What Are Predictive Analytics?

What is predictive analytics?Predictive analytics are a product of statistical analysis, and they represent one of the most potent capabilities of big data. Looking at current trends and where they are likely to lead has become one of the most important practices in the contemporary business climate.

Because of the huge amount of risks involved in scaling or even trying to maintain small but steady growth, businesses and the brands they represent must know what lies ahead. Predictive analytics gives them that power, and it allows them to make sweeping decisions without having to rely on “gut feelings” or guesswork.

At the same time, predictive analytics are not infallible. Testing your models for accuracy and experimenting with new ones will keep your business on the right track. After all, everyone knows what they say about making assumptions: sometimes you’re wrong.

Building Predictive Models

The data science behind crafting a predictive model may seem like hoodoo or wizardry, but the steps can actually be broken down quite basically.

First, a business must have data. Whether that data comes from their own systems, aggregated industry-wide sources or third-party vendors is not particularly important. Actually, having data from all three sources can help businesses “round out” their data with a fuller perspective or compare their in-house data to industry benchmarks.

Regardless, it all starts with the data. After that data is cleaned up by removing outliers, duplicates and other such skewing factors, it can be processed to create some sort of metric such as the number of customer orders over time.

A metric like this is interesting, but not terribly meaningful without a relationship to others. For example, is the number of customer orders over time affected by the amount of site traffic coming from social media? To answer this question, a “regression analysis” is formed that basically gauges how closely the two are related.

With a strong correlation, a predictive hypothesis model can be generated that essentially says: a boost in X number of visits from social will equate to a Y boost in customer orders processed. Your business can now develop a strategy to boost orders by trying to draw more traffic to your website via social.

Examples of Predictive Analytics Capabilities

The truth is that predictive analytics are everywhere, and they existed before big data ever became a unique concept. Something as simple as noticing that a certain type of consumer is more likely to purchase a certain product led companies to pursue that consumer through targeted advertising efforts. Backing up these decisions with great big heaps of data only makes them more accurate.

Other possible examples of predictive analytics include:

  • An auto manufacturer will sell fewer convertibles in America in 2016 because of El Niño, causing them to manufacture a smaller number of that model
  • Insurance companies use demographic data to predict how much of a risk a new customer could be and thus how high should they charge for premiums
  • Certain items are bought together in higher frequency, allowing websites or sales associates to suggest an item and gain an increase in basket size
  • A power company predicts where the highest number of downed line incidents will occur, informing them where to build a new line servicing station closest to the problem spots

A Word of Caution

The whole point of looking at data to make predictions is to ensure that you are not simply guessing. Businesses should follow through with this practice by constantly testing their models for accuracy while creating new hypothetical models that account for unconsidered variables or emerging trends.

Finally, heed the age-old statistical axiom: “correlation does not mean causation.” All one has to do is visit the hilarious “Spurious Correlations” website to understand that, no, people eating more margarine does not cause divorces in Maine. For this reason, data scientists and marketing departments must use critical thought and creativity when crafting models, not just obey what the data tells them. Otherwise, Nicholas Cage has a lot of pool drownings to answer for.
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