Somewhere in a sleepy North London suburb, a shopkeeper ritualistically opened his daily newspaper. Eyeing the weather report, he moved a bin of black umbrellas to the front of the store, just inside the door where they could easily be seen by customers needing a quick respite from the approaching rain.
copyright by www.econsultancy.com
Written by Lori Goldberg, Silverlight Digital
The year is 1861 and a weather forecast, first published in London’s daily newspaper The Times, had likely influenced the purchase of an umbrella or two.
Over a century later, American Jule Gregory Charney – who is considered the father of modern meteorology, teamed with his Norwegian and American counterparts in mathematics, meteorology and computer programming to develop the first computerized program derived for the prediction of weather. Their computerized approach was perhaps the first example of (also known as ) influencing consumer behavior through weather reporting.
With this , shopkeepers and advertisers could effectively move merchandise associated with changes in weather, from simple umbrellas to pharmaceuticals, clothing, vacations, and air conditioners.
Today, IBM’s The Weather Company provides actionable weather forecasts and analytics to advertisers with relevance to thousands of businesses, globally. Through the speed and agility of digital advertising, ad campaigns can flight and pause with the precision of changes in the weather… and as we know, the weather always changes. Ads for cold weather products can appear when local temperatures drop below 68 degrees, while ads for Caribbean vacations can target New York days before an approaching snowstorm.
In the last 20 years, has flooded the advertising market by helping to scale operations through programmatic and content creation, emulating human conversation via chatbots and virtual personal assistants, and refining advertising platforms to understand consumer intent.
Just as our ability to forecast weather allows us to target advertising dollars, is influencing more and more advertising decisions on our behalf. To this point, below is a brief history of advertising’s use of and perhaps a glimpse of the future.
1998 – thinks you’ll like this book
The concept of clustering consumer behaviors to predict future behaviors began at Columbia University in a report on “digital bookshelves” by Jussi Karlgren, a Swedish computational linguist. And it was in 1998 that Amazon began using “collaborative filtering” enabling recommendations for millions of customers.
Today, Spotify recommends music you may like, Netflix suggests films and television programs you may like, and Facebook suggests friends you may know. This all comes from -based clustering and interpreting of consumer data paired with profile information and demographics. These -based systems continually adapt to your likes and dislikes and react with new recommendations tailored in real-time.
2013 – targets the labor of content creation
With the increasing popularity of content marketing, more content means more advertising opportunities. But the cost and pace of good journalism are considered too slow given volume of ads and eyeballs to be had. The solution: Yahoo’s Automated Insights Wordsmith Platform (now Verizon’s) uses to scan billions of daily sports-related data points (scores, statistics) and structure the information in computer-generated articles summarizing games, informing fantasy sports fans, and reporting stats.
Articles are produced with speed and scale never possible by human journalists. The produces natural language content and adjusts for tone and personality, giving each piece a specific journalistic attitude. Automated Insights published 300 million pieces of content in 2013 and has far exceeded 1.5 billion annually since. […]