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What Exactly is Predictive Analytics?
From The Buxton Co
The term “predictive analytics” is one that is used often in business today. But if you ask the average professional to define it, you will likely receive a half-mumbled response about “data” and “statistics.”
Let’s clear up the confusion.
Predictive Analytics Defined
Predictive analytics involves using data about things that have already happened – such as sales transactions, customer behaviors, and competitors moving into a market – to forecast a future outcome, such as sales performance.
In short, it uses the past to predict the future based on the assumption that what happened before is likely to happen again.
But how exactly does a predictive model work and what type of data is necessary?
Start with the Right Data
When you think about all of the data from “the past” that could be used to predict the future, the list is enormous. Each major category of data has many subcategories.
For example, the presence of “customers” in a company’s trade area is obviously important, but how do you define “customers?” Demographic data only scratches the surface of the psychographic, or lifestyle, data that is now available for individual households. Maybe your best customer isn’t just a 25-year-old female with a $40,000 annual income but a single, 25-year-old female with a $40,000 income who lives in the downtown area of a second tier city, does not use a credit card, and enjoys creative hobbies and making home-cooked meals.
Competitors also matter, but which competitors matter the most? And what if being near a competitor actually helps your business by attracting more traffic?
Does it help your business to be near a traffic “generator,” such as a sports complex, business park, or transportation hub?
Is your business seasonal? Does it perform differently in one region of the country than in another? What happens if you increase the square footage of your store or begin to sell products online?
Developing Predictive Models
While the word analytics conjures up images of smart technology spitting out answers to complex questions, there is a human element involved in making this possible.
In order to develop a predictive model, which is the tool used to provide “automatic” answers, a human analyst starts by understanding the company and the questions they would like to answer using the model. This helps the analyst to identify relevant data features and to select data variables that are likely to be “causal” (meaning they have a cause-effect relationship) to the outcome.
The analyst then tests each variable in combination with the others, using various statistical methods, to determine the combination and weighting of variables that yields the greatest predictive power.
Once the predictive model is developed, it is tested to see how well it holds up in real life before being automated and deployed into a user-friendly tool. The output depends on what the model is supposed to forecast, whether revenue, patient visits, or another performance metric.
The Bottom Line
When combined with qualitative factors, predictive analytics are powerful support tools for the business decision-making process.
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