Use of AI in marketing: present and future
Artificial intelligence is top of mind for many professionals in the marketing world. AI helps to optimize the budget, specific KPIs, retargeting platforms and even more. And everyday, more marketers are choosing AI-powered tools instead of traditional, commonly-used ones. But what really is the presence and the future of AI in marketing?
Table of Contents
You will work with the AI. The use of artificial intelligence is becoming increasingly commonplace in the marketing field. There are also a lot of buzzwords flying around in regards to AI: machine learning, Google AI, Skynet, etc. None of them explains the most critical aspects of AI in marketing and why you should get used to working with your cyber colleagues.
Looking at AI in the present, the picture is overall positive:
- The global market value of AI — $93.53 billion and is expected to grow at CAGR 40% until 2028;
- Marketing agencies & in-housing companies are raising their investments into AI;
- The outlook on AI is overall a positive one, expecting to create more jobs.
What are we talking about?
Talking AI is complicated. There is no one singular way to define an AI that we are using today. Rather, it’s a list of types of artificial intelligence that are each good at some tasks. And not all of them are relevant to marketers.
Currently, we have these MOST COMMONLY used AIs — some of them you already heard about:
- Machine learning (ML) — the most popular, widely used type of AI.
- ML relies on interconnected algorithms that are constantly tested and improved, creating increasingly better algorithms;
- Google AI is one such algorithm production line and warehouse;
- ML is the most popular AI in marketing, used for product suggestions, keyword research, content planning, etc;
- Click here to better understand how machines learn.
- Natural language processing (NLP) — a type of AI that processes and interprets human language.
- NLP tackles the biggest issue AI has: understanding of context. By processing human language, NLP AI can understand the true intention of words.
- NLPs are used for chatbots, virtual assistants, email sorting and other customer service-oriented business processes.
- NeuraFlash is an example of NLP partnered up with SalesForce to provide AI-powered customer service.
- Find more information about NLPs here.
- Expert systems (ES) — an AI that is trained to store data in a single field and extract information based on inference rules.
- ES is designed to rummage through data it stores and give the most accurate answer to a question in the field. The more data it has, the more of an expert it becomes.
- The most popular ES right now is IBM’s Watson — an AI designed to help doctors arrive to a diagnosis.
- Although slightly drier than others, here’s a good introduction to expert systems.
A BIG disclaimer is incoming: there are more types of AI in varying stages of development. These are only examples and the 3 most popular and relevant to marketing. If you want to go down the rabbit hole and learn more about AI, here’s a good place to start.
Machine learning in marketing. That’s what really interests us, and brings the highest benefits. We have covered what it’s like now in the present. ML needs a lot of data to test its algorithms against. And while we are already seeing a lot of positive impact on data analytics, digital advertising planning, and other instances — there are future hurdles ML AI has to overcome.
The 3 big hurdles
1. AI use in creative marketing fields.
Creativity takes a human. Even though this article is quite technical and uses a lot of sources, it still took creativity to construct. Your social media post took inspiration from a picture you took, a video you filmed, or a text you read. It still takes a human that instinctively understands the context in order to produce creative work.
That doesn’t mean that AI can’t participate. In the world of journalism, news giants like BBC, Forbes, The Washington Post, MSN, and others are using some form of AI to help them get first drafts of stories a human later fixes. An AI here saves time by providing grounds instead of a blank page.
Still, it’s a long way to go until true creativity. While ML can extrapolate from existing information, it would take many more algorithmic connections to truly produce creative work. We’re not there yet. The question also remains: do we need to go that far?
2. Increased AI use in data processing and how 3rd-party cookie removal will affect that.
Right now, the most popular uses of AI in marketing are all technical and largely numerical. Based on studies, there are 5 categories where use of ML and NLP AIs is prevalent (and somewhat successful). Some examples I’m sure you’re familiar with are:
- [Planning] Keywords and topics for content optimization (this article is NOT written on relevant keywords);
- [Production] Content created should be data-driven (this article is NOT written because data said so);
- [Personalization] Highly targeted content promoted to select users (this article should be targeted);
- [Promotion] Audience adjustment based on behavioral data;
- [Performance] Measure ROI for each channel, campaign or time interval.
Notice that 4 out of 5 categories rely heavily on user data. That’s why we all have cookies helping algorithms watch our behavior online: so ML can learn about us and help adjust business strategies accordingly.
Another indication that AI is focused on quantitative marketing tasks and the trend will only increase.
What happens when cookies will no longer apply? It won’t be quite the apocalypse that we tend to imagine, but it will get harder to track users. GDPR already curbed individual user tracking significantly and this trend is unlikely to stop. Google’s set to introduce cohort tracking as opposed to personal, but that story will unravel in 2022 at the earliest.
All we know now is that AI needs data to learn. It will still be learning for sure, but it might do so in different ways.
3. Move towards hyper-automation.
Hyperautomation is a term that describes constructive and planned automation of as many business processes as possible. The use of AI is only one of many possible tools to attain the status of a hyper-automated company.
The trend of automating as many business processes as possible is very real. Now and in the near future.
Why is this a hurdle? The biggest question is not the fear of employees losing their job. Earlier we saw that AI is in fact a job-creating force. The issue is training and readiness for working hand-in-hand with AIs.
70% of participants in the study done by Drift and Marketing Artificial Intelligence Institute say they don’t have the necessary knowledge or training to adopt AI. Not to mention fully automate their business processes.
Marketing employers will have to contend with this fact, train their employees AND themselves if they want to keep up with the competition.
Human, Robot + Marketing
AIs are here to stay. Our cyber partners are helping us achieve productivity and to keep up with the pace of modern digital marketing. Our greatest challenge right now is to prepare for them adequately. Keep up with the creativity, but do it in a smart, organized and productive fashion — with AI helping you.