The future is the ultimate unknown. It’s everything that hasn’t happened yet.
Prediction as a capability is booming. It reinvents industries and runs the world. More and more, predictive analytics drives commerce, manufacturing, healthcare, government, and law enforcement. In these spheres, organizations operate more effectively by way of predicting behavior—i.e., the outcome for each individual customer, employee, patient, voter, and suspect.
Predictive analytics’ expansive deployment has taken hold. Accenture and Forrester both report that predictive analytics’ adoption has more than doubled in recent years. Transparency Market Research projects the predictive analytics market will reach $6.5 billion within a few years. Predictive analytics is becoming a standard safeguard for business, and even demand from consumers for its capabilities promises to surge.
New groundbreaking stories of predictive analytics in action are pouring in. A few key ingredients have opened these floodgates:
I’ve listed below a slew of examples—from the likes of Facebook, the NSA, Hillary for America, Uber, Airbnb, Google, Shell, UPS, Amazon.com, ConEd, Yahoo!, and the U.S. government. These examples are new in this year’s Revised and Updated edition of my book, Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. With these newly added cases, the book’s central compendium of mini-case studies has grown to 182 entries (most were sourced from presentations at Predictive Analytics World, the event series I founded—for more information about each example, access the book’s Notes PDF, available at www.PredictiveNotes.com, and search by organization name).
Below are 7 of the 22 examples in this article as it was originally published by Big Think (you are reading an excerpted version—click through for access to the complete article).
EXAMPLES OF PREDICTIVE ANALYTICS:
|Which Facebook posts you will like in order to optimize your news feed||Facebook: Predicts which of 1,500 candidate posts (on average) will be most interesting to you in order to personalize your news feed. To optimize the order of content items, the News Feed ranking algorithm weights around 100,000 factors such as recency, likes, clicks, shares, comments, time spent on posts, poster popularity, your affinity for the poster and content area, and measures of relevance and trustworthiness. This intensifies the “addictive” engagement, with two-thirds of Facebook’s 1.44 billion monthly users logging in daily.|
|Terrorism||The National Security Agency: Obtained software solutions for and core competency in predictive analytics. It’s clear that the NSA considers predictive analytics a strategic priority as a means to target investigation activities by automatically discovering previously unknown potential suspects.|
|Where you are going||Uber: Can predict the specific destination address of San Francisco riders based on exact drop-off location with 74 percent accuracy, despite, for example, how many businesses there are within 100 meters in a typical city area (just taking the closest candidate address achieves 44 percent accuracy).|
|Acceptance of booking request in order to match guests to hosts||Airbnb: Rank orders accommodations that fulfill a user search in part by the predicted probability each host would accept the user’s booking request. By surfacing likely matches more prominently, the company increased booking conversions by nearly 4 percent—a significant gain considering its estimated annual booking of over 12 million guest nights.|
|Oil refinery safety incidents||Shell: Predicts the number of safety incidents per team of workers at oil refineries, globally. One example discovery: Increased employee engagement predicts fewer incidents; one percentage point increase in team employee engagement is associated with a 4 percent decrease in the number of safety incidents per FTE.|
|Maritime incidents||RightShip: Predicts dangerous or costly maritime incidents in order to assess vessel risk that informs shipment decisions when selecting between vessels. The 10 percent highest-risk vessels are three times more likely than average to experience an incident in the next 12 months, and are 16 times more likely to incur a casualty than the 10 percent least risky. Risk assessment is based on vessel age, type, carrying capacity, origin, registration, ownership, management, and other factors.|
|Voter persuasion||Hillary for America 2016 Campaign: Given Obama’s success with persuasion modeling in 2012, Hillary Clinton’s 2016 campaign appears to be planning to employ it as well. Analytics job postings reveal they’re going to be “helping the campaign determine which voters to target for persuasion.”|
Click here to access the full article at Big Think (15 more examples, plus conclusions).
Adapted with permission of the publisher from Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Revised and Updated Edition (Wiley, January 2016) by Eric Siegel, Ph.D. Siegel is the founder of the Predictive Analytics World conference series— which includes events for business, government, healthcare, workforce, manufacturing, and financial services —executive editor of The Predictive Analytics Times, and a former computer science professor at Columbia University. For more information about predictive analytics, see the Predictive Analytics Guide.