technology to do this type of predictive analysis exists and recently I looked at a model that was built by a Social Media Explorer partner that made my mouth water. The bad news is that it’s not cheap. It requires phD level scientists and a ton of analysis on existing data to build the right algorithms, so the price tag is going to be in the high six-figures to low seven-figures. However, there is a market for this today. Companies who see a return in the millions with a simple move of a needle can justify that expense today. Obviously, we hope that the cost of the technology comes down and makes it approachable for more mid-sized companies as well. But this post isn’t really about the fact that the technology exists, in reality bigger companies have been trying to build this in-house for the last decade.
What I want to talk about is what you would do with the data. As executives and marketers we are always on the hunt for better data that will help us drive decisions. However, I wonder if in the face of the best data we have ever seen if we will actually listen to it. Think about that for a little while. If your predictive software predicts that you should decrease advertising spend and increase content marketing significantly would your executive team actually do it? Or what if it was the converse and told you to significantly decrease the spend in social media and content marketing and increase advertising spend to drive immediate revenue gains? Would your executive team do that?
Unfortunately, for too many companies they would say yes to the first scenario and no to the second. As executives and marketers we have to find a balance between short-term ROI and building for long-term gains. We think our decisions would be so much easier if we had reliable data that provided an absolute clear outcome for decision-making. But in fact, better data may make our decisions even more difficult.
There are two sides to this coin. If we don’t listen to the results from the predictive model what good is the predictive model to begin with? How can we possibly justify the effort and expense if we won’t trust the model to help us make the right decision? I can’t think of anything more ridiculous than finding the optimal way to spend that extra $30k and then ignoring it based on our gut.
On the flip side, could the predictive model lead us to making decisions that benefit us in the short-term, but hurt us in the long run? Arguably, there is no predictive model that could’ve predicted the impact social media would have on businesses. By its nature, predictive modeling is relying on past performance to predict future performance. It will always have a blind spot where there historical performance doesn’t exist.
Is our future success found somewhere in the success of yesterday? Or do we continue to rely on our instincts to find the golden nugget that a predictive model would easily miss?
What do you think? Where does predictive modeling fit into decision making? Will it be good for businesses? Could it prevent organizations from investing in new technologies and channels that take time to build ROI? How should we balance the need for short-term return against long-term gains? Leave a comment and let’s have a healthy debate on the role of predictive modeling versus gut instinct.