5 examples of Artificial Intelligence (AI) in marketing strategies and 3 beyond
The top three most significant challenges companies face when considering the implementation of AI are staff skills (56%), the fear of the unknown (42%), and finding a starting point (26%). Where do you begin when planning to move towards AI and a data-based approach in marketing strategy? What are the most exciting or useful AI examples? In this article, I’ll show you both solutions that set the human horizon for the future and those that are already being used (and that work!) in marketing.
What is Artificial Intelligence (AI) in Marketing?
AI marketing makes a bridge between data science and the need for personalization, fast scaling, and being objective, data-driven and customer-oriented. It augments digital marketing teams by performing more tactical tasks that require little human nuance, but much, much effort and time.
Artificial intelligence in the form of algorithms and machine learning, fed with data, in hands of data experts improves work, boosts marketing campaigns’ performance and ROI with less worker engagement.
What is Artificial Intelligence per se?
AI refers to machines, algorithms and programs fed with huge amounts of data (text, images, videos), recognizing patterns and learning to perform human-like tasks. It depends heavily on deep learning, which is connected to natural language processing.
AI has a wide spectrum of use from instrument playing, speech and face recognition, and text analysis to self-driving cars.
Artificial intelligence beyond digital marketing. Most impressive solutions
Before I move on to real cases of AI usage in marketing strategies, let’s explore some projects that have pushed various fields (or even the world) forward. By introducing them I want to show the horizons of today’s technological development – what we’re aiming at and what is possible in the future. Yet for now, this is still pretty exclusive.
Transport innovations: self-driving cars from Google, General Motors, Tesla
Huge plans and announcements about self-driving cars started years ago. But there are some companies that got more serious about it than others – like Google. The giant that is best known for creating the most powerful search engine and advertising platform is one of the leaders in the autonomous car industry. How come?
When evaluating their progress in this industry, we can consider 2 factors: miles the car has driven and disengagements — moments when a human has to take over because the computer couldn’t handle a situation — per mile driven. And although many companies working on autonomous cars don’t share statistics, Google and its sister company Waymo do.
There are other players like General Motors or Tesla. Lately, Volvo hit the market with new technology from Nvidia. Although the fruits of their work – autonomous cars – are still not available on the mainstream market, there’s proof that the future is AI-driven.
Medical innovations: Artificial Intelligence has helped to predict the spread of COVID-19 and create the vaccine
Nine days before the World Health Organization released its statement alerting people to the emergence of a novel coronavirus, the BlueDot – artificial intelligence platform, reported “unusual pneumonia” cases happening around a market in Wuhan, China.
The foundation (and fuel) of BlueDot is big data. The platform uses natural language processing and machine learning to analyze data from hundreds of thousands of sources, such as statements from official public health organizations, digital media, global airline ticketing data, livestock health reports and population demographics. It’s able to process tons of information every 15 minutes, 24 hours a day.
Another example is the use of AI in COVID-19 drug and vaccine development.
“In the last decade, machine learning-based models, trained on specific biomolecules, have offered inexpensive and rapid implementation methods for the discovery of effective viral therapies. Given a target biomolecule, these models are capable of predicting inhibitor candidates in a structural-based manner. If enough data are presented to a model, it can aid the search for a drug or vaccine candidate by identifying patterns within the data.”
Copywriting innovations: Artificial Intelligence created an interview between Tim Ferris with Mark Aurelius
What if machines could write simple texts? What if they bring to life ancient ideas or figures? The question was answered recently.
GPT-3 – the AI model by the Open AI team was fed with almost all of the data on the internet. It famously has the largest neural network ever created, with 175 billion parameters. It has access to huge amounts of public and historical data, so it knows surprisingly well how to emulate people. And how to chat, for instance…
That’s how this AI could write an interview between the contemporary American entrepreneur, lifestyle guru and investor, Tim Ferris and Roman emperor, writer and philosopher, Mark Aurelius about Stoicism. The whole piece was created by GPT-3.
We’ve heard the story of eBay adopting AI to scale their marketing, with NLG algorithms writing personalized messages to customers.
But a whole article written by artificial intelligence seems even more surprising. Check out the interview on your own.
Algorithms and AI in marketing strategy – based on case studies
There is so much more to come when it comes to adopting AI in everyday life and business. The world’s aim is autonomous intelligence – as envisioned by market giants. Although we already know that this is within reach, it will take many years before access to solutions at this level will be universal. Autonomous cars are not yet driving on city streets. Robots do not make our advertisements yet; nor do they write advanced books or achieve dizzying sales results.
In fact, we are at a moment when we are adapting tools to automate more and more difficult tasks. AI in marketing works best in analytics and processing large amounts of data. Algorithms can support us in automating and scaling simple, mechanical tasks, which a human simply cannot do in a short time. That’s why the best solution is to look soberly at the possibilities of algorithms and adapt them step by step. Not as a revolution in your digital marketing department but as thoughtful and accessible improvement.
Let’s explore some real-life cases of AI and algorithms in marketing strategy based on our projects and market practices.
1. Algorithms will optimize the operation of the campaign
“I’m setting up marketing campaigns on Facebook. I want to spend $10,000 on it. What reach and traffic should I expect? How will the system adjust adverts to the target audience?”
- uses machine learning to estimate action rate and the ad quality score even before we implement the campaign
- improves ad delivery
- support campaign performance analysis
How does Facebook estimate and predict ad delivery?
To estimate the results of ads, AI predicts an individual’s likelihood of taking the advertiser’s desired action. Facebook models consider that person’s behavior on and off Facebook, as well as other factors, such as the content of the ad, the time of day, and interactions between people and ads.
Ready-to-use solutions from the market giants (Facebook, Google) are the most basic and widely used examples of algorithms improving human work.
At Whites Agency we have collected more custom and comprehensive examples from work with our clients. You’ll find them below.
2. Algorithms will determine where the competition has an advantage
Marketer: “I want to position myself better in search results on foreign markets.”
- automatically selects competition
- uses machine-learning to analyze ranking factors (work, which would take a specialist 20 days, takes 17 minutes)
- auto selects the most important ranking parameters for given phrases
- compares customer data against other, competing websites
Data-driven Marketer: Now I know that in 238 articles I should create longer titles with the key phrase in the third position. My competition in the British and Dutch markets does this. My competition adds 3x more photos and videos to articles. After introducing these changes, the organic traffic to my website grew by 150%, and I did not write a single additional article.
3. Algorithms will optimize advertising costs
Marketer: “My digital marketing budget is 1,000,000 euros per year. Should I spend it on paid activities on FB, on Google, on advertising networks or on content marketing and SEO?”
- analyzes the content of the campaign and chooses the most effective ones
- analyzes the structure of marketing content and recommends changes for the assumed goals
- adjusts the budget to the conversion data or expected reach
Data-driven Marketer: The results from all my content distribution channels (SEO, social media, paid campaigns, PR, video) are collected on one platform and analyzed for profitability. The conclusions from these analyses are then used to optimize specific activities on each of the channels. Thanks to this, I operate in 10 markets, but my department consists of 2 people.
4. Algorithms will indicate the content and topics which our communication is missing
Marketer: “I spend a lot on SEO content, descriptions and content marketing. I don’t see the expected results. Reach and conversion do not increase, and expenses do.”
- analyzes whether the content is considered expert by Google, in this case, medical
- analyzes the competition’s expert content
- recommends combining existing content
- recommends changes to titles, descriptions, media use (video, photos)
- recommends areas where we find a “content gap” vs the competition.
Data-driven Marketer: I should remove 3592 subpages from my directory. I should merge 1348 categories of my e-commerce site to increase their authority according to Google rating. I should write 238 articles on 54 phrases and they should be packed with at least 1 video and 3 images.
5. Algorithms will automate the campaign management process
Marketer: “I run a campaign in 14 markets at the same time. I have a very extensive product feed (hundreds of thousands of products) and I still want to conduct efficient and precise communication with my clients.”
- plugs into the existing data environment and product catalogs
- works with Facebook and Google
- cooperates with the Customer Data Platform and Marketing Automation
Data-driven Marketer: I can run an SEM and Facebook Ads campaign for 380,000 varieties of products from my e-commerce catalog at the same time. The algorithm helps me create visuals in 10 languages at the same time and only runs campaigns that achieve ROAS above 4.
AI in Marketing: Summary
If you want to move your company towards AI marketing along with the market leaders, you don’t need to chase the most groundbreaking innovations. Start with proven paths: automation, data aggregation, and implementing automation in your daily work. The next step is to introduce intelligent algorithms. Only then will your environment, data and department be ready for a real AI revolution.
Start with what you have. Ask yourself if any processes cannot be automated or accelerated. Maybe when looking for data or analyzing your own products, you want to do a deep market analysis that your people can’t do on their own? If you come up against a wall with a project or traffic growth has stopped – perhaps it’s worth putting your business in the hands of algorithms. The technology is there, now it’s time for you to use it.
Learn more about the use of artificial intelligence and machine learning in digital marketing from our related blog posts:
- How do automation and machine learning support Online Marketing?
- The attention mechanism and deep learning – a gem among state of the art NLP
- Machine Learning in SEO – the key to great website content in 2020
- How to use machine learning to automate near-duplicate content detection?