When it comes to technology management, planning, and decision making, extracting information from existing data sets—or, predictive analysis—can be an essential business tool. Predictive models are used to examine existing data and trends to better understand customers and products while also identifying potential future opportunities and risks.1
These business intelligence models create forecasts by integrating data mining, machine learning, statistical modeling, and other data technology.
Who uses predictive analytics?
Technology management teams in marketing, finance, insurance, retail, tourism, healthcare, pharmaceutical, and utility companies alike rely on predictive analytics to plan for the future or improve the customer experience. Take Netflix and Amazon for example. Netflix learns which movies viewers are likely to enjoy and Amazon predicts what a customer will buy—even going as far as to patent “anticipatory shipping,” which would deliver packages to a geographic region before a customer actually buys them.2
Benefits of Predictive Analytics
In its multiple forms—predictive modeling, decision analysis and optimization, transaction profiling, and predictive search—predictive analytics can be applied to a range of business strategies and has been a key player in search advertising and recommendation engines.3 These techniques can provide managers and executives with decision-making tools to influence upselling, sales and revenue forecasting, manufacturing optimization, and even new product development. Though useful and beneficial, predictive analytics isn’t for everyone.
Drawbacks and Criticism
A company that wishes to utilize data-driven decision-making needs to have access to substantial relevant data from a range of activities, and sometimes big data sets are hard to come by. Even if a company has sufficient data, critics argue that when anticipating human behavior computers and algorithms fail to consider variables—from changing weather to moods to relationships—that might influence customer-purchasing patterns.
Time also plays a role in how well these techniques work. Though a model may be successful at one point in time, customer behavior changes with time and therefore a model must be updated. The financial crisis in 2008-2009 exemplifies how crucial time consideration is because invalid models were predicting the likelihood of mortgage customers repaying loans without considering the possibility that housing prices might drop.4
A thorough understanding of predictive analytics can help you with business forecasting, deciding when and when not to implement predictive methods into a technology management plan, and managing data scientists.