From algorithms to advertising: 7 steps to introducing AI to marketing

Artificial intelligence — once the rarefied domain of big-name, ambitious projects like Google’s self-driving car or IBM’s Watson — is now finding its way into everyday business.

In advertising and marketing specifically, brands might not be completely overhauling their existing ad tech and martech stacks to make room for AI just yet, but many are getting a feel for it by experimenting with single-touch AI solutions that focus on isolated tasks, like recommendations, ad buying and optimization.

The coming wave of AI in marketing will be defined by the automation of complex, multi-step processes — not just one-off aspects of a larger campaign. For brands, this will mean relinquishing control, trusting the technology to come in and quickly understand processes comprised of numerous tasks, channels, people and procedures, without messing things up.

Before handing over the reins, it’s helpful to understand how AI works — and how entire human thought processes are converted into algorithms.

For all its complexity, here’s a simplified look at seven steps to introducing an AI that can automate holistic digital marketing programs from start to finish.

  1. Obsessively observe and unravel every step in the process

Creating artificial intelligence for “self-driving” marketing technology is not so different from creating AI for a self-driving car. In the case of the car, it must know how close it is to other cars. It must know how to make a turn and ensure that it’s in the right position at the end of the turn, when to hit the gas pedal to go faster, what the road conditions are like, and so on — all without the driver telling it what to do.Like driving a car, many of the thought processes that go into the day-to-day execution of marketing programs also happen automatically and largely on the subconscious level. Transforming these subtle processes into a tangible series of algorithms means isolating logic and reasoning that humans often aren’t even aware they’re engaging in.This begins with the acute observation of marketers and account managers as they execute each step of a process, over and over again. Often, things that seem trivial — like determining which image and headline combo work best for Facebook, how much budget to spend where, or picking keywords for a search campaign — are critical parts of a larger process.

  1. Understand why human marketers make the decisions they do

The AI doesn’t just need to know what steps to take; it ultimately needs to understand why each decision was made, whether it was based on experience, logic and reasoning or simply knee-jerk instincts.This requires asking marketers and account managers to describe their decision-making process, which can be difficult considering that, as we’ve already discovered, they have no idea what motivated them half the time: Why did you keep these words and ditch those ones? How did you decide your bid size? Say you see a keyword doing well and you increase it by 20 percent — how did you choose 20 percent? What is the best time of day to send stuff to that person? Okay, what about that other person?

  1. Teach technology how to understand abstract information, such as creative Data in the form of words and numbers are unquestionably the domain of AI.

So, what happens when technology is asked to process and make decisions that are more creative in nature?For a human, understanding why certain images and text make more sense as a first interaction with a consumer rather than as a secondary or final interaction is almost second nature. A machine, on the other hand, needs to be told (or programmed) with this knowledge in order to be able to judge images and text, determine where they should appear along the journey, and not have to rely on humans to make these decisions for it.

Read all: From algorithms to advertising: 7 steps to introducing AI to marketing

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Where is data-driven marketing headed in 2017? | Econsultancy

‘Data-driven’ is one of those terms which seems unnecessary for marketing.

Surely all marketing uses data to some extent, so why does there need to be a distinction?

As marketing increasingly moves to digital platforms, however, the concepts behind the term ‘data-driven marketing’ have become distinguished from more traditional marketing and even have their own vocabulary.Terms like programmatic buying, real-time bidding (RTB), data management platform (DMP), customer data platform (CDP), and attribution modeling are now standard lingo when talking about using data for marketing nowadays. Without some grasp of these terms and the concepts behind them, marketers can quickly become lost when speaking with others in the biz. Perhaps, then, it does make sense to talk about ‘data-driven’ marketing differently from other marketing which focuses more on the ‘four Ps’ or ‘STP marketing’.

For readers who feel that they need to catch up in this area, Econsultancy has a number of blog posts on these topics and Econsultancy subscribers can consult our recent research covering programmatic, data-driven branding and the role of the CRM in data-driven marketing.But for those who are familiar with these concepts, the next question is:

For the answer go to the Source: Where is data-driven marketing headed in 2017? | Econsultancy

Gil Press: Top 10 Hot Artificial Intelligence (AI) Technologies

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The market for artificial intelligence (AI) technologies is flourishing. Beyond the hype and the heightened media attention, the numerous startups and the internet giants racing to acquire them, there is a significant increase in investment and adoption by enterprises.

A Narrative Science survey found last year that 38% of enterprises are already using AI, growing to 62% by 2018. Forrester Research predicted a greater than 300% increase in investment in artificial intelligence in 2017 compared with 2016. IDC estimated that the AI market will grow from $8 billion in 2016 to more than $47 billion in 2020.

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“Artificial Intelligence”

Coined in 1955 to describe a new computer science sub-discipline, “Artificial Intelligence” today includes a variety of technologies and tools, some time-tested, others relatively new. To help make sense of what’s hot and what’s not, Forrester just published a TechRadar report on Artificial Intelligence (for application development professionals), a detailed analysis of 13 technologies enterprises should consider adopting to support human decision-making.

Based on Forrester’s analysis, here’s a list of the 10 hottest AI technologies:

  1. Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights. Sample vendors: Attivio, Automated Insights, Cambridge Semantics, Digital Reasoning, Lucidworks, Narrative Science, SAS, Yseop.
  1. Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications. Sample vendors: NICE, Nuance Communications, OpenText, Verint Systems.
  2. Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager. Sample vendors: Amazon, Apple, Artificial Solutions, Assist AI, Creative Virtual, Google, IBM, IPsoft, Microsoft, Satisfi.

Read all http://www.forbes.com/sites/gilpress/2017/01/23/top-10-hot-artificial-intelligence-ai-technologies/#4a2fe9d442de

McKinsey & Company: The case for #digital reinvention

Digital technology, despite its seeming ubiquity, has only begun to penetrate industries. As it continues its advance, the implications for revenues, profits, and opportunities will be dramatic.

As new markets emerge, profit pools shift, and digital technologies pervade more of everyday life, it’s easy to assume that the economy’s digitization is already far advanced.

According to our latest research, however, the forces of digital have yet to become fully mainstream.

On average, industries are less than 40 percent digitized, despite the relatively deep penetration of these technologies in media, retail, and high tech.

As digitization penetrates more fully, it will dampen revenue and profit growth for some, particularly the bottom quartile of companies, according to our research, while the top quartile captures disproportionate gains.

Bold, tightly integrated digital strategies will be the biggest differentiator between companies that win and companies that don’t, and the biggest payouts will go to those that initiate digital disruptions. Fast-followers with operational excellence and superior organizational health won’t be far behind.

These findings emerged from a research effort to understand the nature, extent, and top-management implications of the progress of digitization. We tailored our efforts to examine its effects along multiple dimensions: products and services, marketing and distribution channels, business processes, supply chains, and new entrants at the ecosystem level.

We sought to understand how economic performance will change as digitization continues its advance along these different dimensions.

What are the best-performing companies doing in the face of rising pressure? Which approach is more important as digitization progresses: a great strategy with average execution or an average strategy with great execution?

The research-survey findings, taken together, amount to a clear mandate to act decisively, whether through the creation of new digital businesses or by reinventing the core of today’s strategic, operational, and organizational approaches.

More digitization—and performance pressure—ahead

According to our research, digitization has only begun to transform many industries. Its impact on the economic performance of companies, while already significant, is far from complete.

Read all: The case for digital reinvention | McKinsey & Company

The business case for digital transformation | ZDNet

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Digital transformation investments are ultimately about business survival through disruption.

Such investments have a direct impact on customer expectations and go beyond the traditional ROI. The scope for disruption spans the entire customer life cycle, affecting everything from the supply chain to after-sales support.

Forrester recently researched the impact of digital adoption on CX and ROI and some of our key takeaways are below:

Disruptive transformation must be viewed as a strategic investment. The real value of digital transformation investments relates to long-term revenue growth, not short-term technology ROI.

Bolt-on digital projects do not change the fundamental value relationship that you have with your customer. To maximize the impact of digital investments, business and technology leaders must learn to value such investments through the eyes of the company’s customers.

A classic ROI calculation is neither always feasible nor desirable for digital investments. Digital transformation changes business processes and models. ROI works for single digital initiatives, but not for shifts in business models.

Digital investments aimed at disruptive change across the enterprise challenge traditional ROI calculations. Attributing benefits like customer satisfaction, group productivity, and group revenues — let alone business survival — to a single digital investment is impossible because so much of the impact of digital transformation is cumulative.

Read all: The business case for digital transformation | ZDNet