Predictive Analytics

Predictive Analytics

Predictive analytics is performed to predict what may happen in the future. It builds off of descriptive analytics by using algorithms and machine learning to make projections based on trends and patterns in data that’s already been collected.

How Predictive Analytics Works

Unlike descriptive analytics, predictive analytics isn’t exact. It’s a probability that answers the question, “what could happen based on the historical data?” 

To make projections, historical data is gathered from a variety of sources and thoroughly reviewed. The objective is to identify patterns as well as variables in the data. Analysts then create algorithms based on the existing data and fill in the blanks using hypotheses. 

A variety of techniques are used in predictive analytics, including: 

  • Pattern Identification
  • Root Cause Analysis 
  • Predictive Modeling
  • Forecasting
  • Monte-Carlo Simulation
  • Sentiment Analysis

Each technique answers a different type of question about future performance in order to reach a projection. The findings produced by predictive analytics can help you optimize marketing, sales funnels and revenue generation while also revealing opportunities for automation (prescriptive analytics). 

Benefits and Drawbacks of Predictive Analytics

The major benefit of predictive analytics is clear. Predictive analytics can help a company better prepare for the future whether it’s launching a new product feature or bracing for an economic downturn. This gives a company a competitive edge and the ability to better serve its customers. Predictive analytics can also help businesses set more realistic goals, expectations, and growth projections. 

Of course, predictive analytics is not an exact science. Rarely are projections 100% accurate, especially if you run into challenges. A shortage of data, overly complex predictive models and unforeseen events that never happened in the past could throw off projections. New technologies and innovations can also disrupt data patterns in such a way that projections are far from accurate.

Because of these shortcomings, predictive models should always be heavily scrutinized in order to gain real value from them.  

Testing the Accuracy of Predictive Analytics Assumptions

Predictions aren’t perfect. Even though the data strongly suggests a causation, outcome or response there could be underlying discrepancies. You may also find that the prediction is spot on in some scenarios and way off in others. 

The key to circumventing inaccurate assumptions is to test your hypotheses. You have to query the data, create predictive models and dig deeper to discover if there is a true causal relationship between the data and a specific outcome.

A/B testing should also be used to test promising models before making any changes. This will give you a much better idea of how accurate a prediction is likely to be in the real world.  

As you gather more data you’ll be able to refine the predictive models based on what users actually do in real world situations. This information can be applied to future predictive models to streamline the process and improve accuracy. 

Implementing Predictive Analytics

Predictive analytics can be tricky. While 90% of companies say they use descriptive analytics, far fewer implement predictive analytics. Virtually every type of business can benefit from predictive analytics – if it’s done correctly. 

If you want to venture into predictive analytics it helps to have the right people and tools in place to fully capitalize on data-driven projections. For example, a data analyst that specializes in predictive analytics and forecasting can be a valuable addition to your team. They can help direct the procedures and take the lead in developing predictive models.

An analytics platform that is capable of not only aggregating data but also identifying patterns and anomaly detection makes it much easier to parse out meaningful insights.

It’s also a good idea to create predictive analytics guidelines that outline the process of analyzing data, creating predictive models and testing projections. The guidelines will ensure consistency even if your analytics team changes.

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