When data is mined and manipulated to produce meaningful information about possible events and trends in the future, the data is said to have been predictive. The branch of data science that deals with these predictions is called predictive analytics, or the usage of historical data to produce, emulate, or predict hypothetical situations and scenarios.
Predictive analytics relies heavily on statistics and mathematics to convert the data to either a predictor or a predictive model, which are just variables that describe the future behavior or direction of a particular dataset, whether these are customers, stock prices, or consumer interests.
Looking back to the past is looking into the future
In predicting a certain event, multiple factors are taken into consideration: related historical events, frequency of occurrence of said events, and the presence of certain elements that could have influenced the past. These are then modeled and subjected to mathematical study to see if the likelihood of an event can be quantified.
For example, Uber has developed a statistical predictive model that allows their system to hypothesize with 75% accuracy where you’re headed based on your previous rides. To do this, they take in the uniqueness of the behavior of the rider, his previous rides at or on around the same time of his current request, and even the landscape around him—just some of the data they store on their databases when you use their app—and plugs them into a proprietary model that they developed to accurately arrive at your probable destination.
Even in healthcare, one of the greatest challenges of hospital systems nowadays is reducing the rate of readmitted patients. To effectively do this, they create medical cohorts, or groups of people who exhibit certain conditions, and predict the probability of their getting admitted in a future time. A simple example is using the data of patients with diabetes that were readmitted due to an infected wound because of being in a high-risk blue-collar job and transforming this into a predictor for future patients that meet the same conditions—thereby allowing hospitals to prepare better when dealing with these patients.
Predictive analytics in business
One advantage of leveraging predictive analytics in business is being able to tailor-fit entire processes to go along with market trends. Manufacturers of clothes and dresses can predict when a certain fashion is in our out of season based on past consumer purchases. Call centers can use past workforce distribution or utilization to predict when or how many agents need to be fielded during the holidays. Even better, some sellers can predict based on previous buyer behavior when is the best day to enforce discounts on their products to maximize profit while operating at a loss.
Similar to market intelligence data, specific CRM tools can do predictive analytics to give decision-makers an extra advantage by using data they already have. There are also software out there like RapidMiner or STATISTICA that can be utilized to crawl the web for data that meet specific criteria.
FREE EBOOK: 21 Tips Seasoned Sales Reps Won't Tell You
Sell smarter. Close more.
- The tips include:
- Recognizing buying cues
- How to handle follow up calls
- Working on your speaking voice
Latest posts by Patrick Hogan (see all)
- How to Drive CRM Adoption to Get the Most Out of Your Sales Tools - November 23, 2016
- How to Build a Powerful Sales Stack for 2017 - November 21, 2016
- Understanding the Importance of Product Knowledge in B2B Sales - November 2, 2016