Don’t worry – we’re not about to get too technical, but we are going to explain why predictive analytic software matters and how it’s more than just compiled statistics.
So, let’s dive in.
Here’s the difference between predictive analytics and statistics
The simplest way to explain the difference between predictive analytics and statistics is this: Statistics is a mere collection of numerical facts which in turn gives you a specific data set, i.e. the number of bids of you won in “x” city over the last 6 months. Predictive analytics, however, takes those data sets to actually make predictions for future events, like what number you’ll have to bid to win the next job in “x” city.
Truth is, analytics would be nothing without statistics to build upon. Jeffrey Strickland, Author of Predictive Analytics Using R writes, “Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, machine learning, computer programming and operations research to quantify performance or predictions.”
Think of it this way.
Statistics in its raw form is current and/or historical information that’s compiled to show the most common occurrence of an action (the “mode”), the average occurrence (the “mean”), and the middle ground (the “median”).
Without analytics, statistics just are what they are. They’re facts.
But with analytics, and the help of things like machine learning and computer programming, you’re able to take those facts and turn them into meaningful, actionable predictions.
Real-life examples of predictive analytics:
- Airlines use predictive analytics to set ticket prices. Identifying how many customers flew to, say, Florida over Spring Break in 2019 (statistics) they’re able to predict how many will go again in 2020 and set ticket prices accordingly (analytics).
- Optimized marketing campaigns. Using existing customer data such as purchase history, dollars spent, age, gender, etc.. (statistics), you can more accurately predict where you should spend your marketing budget to attract new prospects and get a return on your investment (analytics).
Still with us? Good.
To wrap this up, it’s important to note that predictive analytics is the foundation for so many business technologies. SAS explains why: “More and more organizations are turning to predictive analytics to increase their bottom line and competitive advantage.”
It’s easy to use, it gives you a competitive edge, and it produces valuable insights you can use to grow your business.
Now you’re ready so let’s put your statistics into an actionable prediction!