This post originally appeared on the Marketing Artificial Intelligence Institute blog and was republished with permission.
You can read the original here: https://www.marketingaiinstitute.com/blog/do-i-need-ai-the-question-every-marketer-should-be-asking
Do you need AI?
Given all the hype, and forecasts of trillions of dollars in annual impact, whether you need AI in your marketing is a logical question to ask.
But, it’s the wrong question, at least to start.
The better approach is to consider what outcomes matter to your business, and then ask if there’s a smarter way to achieve them.
Consider these 10 questions in relation to your own plans and goals:
- Is driving costs down and revenue up important?
- Do your consumers demand personalization and convenience?
- Would you like to better predict consumer needs and behaviors?
- Do you want more actionable insights from your marketing data?
- Do you want to reduce time spent on repetitive, data-driven tasks?
- Do you want to get more out of your marketing technology stack?
- Are you under pressure to generate greater ROI on your campaigns?
- Do you want to gain a competitive advantage in your career, and for your company?
- Do you want your marketing to be more intelligent?
- Do you want your brand to be more human?
If you answered yes to any of these, then it’s critical that you understand what AI is, and what it’s capable of doing.
Because without understanding, you can’t possibly determine the importance of AI to your company now, and in the future. Nor can you ask the right questions of marketing technology vendors touting AI and machine learning in their messaging.
But, once you understand it (and I mean truly understand it, so that you are confident in your ability to explain it to a friend or co-worker), then its potential to transform your marketing, and business, becomes obvious.
So, let’s take a step back for a moment to level set on definitions. Simple, non-technical definitions designed to make AI make sense to marketers.
>>>Related: The Ultimate Beginner’s Guide to AI in Marketing
What Is AI?
According to Demis Hassabis (@demishassabis), founder and CEO of DeepMind, artificial intelligence is, “the science of making machines smart.” This is by far the best, most approachable definition I’ve found, and my favorite. And since I’m writing the article, we’ll go with it. 🙂
So, to borrow from Demis, we can think of marketing artificial intelligence as, “the science of making marketing smart.”
Machine learning is the primary subset of AI. So, yes, in case you hear otherwise (which you will), machine learning is a form of AI.
Machine learning is literally a system that learns. It takes data in (i.e. words, images, videos, numbers, voice, etc.), discovers insights and finds patterns that marketers would often miss (or never think to consider), and then makes predictions about future outcomes (e.g. what will happen next, or recommendations of what to do next).
And, most importantly, it continues to evolve and improve based on new data. In other words, it gets smarter. The human still often tells the system what to predict, and decides what to do with those predictions, but machine learning can give marketers superpowers when they have the right data.
This has obvious application to every area of marketing where data lives (e.g. analytics, automation, advertising, call center, chat, content, email, sales, search, social, website), but more on AI use cases in a minute.
Deep learning, which you hear about in the news these days, often related to facial recognition and computer vision, is a subset of machine learning. Deep learning tries, in a way, to emulate how the human brain works in order to give machines the ability to see, hear, speak, move and understand. Technologies such as Alexa, Siri, Google Assistant, iPhone Face ID, and Tesla Autopilot would not be possible without deep learning.
That’s it. That’s as complicated as I’m going to get with terminology and as simple as it needs to be for most marketers. But, if you want to continue exploring what AI is, here are two great articles from Karen Hao, senior AI Reporter, MIT Tech Review, as well as her Marketing AI Conference (MAICON) 2019 keynote:
What Are the Use Cases for AI in Marketing?
Now that we have a basic understanding of AI, machine learning and deep learning, it’s helpful to consider actual marketing use cases that make the technology more tangible.
Think about all the repetitive tasks your team performs every day, such as: drafting social media updates, writing data-driven blog posts, personalizing emails and website copy, tagging images, A/B testing landing pages, building lead nurturing workflows, developing advertising copy, crafting email newsletters, responding to consumer chat inquiries, managing paid media spend, conducting keyword research, finding insights in analytics and recommending strategies (to name a few).
Every one of those activities, and hundreds more, can be done more efficiently using AI technology that’s available today. In most cases, AI functions as an assistant, intelligently automating parts of a job or task, not replacing the need for human involvement.
Marketing artificial intelligence is the science of making marketing smart.
Using our AI Score for Marketers assessment tool, we’ve asked hundreds of professionals to rate the value of intelligently automating more than 60 common AI use cases.
All use cases are scored on a 1 – 5 scale (1 = no value; 5 = transformative) based on the same question: “Assuming AI technology could be applied, how valuable would it be for your team to intelligently automate each use case?”
Respondents are guided to consider the potential time and money saved, and the increased probability of achieving business goals.
Here are the top 15 marketing AI use cases, with average rating, according to our analysis of 210 respondents. As you can see, the highest rated use cases were scored between 3 and 4, indicating they are of moderate-to-high value for marketers.
- Analyze existing online content for gaps and opportunities. (3.88)
- Choose keywords and topic clusters for content optimization. (3.72)
- Construct buyer personas based on needs, goals, intent, and behavior. (3.71)
- Create data-driven content. (3.70)
- Discover insights into top-performing content and campaigns. (3.64)
- Measure return on investment (ROI) by channel, campaign, and overall. (3.64)
- Adapt audience targeting based on behavior and lookalike analysis. (3.64)
- Optimize website content for search engines. (3.55)
- Recommend highly targeted content to users in real-time. (3.52)
- Assess and evolve creative (e.g. landing pages, email, CTAs) with A/B testing. (3.47)
- Deliver individualized content experiences across channels. (3.44)
- Define topics and titles for content marketing editorial calendars. (3.43)
- Predict content performance before deployment. (3.41)
- Forecast campaign results based on predictive analysis. (3.40)
- Build media and influencer databases based on interests, audiences and intent. (3.37)
Check out the following articles for some additional, category-specific use cases in advertising, analytics, content marketing, conversational, email, PR and communications, sales, SEO and social media.
AI is just smarter marketing technology.
How Do I Get Started with Marketing AI?
With so many potential use cases, it can be daunting to figure out where to start. What you need are quick-win pilot projects with narrowly defined use cases and high probabilities of success. This helps build momentum, as well as executive support for AI.
When piloting AI in your organization, you want to focus on one use case at a time, since AI is built to do very specific tasks. The key is thinking about everything your team does regularly, and then considering two primary factors:
- The value to intelligently automate all or portions of that activity, with value being defined by potential time and money saved, and the increased probability of achieving business goals.
- The ability to intelligently automate the activity, based on existing AI tech, or solutions that could be built with the right resources. (Note, you may need some help to properly assessing this factor, until you get more advanced in your understanding of AI’s capabilities and the vendor landscape.)
>>>Learn more about our Marketing AI Consulting, and Piloting AI Workshops.
Keep in mind, a little bit of AI can go a long way to reducing costs and driving revenue when you have the right data and the right use case. You don’t necessarily need to go from all-manual to fully autonomous in order to see massive returns.
Besides, there is no such thing as a fully autonomous marketing AI solution (i.e. one that requires no human involvement or oversight), so ignore any vendor that claims otherwise.
Which brings us to our next topic . . .
>>>Want to get started with AI? Check out our free Piloting AI On-Demand Webinar.
How Do I Buy Smarter (i.e. AI-Powered) Marketing Technology?
At the end of the day, AI is just smarter marketing technology. But, it’s what you should be demanding from the vendors in your tech stack.
However, it’s important to know that all AI is not created equal.
Just because marketing technology companies claim they use AI, machine learning or deep learning, doesn’t necessarily mean their solutions are actually much more intelligent or efficient than what you’re already using.
Often times there is some form of AI—such as natural language processing (NLP)—used in limited features within a product, but the solution as a whole probably isn’t as advanced as their marketing messages may lead you to believe.
Many marketing technology companies are just starting to experiment with AI themselves, and while they may have roadmaps for more integration of AI moving forward, it’s still early. So, they’re caught in a tough spot. They want to tout the intelligent elements of their products, but they don’t want to overpromise what it will deliver in the short term.
Conversely, the tech companies who are actually building robust smarter solutions, and have been applying AI for years, struggle with how much to use AI in their messaging because they aren’t sure if it means anything to marketers (and it doesn’t, to most).
>>> If you’re with an AI-powered vendor that makes marketing smarter, we want to share your story. Submit an Editorial Spotlight Form to be featured on the Institute.
The other reality is that many of the marketers and salespeople at these marketing technology companies who are responsible for branding and selling the AI technology don’t actually understand it themselves.
This all causes a big disconnect in the market, which creates confusion and frustration on both ends.
The more you understand AI and what to look for in solutions, the greater chance you have of finding the right technologies that create value for your company.
Challenge the vendors who claim AI to explain, in simple terms, how the technology works, and how it is smarter than what you’re doing now. Ask them how it will save time and money, and increase your likelihood of achieving success?
If the sales rep can’t talk in plain terms about the value of their AI, then ask to have one of the engineers walk you through it.
And, the general rule of thumb is, don’t buy it if you don’t understand it.
After all, AI isn’t magic, it’s math.
Be Curious. Explore AI.
So, back to the original question. Do you need AI?
Yes, you do. It may be simple projects to start, but don’t wait for the marketing world to get smarter around you.
AI enables professionals across all marketing categories to solve problems and achieve goals more efficiently at scale than with traditional marketing technology solutions.
Smarter marketing technology (i.e. AI-powered) automates manual tasks and creates more intelligent marketing strategies, which drives improved performance and ROI.
Take the initiative now to understand, pilot and scale AI in your organization.
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