Knowing “what” happened and “why” it happened are two crucial first steps to addressing business problems.
These insights are referred to as descriptive and diagnostic analytics, and you can read more about them in our guide on the four types of data analytics.
While describing the problem and understanding its source is important, these insights are retrospective. To take action, you need to have data-backed, proactive insights on what to expect next. This is referred to as predictive analytics.
What is predictive analytics?
Predictive analytics uses historical data to forecast future outcomes. This is done through a variety of statistical techniques like data mining, machine learning, and predictive modeling. Predictive analytics falls under the umbrella of advanced analytics.
What are the benefits of predictive analytics?
- Gain a competitive advantage
- Gain a deeper understanding of customers
- Find new product/service opportunities
- Reduce cost and risk
- Address problems before they occur
How predictive analytics work
Like any data analysis process, predictive analytics first requires defining a need. Are you looking to predict which pieces of content will perform well? Predict customer buying habits? How about customer churn rate? Define these needs first, and then collect data from relevant sources like CRM, ERP, marketing automation, and others.
Next, predictive analytics software mines through the collected data and extracts predictive insights. These insights are then visualized in a way for business users to interpret the results. The example below shows a basic predictive visualization on forecasting website traffic for Q1 and Q2 after a sharp fall:
Depending on the complexity of the tool and its niche, the insights and visualizations can vary. So, finding the right tool for your business requirements is very important.
The final step is taking action based on the trends and patterns found. This requires strong business acumen to identify areas of opportunity. Having the assistance of data analysts and/or scientists also helps.
There is also prescriptive analytics, which takes insights a step further by providing calculated next steps, however, these analytics are rarely seen today.
Who uses predictive analytics?
Advanced tools are no longer reserved for large organizations, and many industries today are tapping the uses of predictive analytics.
For example, the healthcare industry has adopted analytics to predict negative health outcomes in at-risk patients. These predictions allow for earlier detections of diseases in patients who may not even be experiencing symptoms.
In the manufacturing industry, predictive maintenance is crucial to the upkeep of expensive factory equipment. Sensors attached to machinery feeds real-time data to analytic tools and reveals any cautious activity.
Across many sports, predictive analytics are key to maintaining a competitive edge. Predicting player regression and productivity is perhaps one of the largest use cases of analytics in sports today. Even small market teams use predictive analytics to structure player contracts and avoid mishaps down the road.
|Related Content: Here are eight examples of predictive analytics used in HR, cybersecurity, weather forecasting, and other industries.|
Challenges to predictive analytics
Like any data analysis, the insight can only be as accurate as the data behind it. Data from irrelevant sources can just create noisy results that provide little value to a business. Data that is inaccurate or manipulated can also skew results, this is why data professionals spend a good portion of their time cleaning data.
Predictive analytics uses statistical methods to forecast future outcomes, but it’s up to a business to interpret the results and take action. That’s why it’s important to have the right tool and team on-hand for any analytic project, especially one that involves prediction and forecasting.
Interested in learning more about data analytics? Check out our complete beginner’s guide with tons of informative visuals.