Artificial Intelligence Potentials in B2B Marketing – D&B

ai-dnb
AI Marketing

By Leslie Hancock
Founder & CEO
CreativeCafeHQ.com

I did an amusing and highly informal survey, asking various people in my life what they think about when I mention “artificial intelligence” (AI). Not surprisingly, most think of human-like robots or omniscient, self-aware computer systems as portrayed in The Terminator, Ex Machina or Person of Interest. There’s the unavoidable association with a dystopian future in which the machines wake up and decide humans are a blight that must be eradicated. Even luminaries like Stephen Hawking and Elon Musk have warned us of the potential dangers of AI. But the general public still seems to think of AI technology as something to worry about in the distant future, not something that’s already a factor in almost every industry and aspect of our everyday lives.

The truth is that AI is already here, and it’s pervasive. Though most AI tech is nowhere near passing the Turing Test and taking over the world, it’s absolutely already transforming the way we do business. Whether they consider AI technology to be a friend or a foe, many B2B marketing leaders see the potential—and the inevitability—of AI. In fact, most CMOs in five global markets believe artificial intelligence will surpass social media’s influence in the industry. Nearly six in 10 believe that within the next five years, companies will need to compete in the AI space to succeed.

The Dawn of the Cognitive Era

Joanna L. Batstone, PhD, Vice President and Lab Director at IBM Research Australia and Chief Technology Officer, IBM Australia and New Zealand, says those involved in serious information science understand the enormous potential of intelligent systems.

“Cognitive computing systems learn at scale, reason with purpose and interact with humans naturally,” Batstone explains. “They learn and reason from their interactions with us and their experiences with their environment.”

Intelligent systems can go beyond answering numerical problems to offer hypotheses, reasoned arguments and recommendations. However, Batstone reassures anyone who might still be nervous about the nature of AI.

AI Is as Much About “Nurture” as “Nature”

But don’t worry, AI marketing (AIM) isn’t putting human marketers out of work anytime soon. Instead, it has the potential to be the connective tissue among martech systems, augmenting humans’ ability to make sense of and take action on data.

Wayne Sadin, Chief Digital and Information Officer at Affinitas Life, says, “Humans are still very much driving the train. It’s just a much faster, more powerful train now.”

Sadin urges marketers to rely on machine learning to perform rote tasks (like watching social media posts) in order to free teams to do higher-level creative work. By interconnecting social data to web analytics to a CRM database to external data and more, B2B marketers can use AI applications to automate and personalize many interactions that used to be time-intensive and far less efficient.

For this reason, Sadin thinks of the “A” in “AI” as augmented instead of artificial. “AI is part of the trend towards ‘Augmented Everything’: brains (AI), muscles (robots), vision (AR/VR),” Sadin says. “It makes workers smarter, stronger and faster. It’s not some central overlord machine, though. It’s just smart people taking advantage of data that’s already there and intelligent martech that is getting more and more capable of adapting to changing customer behaviors and expectations.”Sadin cautions that AI has to be carefully nurtured and guided. It can only do what we tell it to do and learn what we tell it to learn. AI can’t connect data and martech systems on its own without a human telling the technology how to make the connections and what to do with the data it takes in. (Maybe Maciej Ceglowski puts it best: “I find it helpful to think of algorithms as a dim-witted but extremely industrious graduate student, whom you don’t fully trust.”)

To train AI apps to be genuinely useful and not just more chaos cluttering up the martech landscape, we have to tie machine learning to business goals and ethical standards and be very, very specific about the data we feed into the AI to train it. We have to set limits and maintain good data hygiene so the AI makes the connections we want and stays on track. Without reasonably clean, complete data, AI is just garbage in, garbage out. And without encoding careful ethical parameters to nurture the AI’s learning and development.

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AI Marketing Requires a Different Mindset

AI is already proving to be more of a friend than a foe to B2B marketers. To get the desired results, though, not only do marketers have to properly nurture AI technologies, we also have to embrace their potential—even when there’s the possibility that AI will eventually render our current jobs obsolete.

Paul Greenberg, independent consultant and author of the best-selling book CRM at the Speed of Light, points out that marketers have to embrace a very different mindset moving forward. Marketers are trained to target personas—representations of broad groups of people with similar characteristics and drivers—and produce content that appeals to them. But now customers expect an extremely high level of personalization and real-time interactions with brands across a multitude of devices and channels.

These technologies will nevertheless grow in popularity. Greenberg expects these applications to be acquired by, and absorbed into, big marketing clouds like IBM, Oracle and Salesforce, so you should expect to see them soon in your martech portfolios if you don’t have them already.

As for what we can and should expect AIM technology to do today and in the future, Greenberg points to one of his favorite marketing videos of the past few years, Corning’s “A Day Made of Glass.” This video was not produced using AI, but it’s the kind of thing Greenberg believes we can and should be leveraging AI to create. He says the Corning video brilliantly connects with consumers’ emotions to create a human-centric vision of the future with extremely broad appeal.

“AIM technology can already do this if we ask it to. It’s not way off in the future,” Greenberg says. “Intelligent systems can already make the connections between systems of record and systems of engagement to understand what would emotionally engage people. These technologies can independently test and learn from people’s reactions enough to change their approach and build highly personalized, relevant and contextual custom content for prospects and customers in real time.”

So is AI a friend or a foe to B2B marketers? The answer is yes. AI is what you make it. It all goes back to how you feed, nurture and train your intelligent systems. Just as you wouldn’t toss a five-year-old child into a mosh pit to learn how to dance, you can’t leave AIM technology on its own to learn from unlimited inputs and “dirty” data sources. If you train it well and use it to build interconnections across your organization and beyond, AIM can significantly augment your human marketing team’s capabilities to anticipate and connect with much larger and more diverse audiences over time.

Source: Artificial Intelligence Potentials in B2B Marketing – D&B

Retargetting is so Flintstones



By Charlie Tarzian, Founder, The Big Willow

So we’re at least 7 years into exchange based media and we’re still complaining about retargeting. Consumer complaints are that it is mindless or creepy. And on the professional side, clients want more control over who to retarget and when.

Meanwhile, back at the zombie ranch of retargeting there has been very little innovation or progress made on some basic requirements that would change the game.

The biggest issue is lack of integration into the enterprise. And by enterprise I mean those data repositories and systems that run any company. The fact that brands continue to chase us with acquisition based messaging even after we have purchased is clearly a missed opportunity. Which begs the question: Is retargeting that misunderstood that it falls to the bottom of the data integration wish list? Do brands understand the magnificent fail of not knowing who their new customers are and the state of play between any one consumer and their company?

Without question brands need to start paying attention to this continual consumer aggravation.

Recently, a B2B client said in a meeting: We are only really interested in investing in our targeted client list. We want to know when they come to our site, we want to know when we serve them ads, when they open and click through our ads, when they follow and share our social links, etc…

So why, she asked, are we retargeting everyone that comes to our site? Our targeted list represents less than 10% of everyone that comes – so why can’t we suppress retargeting to the other 90% of the audience we are not interested in at this time? And what about if we want to retarget based on which part of the site and which product they were engaged with?

Her company’s agency responded: That’s not how it works. There is no way to discriminate. Anyone that comes to the site becomes part of the retargeting pool.

So – indiscriminant retargeting is what it shall be!

Now, on the other side, retargeting relies on building sizable pools of audience to drive the cost of bidding down – meaning the more you have in the pool and the more you have to choose from the better chance you have of winning a certain percentage of your bids and of keeping the costs down. Retargeting buys can be two times the cost on a CPM basis when compared to straight CPM buys. We get that. But retargeting parameters should be no different than how you would set up any DSP-based campaign. You should be able to create whitelists with rules we use in any campaign: only these IP addresses, or these devices or cookies, or customers that have contracts coming up, etc… I only want to target those and with the right context.

If you think about it – instead of relying on the primitive, non-evolved way retargeting is done today, we should be thinking about moving the heavy lifting of retargeting to the same data-driven approach we take through our DMP’s-to-DSP’s-to-ad servers process. That’s how we operate the foundational aspects of our media stack today, so why can’t we use the same stack to inject logic, filtering and knowledge into retargeting.

I can tell you we are working on this issue and I have to imagine others are as well. We call it Filtered Retargeting and to be honest – it is not retargeting as much as it is sequencing messaging based on using historical data. Historical can mean 10 minutes, 10 days or 10 weeks – but the strategy relies on being up to date with previous interactions across data sets and systems. But that is just one dimension.

The other is getting client organizations to architect how key customer data gets into the marketing stack with the aforementioned frequency. When someone makes their first purchase, reactivates, buys a new service, upgrades, etc… the marketing operations world must be updated and rules put in place to allow a change in how we communicate to that individual and/or company. This is the promise of both DMP’s and of an ALWAYS ON marketing stack.

All to say, retargeting has withered on the vine for so long and yet could be so much more effective in enhancing relationships.

Let’s put some of that great thinking that has created so many innovations and breakthroughs into this issue so we can stop talking about it. Selfish as it is, I am tired of retargeting being the subject of dinner party conversations!

What are your thoughts? And who do you think is and/or should be solving for this lack of progress?

Google’s Artificial Intelligence Masters Atari Video Games

atari-brainDeep learning, one of the hottest topics today in artificial intelligence (AI), has taken another leap forward with DeepMind’s latest announcement.

Source: Google’s Artificial Intelligence Masters Atari Video Games

Think you’re good at classic arcade games such as Space Invaders, Breakout and Pong? Think again.

In a groundbreaking paper published yesterday in Nature, a team of researchers led by DeepMind co-founder Demis Hassabis reported developing a deep neural network that was able to learn to play such games at an expert level.

What makes this achievement all the more impressive is that the program was not given any background knowledge about the games. It just had access to the score and the pixels on the screen.

It didn’t know about bats, balls, lasers or any of the other things we humans need to know about in order to play the games.

But by playing lots and lots of games many times over, the computer learned first how to play, and then how to play well.

A Machine That Learns From Scratch

This is the latest in a series of breakthroughs in deep learning, one of the hottest topics today in artificial intelligence (AI).

Actually, DeepMind isn’t the first such success at playing games. Twenty years ago a computer program known as TD-Gammon learned to play backgammon at a super-human level also using a neural network.

But TD-Gammon never did so well at similar games such as chess, Go or checkers.

In a few years time, though, you’re likely to see such deep learning in your Google search results. Early last year, inspired by results like these, Google bought DeepMind for a reported $400 million.

Many other technology companies are spending big in this space. Baidu, the “Chinese Google”, set up the Institute of Deep Learning and hired experts such as Stanford University professor Andrew Ng. Facebook has set up its Artificial Intelligence Research Lab which is led by another deep learning expert, Yann LeCun. And more recently Twitter acquired Madbits, another deep learning startup.

The Secret Sauce of Deep Learning

Geoffrey Hinton is one of the pioneers in this area, and is another recent Google hire. In an inspiring keynote talk at last month’s annual meeting of the Association for the Advancement of Artificial Intelligence, he outlined three main reasons for these recent breakthroughs.

First, lots of Central Processing Units (CPUs). These are not the sort of neural networks you can train at home. It takes thousands of CPUs to train the many layers of these networks. This requires some serious computing power.

In fact, a lot of progress is being made using the raw horse power of Graphics Processing Units (GPUs), the super fast chips that power graphics engines in the very same arcade games.

Second, lots of data. The deep neural network plays the arcade game millions of times.

Third, a couple of nifty tricks for speeding up the learning such as training a collection of networks rather than a single one. Think the wisdom of crowds.

What Will Deep Learning Be Good For?

Despite all the excitement about deep learning technologies, there are some limitations to what it can do.

Deep learning appears to be good for low-level tasks that we do without much thinking. Recognizing a cat in a picture, understanding some speech on the phone or playing an arcade game like an expert.

These are all tasks we have “compiled” down into our own marvelous neural networks.

Cutting through the hype, it’s much less clear if deep learning will be so good at high level reasoning. This includes proving difficult mathematical theorems, optimizing a complex supply chain or scheduling all the planes in an airline.

Where Next for Deep Learning?

Deep learning is sure to turn up in a browser or smartphone near you before too long. We will see products such as a super smart Siri that simplifies your life by predicting your next desire.

But I suspect there will eventually be a deep learning backlash in a few years time when we run into the limitations of this technology. Especially if more deep learning startups sell for hundreds of millions of dollars. It will be hard to meet the expectations that all these dollars entail.

Nevertheless, deep learning looks set to be another piece of the AI jigsaw. Putting these and other pieces together will see much of what we humans do replicated by computers.

If you want to hear more about the future of AI, I invite you to the Next Big Thing Summit in Melbourne on April 21, 2015. This is part of the two-day CONNECT conference taking place in the Victorian capital.

Along with AI experts such as Sebastian Thrun and Rodney Brooks, I will be trying to predict where all of this is taking us.

And if you’re feeling nostalgic and want to try your hand out at one of these games, go to Google Images and search for “atari breakout” (or follow this link). You’ll get a browser version of the Atari classic to play.

And once you’re an expert at Breakout, you might want to head to Atari’s arcade website.

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Source: Why Marketers Should Consider Artificial Intelligence When Reaching Out To Consumers

3 Levels of Data Analysis to Revitalize Your Automated Email Programs 

Optimizing for incremental percentage gains on your email statistics could make a monumental difference in revenue and brand perception in the following weeks and months.

Source: 3 Levels of Data Analysis to Revitalize Your Automated Email Programs