Predictive buying

Predictive Buying is the name of the industry dedicated to algorithmic consumer analytics yielding future buying patterns. The primary nature of data mining,[1] analysis and extrapolation have their roots in game theory,[2] rule of inference[3] and regression models.[4] Predictive Buying is an integration of predictive analytics[5] and the methods of permission marketing.[6]

Benefits

While direct marketing and content-relevant ads have expanded the personalized nature of individual consumer's experiences and communication with businesses,[7] 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[8] to examine the content of all of their related social media,[9] trusting the exchange will provide for a better purchasing experience.[10]

History

Predictive buying is an applied derivative of Artificial Intelligence[11] 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[12] and Oskar Morgenstern introduced the Game Theory[13] 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[14] and Pandora have pioneered predictive consumer behavior based on history and algorithmic processing.[15]

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.

gollark: Cancer is widely considered not good.
gollark: Joe Biden contains particles, after all.
gollark: Well, according to physics, every particle in the universe has an effect on every other particle, so that makes sense.
gollark: Anything anyone disagrees on in any way is politics.
gollark: For any actual high-throughput writing I just use a computer, because anything else would be silly.

References
  1. Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN 0-471-22852-4. OCLC 50055336
  2. Fudenberg, Drew; Tirole, Jean (1991), Game theory, MIT Press, ISBN 978-0-262-06141-4 .
  3. Boolos, George; Burgess, John; Jeffrey, Richard C. (2007). Computability and logic. Cambridge: Cambridge University Press.
  4. M. H. Kutner, C. J. Nachtsheim, and J. Neter (2004), "Applied Linear Regression Models", 4th ed., McGraw-Hill/Irwin, Boston
  5. Agresti, Alan (2002). Categorical Data Analysis. Hoboken: John Wiley and Sons. ISBN 0-471-36093-7
  6. Scott, David Meerman (2007). The new rules of marketing and PR how to use news releases, blogs, podcasts, viral marketing and online media to reach your buyers directly. Hoboken, N.J.: J. Wiley & Sons, Inc.. p. 162. ISBN 978-0-470-11345-5.
  7. O'guinn, Thomas (2008). Advertising and Integrated Brand Promotion. Oxford Oxfordshire: Oxford University Press.
  8. Godin, Seth (1999). Permission Marketing: turning strangers into friends, and friends into customers. New York: Simon & Schuster. ISBN 0-684-85636-0.
  9. V. Buskens, “Social networks and trust,” in The Netherlands:Kluwer Academic Publishers, 2002.
  10. U. Kuter and J. Golbeck, “Sunny: A new algorithm for trust inference in social networks using probabilistic confidence models,” in AAAI, 2007,
  11. John R. Davies, Stephen V. Coggeshall, Roger D. Jones, and Daniel Schutzer, "Intelligent Security Systems," in Freedman, Roy S., Flein, Robert A., and Lederman, Jess, Editors (1995). Artificial Intelligence in the Capital Markets. Chicago: Irwin.
  12. Poundstone, William (1992), Prisoner's Dilemma: John von Neumann, Game Theory and the Puzzle of the Bomb, Anchor, ISBN 978-0-385-41580-4 . A general history of game theory and game theoreticians.
  13. Maynard Smith, John (1982), Evolution and the theory of games, Cambridge University Press, ISBN 978-0-521-28884-2
  14. "Archived copy". Archived from the original on 2014-11-02. Retrieved 2010-11-24.CS1 maint: archived copy as title (link)
  15. John, Joyce (September 2006). ""Pandora and the Music Genome Project"". Scientific Computing. 23: 40–41. ISSN 1930-5753. Archived from the original on 2012-02-16. Retrieved 2008-08-03.
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