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Predictive Analytics Drivers

by Wendy Buxton | Jan 24, 2019

Although predictive analytics might feel like a buzzword, it’s quickly becoming the standard in logistics. The value of synthesizing all the data we have the power to collect to make more informed business decisions is unmatched, especially if it means getting an edge on your competition.


But how do predictive analytics actually work? What does it mean to apply predictive analytics to a particular data set?


The algorithm is everything

Predictive analytics differs from traditional analytics because it focuses on upcoming trends rather than current events.


The technology that powers predictive analytics relies on several technologies, including statistical modeling and machine learning. These techniques sift through your data to find patterns that help forecast events. Such strategies include:


  • Models, which are templates that help predictive analytics pinpoint particular customers or incidents
  • Decision trees, which show the statistical probabilities of certain outcomes based on a tree-shaped diagram
  • Regression techniques, which can help determine the relationship between variables and forecast values
  • Neural networks, which mimic human thought to identify relationships in a data set
  • Clustering algorithms, which organize customers or data sets


These techniques are the framework for decisions that can be instantly generated based on the data collected. Modeling, regression techniques, and clustering algorithms can also be applied to traditional analytics. Predictive analytics is set apart by making short- and long-term recommendations, rather than simply analyzing historical data.


The generation of these recommendations is getting faster, thanks to artificial intelligence and machine learning. This technology also allows for more complex predictions, going from “Will a customer buy this specific product?” to “At what point would a customer not purchase this specific product?”


Take predictions with a grain of salt

The parameters of your algorithm dictate the output of the program. If your model isn’t optimized, the algorithms can produce faulty or less accurate recommendations. The quality of your dataset can also cause major problems. In 2017, researchers found that only 3% of companies met basic quality standards for data. Therefore, a lot of data is miscategorized, misidentified, missing, or just plain wrong.


Unless you’re 100% sure that your data is squeaky clean, don’t consider predictive analytics to be a crystal ball. There are ways to counteract data issues, especially by ensuring that you employ data engineers who are experienced in dealing with erroneous data and aligning data across systems. However, random happenstance always has the potential to confound “perfect” systems. At the end of the day, you can’t plan for — or predict — everything.


This isn’t to discount the value of predictive analytics, especially with the massive data sets we have available in logistics. By 2023, the global market for these technologies is expected to hit $14.9 billion. But the reliance on larger, older, and often unmanaged data sets can create more problems than benefits. Data also can’t anticipate market changes — at least, not for now — so old-fashioned human analysis is still incredibly important when making business decisions.