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Community Blog Getting Started on Predictive Analytics

Getting Started on Predictive Analytics

You won’t be able to see the future with predictive analytics, but you will be able to forecast likely trends and patterns.

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You won’t be able to see the future with predictive analytics, but you will be able to forecast likely trends and patterns. Essentially, it’s similar to weather forecasting, where the basic premise is to use past data to guide our thoughts for future outcomes. Here are three ways to begin scratching the surface of predictive analytics.





  • Determine your objectives - As with any project or major undertaking, you must have a clear picture in your mind of what you want to achieve. The nature of predictive analytics can be very open, and as a result, possibilities of what you can achieve may be extensive. Avoid jumping straight into rafts of data. Instead, plot down your overall desired outcome in natural language. You’ll then be able to work out how that objective gets measured with which pieces of data.



  • Structure your data - Any form of data analysis must begin with organizing data. With data coming from all sorts of sources and in different formats, it’s impossible to begin without having everything structured first. You will want to try to ensure that you have consistent parameters and answer options. This will give you the platform upon which to proceed with analysis.


  • Experiment and mine - Statistical analysis will help as you mine the data. This is the time to be a bit more creative with how you view data. There are going to be so many parameters and variables that patterns will reveal themselves as you begin to pair up different ones against each other. By experimenting with the relationships, you will discover new causes and effects that will form a part of your forecasting.


Giving yourself an edge in the marketplace


By following the steps above, you will be able to start using predictive analytics to forecast important developments, such as changes in the performance of your competitors, predicting risk or the changing preferences of your clients.


For example, U.S. retailer Macy's is using predictive analytics to better target consumers and develop more tailored digital marketing campaigns. After developing 20 predictive models and deploying better targeted e-mails, the retailer saw an 8-12 percent increase in online sales.


With the explosion of data available to most businesses today, there is little excuse not to leverage that data to power predictive insights that can help your business survive and even thrive in an increasingly demanding and competitive marketplace.

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