Jump on LinkedIn, and you’ll see countless discussions about AI and machine learning in digital marketing.
The hottest digital marketing trend is promising insane results and making copywriters and designers quake in their boots as new algorithms increasingly prove to be more intelligent and capable of nuance than previously thought.
So, what’s it all about?
In this article, we’re getting stuck into the most effective applications of AI and machine learning in digital marketing – how they work, why they work, and what results real businesses have experienced.
Let’s get beyond the buzzwords and find out exactly how businesses and marketers can use this emerging technology to boost revenue, improve their products, and build more meaningful customer experiences.
What Is AI And Machine Learning?
Artificial intelligence (AI) and machine learning are related but distinct fields within technology.
AI is more of an umbrella term that refers to computers or machines performing traditionally ‘human’ tasks, such as learning, problem-solving, and decision-making. Apple’s Siri and Amazon’s Alexa are classic examples of AI used daily.
Machine learning is a category within the AI umbrella. It refers to algorithms that can learn from data to improve their capability over time. They can be trained on huge datasets in supervised or unsupervised environments to the point where they can make accurate predictions based on how they’ve seen data being labelled.
One of the most powerful examples of machine learning that we use every day is Spotify; its algorithm learns about our music tastes and uses our behaviour to create our own personalised playlists.
Benefits Of AI And Machine Learning In Digital Marketing
As technology develops, marketing becomes more competitive, and resource crunches drive more cost-effective decision-making, AI and machine learning have become crucial components of digital marketing.
At first, marketers might feel a sense of foreboding – even panic – at the rise of AI in their industry…
…but then they discover that the benefits of AI and machine learning make them better at their jobs, rather than redundant.
6 Ways To Leverage AI And Machine Learning In Digital Marketing Now
Below, we dive into six trending applications of AI and machine learning in digital marketing.
Each section dives into how the strategy works, why it’s effective, and the real results it provides to businesses.
Don’t have time to read the whole thing?
No worries, we’ve summarised the key insights for you here:
Area of Digital Marketing
How AI & Machine Learning Helps
Collects and analyses feedback from customers
Automates customer service and lead generation
Enhances understanding and segmentation of target audiences
Analyses customer behaviour and sentiment
Trawls through the internet to find the most relevant and profitable topics
Writes, tests, and optimises copy
Social listening and sentiment analysis
Collects, categorises, and responds to customer feedback based on a number of factors
Reveals where your customers are talking about you online
LTV and churn rate forecasting
Helps marketers set and allocate budgets based on the forecasted revenue that will come from different audience segments
Enables campaigns to be evaluated, adjusted, and targeted based on business goals around decreasing churn for a particular audience segment
Maximises average order value, minimises cart abandonment, and increases engagement by recommending products based on a customer’s historical purchases or engagement with the company
Offers a far less lame alternative to using stock images
Boosts creativity for design teams
Provides a low-cost imagery option for startups
But if you’d like more than just the bare bones, read on and enjoy our deep dive into these six major applications and how AI is transforming the marketing world.
Before we discuss traditional chatbots, we need to bring your attention to an insanely disruptive chatbot technology that’s taken the world by storm – ChatGPT.
OpenAI’s ChatGPT is the most sophisticated chatbot we’ve ever seen, and marketers are losing their minds over it.
It’s capable of holding natural conversations with users, providing thorough answers to almost any question, and even coming up with creative responses to the most descriptive prompts.
Check this out:
So, what does this mean for marketers?
It’s still early days, but we can see a major positive and a few major negatives to this technology:
Creative support: ChatGPT can help marketers by generating ideas for content and producing basic drafts that specialists can then perfect with their expertise and flair.
Google killer: Although ChatGT isn’t connected to the internet (yet) and can’t provide answers based on current affairs, its sophisticated answers could lead users to prefer it over Google. This could destroy traffic from search referrals and turn the SEO game on its head.
We discuss its impact on content creation later on in the blog.
While it’s impossible to draw concrete conclusions yet, we strongly advise marketers to follow the development of ChatGPT and test it out for themselves.
Now, let’s discuss how you could use a more ‘traditional’ chatbot on your website or social media channels…
Traditional chatbots work by using natural language processing (NLP) algorithms to analyse someone’s question, understand it, and respond to it.
You can build a bespoke chatbot if you have a specialised design team, or are confident with working with freelance chatbot developers.
Depending on a chatbot’s sophistication, it can respond to customer messages based on:
A set of pre-programmed responses that marketing teams prepare based on their experience and pre-existing data about customers, products, and services.
A machine learning algorithm that enables the chatbot to independently provide valuable and relevant information based on the data it’s collected from other customer interactions.
Whether the matter is too complex and needs to be raised with a human representative of the company.
Chatbots are a brilliant application of AI and machine learning for customer service, sales, and marketing. Essentially, their value to marketers is in the data they collect about your users and visitors.
Map, segment, and learn more about your target audience
Collect and analyse feedback from customers to further improve messaging and brand positioning
Develop and prioritise marketing campaigns based on the most common queries or requests from your audience
Further personalise your sales copy based on customer behaviour and response to messages
Automate marketing tasks like sending promotional messages, social media posts, and personalised emails
Capture high-quality leads to support your sales team
When done well, chatbots are an excellent AI marketing tool helping industries save up to $11bn annually on functions that would be completed manually, take much longer, and be less accurate because there’s more reliance on assumptions or gut instinct.
Telecommunication company Telenor responded to its customers’ decreased interest in phone communication by investing in its own chatbot, Telmi. Using NLP and machine learning, Telmi could handle conversations with customers.
The instantaneous, valuable responses to customers earned Telenor a 20% increase in customer satisfaction and a 15% increase in revenue.
A well-built chatbot enhances your brand identity and customer loyalty – especially when brands are able to infuse their brand voice, humour, and personalised messages to give customers an almost-human interaction.
2. Content Marketing
Content marketing is arguably the most creative aspect of digital marketing – so, how do robots help?
Using algorithms to curate content based on a large pool of information
Analysing customer behaviour and sentiment to help marketers understand their audience and improve messages
Automating content marketing tasks such as social media posting and email newsletters
Testing and optimising website elements to improve SEO and improve traffic
The fundamental value of AI and machine learning is the insights it brings you about your customers – who they are, how they behave, what they want, and how they respond to different messages.
Here are a few tangible examples of popular AI and machine learning tools in content marketing…
What it does
It’s the AI tech behind a bunch of Adobe products, including Creative Cloud, Experience Platform, and Document Cloud.
It can be used to automatically tag and organise images, transcribe audio and video files, and provide customers with personalised product recommendations.
It helps content marketing teams improve their content by analysing web pages to identify customer intent.
Enables marketers to save time on content strategy, understand their audience, choose more relevant and profitable topic opportunities, and automate content brief creation.
An AI writing tool that uses machine learning to write marketing copy and other types of sales content.
Uses sentiment analysis to help marketers craft more effective and tailored marketing messages to help maximise conversions.
There’s a lot of hype about the impact of AI and machine learning on content marketing – particularly, the risk it poses to creatives and their jobs.
So, can you really just get an AI tool to write all your website copy, emails, and blogs?
Technically, yes, but it’s a hot debate on LinkedIn.
The general consensus is that AI writing tools can dramatically cut costs on bloated content creation processes – which is especially helpful for startups with limited resources.
For example, EcomHype used SmartWriter’s Outreach Tool to help personalise its emails without having a copywriter pour over each interaction. As a result, they saw a 40% increase in appointments booked!
AI content marketing tools are also hugely helpful in the brainstorming and drafting process. Getting ‘pen to paper’ can be difficult for copywriters and designers, so just having a rough draft created by an AI tool can kickstart the creative process.
3. Social Listening Tools And Sentiment Analysis
Social listening and sentiment analysis tools work by using AI and machine learning to automatically identify and categorise online discussions based on what’s being said and how it’s being said.
These days, people are upfront about their experiences with a business. And, while sometimes it’s a bit much…
…the feedback and reviews can be golden opportunities for marketers to improve their campaigns, branding, and interactions.
Businesses receive a tremendous amount of messages from their audience every day, from social media posts, to third-party reviews, to phone calls with customer service reps.
You’d need a literal army of marketers to read every message and categorise every message!
Identify trends in customer feedback so they can better understand what customers care about and how their product or service could be improved
Improve customer satisfaction by responding to customer feedback more quickly and effectively
Spot new opportunities to engage with customers by seeing what platforms they use online
A classic use case for social listening tools is with airlines, who have to process and respond to thousands of customers’ feedback every day.
Since Southwest Airlines created a Listening Centre to handle customer feedback, they’ve been able to track and categorise feedback and maintain a response rate of <15 minutes!
With this organised information at their fingertips, marketers can use AI to utilise and respond to feedback in a way that improves the brand reputation, quality of service, and customer loyalty.
4. LTV And Churn Rate Forecasting
Imagine you’re the head of marketing for a SaaS company.
You’ve put together an expensive video marketing campaign – only to find that the audience you’re targeting is most likely to use the freemium version of your product.
Set and allocate marketing budget based on how much revenue each new customer will bring
Evaluate and adjust marketing campaigns based on whether targeted audiences are continuing to pay or ditching their subscription
To get the most rapid and accurate forecasts for LTV and churn rate, marketers have to build and train a machine learning algorithm.
The algorithm can then calculate LTV and churn forecasts from a huge variety of sources, including:
Historical data about a company’s customer base
Engagement with past marketing campaigns or materials
Once the algorithm has been trained, it can make accurate predictions about the LTV and churn rate of new customers based on the characteristics it’s learned from current customers.
With this information, marketers can then make informed decisions about their marketing strategy and stay ahead of changes in customer behaviour.
Identify high-value customers and target them with personalised offers, such as special discounts or incentives to continue to retain them as customers
Develop more personalised marketing strategies by tailoring more persuasive or enticing campaigns towards audience segments with the highest churn rate, so they’re able to get more value from the product or service and are more likely to continue their subscription
Improve targeting and messages by understanding the factors that influence both customer LTV and churn rate and capitalising on the sales copy or type of content that customers engage with the most
Employing your own data scientists is expensive. Still, you can benefit from advanced machine learning algorithms using leveraging tools like Amplitude.
Amplitude streamlines data science for teams, calculating LTV and churn and providing insights into the factors contributing to those metrics – NBC used Amplitude’s product intelligence capability to double customer retention!
5. Product Recommendation Systems
AI and machine learning help marketers provide what their customers want most:
AI and machine learning in digital marketing can be used to show customers their own personalised brochure of available products, recommended based on their purchasing history and engagement with the company.
Natural language processing, which considers the context of customer reviews and feedback, and
Collaborative filtering, which analyses the behaviour of similar customers to make recommendations
For example, if you’re a jewellery company and a customer has purchased earrings from you before, the algorithm will show them matching styles or similar pairs of earrings either at the checkout, on their next visit, or in the next paid ad they see on social media.
Data isn’t only collected from past purchases, but also from adding items to a wishlist, saving them on social media, or adding them to a cart only to abandon them later.
By leveraging these automated tools, marketers can see immediate results in increased order value, reduced cart abandonment, and increased engagement with their content.
Sportswear brand Kappa increased average order value (AOV) by 11% simply by setting up product recommendations on their online store
Children’s clothing brand Andy & Evan increased customer engagement by 50% by displaying product recommendations
By collecting data on how customers have previously interacted with a company and its products, product recommendation algorithms make it easier for customers to find and purchase what they’re already interested in.
So, what tools are available to help marketers take advantage of product recommendations?
Amazon Personalise is a customisable machine learning service that collects data from customer behaviour and preferences on your storefront to generate personalised recommendations. However, it’s only available as an integration with other Amazon services like Amazon Web Services (AWS) or Amazon SageMaker.
Recombee is a specific product recommendation platform that uses a machine learning algorithm to generate product recommendations for your customers. It’s compatible with popular website hosting services like Shopify and WooCommerce, and you can tailor it to the specific needs of your business.
Product recommendation systems are a win-win: customers get a quicker and more valuable shopping experience, and marketers are able to learn more about what customers want and how they engage with products.
6. Image Generation
You might have seen the AI-generated image phenomenon circulating social media.
Type any prompt into a platform like DALL·E 2, and you’ll get a series of realistic images depicting your prompt – no matter how abstract or silly!
For example, here’s what it came up with when I said, ‘Spaceship from Malta leads a marketing meeting’:
AI-generated image platforms work by building an algorithm and training it on massive datasets of images – we’re talking millions of images. Using their expansive knowledge of what things look like, based on their training, the algorithm can then generate new images with slight variations.
Once the new algorithm is trained, it can generate images based on input from the user. For example, the user could upload a photo of themselves, and the algorithm could show them what they’d look like wearing a particular outfit or with a different hairstyle.
In the case of DALL-E 2, it can generate images simply from written prompts – so the possibilities are endless!
It’s all very fun, but how do AI-powered image generators help with digital marketing?
Minimising reliance on stock images: until now, stock images have been arguably one of the only sources of free and high-quality photography for websites, blogs, and other marketing collateral. But bland, overused stock imagery isn’t great for brand awareness – and savvy customers can quickly make judgements about the quality of your company based on whether you invest in your own photography!
Mock-ups for design elements: to get the ball rolling, AI-generated imagery can be used by design teams to quickly and collaboratively bring concepts to life and brainstorm different variations. This is especially handy for lean startup teams who want to minimise costs on sophisticated design tools or design specialists.
Boost originality: for creatives who are fearful of falling into the trap of coming up with similar ideas over and over again, AI-generated imagery can be an effective way to break the mould and come up with something completely new.
But there are a few concerns to bear in mind before jumping on the AI bandwagon for your business’s imagery.
For example, products that are generated by AI are unlikely to be 100% accurate, and so customers could be at risk of making purchasing decisions based on misrepresented products.
The other concern is that, as the algorithms become more sophisticated, companies might not stipulate whether they’re using them, leaving customers unsure of whether to trust online stores.
Keen to learn more about DALL-E 2 and AI-generated imagery? We discussed it at length in our podcast with our very own Website Guru, Benjamin. Give it a listen here.
What Is The Future Of AI In Marketing?
With so much change happening in the last few years, it’s tricky to predict what the future of digital marketing will look like.
But if we had to get out our crystal ball , we imagine that AI in marketing will take care of technical marketing tactics, like PPC, A/B testing, and website optimisation.
Absorbing these tasks means that marketing teams will be less reliant on technical specialists, with more value placed on creative and strategic thinkers with lateral skills.
This new wave of generalist marketers will be more agile, more experimental, and able to use AI-driven insights to connect with customers on a more meaningful level, prioritising real value over cheap messages.
Spotify Wrapped and Discover Weekly are classic examples of how machine learning and human creativity can work together to deliver wonderful user experiences and raise awareness about up-and-coming artists.
AI is so much more than hype. According to McKinsey, AI and machine learning can create $1.4 trillion to $2.6 trillion of value in marketing and sales.
The only question now is: will your business jump on it?
Final Thoughts: AI Is Our Assistant, Not Our Replacement
This article walks through just six of the many applications of AI and machine learning in digital marketing.
While it’s easy to feel intimidated by the rise of AI in marketing, we think this is an exciting thing for marketing, businesses, and consumers.
Ultimately, AI and machine learning are helping marketers be better at what they do.
By taking care of the technical legwork, AI empowers marketers to be better strategists and make decisions that boost revenue and create more valuable and meaningful customer experiences.
At Growth Gurus, we’re excited to keep discovering and testing AI and machine learning tactics. Once we’ve fine-tuned the methods and feel confident that they work, we’ll start applying those strategies to our customers so they can see epic results!
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