Predictive Buying is the name of the industry dedicated to algorithmic consumer analytics yielding future buying patterns. The primary nature of data mining, analysis and extrapolation have their roots in game theory, rule of inference and regression models. Predictive Buying is an integration of predictive analytics and the methods of permission marketing.
Contents
Benefits
While direct marketing and content-relevant ads have expanded the personalized nature of individual consumer's experiences and communication with businesses, predictive buying intelligence bridges between consumers and the products they want. Even if the consumer does not know of a product's existence, predictive buying technology can, through an analysis of the consumer's interactions, purchase history and other factors, bring that product to the consumer’s attention.
Limitations
While many products are relevant, there will never be a perfect match every time, just as there isn’t always a perfect match with human intelligence when buying a product. This condition is especially true when the predictive buying analysis is based on limited data sets.
The future
The future for predictive buying however is unlikely to be impeded by limited data sets. Trends in increased internet usage, the widespread popularity of social media and the data which can be obtained from an online merchant's website optimization analysis add daily to the diversity of data sources which can be mined, analyzed and extrapolated to accurately predict the products an individual is willing to purchase. Data sets are dramatically augmented when consumers give permission to examine the content of all of their related social media, trusting the exchange will provide for a better purchasing experience.
History
Predictive buying is an applied derivative of Artificial Intelligence which may have some of its earliest roots manifest in Greek mythology. Consider Hephaestus and Pygmalion which used the concept of intelligent robots (such as Talos). Through the years, there have been many advances in the philosophy of intelligent programming, but in 1945, John von Neumann and Oskar Morgenstern introduced the Game Theory which introduced artificial intelligence. Vannevar Bush followed up later that year with an article in July 1945 in The Atlantic Monthly titled As We May Think focusing on a vision of future computers assisting humans in many activities.
It wasn’t until 1951 that the first AI programs were actually written by Christopher Strachey and Dietrich Prinz to run on the Ferranti Mark1 machine of the University of Manchester to play checkers and chess.
In the 1990s data mining became a key development which eventually led to behavior-based robotics, such as with Polly, the first robot to navigate using vision and operate at animal-like speeds.
In 2005, recommendation technology based on tracking web activity or media usage brought artificial intelligence to marketing.
TiVo Suggestions and Pandora have pioneered predictive consumer behavior based on history and algorithmic processing.
Predictive buying today
Websites such as Amazon.com, @WalmartLabs and Imply Labs utilize predictive buying intelligence to recommend relevant products to consumers based on social media usage.