How To Use Predictive Modeling
While the idea of predictive modeling may not be readily familiar to most, just about every person has been exposed to its impact on our lives. Case in point: weather forecasts. Meteorologists are, in essence, using known data, as well as historical data, and using it to help create models the likes of which will predict what weather will look like over the course of time.
You may have even noticed that in recent years, weather forecasts have started mentioning a number of weather models. There are a plethora of global models, American models, as well as European models. This is all done in an attempt to bring the most accurate forecast to the masses as humanly possible.
The same idea also applies to marketing. Predictive modeling, when used well, can actually do wonders for campaign implementation and overall marketing strategy. It also does a great job of helping businesses understand correlations between product/service purchases and how best to optimize them.
Predictive modeling is about looking to one’s past to help predict the future. That may come across as a bit outlandish & impossible to some. But in the hands of a the best data scientists, using known data to help build models that predict outcomes for new data can feel like having a proverbial crystal ball. Coupled with detailed audience definition and customer segmentation, predictive modeling can reach levels incredible levels of accuracy.
Keep in mind that predictive modeling is all about the identification of patterns in known data. As more data is added to the mix, the more intricate the models become. Predictive modeling can be as simple as asking important questions if only to understand what it is that the modeling is trying to accomplish. Perhaps the best question one might ask is not so much about how x relates to y when it comes to one’s business. The better question has to more to do with if you know x, can you predict y?
For as general and as straightforward as predictive modeling seems to be, it does involve some high-level techniques and skills sets. These include but aren’t limited to:
- Applied Techniques — Naive Bayes, k-nearest neighbors, neural networks
- Model Building — training set data cleanup & data prep skills, framing question/challenge correctly
- Model Application — Interpretation of results
It is important to note that predictive modeling can have some pitfalls. The best data scientists and marketing firms are incredibly adept at avoiding these pitfalls because they’ve seen it all and done it all. Furthermore, they know what it takes to get past what others might find unconquerable. Some marketing firms make mistake like not having enough data. Others end up over-testing models. Still others overlook a company’s goals & main issues. This, in turn, makes their data unfocused and fairly useless. Even when the numbers show some level of statistical significance, it may not equate to business insight.
In order for predictive modeling to work best, it is imperative for a company to team up with a firm that specializes in this type of work. Data-driven results are THE best way to move forward when putting together strategies for everything from PPC campaigns to geofencing.
Here at MindEcology, we are an Austin advertising agency that knows the ins and outs of predictive modeling. Our data work is focused on results & making sure we help our clients achieve their goals, period. Click here to connect with our team today & let’s make it happen!