predictive analytics

2月 102013
 

Netflix has made a big splash in the news with its use of big data. By analyzing millions of data points about the viewing habits of its customers, the movie delivery giant used the insight it gained to devise the "perfect show". One of the defining characteristics of the show, aside from its cast and story line, is in its packaging. House of Cards is delivered in a series of episodes, but released all at once, thus enabling the audience to indulge in "binge viewing".

In my household we are familiar with binge viewing. We don't watch a lot of television, and "media" is all-but-banned during the week when school is in session. That means that during weekends and holidays, in between other scheduled activities, the family lets loose. And since we don't subscribe to any other cable or satellite programming, Netflix receives the biggest share of our attention.

You might remember how I've used SAS to analyze our personal Netflix viewing history. This graph (produced using PROC SGPLOT) shows how our per-day minutes-of-watching has grown ever since Netflix introduced television streaming (around April 2010).


As you can see from the data points on the right side of the plot, we've had our share of Netflix binges. Here's the breakdown by day-of-week. Saturday accounts for 23% of our viewing minutes, while Friday-Sunday add up to 53%. (That accounts for 21,517 minutes -- that's 358 hours! Just imagine if we did watch a lot of TV...).


Okay, so if Netflix's predictive model indicates "viewers watch a lot in one sitting", then our behavior (sometimes) fits that model pretty well. But what about the content? Here are the top 10 serial shows that we've streamed using our account:


I think that this is where our household viewing habits depart from the predicted model. And while House of Cards might be a compelling show, it's not likely to appear on our top 10 chart anytime soon. If Netflix had wanted to design a show especially for us, the plot pitch might have sounded like this:

A groundbreaking sci-fi historical fiction series, featuring several British and Australian actors. Chunked into 22-minute episodes, the stories revolve around two smart boys -- one a fake psychic and the other an OCD-afflicted detective -- who travel through time and space. With their only goal to fill their summer vacation with interesting activities, they consistently foil the evil plots of the antagonist, Dr. Doofenshmirtz. Subplots revolve around Dr. Doofenshmirtz acquiring and dispensing with several wives (his "evil queens"), who seem to always fall victim to his many "-inators".

Will the boys ever get "busted"? Not if the mermaids have anything to say about it.

I'm not sure that such a show would be a commercial success, even though it might be a big hit in the Hemedinger household. It was wise of Netflix to use the accumulated viewing habits of all of its customers, and not just me, to train its analytical models. And I think the House of Cards fans will agree with that.

tags: big data, Netflix, predictive analytics
10月 252012
 
Jean Paul Isson (Global Vice President of Business Intelligence and Predictive Analytics, Monster Worldwide, Inc.) and Jesse Harriott (Chief Analytics Officer, Constant Contact) know a thing or two about business analytics. With almost 40 years of experience between them, they've handled it all—from web mining solutions to business intelligence, predictive [...]
9月 152012
 
Did you ever experience a time where you hear or see the same thing over and over again? Whether you chalk it up to coincidence, immersion or saturation, you clearly start seeing the same ideas or topics discussed in multiple places. Lately, I have been hearing about the topic of [...]
9月 122012
 
Antonia de Medinaceli is the Director of Fraud Analytics at Elder Research Inc. Her keynote speech focuses on using data mining and predictive analytics to find fraud, waste and abuse, and begins at 9:15 a.m. ET on Wednesday, Sept. 12. View the live blog. tags: analytics, Discovery Summit, Discovery Summit [...]
9月 112012
 
Ian Ayres is a lawyer and an economist. He is the William K. Townsend Professor at Yale Law School, the Anne Urowsky Professorial Fellow in Law, and a Professor at Yale's School of Management. He is the author of Super Crunchers and Carrots and Sticks. He delivers a keynote speech [...]
8月 142012
 
“Analytics are no longer just a nice thing for an organization to have,” says Antonia de Medinaceli, Director of Fraud Analytics at Elder Research, a leading consultancy in data mining, predictive analytics and text mining. She believes analytics are a must-have. We've invited her to speak at Discovery Summit 2012 [...]
6月 052012
 
Fraud detection presents myriad analytical challenges: gathering sufficient known cases to make typical modeling techniques possible, gathering inputs from disparate data sources, and combining expert knowledge from investigators with findings to be gleaned from the data in an efficient way. Of course, analysts can fall into the trap of thinking [...]
4月 062012
 
A small, North American marketing firm (a division of a much larger international firm that provides data-driven, multichannel marketing solutions) provides its clients with "effective one-to-one marketing and ROI-focused strategies by applying advanced predictive analytics." Two key client models are outlined in the Post-It Note below. This is a case study that would fit [...]
3月 152012
 
Automated fraud detection systems are becoming more common in the insurance industry as the technology improves and the benefits become more evident.  Many companies have embraced this change and are showing measurable and significant returns on their investment.  The quantifiable benefits are numerous – an increase in quality and quantity [...]