Analytics Innovation, Issue 1

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ANALYTICS INNOVATION AUG 2015 | #1

FIGHTING ISIS WITH BIG DATA Nowhere does the power of Big Data cause more concern than when it’s in the hands of the NSA. We look at whether it can really help win the war on terror

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Social media analytics and the film industry We ask if film studios can stop putting out duds simply by looking at peoples Twitter feeds 17

Should consumers be concerned about data privacy?

Advances in Big Data over recent years mean that more of people’s personal information is available than ever. We look at whether it’s time to worry

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ISSUE 1

EDITOR’S LETTER Welcome to the 1st Edition of Analytics Innovation Magazine

e have seen the readership of our website grow significantly over the past year, and for all of you familiar with it, we hope that you have enjoyed the content. For those of you who have yet to visit us, you can do so at channels.theinnovationenterprise.com/analytics.

This is the first issue of our analytics magazine, which we’ve created as part of our constant drive to share ideas in the field. Subsequently, we’ve aimed to provide you with articles covering a range of topics, and demonstrate how analytics is being applied across a variety of industries from the everyday, to the more unusual.

Hollywood has come in for criticism over recent years because of the number of sequels and remakes being put out, but how else can they predict whether a film will be a hit? Sam Button asks if social media analytics can help.

We also have insight from Euan Hunter, who looks into how analytics were used in the last two U.S. elections, and what this is likely to mean for 2016.

Working through mountains of unstructured text is a massive challenge for any company, and Elliot Pannaman investigates whether Text Analytics can provide the necessary tools.

The ISIS situation in Syria and Iraq is ongoing, and the terror level is severe. I look at whether big data analytics could be the tool that turns the tide?

In a fast paced world, companies now need their information ahead of time if they are to stay competitive.

Olivia Timson looks at how predictive analytics can help achieve this.

David Barton asks whether consumers should be concerned with data privacy, and what retailers can do to ease their fears.

In addition to this, I look at one of the stranger test cases for how analytics is being put to use: the adult film industry.

If you like the magazine please share it. If you have any comment on the magazine or if you want to submit an article, please let me know via jovenden@theiegroup.com

James Ovenden Managing Editor

Are you are looking to put your products in front of key decision makers?

For Advertising contact Euan at ehunter@theiegroup.com

CONTENTS 04 | FIGHTING ISIS WITH BIG DATA

Nowhere does the power of Big Data cause more concern than when it’s in the hands of the NSA. We look at whether it can really help win the war on terror.

07 | MAXIMIZING BUSINESS PERFORMANCE WITH TEXT ANALYTICS

Companies have swathes of unstructured text that needs searching. Elliot Panaman examines how analytics can help.

17 | SHOULD CONSUMERS BE CONCERNED ABOUT DATA PRIVACY?

Advances in Big Data over recent years mean that more of people’s personal information is available than ever. We investigate whet her it’s time to worry.

25 | CAN ANALYTICS SAVE THE ADULT FILM INDUSTRY?

The bizarre world of pornography is in trouble.

Could big data save it?

MANAGING EDITOR | JAMES OVENDEN EDITOR |

SIMON BARTON CONTENTS

ART DIRECTOR | NATHAN WOOD CONTRIBUTORS | ELLIOT PANAMAN EUAN HUNTER DAVID BARTON OLIVIA TIMSON SAM BUTTON

brought to you by

09 | SOCIAL MEDIA ANALYTICS AND THE FILM INDUSTRY

We ask if film studios can stop putting out duds simply by looking at people’s Twitter feeds.

13 | TAKING POLITICAL CONTROL WITH BUSINESS ANALYTICS

The Democrats used digital analytics to great effect in 2008 and 2012.

Will they continue to do so?

brought to you by

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FIGHTING ISIS WITH BIG DATA

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Nowhere does the power of Big Data cause more concern than when in the hands of the National Security Agency. Though it’s not the idea of Big Data that disturbs people, so much as it is the idea of mass surveillance. Privacy panic is all the rage, and while people fear terrorist attacks, they don’t necessarily believe preventing one is worth someone reading their e-mails.

The case for using the Big Data gained from mass surveillance only holds up if it actually works though.

In a Financial Times blog, Zeynep Tufekci argues that analytics are, by their very nature, the wrong tool to be using. Big Data analytics, she says, ‘when conducted on massive datasets can be powerful

in analyzing and identifying broad patterns, or events that occur regularly and frequently, but are singularly unsuited to finding unpredictable, erratic, and rare needles in huge haystacks.’

Privacy panic is all the rage, and while people fear terrorist attacks, they don’t necessarily believe preventing one is worth someone reading their e-mails

Tufecki argues that government forces would be better prepared to prevent attacks by paying attention instead to the causal chain that leads terrorists on their path.

Which, to continue her metaphor, isn’t a very good way of finding a needle in a haystack either. The best way to find a needle in a hay stack is with a giant magnet, though it’s unlikely that this would be a successful way of catching terrorists.

This is besides the point anyway, as the two things are not mutually exclusive. One of the advantages of Big Data is that it can be used to build profiles of potential terrorists and locate areas with large numbers of people who match these criteria.

6 One of the main tools being used against ISIS is social media analytics. ISIS is renowned for their social media prowess, with the use of Twitter and Facebook central components of a recruitment drive that has seen them become the most popular organization for fighters coming from foreign countries.

ISIS is renowned for their social media prowess, with the use of Twitter and Facebook central components of a recruitment drive that has seen them become the most popular organization for fighters coming from foreign countries The terrorist group and its supporters send an estimated 200,000 tweets a day, a ripe number for analysis that can be used to leverage a number of actionable insights. Big Data taken from social media can establish what motivates terrorists and determine the characteristics of potential recruits. A massive data mining project conducted by the Qatar Computing Research Institute in Doha analyzed data from social media to find the origins of support for ISIS, looking at more than 3 million tweets sent over a three month period, from which they created an algorithm that could identify user sentiment to an 87% level of accuracy.

The article is also flawed in that Tufecki makes a number of assumptions as to the success, or

FIGHTING ISIS WITH BIG DATA

lack there-of, of Big Data as an anti-terrorism tool. She cites several examples of terrorist atrocities in the West as examples of Big Data’s failings, but neglects to mention times it may have been successful, mainly because she doesn’t know.

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The simple truth is that there is no easy solution for terrorism, and lone wolves are always going to occur as they are incredibly difficult to prevent. However, it would be foolish to disregard Big Data as a tool in the fight.

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MAXIMIZING BUSINESS PERFORMANCE WITH TEXT ANALYTICS ELLIOT PANNAMAN INTERNATIONAL EVENTS DIRECTOR

A recent Allied Market Research study found that retailers are the biggest users, accounting for one-third of the market

Predictive algorithms are enabling the examination of huge amounts of text for useful patterns and trends, in a process that is known as ‘text analytics’. While analysis of structured information in Big Data is an easy way of discovering a wide variety of things about your business, text analytics can track far more nuanced insights. It can be used to find out how your customers feel about you and your product or service, and, often more importantly, why they feel that way.

Text analytics take swathes of unstructured text - the kind of amount that a human being could not process - and applies Big Data techniques to them. It has been used for a number of purposes.

For example, two PhD students at the Stanford Literary Lab fed the entire content of 2,958 19th century novels through a series of Big Data analytics tools. They drew a number of insights about

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MAXIMISING BUSINESS PERFORMANCE WITH TEXT ANALYTICS

the real world at the time, noting that words describing action and body parts became more prevalent as the century went on. The researchers increasing urbanization during the 19th century brought people closer together physically, which made people’s bodies and actions harder to ignore.

There are still obvious issues with text analytics.

Many words have different meanings in different cultures

There are also a number of practical business implications for text analytics - customer experience management, brand monitoring, compliance, and business intelligence are all improved with the use of text analytics. A recent Allied Market Research study found that retailers are the biggest users, accounting for one-third of the market. They can use it to look for the number of key phrases in online reviews that describe an attribute or aspect of a shop, or look at tweets and Facebook posts in order to gauge sentiment on social media.

There are still obvious issues with text analytics. Many words have different meanings in different cultures. For example ‘bad’ would usually be used to express a negative sentiment. However, in some cultures bad means good, so text analytics tools may end up confusing a negative sentiment with one that is actually positive. In order for text analytics to function properly, there must be a high degree of cultural context. For a computer to understand this context, they must understand every human’s personal experiences. This requires a massive concept matrix, which many firms are attempting to build, such as IBM with their Watson super-computer.

One example of text analytics being used to great effect was during the US election. Rayid Ghani, former Chief-Scientist at Accenture Technology Labs, implemented a massive text analytics program while working under Barack Obama, known as Project Dreamcatcher. Project Dreamcatcher took voters’ own words, noted down on clipboards by canvassers at the door or during a phone calls, in an online signup sequence or a stunt like ‘share your story.’ These were then analyzed to discover what voters were interested in and why, with keywords and context isolated and statistical patterns gleaned from the examples of millions of voters to discern meaning.

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CAN SOCIAL MEDIA ANALYTICS HELP END BOX OFFICE FLOPS? SAM BUTTON DEPUTY HEAD OF ANALYTICS

10 10 William Goldman once famously said in his insider look at Hollywood, Adventures in the Screen Trade, ‘Nobody knows anything... Not one person in the entire motion picture field knows for a certainty what’s going to work. Every time out it’s a guess and, if you’re lucky, an educated one.’ Movies are big business, and big money is spent on trying to find a box office hit. There are very few other industries that spend the sort of exorbitant sums the film industry does based simply on guess work.

The film industry may have seen many massive successes, with films like The Avengers and Avatar grossing well in excess of a billion dollars, but it has also seen a number of cataclysmic failures.

Keanu Reeves’ 2013 bomb 47 Ronin lost roughly $150 million, while critics’ ‘favourite’ John Carter lost in excess of $100 million.

And the impact for studios can be devastating. Cutthroat Island, Carolco Pictures’ bombastic pirate adventure, tanked so colossally in 1995 that it put the studio out of business. Interestingly, Carolco is making a comeback this year, embracing the trend for 90’s nostalgia.

Predicting whether or not a film is going to be a hit is an exceptionally imprecise science. Analytics have had a massive impact across a range of industries, but such is the nature of the film industry that most are sceptical as to their potential applicability - especially at the early investment stage.

However, as the market gets tighter and more competitive, and studios rely increasingly on their blockbusters to carry their less profitable films, they are looking at analytics to predict the box office success of a film at earlier and earlier stages. Social media analytics in particular is proving

CAN SOCAIL MEDIA ANALYTICS HELP END BOX OFFICE FLOPS?

IBM has applied analytics models to anumber of Bollywood films, looking at social media to build what it calls a social sentiment index (SSI)

CAN SOCAIL MEDIA ANALYTICS HELP END BOX OFFICE FLOPS?

a useful tool, helping studios to judge how to go about marketing a film. Wilson Raj, Global Customer Intelligence Director for SAS, notes that: ‘Social is just another stream of unstructured data to us.

We see it as an opportunity and challenge to integrate it with other business insights such as web behaviors, campaign interactions and purchase histories.’ This philosophy is now being widely applied.

Social is just another stream of unstructured data to us. We see it as an opportunity and challenge to integrate it with other business insights such as web behaviors, campaign interactions and purchase histories

Fizziology is one company datamining social media to predict how a film goes down with audiences.

Fizziology uses a number of different metrics to evaluate the sentiment towards a movie. These range from the more obvious, such as how many times a film is mentioned, to deeper metrics such as the demographic of those mentioning the film and what other topics are being mentioned in conjunction with it. Their methods were notably successful in marketing Pitch Perfect. Having been asked by Universal to look into how the film was being discussed on social media, analysts at the marketing firm discovered a number of things. For instance, rather than drawing positive remarks from a predominantly female audience with an interest in its supposed spiritual cousin Glee, as had been expected, they found that it was actually males showing an interest - mainly college students, and mainly not Glee fans. They found that people were referencing certain characters, such as ‘Fat Amy’, and that people were already widely quoting the film, suggesting that it had a good chance of becoming a cult hit. This allowed Universal to change its pre-release strategy for the film, which they did with great success.

The use of analytics is not just limited to Hollywood. The world’s biggest film industry, Bollywood, is also getting in on the act. IBM

11 has applied analytics models to a number of Bollywood films, looking at social media to build what it calls a social sentiment index (SSI). SSI revealed a number of things about which films would be successful, including that there is a high probability that films with a political bent would do well.

By adopting such techniques, studios may even be able to get an idea of how successful a film will be from conception. While it could be argued that this will hamper creativity in the movie industry and neuter risk taking, it is also the case that studios need blockbusters to pay for a large percentage of their films, and the better the chance they have of success, the more small films they will be able to afford to take a chance on.

Nobody knows anything…

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PREDICTIVE ANALYTICS INNOVATION NOVEMBER 11—12 2015 | CHICAGO

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TAKING POLITICAL CONTROL WITH DIGITAL ANALYTICS EUAN HUNTER

One of the main elements of Barack Obama’s victory in 2012 was a massive analytics drive, led by campaign manager Jim Messina

ANALYICS THOUGHT LEADER

‘Political organization is basically a matter of list-keeping. You canvas a state by foot and by phone to find out who is for you, who is against you, and who is uncommitted. Once you have the list, you cross off the ones against you, barrage the uncommitted with pleas and information, and make sure your supporters get to the polls.’

Hunter S Thompson wrote this quote while following candidates in

the 1972 Democratic primary. The principles of political organization remain very much the same.

The reliance on lists is certainly still there. These lists, however, now contain substantially more information than when George McGovern ran against Nixon in ’72.

One of the main elements of Barack Obama’s victory in 2012 was a massive analytics drive, led by campaign manager Jim Messina. Analytics was applied to

14 every aspect of the campaign, and data-driven decision-making was used to choose advertising buys, raise money, and model voter movements as the race came down to the wire. Messina later said: ‘We were going to demand data on everything, we were going to measure everything... we were going to put an analytics team inside of us to study us the entire time to make sure we were being smart about things.’

To achieve this, Messina employed a 100-strong analytics team, 50 of whom worked in a dedicated analytics department. A further 20 more were embedded throughout the headquarters, while 30 were sent out into the field. They worked their way through dozens of terabytes of data using a combination of the HP Vertica MPP analytic database and predictive models with R and Stata. Every aspect of the campaign was geared towards getting as much data as possible. All staff across the entire organization were regularly evaluated on their input levels, to ensure that they were putting in every last bit of data so it could all be collected.

While profiling potential voters is nothing new, it has never before been done on the scale seen during the Obama campaign.

Messina’s army of data crunchers managed to create a so-called ‘megafile’ on potential voters.

This didn’t just show how to find voters and get their attention, but allowed them to run tests which predicted the specific kinds of appeals that would persuade different types of people. Using this information, they were also able to rank call lists in order of ‘persuadability’.

Analytics were also used out in the field. Using Airwolf, volunteers out canvassing would record a voter’s particular interests and feed them

TAKING POLITICAL CONTROL WITH DATA ANALYTICS

While profiling potential voters is nothing new, it has never before been done on the scale seen during the Obama campaign

TAKING POLITICAL CONTROL WITH DATA ANALYTICS

into HP’s Vertica database. The digital team would then run email blasts from the local organizer that corresponded to a voter’s favorite campaign issues, helping to target their messages.

Consumer data was also examined to determine who was giving money to the campaign. One of the most important aspects of Obama’s victory was fundraising.

Before Barack Obama, the Republicans historically raised substantially more than the Democrats. Since 2008, however, the pendulum has swung firmly in favour of the Democrats, despite a number of heavy hitters such as the Koch Brothers backing the Republicans with substantial sums.

This was thanks, in large part, to the substantial number of smalldollar donations raised by a digital grassroots campaign. Anyone who ever donated money to the Barack Obama campaign will be well acquainted with the deluge of e-mails it sparked, and they may - at least they would have been before the e-mails started - be glad to know that it worked.

The Republican campaign, meanwhile, saw no need to build the same sort of in-house data

analytics departments until lateon, preferring to outsource it instead. Mitt Romney’s Digital Director, Zac Moffatt, said in July: ‘I don’t think we thought, relative to the marketplace, we could be the best at data inhouse all the time.’ When they eventually did set about building an in-house team, it was a tenth the size of Obama’s. A massive gulf has developed between the Republicans and Democrats in terms of how many people they employ for their campaigns with experience in technology and data. Indeed, between 2004 and 2012, Democratic campaigns hired 503 staffers in digital, data, and analytics, while the Republicans hired just 123.

The Republican campaign, meanwhile, saw no need to build the same sort of in-house data analytics departments until late-on, preferring to outsource it instead

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The Democrat’s focus on analytics is set to continue into the next election under likely candidate Hilary Clinton, who it is believed will hire 1,000 ‘data geeks, techies and digital gurus’ if she’s successful.

And the Democrats already have a well-oiled data analytics machine and wealth of expertise in place to build upon. It is likely that it will be some time before the Republicans are able to catch up, even if Donald Trump has thrown his toupee and $8 billion chequebook into the ring.

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HR & WORKFORCE ANALYTICS INNOVATION

SEPTEMBER 22—23 2015 | CHICAGO

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SHOULD CONSUMERS BE CONCERNED ABOUT DATA PRIVACY? DAVID BARTON HEAD OF ANALYTICS

Right to privacy has always been tricky to define legally, with many of the repercussions falling under the purview of other laws, such as theft, trespass, and defamation.

What’s left is emotional distress, which is a fairly nebulous concept.

The same is true of data privacy, with the added complication that it is based online and thus harder to regulate. The advances in Big Data over recent years mean that people’s personal information is being generated, collected, harvested and processed at an unprecedented rate, and this is only set to intensify. Research released by YouGov in April,

meanwhile, found that 72% of British adults were concerned about their private information online, citing fears of hackers and unauthorised access to their data.

When asking whether consumers should be concerned about data privacy, it is important to first of all establish what these concerns are. The Guardian refers to the YouGov survey as occurring in a ‘post-Snowden world’, conflating people’s fears with Edward Snowden’s revelation of mass collection of private data by the security services. Concerns over the erosion of liberty are, of course, valid, but whether it is the primary cause of concern for 72% of British adults is highly debatable.

Research released by YouGov in April found that 72% of British adults were concerned about their private information online, citing fears of hackers and una uthorised access to their data

18 People also dislike their personal space being violated. Clearly, this sense of outrage is unlikely to be present when details have been willingly shared, as they often are.

However, when the information disseminates to people who you don’t believe you’ve given permission to have it, that sense of violation creeps back in. On the flip side, as data gets bigger and more aggregated, it often becomes more anonymous. It is, for the most part, someone you don’t know looking at numbers on a screen which can’t be related back to you.

The question is, what can regulators really do to prevent cyber crime? If your house keeps getting broken into, do you get better locks put in, or do you set limits on who is allowed with 10 feet of your house?

There are also examples of firms doing too good a job with their data analytics and bringing the veil of anonymity crashing down. Target is one infamous example. The US retailer would send coupons to women it believed to be pregnant based on their

SHOULD CONSUMERS BE CONCERNED ABOUT DATA PRIVACY?

browsing and buying habits. One day they were called by the irate father of a young girl who had been sent such coupons, claiming that she wasn’t pregnant. It was later revealed that she was, but it was being kept secret, and the coupons had exposed her.

Most people’s fears around the large amounts of their personal information on the internet center around the likelihood of theft.

As the amount of personal data increases, so too do the number of opportunities for it to be stolen.

It is vital that companies do everything in their power to prevent people’s personal information from being exposed. And this is still not happening. A recent report from McAfee found that nearly 90% of SMEs in the US do not use data protection for company and customer information, and less than half secured company email to prevent phishing scams.

Poor security does not simply impact the customer though, but the company as well. According to PwC, the average cost of a firm’s worst security breach is rising significantly. For small businesses, the worst breaches cost between £65,000 and £115,000 on average, while for large firms, the damage is between £600,000 and £1.15m.

SHOULD CONSUMERS BE CONCERNED ABOUT DATA PRIVACY?

The question is, what can regulators really do to prevent cyber crime?

If your house keeps getting broken into, do you get better locks put in, or do you set limits on who is allowed with 10 feet of your house?

It is right that customers’ private details are properly secured, and that any sharing is completely transparent, and this will help prevent theft. However, regulators’ ability to enforce such rules is questionable, and they shouldn’t have to. As the internet becomes even more deeply ingrained in people’s lives and knowledge around data improves, any firm that does not have such processes in place will lose customer trust, and the penalties will be more severe than any regulator could inflict.

Right to privacy has always been tricky to define legally, with many of the repercussions falling under the purview of other laws, such as theft, trespass, and defamation. What’s left is emotional distress, which is a fairly nebulous concept

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MAKING FASTER DECISIONS WITH PREDICTIVE ANALYTICS OLIVIA TIMSON ANALYTICS THOUGHT LEADER

When making a big decision that has a companywide impact, there are normally a number of departments with vested interests involved

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MAKING FASTER DECISIONS WITH PREDICTIVE ANALYTICS

Over the past several years, companies have moved from simply asking where to get the data from to support their decisions, to asking how they are going to leverage it to create actionable insights.

By using predictive analytics, it is possible for a company to make better and faster decisions, and at a lower cost - either by using them to supplement human decision making processes, or replacing them entirely.

Predictive analytics use statistical or machine-learning techniques to analyze current and historical facts, and find relationships and patterns that can be used to predict future events. A basic example of its application is in retail banking.

Banks can use predictive models to look at customers’ spending patterns. They can then use this information to predict their financial and life events and send them more relevant offers, make a decision as to whether to give them a loan, and so forth.

Predictive analytics use statistical or machinelearning techniques to analyze current and historical facts, and find relationships and patterns that can be used to predict future events

In decision making, the more that you know about the likely outcome, the more confident you can be that you decision is the right one.

Predictive analytics allows you to get an idea of every possible eventuality, so you can weigh up the risks and the potential return on investment (ROI). It enables you to see every factor that could play a role in the outcome, including some that you may not have thought relevant.

Another advantage of predictive analytics is that it helps to remove politics from the decision making process. When making a big decision that has a companywide impact, there are normally a number of departments with vested interests involved, often relying on different experiences

MAKING FASTER DECISIONS WITH PREDICTIVE ANALYTICS

and knowledge bases that may run contrary to those of others. Using predictive analytics, it is possible to gain a single version of the truth that can override such ulterior motives.

According to Wayne Eckerson’s 2014 study, Making Predictive Analytics Pervasive, the number of organizations that reported successfully implementing them actually dropped from 21% to 18% in 2014

Awareness of predictive analytics is growing, and companies that have adopted the methods are reporting success and seeing increased ROIs. However, according to Wayne Eckerson’s 2014 study, Making Predictive Analytics Pervasive, the number of organizations that reported successfully implementing them actually dropped from 21% to 18% in 2014. One aid that could see this change is the new IBM Industry Analytics Solutions, which is designed to provide interactive and role-specific dashboards that business users can share predictive insights on. These are visible across teams and organizations, increasing the speed at which decisions are made.

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Another useful tool in optimizing predictive analytics to produce better choices is decisionmodeling. Decision-Modelling is the structure of the decisionmaking involved in a business scenario that has to be made repeatedly, such as pricing deals.

It breaks the process down and identifies what information and knowledge was required and where. It is also possible to link decision requirements models to KPIs and metrics, making it apparent which metrics will improve if the decision making does. By doing this, it’s possible to see how predictive analytics can add value, and how ROI can be measured later.

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SOCIAL & DIGITAL ANALYTICS INNOVATION SEPTEMBER 24—25 2015 | MIAMI

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CAN ANALYTICS SAVE THE ADULT FILM INDUSTRY

BY JAMES OVENDEN

MANAGING EDITOR

At the heart of pornography is sexuality haunted by its own disappearance

According to sociologist Jean Baudrilliard, at the heart of pornography is sexuality haunted by its own disappearance. At the heart of the pornography industry, however, is a group of entrepreneurs who are turning to data analytics for insights into the viewing habits of their customers.

The pornography industry is besieged with the same problem that the music industry has faced since Sean Parker released Napster: with so much of their content now freely available online, nobody wants to pay for it anymore. This problem is exacerbated by the fact that they have a clientele who are particularly reticent to leave a digital footprint of their sexual proclivities for fear of others, spouses in particular, finding it.

TAKING POLITICAL

26 This has had heavy financial implications. As a result of the explosion in free sites, global revenue in the industry fell by 50% between 2007 and 2011. Kink.com, a San Francisco porn conglomerate that had 205 million page views in 2013, made its first ever loss in 2011.

Despite this, pornography still accounts for 30% of all data transferred over the internet. Porn sites get more visitors per month

CAN ANALYTICS SAVE THE ADULT FILM INDUSTRY? than Netflix, Amazon, and Twitter combined. Manwin, which owns most online adult sites, including Brazzers and RealityKings, receives in the region of 16 billion visitors per month.

With so much data available about the tastes of their customers, it is a prime example of an industry that could embrace Big Data as a resource for harnessing customer insights that improve their functionality and drive their clientele

Some argue that the industry is too segmented for analytics to be of use, with the whimlike nature of most consumers’ fantasies meaning that any insights gathered fast become irrelevant

ICAL CONTROL WITH DATA ANALYTICS

CAN ANALYTICS SAVE THE ADULT FILM INDUSTRY? down revenue making streams.

The key to survival now for adult websites is retaining the customers they still have, and attempting to find something they can offer that free sites can’t. Free sites such as YouPorn, on the other hand, are using them to drive visitors towards their fee paying options. This is made possible by the personalized service that analytics can enable, and many companies are now using it to leverage actionable insights.

Adult film production studio Pink Visual, for one, has analyzed user data to discover who is accessing their higher-quality videos, and now streams its sites via Wi-Fi as they are better equipped to deal with the bandwidth loads. It has also learned to look at which countries have a high level of traffic but are producing limited financial returns, and can subsequently block such regions to free up bandwidth.

Websites can also look at what time their users are logging on, and target viewers with offers and special shows, particularly the live-cam sites. Some argue that the industry is too segmented for analytics to be of use, with the whim-like nature of most consumers’ fantasies meaning that any insights gathered fast become irrelevant. These whims, however, can also be seen as trends when analytics are applied, and by following them, websites can keep on top of what’s popular, and make predictions as to what these trends will be. Analytics have even enabled producers to get an idea of what kind of star or starlet their customers are looking for.

27 There could also be an opportunity for more run of the mill companies to exploit the insights garnered from pornography. Sex sells, and an understanding of the sexual appetites of certain demographics and regions could be of an advantage to advertisers. Peter Acworth, founder of Kink.com, has argued that advertisers would have little interest in the personal data they garner, telling NPR.org: “Who would want to buy data pertaining to whether somebody likes bondage or spanking?” But third party advertising companies are tracking them in their hundreds, and this could be another potential revenue stream.

As a result of the explosion in free sites, global revenue in the industry fell by 50% between 2007 and 2011 The porn industry has faced massive upheaval before. Indeed, it is often one of the driving forces behind the early growth of new viewing formats, from the VCR to cable television, and ironically the World Wide Web, and the public’s appetite for their wares means it will be interesting to see how the industry adapts to the new challenges facing them.

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