Is there room for another genius in the marketing tech and intelligence space? Datorama, the New York-based data management and activation platform, believes there is, and today launches Datorama Genius, an AI engine designed to surface optimization paths for platform users.
In a conversation before the launch, Datorama's CMO Leah Pope broke the Datorama platform down into three layers: an integration engine, an insights engine, and an activation engine. "There's intelligence in all three buckets," she said. Genius puts AI into the insights layer. 

I’ve been noticing how articles about how AI, robots, or automation will impact the future job outlook all seem to reuse the same terms, like “disrupt”, “steal”, or “threaten”. The thesaurus has only so many terms to go around I suppose. I got to wondering which terms were most popular, and then how they’ve changed …

Food delivery company DoorDash says personalized restaurant recommendations based on AI are seeing a significant lift in orders, compared to regular recommendations based on popularity.

In an interview with VentureBeat, DoorDash product manager Jimmy Liu said customers who saw personalized recommendations on average “were over 25 percent more likely” to place an order versus people who saw the most popular restaurants in their area.

Liu said the 25 percent lift from recommendations came specifically from email campaigns. Machine learning (ML) based recommendations made within DoorDash’s app saw a lower lift on orders, Liu said. That’s logical, he said, because if someone is already within the app, they are already showing an intent to order — and so getting an additional lift from recommendations is comparably harder to do than for email, where someone may not be actively searching.

Many people—including the media—are quickly jumping to conclusions about how we will be getting “intelligence in a box,” whether these devices are helping patients, doctors, law enforcement professionals, or white-collar workers across a wide range of industries. The “box” idea, however, is not the right metaphor—far from it. It won’t be a box, but a stack of technologies connected to thousands of sensors facilitating a system that learns about something and creates its own rules. This “black box” idea is wrong because it’s very dangerous to leave such things to a “box,” with very little understanding of what it’s actually learning and the rules it’s creating.

The long-standing debate about AI systems as complements or substitutes for human labor is not relevant. It will become part of our intelligence system, affecting enterprises and individuals, managers and consumers. One thing is for sure: many jobs will disappear and many new ones will be invented and hopefully soon enough

Bitvore is a search engine that uses artificial intelligence to deliver proactive and personalized data to businesses in real-time. Referred to as ‘precision intelligence’, Bitvore frequently sources and filters through information, helping businesses spot untapped sales opportunities, trace valuable trends, and identify potential risk factors. The platform filters insights based on specific needs, then instantly delivers those results to back to business leaders.

Today, Bitvore has over 200 machine learning algorithms that analyze each piece of information flowing through their system, with over 350,000 pieces of data circulating daily. Bitvore has raised $8.2 million to date, receiving a recent $3.45 million round in January of this year.

Mapping the most effective AI technologies for marketing across the customer lifecycle AI technology is a hot topic in marketing at the moment, but AI is a. Marketing topic(s):Marketing innovation. Advice by Robert Allen.

We are experiencing an historic moment where we have more data than ever. We are overwhelmed by data. Yet, although the data volume continues to burgeon, companies often struggle to harness it. Indeed, the way they take decisions isn’t really changed over the past decade. We’re not able to use the data we have. Or at least, we use data, but they do not influence our decision as one might expect. Identifying patterns that enable you to make the right decisions at the right time is what could really make the difference.

Massive data volume generated from a side. The inability to decipher that data chaos and pull out actionable insights from the other side. This is where Artificial Intelligence comes in as a connecting link. One of the techniques widely used to reach Artificial Intelligence is Machine Learning. Machine learning relies on two key elements: algorithms and data sets to train those algorithms.

Thank about all the structured, repetitive and rules based tasks you might do on a regular basis as part of your job as a marketer. They are all open to being completed by an AI service. Not only could they be completed by a computer, but they could be done faster and with fewer errors. That could free marketers up to spend time on managing even more programs without additional staff. Now, if you’re wondering what roles and tasks are at risk, Loren McDonald of IBM Watson Marketing shared this list:

• Easily repeatable
• Data-centric
• Tasks that improve with learning
• Rules drive tasks
• Reporting
• Customer and segment analysis
• Campaign automation
• Media buying
• Campaign testing

It’s important that marketers understand the difference between ML and Artificial Intelligence (AI), the latter of which is – at this point – aspirational for marketing. Machine learning is a foundational aspect to AI, and you need to ensure the technology you purchase is implementing ML right.

Functioning properly, machine learning provides the algorithms that make machines smarter but keep in mind that a lot of these algorithms are commoditized – many products use them. It’s how they are used in combination that often differentiates a solution from others. 

There’s a reason ML is used for attribution – it helps improve the accuracy of the analysis. Consider the amount of historical data you need to analyze; no human can accurately analyze all that data and come back with realistic insights. The same for predictive analysis. And ML can learn as the data grows.

Intelligence tools are one part of the marketing stack, and that can make it challenging to do attribution and other analysis. This is starting to change. 

What now seems clear is that the future of ABM — or at least high quality ABM — has as much to do with AI and machine learning as with the MA hub. Why?
"It's about intimacy at scale," said Demandbase CMO Peter Isaacson in his keynote. "As marketers we've fallen in love with volume. We've replaced intimacy with volume. That's just a broken system." Ironically, it's the machines which can bring human intimacy back. It's precisely AI that creates the potential to market to hundreds of accounts — and the individuals associated with those accounts — in a personal way. As Alan Fletcher, Chief Product Officer, told me later in the day, it's that old marketing goal "real time engagement" — the ability to catch the right person at the right time and say: "You need to read this now."
The AI emphasis suits Demandbase very well, of course. Last year it applied AI under the hood to power its DemandGraph offering — which essentially tracked and predicted the behaviors of B2B buyers around the globe by interpreting hundreds of terabytes of unstructured data and billions of web interactions: impossible without intelligent machines.

Here are the elements I’ll cover:

+ AI | Now | Local Maxima.
+ AI | Now | Global Maxima.
+ What the heck is Artificial Intelligence?
+ Machine Learning | Marketing.
+ Machine Learning | Analytics.
+ Artificial Intelligence | Future | Kids.
+ Artificial Intelligence | Worry about Humanity.

The use of artificial intelligence apps and machine-learning in business is growing. Check out this infographic to see which tasks are appropriate for AI implementation and how you might benefit.

Sixty days as the CEO of Kahuan, Patel shared lessons learned about marketing automation including:

• All inventory is either expensive or perishable. Advancements in data science enables CEOs and CMOs to demand an exponentially faster flow of product across demand and supply chains as part of any digital transformation initiative. As a result, smart marketing automation that can convert fast is now unequivocally the tip of the spear for modern marketing leaders.
• However, the consumer’s attention is far more perishable then your inventory. Every brand is now expected to also use smarter marketing automation to ascertain interest and to engage with consumers at the right time, on the right device and with the right message.
• You can’t fake AI, you can’t sugar coat AI, you can’t peanut butter AI. Mass personalization at scale is not a manual endeavor. AI at the core, or bust.
• “Omnichannel consumer experience” is unadulterated hogwash. As an industry, we will have to earn our way to an omni-channel consumer experience and we’re only getting started.

"AI marketing tools are already making an impact on customer profiling, ad targeting, content testing, and optimisation:

1. Content generation
2. Generative design
3. Content targeting
4. Recommendations and content curation
5. Customer segmentation
6. Predictive Analytics"

AI proponents concede that technology will replace some roles, but they believe AI has the potential to create just as many new jobs. Plus, they argue, software could free up marketers to focus on creative, strategic work rather than day-to-day processes.

“Ninety-five percent of the people who came to marketing did not come to this data-driven world. They came for the soft side of marketing. ” Patel said. “Because of AI, you can be that creative person you always wanted to be. You now have systems that don’t require you to become a mathematician.”

“AI will significantly simplify the user experience for many of the martech tools that marketers use, as well as automate a tremendous amount of the ‘manual’ labor associated with marketing programs today,” Brinker added. “It’s unlikely that we’ll be lounging around while robots feed us grapes.”

1) Emotional intelligence
We already have sentiment analysis. Now we need to hook it up to smart systems that help us understand how customers and prospects feel about our brands and our services.

2) Contextual awareness
The time of the day, current location, weather, and nearby events can have a profound impact on what we want, our urgency, and how brands should react. Artificial intelligence systems like bots or chatbots need to know about the world we live in as well as the things we’re asking them for, to better contextualize their responses.

3) Automation of busy work
Marketers still spend far too much time in Excel or other data aggregation and analysis tools. They need AI systems that can ingest data and then respond to natural language questions … as well as suggest fruitful lines of inquiry.

4) Integration with other AI systems
Putting AI in your marketing cloud, another in your chatbot, another in your Alexa skill, and yet more in your multivariate testing tool is great. Much greater is integrating them all into a smart community that can help marketers globally.

5) Understanding the customer journey
Artificial intelligence can help by ingesting vast quantities of data and — in ways that respect individual privacy — highlight common (and not so common) paths to purchase and loyalty.