Showing posts with label Entertainment Industry. Show all posts
Showing posts with label Entertainment Industry. Show all posts

Wednesday, August 14, 2013

Everybody likes to predict, but nobody likes being predictable, nor told what to do

The Netflix algorithm is in the news again.
The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next

Netflix finds rating predictions are no longer as important, trumped by current viewing behaviour, i.e. what you are watching now. However, browsing through the comments, and again, you will see a generally negative reaction. Some people really hate being told what to watch, even if it's just a recommendation. Others say Netflix sucks, because it recommends things they've watched elsewhere. That sounds like a lack of understanding: if you don't tell Netflix you've watched something already, then how could it know?

As "big data" gets more media attention, it is reaching a wider audience who don't yet understand how algorithms work, but only know there are algorithms everywhere in their life, and it's scary to them. The lack of understanding seems to create fear and resentment.

LinkedIn and Facebook's recommendation systems for helping people find colleagues or friends they may know are generally well received, yet these film recommendation systems aren't. The difference between them might underline the success criteria of rolling out such recommendation systems.

Saturday, August 3, 2013

The Slightly Rosier Side of Gambling Analytics

Having posted about the ugly side of analytics - casino loyalty programmes, the Guardian's DataBlog caught my eye with their article on a rosier side of gambling analytics, where UK technology firm uses machine learning to combat gambling addiction.

Of course, a business is still a business. It needs to be profitable, so there are reasons more than just "let's be good". I list out below my take on the reasons for "them" the gamblers clients, and the reasons for "us" the casinos. Note, I simply assumed the machine learning study is sponsored by the casinos.

Just for "them":

Casinos too have a corporate social responsibility (CSP). Helping pathological gamblers, or identifying them before they become one is a nice thing to do.

For "them" and for "us":

More for everyone! They get to play more, and we get to profit more. The more people play a bit for longer is better than playing a lot for a short amount of time due to self exclusion lists. (I'm not sure which is the better evil of the two though...)
That's the business case. It's not all soft and cuddly like the CSP. Well, ok, business cases almost never are.
"If you can help that player have long term sustainable activity, then over the long term that customer will be of more value to you than if they make a short term loss, decide they are out of control and withdraw completely"

Just for "us":

Minimising gambling problems helps keep the country's regulators off the companies' backs, so they don't have to relocate when the country's regulations tighten. Relocation = cost. A lot of it.
"And there's also brand reputation for the operator. No company wants to be named in a case study of extreme gambling addiction, to be named in relation to a problem gambler losing their house"

A side note: This reaffirmed why I don't's a lose-win situation.

"A lot of casino games operate around a return-to-player rate (RTP) whereby if the customer pays, say £100, the game would be set up to pay back an average of £90. Different games will have different RTPs, and there are a few schools of thought on whether certain rates have different impacts on somebody's likelihood of becoming addicted.Some believe that if you lose really quickly, you'll be out of funds very quickly and will leave, and that a higher RTP will keep people on site, but others disagree"

I highly recommend reading the full article on the DataBlog.

Thursday, August 1, 2013

The Ugly Side of Analytics - Casino Customer Loyalty

While listening to This American Life's episode "Blackjack", its Act 2 had me in the car saying, "oh no, they did not!"  The "they" is the Caesars Entertainment Corporations (the casino), and yes, they have a customer loyalty programme that they use to "attract more customers", and claim it's no different than other such programmes in industries like supermarkets, hotels, airlines or dry cleaners.

Well...there is a wee bit of difference.

No one is addicted to dry cleaning.

I am saddened that analytics is used to help the casino loyalty programme and hurt the pathological gamblers. The show indicates that the programme identifies "high value customers" using loyalty cards, tracking all spend and results, and then offer them the "right" rewards to keep them coming back. Most addicted gamblers are "high value customers". The bigger the looser, the more the reward. Rewards include drinks and meals, hotel suites, trips to casinos (if you don't live there), to gifts like handbags and diamonds.

Analytics and Operational Research is supposed to be the Science of Better.

I'd like to call on all professionals in the analytics field to reflect on the moral goodness, or lack of, in your work.

There is still hope though. If casinos can use analytics to identify problem gamblers, then others can too. Given pathological gambling is a mental health issue, is it time for NGOs or governments to catch up with technology and get their hands on those loyalty card data?

Saturday, April 17, 2010

Hollywood stock exchange to become reality?

A year and a half ago, we wrote an article, Forecasting Hollywood movie box office revenue with HSX trading history, based on a talk by Natasha Foutz at the 2008 INFORMS Conference in Washington, DC.

Today I see in the news (Movie futures market approved) that trading of futures related to movies' box office success is about to become a reality. There may be some legal and political obstacles left to surmount, but there may yet be more data to work with in this line of research.

Curiously the article focuses on financial aspects of the new financial instruments rather than the consequences for Operations Research. Market liquidity and hedging by large and independent film financiers is a laudable goal, but think of the statistics!

I would be interested to know what sort of use movie theatres/cinemas could make of these predictions when making operational and strategic decisions regarding film selection and scheduling.

Sunday, October 12, 2008

Forecasting Hollywood movie box office revenue with HSX trading history

Want to know what movies are going to make it or flank it at the box office? Is it going to be a hit, a fast decaying, or a sleeper movie – that is in terms of its box office revenue trend? Natasha Foutz, Wolfgang Jank and Gareth James have attempted to predict the revenue trend of Hollywood movies with 3 principle components (average/longetivty, fast decay, and sleeper effect) in conjunction with Hollywood Stock Exchange (HSX) trading histories. HSX is a virtual stock market of music, TV shows, and movies. The authors claim a high degree of forecasting accuracy using functional shape analysis and regression on the 3 principle components and early HSX trading histories for the individual 10 weeks box office opening revenue of Hollywood movies. If you are really good at this game, you may end up selling your billion-dollar HSX portfolio on ebay, who knows?

Hollywood movies have widely varying box office revenues, some much more profitable than others. Therefore, it is crucial to forecast movie demand decay patterns for movie financing, contracting, general planning purposes, etc. The forecast needs to be made long before the movie release, since planning happens much more in advance, sometimes years earlier. Most movies gain the majority of its revenue in the first 10 weeks of opening, so the model looks at the forecasting of demand decay patterns of the first 10 weeks of Hollywood movies. The use of HSX data is proven to provide more information for the revenue forecasting purposes. Virtual stock markets (VSM), the show of wisdom of crowds, are of no stranger to forecasting complicated issues ranging from election results, NBA championship winnings, to Al Gore’s 2007 Noble Prize winning. The results produced by VSM are very impressive and accurate. For example, the political VSM was said to be 75% more accurate than political polls.

Foutz, Jank and James identified 3 principle components to be used alongside the trading history of HSX: average/longevity, fast decay, and sleeper. Longevity captures the average box revenue over the lifetime of the movie where the trend is relatively smooth (a linear decreasing trend of a log transformation of the revenue figures), such as Batman Begins. Fast decay captures the movies that have great openings but quickly die out, such as Anchorman. Sleeper describes the movies that have a slow start, but with word of mouth (for example), it would pick up momentum in later weeks of the opening, such as Monster or My Big Fat Greek Wedding.

The authors tested out 5 different models of weekly revenue regression over a period of 10 weeks. Each model uses a combination (or the lack of) the three principle components and the trading histories from HSX. The results indicate that movies with higher level of trading activities on HSX at the very early stage (weeks in advance) would more likely have a higher weekend box office opening revenue. How could this finding be used for more meaningful purposes than betting with your friends? For example, theatre owners could better allocate screens and profit sharing, while movie producers could design different contracts for the slow burners than the fast ones. If you are a movie buff, maybe it’s time to get on the HSX for some trading fun instead of crying over the financial stock markets.

Credits: The talk was given at the INFORMS (Institute For Operations Research and Management Science) 2008 conference in Washington DC, in session SA68, by Natasha Z Foutz, Assistant Professor of Marketing, from the McIntire School of Commerce, University of Virginia. The title of the talk was "Forecasting Movie Demand Decay Via Functional Models of Prediction Markets".