“The summer of 1956. New Hampshire’s Dartmouth College.
My name is John McCarthy, and I want to explore the idea of programming machines to use language and solve problems for humans while improving over time.”
Yes, you guess right, the AI was born.
Sixty years later, cognitive scientists, data analysts, UX designers, and countless others are doing everything this pioneering scientist hoped for — and much more.
Think of AI as a machine-powered version of mankind’s cognitive skills. These machines can 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.
Humans can no longer do it alone. AI can create richer, more personalized digital experiences for consumers, and meet customers’ increasingly high brand expectations.
For many marketers, AI is an enigma surrounded by buzzwords. But the irony is, as much as the hype has overstated what AI might do in the next years, the reality of how AI is already used today in marketing is often under-recognized.
- Facebook uses facial recognition to recommend who to tag in photos and allows you to target people based on the data you already have (lookalike audience). I mean, it automatically shows your ads to the right people, at the right time, on the right channel.
- Google uses deep learning to rank search results.
- Netflix uses machine learning to personalize recommendations.
- Amazon uses natural language processing for Alexa.
- The Washington Post uses natural language generation to write data-driven articles.
Your life is already machine-assisted, and your marketing can be, too.
Business transformations driven by the AI revolution are impossible to ignore: in every industry, AI-enabled systems are transforming organizations and the role of the marketer.
AI has real promise to improve most aspects of a company’s operations—and perhaps no business function has the potential to benefit from it like marketing.
The question is: are marketers and their organizations ready for the AI revolution?
The marketing industry stands on the threshold of perhaps its biggest transformation since the introduction of database marketing in the 1970s.
With today’s AI, tools marketers are now able to bridge the gap between the vast amounts of data companies have collected and leveraged these assets to define and execute effective optimized “one-to-one” personalized marketing at scale.
There is no question that companies that embrace AI-driven marketing can and will unlock vast customer and shareholder value.
What Is Artificial Intelligence?
Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.
AI works by combining large amounts of data with fast, iterative processing and intelligent algorithms, allowing the software to learn automatically from patterns or features in the data.
It’s used to describe a suite of unique but related technologies that can simulate human capabilities.
It’s not some singular magic technology that can do everything.
Rather, it’s basically a concept. Working on it and using it we can build a set of individual tools with real capabilities, each at a different stage of development.
Out of these different subsets of AI, there are a few that are particularly applicable to marketing right now.
- Computer vision allows AI to see. This leads to object detection, facial recognition, and visual listening on social media.
- Natural language processing (NLP) allows AI to hear and speak – giving us chatbots, semantic analysis, content generation, and voice search capabilities.
- Machine learning allows AI to learn with data on how to progressively improve performance on a specific task over time, without explicitly being programmed what to do. This gives us content recommendations, lookalike audiences, programmatic advertising, and lead scoring.
The ability of self-improvement provided by machine learning is the most critical subset of AI for marketers.
Automation Is Not Equal to Machine Learning
Maybe you’re thinking you’ve got this whole AI marketing thing covered because you have an expensive marketing automation tool.
Let’s get one thing crystal clear:
Automation is not machine learning. Automation is a technology used to achieve defined outcomes with minimal human intervention.
You still have to design and input marketing logic.
Machine learning enables it to improve from experience, so the machine learns what to do to produce the desired outcome.
The machine is not restricted to pure execution, it also takes care of optimization.
At the core, automation replicates what you do now. It saves time.
If used wisely, machine learning can improve on current tactics to continually drive up KPIs.
So why aren’t we leveraging this technology?
Reason 1: Lack of Technical Skills
Many marketers feel they lack the technical skills to adopt AI. However, this doesn’t need to be so.
In fact, you already know everything you need to get started.
There is a difference between machine learning research, which is all about building better algorithms and is the prerogative of data scientists. On the other hand, applied machine learning, using AI-powered tools to solve business issues, which is what marketers need to do.
Think about it this way:
You may not fully understand the science behind how a microwave works. But that doesn’t stop you from using it to cook.
- Companies without data scientists can still choose the best data (best ingredients).
- Put this information into tools (the appliance).
- To create models (the recipes).
- That produces predictions – a.k.a., enact marketing tactics (the dish).
- The quality of which we can assess by testing (tasting).
And if the dish isn’t tasty, you can modify the recipe, or buy a stove, or get better quality ingredients.
You won’t become a better chef by learning more about the science behind how a microwave works. You won’t become a better marketer by researching the intricacies of data science.
The best way to learn to cook is to just get started.
The best way for marketers to overcome our problem of scale is to roll out any use case of AI.
Reason 2: Fear of losing our jobs
Some teams don’t want to begin AI initiatives as they fear it will cause the next industrial revolution and they will be out of a job – this naturally causes a lot of resistance.
Unless you plan to retire in the next 5 years, artificial intelligence will significantly impact your career in marketing. But this doesn’t mean you’ll be replaced by a marketing robot.
Your job will change from executing repetitive tasks to teaching AI to do those tasks for you.
Allowing you to reinvest your time into creativity and strategy.
But how to teach AI to do something?
Because it sounds very technical.
The most common training method for marketing use cases is supervised learning.
This involves two phases.
The first is the initial teaching process.
Let’s say you have 1 million customer reviews.
No human could read them all, so you want to use machine learning to understand the sentiment, classifying the review as positive, neutral, or negative.
To achieve this, take a sample of those reviews and label each of them with one of your three classifications. Then feed this training data into your machine learning algorithm.
The more data it has, the better it will be at recognizing patterns and over time the more reliably it will be able to classify the sentiment of reviews on its own.
To test its abilities, rather than feeding it labeled data, input the raw data, and assess the quality of the results. Often, if you have done the initial teaching process well, it will already be able to correctly classify a large portion of the data.
And you can move onto the second phase; the ongoing teaching process.
Where regularly you would re-labeling any errors to teach the algorithm what it did wrong, allowing it to continually improve. You may have already been teaching algorithms without knowing it.
Who hasn’t filled in an image-based Google-captcha, marked an email as not spam, or marked fake news on a Facebook post? By doing any of these, you were proving manual verification, adding labels, and teaching algorithms.
Think of launching a machine learning algorithm like hiring a new junior marketer.
The day you onboard it, is the worst day it’s ever going to perform.
It will do the work, but it will make mistakes, so you need to supervise the results, correcting as needed. The longer it works, the better it becomes and the more time you have to reinvest into scaling other marketing channels.
But unlike a human, machines are happy to do the same, very narrow job forever – whether that’s classifying reviews, adjusting ad bids, posting on social media, or forecasting growth.
You are not handing over control of marketing to a machine.
You are teaching them how to collect the information you need or how to execute a specific element of your marketing strategy.
And AI offers a whole new level of scale.
Classifying 1 million reviews is not a problem for a machine learning algorithm.
What’s more, because of this scale, it can produce insights that would otherwise not be available.
The process of collecting and analyzing consumer insights allow you to establish purchasing patterns that reveal what your customer base values. Business insights serve the purpose of building the strongest relationship with a customer, serving up relevant product recommendations, and increasing sales.
Those strategic questions are where marketers should be spending time.
Reason 3: Investment of Resources & Budget
Executives are often concerned about the implementation efforts and costs for AI applications.
So the best place to start is not by asking for more budget and resources, but by asking yourself – are you fully leveraging what you are already paying for?
Consider the AI capabilities of your current marketing toolset.
Marketing automation platforms like HubSpot, CRMs like SalesForce, and advertising tools like Google Ads and Facebook Ads have all incorporated AI into their systems.
If you’re a customer of one of these solutions, their support teams can be a valuable resource to begin your organization’s AI implementation as you can learn from their insights and experience.
It’s a great way to start building up your team competency in AI applications for little to no additional cost.
And what about the AI capabilities of your current tech stack?
AI technologies are not channel-based, they are use-case based.
So if you have a recommendation engine running on your website, why not use this engine to improve the personalization of your email newsletter, push notifications, or chatbot content.
You can use these existing technologies as low investment proof of concept.
So by the time you are asking for additional resources and budgets, your executives are already fully on board.
When you are looking for a new tool, beware of buzzwords.
Many AI solutions aren’t that intelligent. Even when there are the words “AI” or “machine learning” right there in the product description.
Some tools shamelessly use these terms to describe commonplace automation or targeting capabilities. If the vendor can’t explain how the AI works in detail, don’t buy it. If it seems too good to be true, don’t buy it.
Because I’m sorry to say, no marketing AI platform neatly bundles everything up into a single monthly subscription.
That’s because AI should be purpose-built to solve one, well-defined problem. This is why you need to choose the right use case first – as each use case will likely need its tool.
But what you can do is begin to build your artificial intelligence.
AI technology is becoming more affordable and accessible because companies like Google, Amazon, IBM, and SalesForce are offering their algorithms to the world.
Some third-party services are open-source, others are pay to play – but they all give a springboard from where you can customize your solution.
Especially if they offer access to additional data sets to layer onto your 1st party data, making your AI application more powerful.
Reason 4: Quality of Data Sources
The very best AI tools and talent in the world will not deliver results if you are missing the most critical component for machine learning – high-quality data to feed the learning algorithm.
Data quality is probably the single biggest challenge you will face when implementing AI.
As eMarketer notes, data is often old, or in silos, or we just don’t have enough in the first place
And we’re not dedicating resources to fix this.
The problem is, feeding bad data into a good machine learning algorithm won’t give the right answers. Without understanding that data is critical, you are likely to blame the poor outcomes on the AI.
There are things we marketers should be doing to drive for actionable data.
- When was the last time you did a Google Analytics audit?
- Have you implemented structured markup and content tagging?
- Are you using remarketing scripts to collect more user data?
- Are you supporting the collection of data that can be used to identify users across devices and channels, like email addresses?
- Have you got your marketing tools integrated with your Data Management Platform (DMP)?
You need to focus on these areas now, because good AI marketing depends upon having actionable data that is structured, integrated through a common identifier, plentiful, and (most importantly) accurate.
Looking to the Future
Artificial intelligence is changing consumer behavior. Consumers are hit with too much information every day. They don’t want to spend time evaluating all the options.
So they delegate. Think about what is already controlled by AI.
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. 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, 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.
So, what do you say?
Are you ready for the revolution?
Understanding the data, you will understand your customers. We can help you, contact us.