Analytics Innovation, Issue 5

T H E L E A D I N G V O I C E I N A N A LY T I C S I N N O V A T I O N

ANALYTICS INNOVATION MAY 2016 | #5

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Which Social Metrics Matter Predictive Modeling Around Most To Marketers Music Festivals Determining the success of a social media campaign is a challenge for marketers. We look at the metrics that best show whether it’s succeeded, or failed | 6

It’s festival season, and the hoards are once again descending on fields around the world. We look at how analytics is helping to cater for them | 28

Data Science Festival Data Festival June 8 & Science 9, 2016 - San Francisco June 8 & 9, 2016 - San Francisco

Machine Learning Innovation HR & Workforce Analytics Innovation Machine Learning Innovation in STEM Innovation HR &Women Workforce Analytics Innovation Women in STEM Innovation

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ISSUE 5

EDITOR’S LETTER Welcome to the 5th Edition of the Analytics Innovation Magazine

The world is a volatile place. The economy booms, it busts, wars are won, wars are lost, companies rise, and companies fall. The causes of these events are always subjective, and debate is always fierce as to how they could have been prevented and what the implications are. The one constant, however, is data. Data, when collected correctly, always has the truth contained within it, but requires analytics to reveal it.

Having realized this, organizations both private and public are collecting all the data they can, and investing heavily to make sure they can use it positively benefit their organization.

People are constantly confronted with data, either because it’s being collected or because it is being leveraged to impact upon their lives.

It is now such an accepted part of day-to-day life that many of the initial fears around invasion of privacy have been quashed, and people have a far greater awareness of the balance of benefits to sharing it.

However, there is still a substantial animosity towards data, much of which is driven by its association with the various agencies that use it. When governments search emails, it is not Big Data that people fear, it’s the government agencies who they fear will misuse it, and it seems that Big Data gets the flak.

Perhaps oddly, it appears to be governments who attract more ire than corporations from the public for data collection, despite corporations having equally as much. This is an issue of both tangibility and transparency. Governments tend to collect data under the guise of benefiting national security, while someone like Apple does it to provide a more efficient service - a far more tangible benefit than most who feel no more or less secure as a result of the government having read their emails. It is also an issue of transparency. Governments cannot ask people to share their emails with them because those with things to hide will simply click

no. The animosity it sparks should be a lesson to companies in how important it is to be transparent in how they collect data.

In this issue, we look at how the media is starting to portray data is a more positive light, with documentaries such as The Human Face of Big Data putting the practice in a more positive light. Ahead of the summer and festival season, we also look at how predictive modeling is helping to enhance the festival experience, as well as a number of other applications for data analytics, and best practices for utilizing them.

James Ovenden managing editor

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Big Data & Analytics for Retail Summit 16 & 17 June | Chicago Speakers Include:

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contents 6 | WHICH SOCIAL MEDIA METRICS MATTER MOST TO MARKETERS

15 | NEW DOCUMENTARY PUTS DATA ANALYTICS INTO THE MAINSTREAM

Determining the success of a social media campaign is a challenge for marketers. We look at the metrics that best show whether it’s succeeded, or failed

Big data is often associated with invasion of privacy when it’s reported in the press, but a new documentary is presenting the positive side

9 | IS DATA VISUALIZATION THE MOST IMPORTANT STAGE OF DATA ANALYTICS?

Decision makers without technical knowledge need to be able to understand the data to leverage insights, so is data visualization the most important stage of analytics?

12 | AI AND THE FUTURE OF SEX

Artificial Intelligence is set to change the world, and that includes our physical relationships. We investigate how

18 | HOW REAL BUSINESSES CAN USE MACHINE LEARNING

Machine learning is having a profound impact on many things, but it is evolving so fast that real businesses have still not got to grips with how to exploit it 20 | PREDICTIVE MODELING AROUND MUSIC FESTIVALS

It’s festival season, and the hoards are once again descending on fields around the world. We look at how analytics is helping to cater for them

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WHICH SOCIAL MEDIA MATTER THE MOST TO MARKETERS Meg Rimmer, Deputy Head of Analytics Meg Rimmer

According to a recent study by The McCarthy Group, 84% of millennials Data Debater don’t like advertising. The use of ad blockers is on the rise - 41% in the last year - and if you’re thinking sponsored content is the best way around them, you’re wrong. Young people are even skeptical of that. Just 24% of readers scroll down on native ad content on publisher sites, compared to 71% for normal content. The cynical little tykes.

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According to the 2015 Social Media Marketing Industry Report, the top two benefits of social media marketing are increased exposure and traffic — with 90% of marketers citing an increase in exposure and 77% saying increased traffic

In short, getting your message out there is now more challenging than ever, and marketers need every tool at their disposal. Social media is, and has been, a tremendously effective tool, not just in getting a campaign out there, but in opening up new opportunities for discovering who engages with your brand, who actually buys your product, and how best to target them.

However, many marketers have yet to realize these benefits, and still treat social media as if the main aim is just spreading awareness. The most recent CMO Survey estimated that a fifth of marketing budgets is now spent on social media, yet half of CMOs rate its performance as below average. Much of this comes down to basic failures in understanding the data around a campaign, and which metrics actually matter.

Measuring the success of a social media campaign is not easy. All the information is there, but it’s difficult to know whether what you’re looking at is useful or not. Obviously, the first port vof call is still knowing that the message is getting out there.

This means looking at awareness, reach, and frequency. According to the 2015 Social Media Marketing Industry Report, the top two benefits of social media marketing are increased exposure and traffic — with 90% of marketers citing an increase in exposure and 77% saying increased traffic. These are fairly simple to measure. Large numbers of followers on Twitter and Facebook will likely mean a degree of exposure, but far more important is how many likes and comments your posts get. Klout is an online influence gauge that enters several data points across your various social platforms - such as followers, retweets, clicks on links - into its algorithm and gives you a score.

Tools like Google analytics and Bit. ly are easy ways of seeing if people are clicking the links you’re putting out on different platforms to make

sure they are actually increasing traffic to a place where they can buy something.

These are important, but really they just scratch the surface. The key, ultimately, is to look at these alongside a second layer of metrics.

Your social media marketing goals require data that aids your decision-making and correlates with your company’s KPIs. In a survey conducted by Altimeter, it was found that only 34% of businesses feel there is a crossover between their social objectives and their company’s overall targets. You need to know that your social media efforts are generating leads and translating into customers. To do this, you have to be able to plug your marketing analytics into a contact database or CRM. Doing so allows you to connect marketing activity directly to sales activity and provides a full-funnel view of your efforts, as well as balance the right ratio of inbound and outbound methods.

It’s not just a case of directly measuring how well marketing and sales are linking up. You can also look at how customer action translates to sales over time. For example, does someone becoming a ‘fan’ on Facebook translate into increasing the amount they spend with you? If you put a ‘Buy’ button next to the Like and Message buttons, will more people click it? There is data to measure every aspect of your social media campaign that can be useful in some way, but ultimately the main question you have to ask yourself when looking at a metric is whether you know what action to take based on it? If the answer is ‘no action’, then the metric you’re looking at is probably pointless.

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A RECENT BUSINESS-2COMMUNITY survey of data professionals found that the data science skill with the highest correlation to project success was data mining and visualization tools.

Jeff Jonas, an IBM Fellow and Chief Scientist of IBM’s Context Computing group, seems to disagree. He argues that, ‘Contrary to hype, hopes, and dreams, Big Data visualization is generally not helping humans make novel discoveries. Data visualization has two primary purposes: exploration and storytelling.’ He continues, ‘What are the odds this form factor and experience will help someone find novelty – true weak signal; that proverbial ‘needle in the haystack?

Answer: Slim to none.’

Storytelling with data visualization draws an impactful response from the user and reinforces it with numerical evidence

is data visualization the most important stage of data analytics?

Euan Hunter, Analytics Commentator

Jonas is essentially arguing that the primary use of data visualization is to tell stories about the data we already understand. Whether or not he’s right about its failure to help discover new insights - although many would argue he isn’t - does this mean it’s not the most important stage of data analytics anyway? The discovery of novelty is a wonderful thing, but is entirely pointless to a business if

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decision makers can’t be convinced of their existence and take appropriate action. Data visualization is the best way of doing this.

The amount of data that organizations now consume and produce has grown exponentially over the last few years, but as comforting as it might be to have reams of data stashed away, it’s only valuable when you do something with it - analyze them, visualize them. Visualization reveals intricate structure in data that cannot be absorbed in any other way.

Storytelling with data visualization draws an impactful response from the user and reinforces it with numerical evidence. The way the human brain processes information means that presenting data as a story gives everyone in an organization a better understanding of it, and enabling a greater range of people to make sense of what it’s saying is often likely to lead to more insights.

The discovery of novelty is a wonderful thing, but is entirely pointless to a business if decision makers can’t be convinced of their existence and take appropriate action. Data visualization is the best way of doing this Zoomdata CEO Justin Langseth argues that Jonas is wrong to say that data exploration does not lead to unexpected insights, noting that pitting ‘exploration leading to insight vs ‘aha insights’ as separate things’

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is wrong. They are the same, really.’

He argues that, ‘The best visual is the one that allows a normal human with understanding of a business system to quickly see how the visuals match up with the system, learn new unexpected things about the system (business) if there are any, but mostly just match up with their innate understanding of the business.’ In business terms, it is this that is the most important thing.

Data still needs people to apply it to situations. The point of data visualization is to communicate with people and engage them, and on top of this, people still need to be convinced of the quality of the data, something else that data visualization helps with. It shows you if your dataset is incomplete by easily displaying where data is missing on the report, and whether it’s valid - with a quick, preliminary visualization on collected data showing trends that indicate problems in the complete data.

Jonas may be right that data visualization is not the best way of finding insights, but at the moment it is still the best way of using data to support decision making, and this is ultimately the most important stage of analytics.

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AI And The Future of Sex James Ovenden, Managing Editor It was always inevitable that Artificial Intelligence (AI) would eventually be used for sexual purposes. Humanity has no shame.

According to futurologist Dr. Ian Pearson, 2050 will be the year that human/robot sex overtakes human/human. Artificial intelligence researcher David Levy from the University of Maastricht takes things even further, telling LiveScience that people could be marrying robots by 2050. This happens to be 3 years after the singularity when artificial intelligence overtakes human intelligence - which likely means that the dynamics of such relationships are going to be extremely complicated.

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What RealDoll has essentially created is a metal secretary you can fornicate with whenever you want

Many companies are already making progress in the quest for fully fledged AI-enabled ’sex robots’. Californiabased RealDoll, for example, is in the process of developing a robot called Realbotix. According to The New York Times, Realbotix will include convincing AI, with a robotic head that blinks and opens and closes its mouth. And they’ve not stopped there. They want something you can fall in love with. So they’ve added a virtual assistant and companion mobile app, as well as a virtual reality headset that can be used alongside the doll.

Obviously, what RealDoll has essentially created is a metal secretary you can fornicate with whenever you want. This may well be what some men are still looking for, but encouraging such a retrograde attitude is obviously going to raise the hackles of anyone who thought society had moved on from the days of Mad Men. There is now even a campaign to put an end to the development of such technology completely. The leader of this campaign, Dr Kathleen Richardson, has argued that using AI in such a way is unnecessary and undesirable,

noting that, ‘Sex robots seem to be a growing focus in the robotics industry and the models that they draw on - how they will look, what roles they would play - are very disturbing indeed.’ She believes that they reinforce traditional stereotypes of women, and reduce the idea of relationships to something that is purely physical.

In their current form, Richardson may well be right, but there is vast scope for machine learning to be applied that enable sex robots to be more complicated than this. Such algorithms can help the machines learn from their surroundings to deliver emotional responses based both on their user and women as a gender within society. There is also evidence from researchers in New Zealand that replacing human sex workers with robots will have a number of positive ramifications, potentially reducing the spread of STIs and sex trafficking.

The speed at which social norms around sex have evolved since the 1960s has been tremendous, so it seems logical that AI will not take long to become widely / 13

While the success of blow-up dolls evidences a market for such products, the progression from inflatable doll to full blown robot is a big jump

accepted as the new normal. In 2014, a UK poll found that 1 in 5 people would have sex with a robot. However, it’s going to take some getting used to. While the success of blow-up dolls evidences a market for such products, the progression from inflatable doll to full blown robot is a big jump. At the moment, these issues are still some way off. At present, data analytics is being used for sex toys in simpler forms. Berkley-based Lioness, for example, is building a sex toy with five built-in sensors that connect to a smartphone app, providing information such as user’s individual muscle responses, body temperatures, and other physical factors. It then leverages this data to offer insights around the best way to use the device based on individual and aggregate data. It is likely that fully fledged sex robots will be able to offer similar functionality using sensors and data collection. In his 2007 book, Love and Sex With Robots, Davy Levy noted that they will be able to teach us more than is held in all of the world’s published sex manuals combined about lovemaking.

The opportunities to analyze the data are not just profound for sex either, they should be able to tell us a lot about our own physical and mental health. A robot that learns so much about our emotions is sure to be able to pick up signifiers around depression -particularly important for anyone buying a robot wife. Admittedly, there are many legitimate fears over security. As with all IoT, they are often far too easily accessed by proactive hackers, as it is still a fairly immature technology.

This was highlighted recently by software security firm Trend Micro at a media demonstration. They showed that a vibrator could be hacked from a hacker’s laptop. This would lead to some exceptionally personal data being stolen, and could easily leave the victim open to blackmail.

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At the moment, such devices are prohibitively expensive. The Realbotix is expected to cost $10,000 for the head, and $30,000– $60,000 for the accompanying body. Most similar devices range from $5,000 to $10,000, which is significantly more than many would be willing to pay. However, this price will come down, and the technology will vastly improve. Whether this is a good or bad thing remains to be seen.

New Documentary Puts Data Analytics Into The Mainstream Olivia Timson, Research Analyst

BIG DATA IN RELATION TO SURVEILLANCE has long been a mainstay of popular culture, with TV shows like Person of Interest and Mr Robot portraying it as a tool easily misappropriated for shady government agencies to control the masses. The Edward Snowden leaks and subsequent controversy around government attempts to collect personal information has further ensured that the majority of people only really know big data as something used to spy on them.

This is slowly starting to change, as exhibitions like Big Bang Data at Somerset House in London and the recent PBS documentary, The Human Face of Big Data, try and show the other side. The Big Bang Data exhibition, for example, set out to argue that data is the driving cultural force of this epoch, and could either help us create a society that is ‘fairer, more stable and efficient, or be ‘wielded as a means of unprecedented mass surveillance

and commodification’. These have gone some way to introducing mainstream audiences to the more beneficial aspects of Big Data, hopefully helping to change public perceptions while still urging caution around how society adapts to its implementation - something that is going to be even more necessary as Big Data further entrenches itself into our everyday lives.

The Human Face of Big Data is a one-hour documentary film, sponsored by a number of other tech companies, that debuted on PBS in February. It is based, in part, on the book of the same name that came out in 2012, although Rick Smolan, co-author and driving force behind the book, says the movie, directed by his brother Sandy Smolan, presents new examples of big data in action in fields such as health care and government and only actually refers to about 20% of the book’s content.

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These have gone some way to introducing mainstream audiences to the more beneficial aspects of Big Data, hopefully helping to change public perceptions while still urging caution around how society adapts to its implementation

The film is highly stylized, showing a variety of ways that data can be used to visualize and reveal insights about public life, with animated data visualizations showing domestic flight paths across the United States as colorful bursts blooming from major cities on the West Coast and Eastern seaboard.

Such visualizations show that data can not only entertain us, but tell us something we didn’t previously know about ourselves. It also features commentary from a number of industry leaders, including Jack Dorsey, the founder of Twitter and Square, Aaron Koblin, the cofounder and CTO of Verse, and MIT professor Deb Roy. Sandy Smolan noted, ‘Our goal with this project was to spark a global conversation about the human aspects of big data — and how it is changing our lives for better and worse.’

This is shown in the film through a number of case studies. In another section of the film, data reveals some surprising facts about the criminal justice system, with two researchers, Laura Kurgan of Columbia University and Eric Cadora of the Justice Mapping Center, using geographical information systems (GIS) to visualize incarceration patterns for residents of Brooklyn.

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Big data has long been seen a data problem for IT, but people are increasingly realizing it is a huge business opportunity rather than simply a technology ‘challenge.’

The conversation around big data needs to shift away from simply whether it is an invasion of privacy, and enter the public consciousness as a tool that can benefit every facet of our daily lives. Documentaries such as The Human Face of Big Data face an uphill battle to change public perceptions, purely because the narrative of Big Data as an evil monolithic being is far more entertaining than the reality, but data is already a fact of life, and it is also important that we not let fear and paranoia prevent us from making the most of its potential.

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how real businesses can use machine learning

ACCORDING TO GLOBAL CONSULTING FIRM ACCENTURE, intelligent automation powered by machine learning processes is 2016’s biggest tech trend. For tech giants like Amazon and Google, machine learning has long been central to their operations - the most famous example being Amazon’s recommendation engine, which many see as having been the key to its success. Such technology has huge implications for organizations across all industries though, and the wealth of data that companies now hold, along with the rise in affordable products like Microsoft Azure ML and IBM Watson, mean that they are rushing to adopt it. / 18

David Barton Head of Analytics

However, along with any technology, there must be an element of caution exercised in adoption.

David Linthicum recently wrote on inforworld.com that, ‘Machine learning is valuable only for use cases that benefit from dynamic learning - and there are not many of those.’

He continued, ‘The problem is if you have a hammer, everything looks like a nail. Vendors pushing machine learning cloud services say it’s a good fit for many applications that shouldn’t use it at all. As a result, the technology will be over-applied and misused, wasting enterprise resources.’

It’s essential for analyzing users, and the insights it provides can deliver highly-personalized content and ads - far more so than the type of predictive analytics tools that are now commonplace

Linthicum is correct to say that vendors’ sales pitches should be treated with skepticism, but when he says that there are not many use cases that benefit from dynamic learning he seems to be going too far in urging caution. Machine learning is an an extraordinary tool when it comes to analyzing any amount of data alongside every combination of variables, and there are a multitude of use cases out there.

Clearly, as Google has shown, it’s essential for analyzing users, and the insights it provides can deliver highly-personalized content and ads - far more so than the type of predictive analytics tools that are now commonplace. Other firms are now realizing the potential in similar ways. Home Depot, for example, uses it to find goods from its large inventory, such as bathtubs, and connect them with customers’ specifications.

Perhaps the most profound consequence of machine learning for businesses is in decision making.

Analytics has long been used by decision makers as evidence, but only by answering the questions that they think to ask of the data.

Machine learning should push past this and effectively automate the decision making process. In such a world, systems would be proactively informing you about what might happen and what you can do about it.

Essentially, machine learning makes your organization far more proactive than it currently is, and automates tasks in such a way that can greatly reduce costs and wasted human effort. According to Gartner, almost every business unit is going to be interested in these tools. Results have already been seen, with papers showing many cutting costs by up to 70%, and revenue go up 20 times because of increased speed in tracking buyer behavior, and improved customer service.

The one drawback at the moment is people’s comfort with automation and turning things over to machines.

Ultimately, this will change as the results are realized and automation becomes more of a factor in our everyday lives. Business leaders must get accustomed with this technology now. There is, as with any new technology, a risk that will be applied to something that simply doesn’t need it, and money will be wasted, as Linthicum argues.

Education is key to this, and the understanding that just because vendors are trying to push a product, doesn’t necessarily mean they’re not telling the truth.

Machine learning algorithms can also act as a personal assistants.

So when it looks like you’re going to miss a deadline, it can reschedule everything that relies on that deadline being met. For example, if a website is not going to be complete until three days past the deadline, it can recommend you postpone posting promotional materials linking to the site until it is up, all without being prompted.

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Coachella grossed a record-breaking $78 million over two weekends in 2014, and broke its own record in 2015 by taking in $84 million

Predictive Modeling Around Music Festivals Alex Lane, Big Data Commentator MUSIC FESTIVALS ARE - FOR THE MOST PART - A WILD, ANGRY, DEBAUCHED MESS. From the outside, it looks like the ultimate challenge for data scientists - trying to keep track of a sweaty farrago of bodies intent on squandering themselves in every direction. Attempts to determine attendees’ likes and dislikes when many are not in their usual frame of mind may seem futile, but with 32 million in the US attending at least one festival a year, data analytics is necessary to maximize the vast revenues that can be made. / 20

In 2014, the five biggest festivals combined grossed over $183 million in ticket sales, and that’s before taking into account sponsorships, merchandise, and food and alcohol sales. Coachella grossed a recordbreaking $78 million over two weekends in 2014, and broke its own record in 2015 by taking in $84 million. Organizers are now looking at data to figure out exactly what festival goers did over the course of their stay and which elements of the event were most successful by using a host of sensors and apps. Using the insights leveraged, organizers can make improvements both in real time and ready for future events.

Festival organizers are not just using analytics to drive revenue, they are also using it to deal with the range of logistical issues that arise when trying to deal with hundreds of thousands of people in various states of sobriety One of the ways that they are using data is determining which artists to book next year, by tracking how many people went to an artist’s

performance and how many stayed following the previous act by putting sensors at entrances to tents. Jeff Cuellar, the vice president of strategic partnerships at AC Entertainment, works for the organizer behind four-day Tennessee festival, Bonnaroo. He notes that they use a non-traditional census to gather data points about festival goers, asking them psychographic questions like whether they put tabasco on tacos to build a picture of their personalities. This can be used to better predict the attendees at future events, so they can customize the line-up accordingly, introduce other attractions, and tailor their marketing efforts.

Festival organizers are not just using analytics to drive revenue, they are also using it to deal with the range of logistical issues that arise when trying to deal with hundreds of thousands of people in various states of sobriety. There were 450,000 at Coachella, and obviously, this presents a safety problem. They all also need to be fed and watered, and all the waste produced by this disposed off.

With 130,000 attendees, the 2015 Roskilde Festival in Denmark is the largest culture and music festival in Northern Europe. At Rosokile, Copenhagen Business School used IBM Watson analytics in a cloud environment to analyze the behavior of attendees, producing a massive 91 million rows of data for

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every minute. In an interview with theCube, Cheri Bergeron, leader of client engagement and advocacy at IBM, explained that IBM had helped the business school set up a realtime data lab to produce heat maps produced by a mobile phone app that helped track the movements of their crowds, so they could get an idea of when people were most likely to go to food stalls, and where crowd build up may present safety issues. They looked at this alongside other factors such as weather conditions and artist affinity, and organizers were able to organize stalls and staff appropriately, as well as determine set times.

Many believe that music festival market is at saturation point. There are hundreds of new festivals popping up every year across the US, and millennials fork out a fortune for the privilege of attending. Music will always be the focal point of a festival, and the experience that

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people have is something that’s really unquantifiable. Data analytics should, however, help prevent the festival bubble from popping, for the time being at least.

IBM had helped the business school set up a real-time data lab to produce heat maps produced by a mobile phone app that helped track the movements of their crowds, so they could get an idea of when people were most likely to go to food stalls, and where crowd build up may present safety issues

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There are probably of definitions the single job of Head of Innovation and any withemployee, them dozens of perspectives What would happendozens if a company fundedfor every new product idea from no questions asked? on it should beAdobe done.did Without anythat. official credentials the Randall subject will I was asked give my personal account As how an experiment, exactly In this session,on Mark share thetosurprising discoveries of running an in innovation team in the of an innovation-hungry organisation that started on the highfor street Adobe made creating Kickbox, thecontext new innovation process that’s already becoming an industry model and has innovation. grown to employ 16,000 people overa 80 years. In red the box pastpacked year orwith so Iimagination, have learnedmoney that when comes igniting Each employee receives mysterious and ait strange to innovation culture trumps everything and there really aren’t any rules.

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big data innovation

Mark Randall's serial entrepreneurial career conceiving, designing and marketing innovative technology spans nearly 20 years and three successful high-tech start-ups. As Chief Strategist, VP of Creativity at Adobe, Mark Randall is focused on infusing divergent thinking at the software giant. Mark has fielded over a dozen award-winning products which combined have sold over a million units, generated over $100 million in sales and won two Emmy awards. As an innovator, Mark has a dozen U.S. patents, he’s been named to Digital Media Magazine’s “Digital Media 100 and he is one of Streaming Magazine’s “50 Most Influential People.”

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