Predictive analytics is the science of exploiting historical data, analytics techniques and technologies to reliably predict an outcome. Whereas traditional analytics focuses on present insights, predictive analytics is future focused, an amalgam of big data, statistical modeling and other mathematical processes. Companies use predictive analytics to gain insight into past successes and failures, and to forecast events based on understanding controllable variables.
If you’re in a managerial position, you undoubtedly work in project teams with business analysts who use predictive analytics. Their tools and processes inform your decision-making. Fundamentally speaking, predictive analytics relies on strong data, statistics and assumptions.
Components
To conduct cost-effective and profitable marketing campaigns, you need good data. This may include a breakdown of customer segments by demographics, preferred purchasing channels, the most popular products for each segment, and product characteristics.
Using data, you find correlations between variables. A regression analysis is a statistical tool used to understand the relationships among variables and the degrees to which each variable affects purchasing behavior. Ultimately, this process arrives at a score for each customer segment, predicting the likelihood of a purchase. Then you market to the top-scoring customers for each product.
Profitable results should accrue if your statistical models and assumptions are correct. An underlying assumption in all predictive modeling is that history tends to repeat, but this does not always hold true. In fashion, for example, khakis may be popular for ten years and then unfit for anyone under 70. Tracking how variables change over time and how other variables affect them is the trickiest aspect of predictive analytics. Managers must understand the process in order to challenge the underlying assumptions.
Applications
Organizations in every industry can use predictive analytics to reliably forecast trends and events of all kinds, from consumer purchases to employee retention, and they can peer months or even years into the future. According to CIO Magazine, predictive analytics is used by a wide range of organizations, “with a global market projected to reach approximately $10.95 billion by 2022, growing at a compound annual growth rate (CAGR) of around 21 percent between 2016 and 2022, according to a 2017 report issued by Zion Market Research.” A few applications:
Forecasting long-term price and demand ratios. In the energy, utilities and commodities industries, predictive analytics is used to forecast the impact of variables on supply and demand and, thus, on pricing. These variables may include equipment reliability, weather events and industry regulations.
Developing credit risk models. The subprime mortgage crisis of 2008-2009 was a case study in poor risk modeling. The modeling was built on the false assumption that home prices would keep rising and homeowners in a financial bind could tap their equity. Instead, people lost jobs in the recession and were upside down in their mortgages, resulting in foreclosures. Better predictive analytics would have accounted for this possibility, as well as for the many other variables that would have disqualified numerous subprime applicants.
Preventing customer churn. A company can examine customers who have discontinued business and develop a regression analysis that correlates certain attributes and behaviors with a high likelihood of churning. They can then use the results to proactively keep current at-risk customers from taking their business elsewhere.
Assessing risk. The insurance industry relies on predictive analytics to attract and retain profitable customer types and to steer clear of the unprofitable. In auto insurance, good drivers are incentivized to stay, and drivers with recent accidents or traffic violations are discouraged with premium hikes. These actions are predicated on predictive analytics results that suggest someone with a speeding ticket in the past year is five times as likely to make an insurance claim.
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Sources:
Harvard Business Review: A Predictive Analytics Primer
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Neural Designer: 6 Applications of Predictive Analytics in Business Intelligence
–%> CIO: What Is Predictive Analytics? Transforming Data Into Future Insights
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