Welcome to 2019. This year will see the publication of my fourth business book, Welcome to the Machine: A primer on artificial intelligence for Marketers.
Artificial intelligence represents the biggest buzzword to come to the IT industry since big data and Web 2.0. However, it also carries the weight of dystopian science fiction and negative views of robots taking over the world and perhaps your job. Marketing strategists hear AI — or machine learning as it is known to data scientists — will change their business. What is real and what is fiction?
Readers will learn how to approach machine learning and improve their marketing efforts in Welcome to the Machine. Sign up below for updates.
Welcome to the Machine was drafted in 2018. The book is now in its first revision and editing stage. Expect the book to be released in the second or third quarter of 2019.
Why Write a Book on Marketing AI?
Last spring I emerged from a two-year job as a CMO. In order to ensure that I was ready for the current marketplace committed myself to study the latest trends in digital and became increasingly concerned about the emerging AI wave that is spreading through every segment of the communications business.
At the same time, I took on an adjunct position at George Washington University to teach a graduate digital communications class. My research for both myself and the class revealed few texts on marketing and communications AI. The books that I found dealt with AI ethics and impacts on a society level. Two texts were extremely tactical and offered no overarching trend theory or how it would change communications.
So I started researching and writing Welcome to the Machine to fill this perceived need and explain how AI will transform my peers’ careers and their companies. I believe the book will be useful to today’s communicator offering important insights. The book augments digital marketing theory with pragmatic approaches on how to approach and implement marketing communications AI
In my mind, AI represents a sea change trend that will be as impactful on communications as social media was, if not more so. Every marketer and public relations professional needs to understand the impact that AI and data algorithms will make on all aspects of communications, from how they email their customers to qualifying influencers.
I hope you will find this book useful as your career continues into the next decade, and you encounter the certain AI-driven changes ahead.
To develop a wider audience, I started publishing my artificial intelligence articles on marketing via Medium. Subscribe to get there articles as they are published. Here are three published between July and August.
Artificial Intelligence or AI the buzzword suffers from additional unfortunate baggage. Thanks to an overwhelming body of dystopian and hard science fiction, society already has negative notions of AI. Those preconceived notions vilify robotics and AI, recalling a malevolent sentient machine that’s going to wipe out the human race.
Given the current state of machine learning in the space, worries about marketing automation AI and its brethren taking over the world in the next decade seems utterly unfounded to me. However, try convincing the general public of the current benign nature of AI.
That’s what happens when you choose a very unfortunate buzzword to describe your trend.
Permission marketing is not new. But the principles of opt-in marketing are often abused. AI will antagonize rising tensions about privacy and personal data usage.
While marketing as a profession do a great job of talking about ethics and best practices, most marketers do not or can not marshal the internal resources, buy-in, or knowledge to protect their companies from machine learning produced transgressions.
As a result, many brands will have what Brandon Purcell calls a “moral mirror” thrust upon them, showing them and their customers the unconscious biases of their businesses. Unfortunately, these challenges will be aired on the public stage, almost certainly creating a crisis in the worst instances.
More advanced brands in the marketing segment like Marketo are already deploying their early AI solutions. However, their legacy implementations may prove a hindrance compared to next generation AI-based marketing systems that are rising to compete against them.
One such system is Lucy, a platform developed by a company called Equals 3. Now two years old, Lucy has advanced to become a powerful segmentation, advertising and content optimization tool that’s already developed industry recognized campaigns for brands like BMW.
I interviewed Equals3 Co-Founder and Managing Partner Scott Litman, one of the company’s founders and marketing technology veteran. Here are some of the key questions and answers from our conversation together.
Marketers should celebrate the Artificial Intelligence movement or “AI” as the industry calls it. Don’t worry. AI is an overhyped Silicon Valley term that represents more of a natural progression in technology — machine learning — than the birth of sentient beings.
In the marketing technology space, AI attempts to replace many menial marketing tasks. This empowers more creative minds and strategic thinkers to focus on the work they love, rather than the “vulcanesque” data rich tasks that drive them crazy.
The machine learning marketing evolution brings a couple of caveats, of course:
1) First, strategic marketers must comprehend machine learning on macro level and how to use those systems to inform strategy and lead day-to-day tactical exercises.
Second, some more tactical marketing roles will be either replaced or impacted by algorithmic bots.
2) The idea of tasks being performed by bots scares people. But it shouldn’t. Marketers are seeing some of the most time consuming tasks in their business automated, specifically those that revolve around data points. Almost every marketer I know complains about having to do 3x work in 1x space. This relieves much of the pressure.
Still, to not understand machine learning tools antiquates one’s skillset. To stay relevant, marketers need to embrace new technologies and see how they can be incorporated. Algorithms and bots are not sentient. They need guidance to successfully interact with human.
5 Marketing Functions Impacted by Machine Learning
Here are the five marketing functions I believe will most likely get impacted by machine learning over the next three years:
1) Community Management
The inability to scale created the biggest knock on social media. Deploying teams of humans across the Internet to reply, engage, and build communities on various social networks remains the luxury of only the largest brands. Further early iterations of automation via Hootsuite and Buffer received low marks from conversation purists who found these offerings inhuman and unengaging.
Current iterations like Social Drift for Instagram level the playing field. New bots and algorithms are let community managers engage in real conversations while delegating mundane tasks of following, liking, and unfollowing fall to the wayside. Further, these new bots fulfill a critical role in identifying and targeting influencers, reducing hours of research.
Community managers should find their tasks to be more enjoyable and less frantic. They will have many tools to make their jobs more fruitful and successful if they adapt the latest tools. Community managers who fail to adapt will likely find themselves falling to the wayside based on performance.
2) Content Marketing
Content marketers will greatly benefit from machine learning. From finding competitive content and relevant source material to optimizing message and delivery preferences, algorithmic programs will greatly assist research and content creation.
Scoop.it is one content research example with its Internet scouring capability. While very helpful, Scoop.it results requires a lot of weeding. This is common with machine learning apps. The early results are not great. The better bots improve as their algorithm optimizes based on human input.
In the near term, AI is unlikely to replace content marketers, simply make them better and more efficient. However, like community managers, content marketers must stay up to date with the latest machine learning tools in order to stay relevant and functional in their jobs.
3) Customer Service
From chat bots and smart helpdesk sourcing of solutions to actual AI bots answering calls and of course, Alexa, Cortana, Google, and Siri-enabled applications. Yes, there will be a need for real live voices, but only for the most difficult problems.
Unfortunately, this is one of those areas where machine learning will create significant job loss, particularly in call centers. Expect to see customer service costs and jobs reduced significantly over the next three years.
4) Data Analyst
The data analyst, the person who combs through reports to find prescient data points, and then spits out reports for management will likely see their task automated in the very near future. Increasingly, reports will be offered by algorithms, which when steered and customized to a unique business, will eliminate much of the weekly dashboard testing.
Strong machine learning will also identify emergent trends before a human can, too. Lead scoring system Infer offers a great example of superior data analysis, identifying SQL opportunities well before the human-induced lead scoring algorithm used by most marketing automation systems.
A need remains to steer machine learning to source the right quality data points and ensure that algorithms continue to evolve and meet customer and business model changes. The more strategic data scientist(s) will be required for larger enterprises.
In small businesses, the marketing lead will need a deeper understanding of data science to remain relevant and functional. In essence, someone needs to guide and ensure that data analyzing bots are on point and at a minimum providing useful predictive information.
5) Digital Advertising
Digital advertising is becoming a game of bots. It should be no surprise as this is where the money is at. Google leads the AI ad market with its increasingly complex machine learning-based Google Ads solution that cross pollinates multi-channels to reach customers, even on their smartphones.
As machine learning-based advertising platforms mesh with automation systems and CRM databases, marketer interactions with those systems will increasingly revolve around management. Advertising bots will take inputted spend, target audiences and initial creative and then offer machine learning suggestions. There is a great need for ethics here, as evidenced by the Cambridge Analytica scandal.
In many ways the digital advertising manager will simply approve or correct these suggestions, and then allocate resources as necessary to fulfill them. In the near term, digital advertising agencies can get a leg up on customers by mastering the latest bots and algorithms.
These are just a few of the marketing roles that AI will impact over the next few years. There’s a bot to assist almost every marketing function available now.
But is worrying about job or role replacement the best way to approach the issue? Or should we dive in and embrace the tools provided to us?