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How Nanigans’ Predictive Lifetime Value Came to Be [A Timeline]

Written by: Jennie White, Marketing Associate

The first known use of the word “predict” was in 1609; originating from the Latin word praedictus. Asdefined by Merriam Webster, predict is to declare or indicate in advance; especially: foretell on the basis of observation, experience, or scientific reason. Predictions have evolved vastly from 1609, arguably one of the first scientists to use “predictive science” was physicist/mathematician /astronomer, and philosopher, Galileo Galilei, who championed theories like”observational astronomy“.

But this post is not about the 1600’s but the developments of predictive technology throughout history. Nanigans’ mantra is “Predictive Lifetime Value” (pLTV); predictive technology has evolved drastically from the words inception in 1609 so we created a predictive technology timeline to see how our pLTV mantra came to be. Our timeline shows you the history of predictive technology below. Take a look and see how far prediction technology has come.

1944: The Manhattan Project team runs computer stimulation to predict behavior of nuclear chain reactions.

1950: The ENIAC (the first electronic general-purpose computer) generates the first predictive model for weather forecasting

1958: FICO starts using predictive modeling for credit risk decisions.

1973: The Black-Scholes model, a mathematical equation to predict optimal price for stock options was published.

1992: FICO releases real-time analytics to fight credit card fraud.

1995: Amazon and eBay compete to personalize the online buying experience and show products based on a customer’s purchasing behavior.

1998: Google deploys algorithms to maximize relevance in search queries.

1998: The Oakland A’s baseball team uses predictive analytics known as “Moneyball” for a competitive edge when buying players.

~1999: T9 (Text on 9 keys) a technology that predicts words based on the numbers inputed on a mobile phone.

1999: The Music Genome Project launches and uses vectors (list of attributes) to identify similar songs in the same genre.

2000: Google launches AdWords, a new ad product designed to to fine-tune ads in real-time based on a users search queries.

2001: StumbleUpon, a website discovery toolbar combines human opinions and machine learnings to show like-minded people similar websites.

2002: Last.Fm recommendation and music streamer launches; Last.Fm puts its user’s listening data into one place and feeds back relevant song and and artist suggestions based on the larger community’s listening habits.

2003: Google launches its automated content-targeted ads product that serves relevant ads based on websites a user visits. ww

2005: The Music Genome Project rebrands as Pandora Radio and launches a consumer facing product.

2005: Netflix started making recommendations to viewers based on what they watched and their star ratings.

2006: After their 2004 launch, Facebook deploys their first ads in partnership with Chase and Microsoft.

2006: LinkedIn introduces features like ‘Recommendations’ the ‘People You May Know’ based on an individuals contacts and networks.

2008: Google’s Suggest feature arrives on, helping formulate queries, limit spelling errors and reduce keystrokes

2010: Nanigans, one of the first Facebook ad developers launches their Facebook performance advertising software, Ad Engine. Ad Engine uses the Facebook’s robust data (demographics, interests, etc.) to make calculated predictions on customer lifetime value.

2011: Facebook launches Sponsored Stories, enabling marketers to serve ads to based on the interests of a user’s friends.

2013: Nanigans continues to develop their predictive lifetime technology to deliver advertisers their most profitable customers on social and mobile.

What will be 2013’s biggest development in predictive technology? Let us know in the comments!

*Note: dates do not reflect the inception of companies, but do represent the launch of their ‘predictive products’. 

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