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‘Summoning the demon.’ ‘The new tools of our oppression.’ ‘Children playing with a bomb.’ These are just a few ways the world’s top researchers and industry leaders have described the threat that artificial intelligence poses to mankind. Will AI enhance our lives or completely upend them?

WHERE IS THIS DATA COMING FROM?

There’s no way around it — artificial intelligence is changing human civilization, from how we work to how we travel to how we enforce laws. Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

As AI technology advances and seeps deeper into our daily lives, its potential to create dangerous situations is becoming more apparent. A Tesla Model 3 owner in California died while using the car’s Autopilot feature. In Arizona, a self-driving Uber vehicle hit and killed a pedestrian (though there was a driver behind the wheel). Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

Other instances have been more insidious. For example, when IBM’s Watson was tasked with helping physicians diagnose cancer patients, it gave numerous “unsafe and incorrect treatment recommendations.”

Some of the world’s top researchers and industry leaders believe these issues are just the tip of the iceberg. What if AI advances to the point where its creators can no longer control it? How might that redefine humanity’s place in the world? Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

Below, 50 experts weigh in on the threat that AI poses to the future of humanity, and what we can do to ensure that AI is an aid to the human race rather than a destructive force. Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

5. Nick Bilton

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AI WILL HAVE UNINTENDED CONSEQUENCES

Nick Bilton. (Om Malik)

Other experts fear the unintended results of AIs being given increasingly mission-critical tasks. Author and magazine journalist Nick Bilton worries that AI’s ruthless machine logic may inadvertently devise deadly “solutions” to genuinely urgent social problems:

“But the upheavals [of AI] can escalate quickly and become scarier and even cataclysmic. Imagine how a medical robot, originally programmed to rid cancer, could conclude that the best way to obliterate cancer is to exterminate humans who are genetically prone to the disease.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

6. Nick Bostrom

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

WE AREN’T READY FOR THE CHALLENGES POSED BY AI

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff
Nick Bostrom at the Future of Humanity Institute. (Future of Humanity Institute)

Academic researcher and writer Nick Bostrom, author of Superintelligence: Paths, Dangers, Strategies, shares Stephen Hawking’s belief that AI could rapidly outpace humanity’s ability to control it:

“Before the prospect of an intelligence explosion, we humans are like small children playing with a bomb. Such is the mismatch between the power of our plaything and the immaturity of our conduct. Superintelligence is a challenge for which we are not ready now and will not be ready for a long time. We have little idea when the detonation will occur, though if we hold the device to our ear we can hear a faint ticking sound. For a child with an undetonated bomb in its hands, a sensible thing to do would be to put it down gently, quickly back out of the room, and contact the nearest adult. Yet what we have here is not one child but many, each with access to an independent trigger mechanism. The chances that we will all find the sense to put down the dangerous stuff seem almost negligible. Some little idiot is bound to press the ignite button just to see what happens.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

8. Jayshree Pandya

AUTONOMOUS WEAPONS SYSTEMS COULD DISRUPT POLITICAL STABILITY

Few applications of AI are as potentially dangerous as autonomous weapons systems. As DARPA and other defense agencies around the world explore how AI could shape the landscape of modern warfare, some experts are deeply concerned by the prospect of relinquishing control over devastating weaponry to neural networks. Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

Jayshree Pandya, founder and CEO of Risk Group LLC, is an expert in disruptive technologies, and she has warned of how AI-controlled weapons systems could pose an existential threat to world peace:

“Technological development has become a rat race. In the competition to lead the emerging technology race and the futuristic warfare battleground, artificial intelligence (AI) is rapidly becoming the center of global power play. As seen across many nations, the development in autonomous weapons systems (AWS) is progressing rapidly, and this increase in the weaponization of artificial intelligence seems to have become a highly destabilizing development. It brings complex security challenges for not only each nation’s decision makers but also for the future of the humanity.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

9. Bonnie Docherty

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KILLING MACHINES THAT LACK MORALITY

Some view the competition among software developers to create increasingly sophisticated AI as a contest eerily reminiscent of the Cold War era nuclear arms race.

Bonnie Docherty, associate director of Armed Conflict and Civilian Protection at the International Human Rights Clinic at Harvard Law School, believes that we must stop the development of weaponized AI before it’s too late:

“If this type of technology is not stopped now, it will lead to an arms race. If one state develops it, then another state will develop it. And machines that lack morality and mortally should not be given power to kill.”

Science Technology breakthrough cool stuff programming robotic science technology software science technology cool stuff

10. Max Erik Tegmark

INFORMATION WARFARE WILL BE AN EVEN GREATER THREAT

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Technological advancements such as autonomous vehicles represent a paradigm shift in human society. According to Max Erik Tegmark, physicist and professor at the Massachusetts Institute of Technology, they also represent weaknesses that rogue actors will be able to exploit in future wars:

“The more automated society gets and the more powerful the attacking AI becomes, the more devastating cyberwarfare can be. If you can hack and crash your enemy’s self-driving cars, auto-piloted planes, nuclear reactors, industrial robots, communication systems, financial systems and power grids, then you can effectively crash his economy and cripple his defenses. If you can hack some of his weapons systems as well, even better.”

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Sourced from boredpanda

The latest item to qualify in the “the Dystopian Future Is Now” category is a system that aims to help bartenders keep track of their customers with facial recognition and AI.

There’s just one teensy problem: Bartenders are really, really not into it.

The British company DataSparQ announced the launch of the “A.I. Bar” Thursday. It works by using cameras and sensors to place people who come up to the bar in a queue; it’s like a digital “take a number” system, but based on facial recognition.

People can see where they are by viewing themselves on a screen (with a number above their head, depicting their place in line). Bartenders will know who to serve by consulting a tablet that has pictures of all the people, in order of who they should serve first. It’s supposed to also help bartenders keep track of whose ID needs to be checked.

Recently, DataSparQ tested the system at a bar in London. Here’s a video showing how it works.

DataSparQ positions the facial recognition queue system as a win-win. It says customers will have less wait time, and that bars are poised to make more money with faster service. Customers, supposedly, hate waiting for drinks in line. But bartenders think the AI makes a problematic, technological mountain out of a decidedly human molehill.

“I just don’t see what this sort of technology would remotely do to mitigate all of the supposed service delay-based qualms that people have,” Asif Rizvi, a  Brooklyn bartender at The Breakers, said. “At best it’s unnecessary. At worst, it’s yet another harbinger of the impending AI apocalypse.”

Mashable spoke with four bartenders in New York, San Francisco, and Las Vegas about their impressions of the queue system. Here’s what they think about the prospect of gettin’ a little help from an AI friend.

“We know what’s going on”

As a 5-foot-1 woman, there have been times at busy bars when I’ve wondered, “does my bartender even know I’m here?” The short answer is, yes — which is why most of the bartenders I spoke with didn’t see the basic need for this sort of system at all.

“Really good bartenders are really good at their own facial recognition algorithm, which is called common sense,” Rob Ready, the co-owner of the SF bar Piano Fight, said.

Essentially, the problem that this is purporting to solve is that people have trouble getting served in a fair order, in a timely fashion.

“The unspoken presupposition for at least the way they frame it is that bartenders have no idea what they’re doing, and don’t know how to handle this stuff,” Rizvi said. “We know what’s going on. We see it all.”

If the value proposition of the product is to be believed, bartenders, supposedly, have a hard time keeping track of who arrived when, and who needs a drink. According to the bartenders, this is one of the challenges of being a bartender, but not an insurmountable one. In fact, being able to manage this is part of what makes a bartender good at their job.

“The best bartenders are very present, and know what’s going on in their surroundings,” Olivia Hu, the co-owner of Oldtimers in Bushwick, said.

The Breakers, where Rizvi works, is one of those multiple-people-deep, all-the-way-around-the-bar, hot Brooklyn weekend spots. He says that service is challenging, but all bartenders have their patterns and workflow that make them, ya know, qualified for their jobs.

“We’re not computers, obviously, and I guess that’s kind of the point here,” Rizvi said. “But we have a habitual checking of things in certain orders that ultimately will average to everyone getting dealt with evenly.”

In short: Calm down, the bartender will get to you.

“Maybe it could work in a non-tipping culture”

The person this system seems made for is, again, someone who feels like they have problems getting served. But there’s a simple solution to this problem, that doesn’t take money out of the pockets of your hard-working barkeep: tipping.

“It’s the people that don’t take care of the bartenders that tend to get forgotten at the bar,” Daniel Keaveney, a veteran Las Vegas casino bartender who is currently works at the downtown Las Vegas restaurant Esther’s Kitchen, said.

Keaveney stressed that discretion about who gets served is a huge part of a bartender’s job, especially in a place like Las Vegas. Automating the service line simply would not work in a place with a “tipping culture” where high rollers and good tippers expect service, pronto — and where bartenders rely on those tips, too.

Keaveney and the other bartenders acknowledged that tipping culture in Europe, where this product comes from, is very different.

“Maybe it could work in a non-tipping culture,” Keaveney said.

But in a place like Vegas, or even Bushwick, this system would change the way bartenders target tipping customers, especially loyal ones. It is possible, though, that supposedly equitable service orchestrated by AI could provide more tips from happy customers, but the bartenders I spoke to didn’t express that.

Plus, discretion in serving isn’t all about tips.

“It’s a very human interaction”

There’s a sacred relationship between a bartender and a customer, one that is not always necessarily financial. An AI just wouldn’t get that.

“I understand the nuances of serving alcohol,” Hu added. “It’s a very human interaction.”

Bartenders cultivate regulars by actually having conversations. Or bartenders can mete out drinks like justice, awarding the well-behaved, while teaching the assholes a lesson. It’s also up to bartenders to look out for the safety of their patrons. Ultimately, the bartenders believe that an AI system would get in the way of all of that.

“A big part of bars is social interaction,” Ready said. “If you come up to a bar, and you’re a dick to the bartender, then, yeah, it might take them a little bit longer to come serve you the next time around. And that’s good because it teaches people to not be dicks to service industry professionals.”

Bartenders also use a lot of personal judgment when deciding whether or not to hand an intoxicated person another drink. Maybe, eventually, an AI might be able to read that. But right now, a queue system might make cutting someone off way too complicated if they’re insisting on being served because their number is up.

“That is one of the major responsibilities of a bartender, to know when someone might be harming themselves with alcohol,” Hu said. “It’s difficult for a computer to make that call.”

“You’re trying to apply data and tech in a context that is very human,” Ready said. “That’s what doesn’t seem to fit.”

“I’d be creeped out”

DataSparQ said that it interviewed 2,000 people to learn that one of the reasons people leave bars is because they don’t like waiting in lines. But Ready thinks a wait in line is nothing compared to a machine capturing your biometric data and broadcasting it for everyone at the bar to see.

“I wonder if there was a question that said, if you saw yourself in a livestream video behind the bar, and the thing was scanning your face, and recognizing your emotions, where would that fall in the list of reasons to leave that bar?” Ready asked, um, hypothetically. “I’d be creeped out.”

Cities, countries, public, and private spaces are all grappling with whether and how to introduce facial recognition. These bartenders thought a watering hole was not a good place to start.

“There’s a privacy thing within a bar. You’re supposed to feel safe in a bar,” Keaveney said. “Maybe I don’t want to be seen in that bar like that, maybe I’m trying to hide out a little bit.”

To the bartenders, making a service system automated and transparent to customers doesn’t seem worth giving over your precious, biometric data.

“This doesn’t sound like anything more than a glorified take-a-number kind of system, and I don’t see why it has to collect your data in order to do that,” Rizvi said.

“You’re fixing something that’s not really broken”

Mostly, bartenders were flummoxed by how the system would actually help them.

“There’s never a situation where the bar is busy because there’s a ton of people there, and the solution is, ‘someone help these bartenders with facial recognition technology,'” Rizvi said.

First, they questioned how adding the extra step of consulting a tablet would actually make them able to serve more people, as the company claimed in its press release. Wouldn’t having to check a tablet, and match a face to a line item in a tablet, end up taking more time?

“They’re trying to solve a problem by creating a bigger problem,” Ready said.

Next, one of the problems the tech says it aims to solve is people pushing, shoving, and cutting in line. But the bartenders — keen observers of human nature — said that surely customers would find a way to game the system (say, by palling up with whoever’s first in line).

Finally, if someone really is bothered by waiting in line for a drink, they can always just … go to another bar.

“If you’re in a busy bar, there’s going to be a lot of people,” Rizvi said. “If you don’t like that, go to a different bar. There will always will be one.”

Mostly, the bartenders just didn’t see the need for the product in the first place. Yes, bars get busy. But it’s a bartender’s job to manage that, and a customer’s job to trust that person — and treat that person well — in order to keep the bar the functioning, very human place that it is.

“It just feels to me like it’s a fancy use of tech to solve problems that don’t exist,” Ready said.

Or, in Rizvi’s words, “you’re fixing something that’s not really broken.”

Feature Image Credit: Datasparq 

Sourced from Mashable

Sourced from AdAge

In the summer of 1956, 10 scientists and mathematicians gathered at New Hampshire’s Dartmouth College to brainstorm a new concept Assistant Professor John McCarthy called “artificial intelligence.” According to the original proposal for the research project, McCarthy—along with fellow organizers from Harvard, Bell Labs and IBM—wanted to explore the idea of programming machines to use language and solve problems for humans while improving over time.

It would be years before these lofty objectives were met, but the summer workshop is credited with launching the field of artificial intelligence (AI). Sixty years later, cognitive scientists, data analysts, UX designers and countless others are doing everything those pioneering scientists hoped for—and more. With deep learning, companies can make extraordinary progress in industries ranging from cybersecurity to marketing. It’s just a matter of knowing where to start.

Think of AI as a machine-powered version of mankind’s cognitive skills. These machines have the ability to interact with humans in a way that feels natural, and just like humans they can grasp complex concepts and extract insights from the information they’re given. Artificial intelligence can understand, learn, interpret, and reason. The difference is that AI can do all of these things faster and on a much bigger scale.

“In the era of big data, we have the need to mine all of that information, and humans can no longer do it alone,” says Mark Simpson, VP of offering management at IBM Watson Marketing. “AI has the capacity to create richer, more personalized digital experiences for consumers, and meet customers’ increasingly high brand expectations.”

The knowledge companies stand to gain by using AI seems to have no bounds. In healthcare, medical professionals are applying it to analyze patient data, explain lab results and support busy physicians. In the security industry, AI helps firms detect potential threats like malicious software in real time. Marketers, meanwhile, can use AI to synthesize data and identify key audience and performance insights, thus freeing them up to be more strategic and creative with their campaigns.

There’s something else AI is very good at, and that’s improving the relationship between companies and consumers. “Even in its earliest iteration, AI helped companies better understand how to be human,” says Brian Solis, author and principal analyst at Altimeter, the digital analyst group at brand and marketing consultancy Prophet. “The irony is that it took this very advanced technology to make them think differently about how they should communicate with their customers.”

Over the past 50 years, Solis says, advances like speech technology, automated attendants, virtual assistants and websites have opened a chasm between companies and customer engagement while also multiplying consumer touchpoints. But AI has the potential to close that gap.

By helping marketers collect data, identify new customer segments and create a more unified marketing and analytics system, AI can scale customer personalization and precision in ways that didn’t exist before. Connecting customer data from sources like websites and social media enables companies to craft marketing messages that are more relevant to consumers’ current needs. AI can deliver an ad experience that is more personalized for each user, shapes the customer journey, influences purchasing decisions and builds brand loyalty.

IBM’s Watson Marketing is leading the charge with a platform that capitalizes on all that AI has to offer. Products like Customer Experience Analytics lets marketers visualize the customer journey and identify areas where consumers might be experiencing friction. Companies get a more complete view of the customer journey, which they can then optimize to improve customer engagement and conversion rates. Since it’s delivered through a single, unified interface, IBM Watson Customer Experience Analytics makes gaining actionable intelligence a seamless process for brands.

According to market research firm TechNavio, the AI market in the U.S. is expected to grow at a compound actual growth rate of about 50 percent through 2021. In its 2017 report “Artificial Intelligence: The Next Digital Frontier?” the McKinsey Global Institute urges companies not to delay “advancing their digital journeys”—especially when it comes to leveraging AI. “It’s those who understand how to use AI in new ways, to create new mindsets and paradigms, that will instill a competitive advantage that wasn’t there before,” Solis says.

We’ve entered the age of deep learning, and with human guidance AI is finally reaching its true potential. Today, the technology McCarthy and his colleagues dreamed about in 1956 takes the form of AI platforms like Watson Marketing. And now is the right time to truly harness the power of AI and put it to work for business success.

Find out more about how Watson Marketing can uncover insights to help you better understand your customers. Read the Guide.

Sourced from AdAge

Content Provided by IBM with Insider Studios. Insider Studios is the branded content studio for Insider Inc., the parent company of INSIDER and Business Insider.

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is capable of making music, but does that make AI an artist? As AI begins to reshape how music is made, our legal systems are going to be confronted with some messy questions regarding authorship. Do AI algorithms create their own work, or is it the humans behind them? What happens if AI software trained solely on Beyoncé creates a track that sounds just like her? “I won’t mince words,” says Jonathan Bailey, CTO of iZotope. “This is a total legal clusterfuck.”

The word “human” does not appear at all in US copyright law, and there’s not much existing litigation around the word’s absence. This has created a giant gray area and left AI’s place in copyright unclear. It also means the law doesn’t account for AI’s unique abilities, like its potential to work endlessly and mimic the sound of a specific artist. Depending on how legal decisions shake out, AI systems could become a valuable tool to assist creativity, a nuisance ripping off hard-working human musicians, or both.


already face the possibility of AI being used to mimic their style, and current copyright law may allow it. Say an AI system is trained exclusively on Beyoncé’s music. “A Botyoncé, if you will, or BeyoncAI,” says Meredith Rose, policy counsel at Public Knowledge. If that system then makes music that sounds like Beyoncé, is Beyoncé owed anything? Several legal experts believe the answer is “no.”

“There’s nothing legally requiring you to give her any profits from it unless you’re directly sampling,” Rose says. There’s room for debate, she says, over whether this is good for musicians. “I think courts and our general instinct would say, ‘Well, if an algorithm is only fed Beyoncé songs and the output is a piece of music, it’s a robot. It clearly couldn’t have added anything to this, and there’s nothing original there.’”

Law is generally reluctant to protect things “in the style of,” as musicians are influenced by other musicians all the time, says Chris Mammen, partner at Womble Bond Dickinson. “Should the original artist whose style is being used to train an AI be allowed to have any [intellectual property] rights in the resulting recording? The traditional answer may well be ‘no,’” Mammen says, “because the resulting work is not an original work of authorship by that artist.”

For there to be a copyright issue, the AI program would have to create a song that sounds like an already existing song. It could also be an issue if an AI-created work were marketed as sounding like a particular artist without that artist’s consent, in which case, it could violate persona or trademark protections, Rose says.

“It’s not about Beyoncé’s general output. It’s about one work at a time,” says Edward Klaris, managing partner at Klaris Law. The AI-made track couldn’t just sound like Beyoncé, in general, it would have to sound like a specific song she made. “If that occurred,” says Klaris, “I think there’s a pretty good case for copyright infringement.”

Directly training an AI on a particular artist could lead to other legal issues, though. Entertainment lawyer Jeff Becker of Swanson, Martin & Bell, says an AI program’s creator could potentially violate a copyright owner’s exclusive rights to reproduce their work and create derivative works based upon the original material. “If an AI company copies and imports a copyrightable song into its computer system to train it to sound like a particular artist,” says Becker, “I see several potential issues that could exist.”

It’s not even clear whether AI can legally be trained on copyrighted music in the first place. When you purchase a song, Mammen asks, are you also purchasing the right to use its audio as AI training data? Several of the experts The Verge spoke to for this piece say there isn’t a good answer to that question.

During a panel The Verge recently hosted on the state of AI and music at Winter Music Conference, which included Bailey; Matt Aimonetti, CTO of Splice; and Taishi Fukuyama, CMO of Amadeus Code, an audience member asked just that. “What if I wanted to license my catalog to a company so its AI could learn from it?”

“Currently,” replied Aimonetti, “there’s no need for that.”

Even if an AI system did closely mimic an artist’s sound, an artist might have trouble proving the AI was designed to mimic them, says Aimonetti. With copyright, you have to prove the infringing author was reasonably exposed to the work they’re accused of ripping off. If a copyright claim were filed against a musical work made by an AI, how could anyone prove an algorithm was trained on the song or artist it allegedly infringes on? It’s not an easy task to reverse engineer a neural network to see what songs it was fed because it’s “ultimately just a collection of numerical weights and a configuration,” says Bailey. Additionally, while there are scores of lawsuits where artists were sued by other artists for failing to credit them on works, a company could say its AI is a trade secret, and artists would have to fight in court to discover how the program works.

“Getting to that point might only be available to the biggest artists that can afford it,” says Becker.


law will also have to contend with the bigger issue of authorship. That is, can an AI system claim legal authorship of the music it produces, or does that belong to the humans who created the software?

Arguments about whether code can be the author of a musical work in the US are over 50 years old. In 1965, the Copyright Office brought up this concern in its annual report under a section titled “Problems Arising From Computer Technology.” The report says the office had already received one application for a musical composition made by a computer, and it “is certain that both the number of works proximately produced or ‘written’ by computers and the problems of the Copyright Office in this area will increase.”

Despite this early warning flag, current US copyright law is still vague when discussing authorship of works that weren’t created by humans. For now, lawyers are still grappling with the implications of one ruling, in particular, which doesn’t involve computers or AI at all: it’s about a monkey taking a selfie.

The case centered on a crested macaque that picked up the remote trigger for a photographer’s camera and took photos of itself. The resulting debate was over which creator should own the copyright: the photographer who set up the camera and optimized the settings for a facial close-up, or the monkey that pressed the remote trigger and took the photograph.

Ultimately, the US Court of Appeals for the Ninth Circuit decided that the monkey could not hold a copyright. The court made two points: the copyright law’s inclusion of terms like “children” and “spouse” imply an author must be human, and although courts have allowed corporations to sue, corporations “are formed and owned by humans; they are not formed or owned by animals.”

Many outlets used the monkey selfie ruling to discuss implications about artificial intelligence and authorship. If a monkey can’t own a copyright, it goes, then what about a song created entirely by AI? Would authorship go to the humans who created the AI, the AI itself, or the public domain?

The heart of this problem is that current US copyright law never differentiates between humans and non-humans. But, the Compendium of US Copyright Office Practices actually spends a lot of time talking about how humanness is a requirement for being considered a legal author. In an internal staff guidebook for the Copyright Office, the Compendium has a section titled, “The Human Authorship Requirement.” There’s also a separate bit to address copyright when a work lacks a human author. According to the Compendium, plants can’t be authors. Neither can supernatural beings or “works produced by a machine or mere mechanical process that operates randomly or automatically without any creative input or intervention from a human author.”

The Compendium has been updated to note that “a photograph taken by a monkey” cannot be given a copyright. But there’s nothing yet on AI.


mashup of all of these weird problems happened just weeks ago. Recently, the developers behind Endel, an app that uses AI to generate reactive, personalized “soundscapes,” signed a distribution deal with Warner Music. As part of the contract, Warner needed to know how to credit each track in order to register the copyrights. The company was initially stumped with what to list for “songwriter,” as it used AI to generate all of the audio. Ultimately, founder Oleg Stavitsky told The Verge, the team decided to list all six employees at Endel as the songwriters for all 600 tracks. “I have songwriting credits,” said Stavitsky, “even though I don’t know how to write a song.”

It sounds like a ludicrous outcome, but preventing humans from obtaining copyright on AI-assisted works could limit our ability to use these algorithms for creative purposes. “If you accept AI-generated work as a new form of art and take away the intellectual property rights of the person who created the algorithm,” says Klaris, “you’ve basically said, ‘you’re out,’ and take away their incentive to create.”

Endel was able to list its employees as songwriters because, in the US, you only need someone to claim they authored a work. But if there’s pushback — like in the monkey selfie case — authors have to prove that they made the work in question. The same might have to be done for music and AI in order to establish any precedent about how to treat this type of material in copyright law moving forward. And there are a million ways to parse the problem.

For now, there are far more questions than there are answers. If you take these problems a few steps further, you get into issues around AI and legal personhood that start to get “existential,” says Rose. Can software be creative? What if an AI software’s creations belong to no one at all?

“We haven’t figured it out,” Becker says. “This road is literally being paved as we’re walking on it.”

By

Sourced from The Verge

By  Laura Jensen

We live in a world where we are incentivised to share. We digitally share our photographs, thoughts and buying habits more than ever before. There is a wealth of data available on consumers and if brands want to capitalise on this through their social media marketing, they need ways to collect and understand it.

However, the need for this data is far surpassed by the fact that it is unwieldy for brands to manage – the volume and velocity of data alone can make it a monumental task to understand. That is why the industry is changing and adapting to make sure this data is much more accessible.

As we’ve seen in the digital marketing sector as a whole, the use of artificial intelligence (AI) is increasing rapidly. With applications from voice search to chatbots, there’s a wealth of information that AI is already harvesting. But, there will be so much more it can do with social media in the future.

Assisting with social media marketing analytics

Steve Wozniak, when asked what his dream product would do, said that he’d love something that would give him “more time”. When a 400-millisecond Google delay results in 8 million fewer searches, the speed to insight needs to be lightning-fast.

Companies like Brandwatch, which provide a social listening service, are looking to use AI as a way to reduce the number of hours that social analysts spend looking at brand data. Instead of an average of 3.2 hours a week looking at basic analysis, social analysts could get on with the bigger things while AI makes that data easy to understand and easy to access across an organisation.

The way Brandwatch does this is by analysis the peaks and troughs in the charts and pulling together data from a number of different sources. This is then used to work out why charts may have peaked at a particular point – maybe a social post coincided with a news event from the same industry that drove new viewers to that channel. These AI insights make reporting on social media marketing far more straightforward, since they take out the guesswork of social analytics.

Integrating customer experience with social apps

As with chatbots, AI is becoming more of a feature on social channels, integrating customer care and social analytics through customer service.

As Donika Ruseva, the Digital Owner Experience Coordinator from Jaguar Land Rover, says, “There’s no better way to show off your brand than good customer service”. Many brands use automation to implement holding messages for complaints and comments on social media, to varying degrees of success, but there’s more that can be done with social and customer experience.

Messenger apps, such as Facebook Messenger and WhatsApp, aren’t just for personal conversations anymore, but have become social media marketing platforms in themselves, providing access to both commerce and service apps. Personal banking apps, like Cleo and Plum, can help you save or manage your spending, while retailers like Made.com have created a conversational commerce experience for their customers. While these apps already exist, there’s a lot of scope in 2019 to see more businesses from different industries embracing this new use of messenger platforms.

From these AI apps, businesses can gain information on what their customers are primarily using them for, what aspects are important to them, and what trends occur on a regular basis.

AI and customer care

Supported by AI, businesses can achieve the quick, responsive and transparent response times that today’s customers expect. What’s more, AI can analyse what customers say in tweets, posts or comments.

Using AI to analyse sentiment and recognize key terms in messages to identify positive or negative feedback is already available, but there’s much more that AI can do for the customer care aspect of a business.Many AI systems have machine learning and natural language processing (NLP) capabilities, and these are key to real-time self-service on customer service platforms. AI can respond to automated queries quickly and generate responses with accuracy and speed that humans can’t match; this is especially effective, as we’ve mentioned before, when applied to chatbots on messenger services.

There are a number of other services that AI can provide in the customer service sector, such as providing an easier way to identify customer issues on social by processing and learning from gathered information, defining customer behaviour patterns, such as when or how they might complain or talk about a product, responding with suitable solutions, products or discounts after receiving complaints or messages, and much more. We’re looking forward to seeing more intuitive measures, developed over the next year and beyond, applied to social media.

By Laura Jensen

Sourced from Business 2 Community

 

By Matthew Kelleher

We focused our efforts on seeing whether using Predictive Analytics combined with AI driven marketing automation can help improve the customer experience around the key stages of the customer lifecycle – prospect’s first purchase, second purchase, multi-purchase, VIP and churn. Our strategy was to improve marketing performance at each of these stages by using Predictive Analytics to understand where each customer is on their own journey.  When the brand understands the customer’s next likely action, they can specifically target those individuals with more effective comms, ultimately, driving up total customer lifetime value.

Results at each stage of the lifecycle have been excellent. For instance, one brand saw an increase of 83.5% in second purchase rate. This, and other case studies, can be found here. Anyone who attended my presentation at either Technology for Marketing or Festival of Marketing recently, would have seen me present the outcome of the longer analysis to see if they could improve Customer Lifetime Value. For those of you who could not attend, you will have to wait for the release of the new case studies to the website in the next couple of weeks.

The obligatory Q&A session followed my presentations at both these events. But to be honest, I always find these questions instructive and rather good fun. Too often, and I’m not alone in this, I get carried away with what I want to say, and questions illustrate key elements that I’ve missed! So, these were the six questions that were asked (although I must admit I thought there were more) with a few more thoughts than I had time to give on the day.

  1. How has GDPR affected your data gathering? How did you fight an increase (if any) in unsubscribed customers?

    Whilst it has felt like forever, the period since May 25th is still, in the grand scheme of things, relatively short! Our impression is that, in general (can you see me caveating this response very heavily!) the long-term impact on sign ups and consent is relatively little. However, for some organisations their ‘re-permissioning’ experiences have been fairly disastrous. For instance, a database of active contacts of 500,000 reduced to 6,500 (if you are in this group then you are not alone). It’s not the objective of this blog to cast aspersions on the quality of advice given to some organisations, all I can really say is that without the correct permissions, processing data for comms or even for Predictive Analytics is not possible. There are minimum amounts of data required to make Predictive Analytics work, so for many organisations with smaller databases Predictive Analytics may not work and the issues surrounding GDPR only serve to increase that group.

  2. Do you have an example of using Predictive Analytics for recruitment initiatives – getting new customers rather than increasing the value of current customers?

    RedEye has not worked with any organisations to develop models around acquisition. However, our whole strategy is built around recent prospect/customer behaviour as the key driver for predicting their next likely action. Marketers can better understand how an individual prospect or customer is behaving in relation to their brand. By tracking as many interactions, across as wide a number of channels as possible, this can then be compared with the typical behaviour of customers who have completed certain journeys. And this is applicable to many different market sectors.

  3. What were the actions that came out of the predictive model to reduce churn. How were they implemented?

    25 minutes is a very short amount of time to pack in a lot of things. One that I often leave off the list is a detailed description of the treatments employed at each of the stages. But there is a very specific reason for this… the platform RedEye has developed provides the data to the marketer, and it is up to the marketer to then leverage this information. They know their brand and customers better than anyone else. A review of the treatments used by Travis Perkins would be a completely different presentation. Every brand will develop specific treatments and the insight of what Travis Perkins did is therefore of less relevance when we’re looking at how the system was plugged together to provide the outcome. I often say ‘if you knew a specific customer was likely to never buy from you again – what would you want to say to them?’. Every marketer would have a specific answer to this, I am sure!

  4. How did you link website behaviours to an individual? Was it logged in users only?

    At FoM I briefly shot off an answer, which was that we utilise a tag management solution, which was a bit blasé. The RedEye solution has always been built around a personalisation capability centred on the value of an individual’s browsing behaviour, which is also at the core of our approach to Predictive Analytics as described above. We then link this to channel engagement information, transactional data and any other type of data a client has that has a personal identifier of any kind. It is this data that is at the core of the CDP function and therefore the bedrock of Predictive Analytics. With regards to the issue of ‘logged in’, no, the customer or prospect does not need to be logged in, they just have to have given their consent.

  5. Did any of your clients face major hurdles in pulling together all the data from siloed and legacy data pots? If so, how was this overcome?

    I would say that the vast majority of organisations that RedEye work with have internal hurdles with regards to data silos. Some clients who want to input more data find they are restricted by internal systems, and there is very little that RedEye can do to overcome these bottlenecks. But assuming that the data is available somewhere in an organisation, the CDP is there to help marketers resolve these issues. We try to make this work more effectively in two ways. Firstly, we create easier ways to format data into the system, using simple connectors to input (and export) data. And secondly, we offer support staff to help this happen for clients who are resource strapped.

  6. Which is the best CDP you would recommend for publishers?

    If I remember this question from the day it was asked by Nish! Well Nish, as an executive of RedEye I would say get in touch with us! But being a bit more professional, and having asked my colleagues on the Customer Data Platform Institute I would recommend BlueConic and Lytics who I’m informed have good experience working with publishers.

If anyone else has any other questions I would be delighted to do my best to answer them, get in contact with me here.

By Matthew Kelleher

Sourced from Digital Doughnut

By Alice Berg

The bots are here and they here to stay. See, despite the chatbot phenomenon has gained ground only a few years ago, it has since become a full-on revolution worthy of the attention. In fact, by 2024, the bot market size is expected to exceed $1.34 billion. And as you ponder on that, it is also important to note that in 2019, at least 40% of large business ventures will implement the use of chatbots.

Let’s take a look at some AI applications in business in 2019 and beyond.

Chatbots and AI in Customer Service

Presently, bots have already delivered impressive results in customer service. Many businesses have rolled out chatbots to help them in distributing useful information and engaging the customers. For instance, Starbucks uses a system that allows customers to easily and quickly place orders using voice commands. The good chatbot will tell you the total price of your order plus when it will be ready.

Another noteworthy customer service company that uses bots is Lyft. Customers of the service can use online chat (Slack or Facebook) or AI voice chat (Amazon) to request for rides on the service. Lyft’s messenger bot offers the customer vital details. It shows the car model, a photo of the license plate, and the location of the driver.

Here are some practical ways you can use bots in your customer service business:

In the Automation of FAQs

Ask anyone if they read the FAQ section of a website, and you will probably get a hard and repetitive “no.” Rather than customers reading the questions, they would opt to email the company their queries. By leveraging chatbots and artificial intelligence, businesses can use appropriate documents to respond to any relevant questions.

As the Internal Help Desk

When an external customer service agent does not have an answer to a customer’s query, logically they would refer to the internal desk. And more often than not, this desk has to respond to the same questions over and over again. By using bots, internal customer service agents will not have to respond to common concerns.

In Offering Confident Responses

Interactive bots can ascribe a confidence score to their response. And if the score is below the threshold you had set, the bot will automatically contact a live agent and come back with a more satisfactory answer. In turn, the bot through Machine Learning can respond to similar queries in the future.

AI in Online Marketing and E-commerce

Some players in e-commerce have already started leveraging AI, and by the end of 2019, we can expect mainstream acceptance of the same. E-commerce companies such as Sephora, Asos, Sun’s Soccer, 1-800-Flowers, and Nitro Café are some of the companies that are already bringing in dollars in profit by deploying bots.

So, how can e-commerce stores and marketers use bots? For one, they can be used in customer support. Personalization is the key to every marketing strategy. And so far, bots have proven to be quite useful in providing personalized responses as compared to email or social media. People can express themselves much more freely when talking to chatbots. Therefore, to connect more with your customers, you can use a bot to interact with them and build or improve brand loyalty.

Another way e-commerce players are using artificial intelligence is in building interactive sales funnels. Marketers can use AI to group their customers and sell their products or services. A bot offers an opportunity to be dynamic and engaging. If a customer declines to try your service or product, the program can analyze the possible reasons to avoid the cases in the future.

You can also substitute emails with AI. By using a messenger chatbot, you significantly increase your click-through rates as compared to email. Most online marketers are already using chatbots to get information about visitors on their site as pop-up messages.

Some other benefits of using artificial intelligence in e-commerce include recouping abandoned carts, upselling after purchases, generating leads, providing useful AI algorithms for product recommendations, and boosting customer retention.

“83% of people who shop online need support, 56% prefer to get a text message, and 38% of people consider chatbots are even more useful. It is, therefore, good business to invest in a bot as an online entrepreneur,” – noted Andrew Ortiz, marketing specialist at Skillroads.

AI Online Chat in Tourism and Hospitality

Chatbots are already among the top technology trends in tourism and hospitality businesses. And in 2019, there are expected to be adopted on a wider scale. Bots are helping companies in this sector to reduce costs while also providing an excellent user experience. So far, bots have helped tourism and hospitality companies increase customer satisfaction. Some of the firms that have already witnessed the good results of using AI applications include Marriot, Snap Travel, KLM, Waylo, and Wynn.

You can use a bot in your business for different reasons. For instance, you can use it to engage customers before, throughout, and after the trip. Bots can send prospective and existing customers links to personalized content on hotels, destination sites, top restaurants, and so forth. Once a customer has booked a room, for example, a bot can come in to help them check in, request service, suggest activities, order meals from the restaurant, and more. Upon checking out, a bot can assist in collecting feedback and comments from customers.

Bots can also help in personalizing the customer experience. By doing this, they eliminate competition based on pricing. Customers often consider factors such as location and brand along with the price when choosing hotels. A bot can thus assist you to send out personalized packages to your customers and appeal to them. They help you put together all activities that interest the target audience and market the package to them specifically.

Chatbots and artificial intelligence can also be used in anticipating customers. By using predictive analysis, a hotel owner can identify future patterns and send out targeted campaigns to customers. For example, in 2014, Roof Inn used flight and weather data to predict the customers that were likely to face cancellations of their flights. In turn, they were able to send out campaigns on mobile devices to customers in locations that were likely to experience harsh weather. So what can we take from this? Bots can help in anticipating problems and addressing them way before they happen.

Artificial Intelligence in Financial Services and Banking

Financial tech companies such as Fintech are already causing waves in the industry by introducing bots. By 2020, it is expected that 85% of customers will use Fintech Chatbots to manage their bank transactions. Currently, some big banks are already using bots. These include Visa, MasterCard, Chase, American Express, Capital One, PayPal, Barclays, Ally Bank, and Bank of America.

So, how are financial companies using bots? First, Chatbots and AI are helping in smart messaging whereby they warn customers about dangers or other issues affecting their accounts. Second, they can also give you personalized tips on things you can do with your finances. Moreover, they can help you know how you are using your funds, how you are repaying your loans, and how you can save more money. And not to forget, bots are offering customers around-the-clock support and useful insight that improves customer experience.

It is important to note that, bots are expected to save financial institutions over $8 billion per year by 2022.

AI in Human Resource Management and Hiring

One of the big beneficiaries of AI has to be HR and recruiting. Bots have become integral in virtually all aspects of the employee lifecycle from sourcing, to screening, to interviewing, and finally to hire.

Employers are also using bots to boost customer engagement. These systems act as a bridge that connects employees to the existing job systems thus giving them better experience at the workplace. Maya is one of the companies that is automating all the stages of recruitment. SAP, Wade & Wendy, Loka, and SGT STAR are some other firms that are also using bots.

AI in Voice and IOT

Voice-powered assistants are increasingly becoming popular. Alexa, by Amazon, is one of the pioneer voice assistants and accounts for up to 70 percent of the market share. Come 2020; it is projected at least 128 million smart speakers will have been sold.

Final Thoughts on AI for Business

As an entrepreneur, AI is a tech trend to watch out for if you are keen on staying relevant, growing your business, and making a profit. Chatbots are cost-effective, time-saving, and most importantly give your customers a personal touch.

By Alice Berg

Alice Berg is a career advisor, who helps people to find their own way in life, gives career advice and guidance, helps young people to prepare for their careers. You can find Alice on Twitter and Medium.

Sourced from RUHANIRABIN

By 

Among the service industries to benefit the most from the AI breakthrough are the Banking, Insurance, Telecom, and Utility Industries.

It is now no secret that Artificial Intelligence is the next big thing. It is set to transform our entire way of life, from how we live to how we work and how we interact with one another.

In fact, we are already seeing some of the effects of Artificial Intelligence in most industries today.

Artificial Intelligence is currently being utilized in a wide variety of businesses and is widely used in many award-winning apps and software in the market, particularly in service industries, the AI role will only continue to rise.

The service industry landscape has shifted to acknowledge the importance of Artificial Intelligence.

Among the service industries to benefit the most from the AI breakthrough are the Banking, Insurance, Telecom, and Utility Industries.

AI in the Banking Industry

3D illustration of a robot hand holding for a generic credit card with NFC technology. Credit card is fictitious. PixOne / Shutterstock.com

3D illustration of a robot hand holding for a generic credit card with NFC technology. Credit card is fictitious. PixOne / Shutterstock.com

The banking industry has benefited massively from the introduction of AI systems, and will only continue to do so.

Some of the areas that have been impacted include:

–         Customer Experience

AI chatbots are all over the place in the banking industry. Almost every major bank has one. These bots have streamlined customer interactions and improved customer experience and satisfaction. App development companies have made it possible for clients to be able to ask for banking assistance and receive it without having to visit a bank at a physical location. This adds a level of convenience that customers appreciate. This is only achievable with AI.

–         Management of Customer Data

The breakthroughs of AI in the management of customer data is outstanding. For example, when JPMorgan Chase’s Contract Intelligence AI was given the task to review their 12000 commercial credit agreements, it managed the task in a few seconds. This compared to the estimated 360,000 hours manual evaluation normally took. The AI has now officially replaced humans in performing this task, freeing up more human resources for other sectors. Today, similar AI can be used to manage emails, articles, phone calls and other legal documents.

–         Banking Security

AI is changing the way banks secure themselves. Cases of money laundering have previously plagued the industry, but this will all soon change. Banks have started seeking tailored AI solutions to help them combat the vice.

AI in Insurance Industry

AI, artificial intelligence, in modern medical technology. IOT and automation. Wright Studio / Shutterstock.com

AI, artificial intelligence, in modern medical technology. IOT and automation. Wright Studio / Shutterstock.com

AI is literally shaking up the insurance industry. Today, traditional insurance and underwriting are being updated to become more efficient and more consumer-friendly through the utilization of AI technologies. Some of the changes we are bound to see more of include:

–         Micro changes

AI is slowly making it possible for insurance companies to access user data and tweak their offers to suit user behaviour. This is only going to be more prevalent in the future. For example, your car sensor may show that you have a history of reckless driving, and consequently, your car insurance rate may increase by 1%. If you are a very good driver, your car insurance rate may drop by 1%. This tweaking not only rewards good drivers but also results in more revenue for insurance companies.

–         Customer interactions

The insurance industry stands to benefit greatly from the advances being made in AI for customer interactions. Soon, we may see app development companies develop insurance apps with assistants that warn you if you engage in activities that could lower your insurance rate, or alert you when you are doing something that could boost your rates.

–         AI May Make Some Insurance Sectors Obsolete

By 2020, driverless cars will be common on our roads. This shift will bring with it many changes to the insurance industry. The much safer cars will lead to a much lower accident rate for example.

Auto insurance may stop being as lucrative, and insurance companies may have to adapt and switch to ensuring car manufacturers as opposed to individual drivers who will see no need for auto insurance.

AI in the Telecom Industry

Chat bot and future marketing concept . Customer hand holding tablet look for ticket and popup out smart phone screen with automatic chatbot message screen , airport background

Chat bot and future marketing concept . Customer hand holding tablet look for ticket and popup out smart phone screen with automatic chatbot message screen , airport background (Photo Credit: www.shutterstock.com)

The Telecom Industry benefits from the advantages of using AI on three fronts:

–         Customer Service and Retention

AI has enhanced customer service in the Telecom industry. The rise in customer service solutions such as chatbots has eased communication between customers and their Telecom companies. This leads to better user experience and satisfaction. AI can also be used as customer service agents, where they interact directly with clients, making the customer service process more cost-efficient.

–         Sales and Personalized User Experience

AI has also helped companies in the Telecom Industry to improve customer retention and boost the amount of revenue earned per user. The immense power of AI can be harnessed to offer personalized product recommendations to clients, assessing the type of call or data packages that suite a potential client pre-sale to increase sale success rates, and analyzing social media, brand image, and customer feedback and offering recommendations to help make the company better.

–         Network Analysis

AI plays a huge role in the network maintenance of Telecom companies. Optimized networks are a necessity today, especially due to increased data consumption. Telecom companies need to adapt and cater to their users’ needs. In the Telecom Industry, AI is being used as a network maintenance solution with a focus on the creation of self-healing, self-learning, and self-optimizing networks. This approach has proven to be future proof and sustainable.

AI in the Utility Industry

Unrecognizable corporate water utility executive managing meter data via advanced metering infrastructure solution. Industry concept for AMI, SaaS, managed services, MDM, IoT, network as a service. LeoWolfert / Shutterstock.com

Unrecognizable corporate water utility executive managing meter data via advanced metering infrastructure solution. Industry concept for AMI, SaaS, managed services, MDM, IoT, network as a service. LeoWolfert / Shutterstock.com

 The Utility Industry is already gaining a lot from the application of AI technologies. For example, AI is being used in:

–         Yield Optimization

In the utility industry, total yield equals total revenue. An increase in total yield usually means an increase in revenue. The application of AI technologies has led to optimized yields. For example, in the power production sector, power generation efficiency can be optimized by using real-time adjustments across their assets to maximize output while minimizing resource use.

–          Predictive Maintenance

Maintenance is a huge deal when it comes to the utility space. With the help of AI, drones equipped with deep learning algorithms can be deployed to help automatically identify defects and potential failure points, predicting when maintenance will be needed. This does away with costly and inaccurate manual inspections.

–         Customer Insights

AI can be used to help utilities maximize margins and minimize consumption. They can also help craft individual customer offers or deals on their services that will allow utility companies to gain new customers, create a larger user base and boost customer loyalty.

By 

  • Rilind Elezaj is an experienced Digital Marketing Specialist with a demonstrated history of working in the marketing and advertising industry. Rilind possesses a strong entrepreneurial mindset and has devoted his career to enhancing the sphere of digital marketing. In his methodological approach, Rilind integrates web development and other digital marketing solutions to create hybrid strategies that bring the best results.

Sourced from TALK IoT

By Mike Moran 

If you are Amazon with Alexa, clearly your AI needs a personality–Alexa wants to be your helpful friend. You talk to her. She talks back. No problem. But does your business AI need a personality? Everywhere you look, someone thinks it does. IBM wants you to love Watson. SAP has Leonardo. Salesforce has Einstein. For you big companies left: Fermi, Curie, and Plato are up for grabs, I think.

Do we need to anthropomorphize AI to make it marketable? Palatable? Acceptable? Approachable? Is this an important part of AI adoption, or a silly phase we will look back on with disdain? I personally think it’s overkill and might actually backfire as we all become better sophisticated, learning that AI isn’t anywhere near as smart as Albert Einstein, Leonardo da Vinci, or even IBM founder Tom Watson.

Maybe we should be looking for real genius, like the guy who invented soft-serve ice cream, Tom Carvel. I can hear him now, “Look at this AI. It’s beautiful AI. It’s the best AI money can buy.” So, maybe we should name our AI “Tom.” Yeah, not sexy, I know, but that’s the point.

AI is becoming embedded in every kind of software you can imagine, and, at it’s best, it isn’t noticeable at all. It just does the job better.

I think Google has the right approach. Yes, you say “Hey, Google,” when you want to talk to your Google Assistant, but there are countless AI component inside dozens of Google products, starting with Google Search, that don’t need a name. They just work better.

To me, that’s what we really needed. AI that works better, rather than has a cute name.

Full disclosure: I am the Senior Strategist at Converseon and SoloSegment, both of which have AI that works, without any cute names.

By Mike Moran 

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Sourced from B2C Business 2 Community

By  Derek Andersen 

Forbes recently stated that 80% of enterprise companies are investing in artificial intelligence (AI) solutions today. AI is a machine’s ability to imitate intelligent human behavior by perceiving a set of inputs and processing that information in order to reach a desired outcome.

In the martech space, companies are utilizing AI to build customer profiles, resulting in more precise ad targeting as well as unprecedented customization. Below are three recent examples of how companies are using AI to build customer profiles and drive revenue.

1. Teleflora Uses AI to Deliver Personalized Product Recommendations

A recent article in Direct Marketing News details how Teleflora, a leading floral arrangements vendor with more than 15,000 member florists in the US and Canada, uses AI to build customer profiles and provide a personalized touch.

Source: Teleflora.com

When Tommy Lamb, Teleflora’s new director of CRM and loyalty, joined the company, he immediately realized their marketing strategy was underdeveloped. Since customers typically only used Teleflora at spread-out points in the year, the company needed strong remarketing and customer service to build brand loyalty. But rather than providing personalized offers, they only utilized a few generic holiday email campaigns.

A retail marketing platform called Bluecore gave Lamb and Teleflora the AI capabilities they needed in order to execute a three-pronged personalization plan:

  1. Teleflora first created more comprehensive customer profiles by combining their product data with their individual customer data.
  2. Next, Teleflora paired Bluecore’s machine learning capabilities with these comprehensive profiles to anticipate the future purchases of various audience segments.
  3. Finally, Teleflora integrated advanced analytics, allowing them to identify best-selling items and other purchasing trends.

This AI strategy allows Teleflora to accurately anticipate customer needs. They can target customers who are ready to buy and make personalized recommendations, driving customer loyalty and ROI. Then, their analytics solution allows them to view the results and promote high- or low-performing products accordingly.

2. BMW Leverages AI to Personalize Ad Spend and Lower its Cost-Per-Acquisition

In order to execute a recent campaign, BMW Mini worked to connect and organize its data into an actionable format. Its goal: to target adults searching for a premium vehicle who had shown interest in the BMW brand.

Source: BMWBy partnering with ad agency Universal McCann, BMW was able to leverage its first-party data—which included people who had visited the BMW website or were already in their CRM system. BMW used this data to enhance its existing search strategy, ensuring its ads delivered relevant messaging to interested car shoppers.

BMW then utilized an AI solution to optimize the efficiency of its targeted ads. Over time, this solution optimized BMW’s ad targeting so the messaging would reach the right person, based on factors like time of day, previous searches, and BMW website visits. As a result of this strategy, BMW Mini’s conversions tripled and their cost per acquisition declined by 75%.

3. Comfort Keepers Utilizes AI to Target Caller-Ready Audiences

Comfort Keepers, one of the nation’s leading providers of in-home care for seniors, uses AI-powered conversation analytics to understand what happens on calls to each of their 450+ franchisee locations. Since phone calls make up 70% of their marketing conversions, they analyze the calls to determine who each caller is, if they are a quality sales lead (vs. a non-sales call), and if they converted to an appointment or customer.

By using this conversation analytics data, Comfort Keepers is able to fully gauge the success of their marketing efforts and prove it to each of their franchisees. Not only are they able to identify the quantity of the calls their campaigns drove, but they can also understand the quality. For example, rather than simply saying “we drove 2,000 calls this week,” they’re able to identify how many of those calls are potential new customers versus current customers. This gives them a full picture of the ROI from each of their campaigns to each location.

As a next step, once Comfort Keepers understands who converted on their calls and who did not, they can use that same conversation analytics data from AI to retarget their prospects with search, social, and display ads and use good callers in their lookalike campaigns.
Read more at https://www.business2community.com/marketing/how-ai-helps-marketers-build-customer-profiles-and-drive-revenue-02091198

By  Derek Andersen 

Sourced from Business 2 Community