Artificial Intelligence (AI) has transformed the digital marketing landscape, making it more efficient, personalised, and data-driven.
This technology is no longer a futuristic concept but a present-day reality that businesses of all sizes can leverage.
In this blog post, we’ll explore strategies for using AI in digital marketing, how to maximise its potential, different types of AI software, and weigh its advantages and disadvantages.
What is AI in digital marketing?
There isn’t a new product or software that hasn’t been launched in 2024 that doesn’t promote some form of artificial intelligence (AI).
AI simulates human intelligence in software designed to think, learn and perform tasks just as we would. AI uses complex algorithms to process large amounts of information and make decisions or predictions based on that information.
But did you know that not all AI is created equal?
There are different forms of AI software, such as generative AI, which we have all come to know and love as the types that create things for us. i.e: ChatGPT, Dall-E etc.
Although AI seems like something new and exciting, the other forms of AI have been around for years. These “traditional AI” tools are used more for analysing and interpreting data. i.e: Google Analytics.
How To Use AI in Digital Marketing
According to Hubspot “64% of marketing professionals said they use AI in some form.” That doesn’t surprise us based on the wide range of uses we have broken down.
These are just some ideas on using AI in digital marketing.
1. Content creation
It is no surprise that this is number 1 on the list, and I bet it is what most marketers are interested in. Using AI for content creation is the shiny new toy that everyone wants to play with.
Creating content can take many different forms.
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At this stage, most content generated through AI is not ready for immediate publishing. It often needs a little tweaking, and a human touch before it can go out. Our advice is to use AI to create a good foundation for you to work off of.
2. Reporting
Digital marketing is vast and spans multiple different channels, from your website, to email marketing and social media. Each area often has multiple sources on its own for various data.
All this often results in an overwhelming amount of customer data that you need to collect, sift through and understand. Overwhelmed much!?
AI to the rescue!
AI software can process a large amount of data in a short amount of time – much quicker than us mere mortals at least. It can then highlight key areas and even create predictions for the future.
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3. Admin
Let’s be real, no one enjoys admin – so why not let the machines do it. It’s the least they can do if they will eventually take over the world anyway.
Use AI for manual tasks such as scheduling meetings, summarising articles and research, or taking notes.
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4. Customer service
Depending on when you were born, will determine if you have a love or hate relationship with Chatbots. If you were born in the 90s like me, then it is most likely the first thing that you go to when looking for customer service.
Websites have been integrating AI into website Chatbots for years now. All to help customers quicker or direct them to the right team that can help them.
As AI becomes more advanced we will see more Chatbots that can answer questions like ChatGPT, rather than following a traditional funnel path.
They operate 24/7, enhancing customer satisfaction and freeing up human resources for more complex tasks.
✅ Advantages of AI in Digital Marketing
- Efficiency: Automates repetitive tasks, saving time and resources.
- Personalisation: Delivers tailored experiences to customers, increasing engagement and conversions.
- Data-Driven Insights: Provides valuable insights from vast amounts of data, enabling informed decision-making.
- 24/7 Availability: AI tools like chatbots offer round-the-clock customer service.
- Scalability: Easily scalable to accommodate growing business needs.
❌ Disadvantages of AI in Digital Marketing
- Cost: Initial investment in AI technology can be high.
- Complexity: Implementing and managing AI tools can be complex and may require specialized skills.
- Dependence on Data Quality: AI performance depends on the quality and quantity of data available.
- Ethical Concerns: Issues like data privacy and algorithmic bias need to be addressed.
- Job Displacement: Automation may lead to job losses in certain roles.
AI for Digital Marketing Strategies
SEO
Search Engine Optimisation (SEO) is a labour-intensive digital marketing strategy, which makes it the perfect candidate to get AI involved to help ease the process.
At the end of the day, you must remember Google’s number 1 rule: Create high-quality content for your users. Google’s algorithms are smart enough to know when content is created for real humans rather than SEO.
Using AI will get you on the right path and even help with some research, but we don’t recommend copying and pasting the content. That is a sure way to negatively impact the organic traffic to your website.
Here are some ideas of how you can use AI in your SEO strategy:
- Content Creation:
AI tools can generate content ideas, optimise keyword usage, and ensure readability and SEO best practices. - SEO Analysis:
AI-powered tools can conduct comprehensive website audits, identify technical SEO issues, and suggest improvements. - Predictive Analytics:
AI algorithms can analyse data trends to predict changes in search engine algorithms and adjust SEO strategies accordingly. - Natural Language Processing (NLP):
Helps in understanding search intent and optimising content for semantic search.
Tools:
- ChatGPT (our favourite)
- SEO Surfer SEO AI-driven tool for optimising content based on real-time data analysis. Paid
- MarketMuse AI tool for content research, creation, and optimization. Paid
read more
Is SEO Dead? An In-Depth Analysis
Pay-Per-Click Advertising (PPC)
Here are some ideas of how you can use AI in your PPC strategy:
- Ad Targeting:
AI analyses user behaviour data to target ads more precisely based on demographics, interests, and online behaviour. - Ad Copy Optimisation:
AI can generate and test ad variations to determine the most effective copy and design elements. - Bid Management:
AI algorithms adjust bids in real time based on performance metrics to achieve the best possible ad placement. - Predictive Analytics:
AI predicts future trends and adjusts bidding strategies to capitalise on opportunities.
Tools:
- PPC Google Ads Uses AI for automated bidding, ad targeting, and ad optimisation. Free
- AdRoll AI-driven platform for cross-channel campaign reporting. Paid
Social Media
Here are some ideas of how you can use AI in your Social Media strategy:
- Content Curation:
AI tools analyse user preferences and behaviours to recommend relevant content and topics. - Chatbots:
AI-powered chatbots handle customer queries, provide personalised recommendations, and facilitate interactions. - Ad Optimisation:
AI optimises social media ads by analysing performance data and adjusting targeting and creative elements. - Sentiment Analysis:
AI algorithms analyse social media sentiment to gauge audience reactions and adjust marketing strategies.
Tools:
- Meta Business Suite – scheduling & reporting
- Social Media Buffer AI tools for scheduling posts, managing accounts, and analyzing engagement. Free (basic), Paid (advanced)
- Hootsuite Manages social media, scheduling posts, and monitoring conversations. Free (basic), Paid (advanced)
Email Marketing
Here are some ideas of how you can use AI in your Email strategy:
- Personalisation:
AI analyses customer data to personalise email content, subject lines, and CTAs based on user preferences and behaviour. - Predictive Analytics:
AI predicts the best times to send emails and the most relevant content to improve engagement. - Automated Campaigns:
AI automates email campaigns from segmentation to content creation, improving efficiency and relevance. - Behavioural Targeting:
AI identifies patterns in customer behaviour to send targeted emails based on actions and interests.
Tools:
- Mailchimp AI for email automation, personalization, and campaign optimization. Free (basic), Paid (advanced)
- HubSpot Provides AI-powered insights for email marketing and customer communication. Free (basic), Paid (advanced)
Remarketing (Retargeting)
Remarketing is such a powerful digital marketing strategy, that unfortunately falls to the bottom of most small businesses list due to the amount of time and effort it can take to do effectively.
With the introduction of AI, these businesses can now take advantage of remarketing to their existing customer base and potentially drive sales even further.
Here are some ideas of how you can use AI in your Remarketing strategy:
- Dynamic Remarketing:
AI dynamically generates personalised ads based on the user’s past interactions with products or services. - Audience Segmentation:
AI analyses user behaviour data to segment audiences more effectively for targeted remarketing campaigns. - Predictive Analysis:
AI predicts which users are more likely to convert and adjusts remarketing strategies accordingly. - Ad Optimisation:
AI optimises ad creative, messaging, and placements to maximise conversion rates and ROI.
Tools:
- Remarketing Google Ads Uses AI for dynamic remarketing and audience targeting. Free
- AdRoll AI-powered platform for retargeting and optimizing ad campaigns. Paid
Generative AI vs Traditional AI
The difference between generative AI and traditional AI lies in their core functionalities, applications, and the methods they use to achieve their goals. Here’s a detailed comparison:
Generative AI
Generative AI refers to systems that create new content, such as text, images, audio, and video, based on the patterns and structures learned from existing data. It can generate data similar to the input data it was trained on.
Key Characteristics:
- Creation:
Focuses on generating new content. - Learning from Data:
Uses training data to learn the underlying distribution and generate new, similar data. - Models Used:
Includes models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models (e.g., GPT-3).
Examples:
- GPT-3 by OpenAI: Generates human-like text for various applications.
- DALL-E by OpenAI: Generates images from textual descriptions.
- DeepArt: Creates artistic images based on the style of famous artists.
Traditional AI
Traditional AI encompasses a broad range of AI systems that focus on performing specific tasks or solving particular problems through predefined rules, patterns, and statistical methods. It includes both rule-based systems and machine-learning models designed for classification, regression, and prediction.
Key Characteristics:
- Task-Specific:
Focuses on performing specific tasks accurately. - Pattern Recognition:
Identifies patterns and relationships in data to make decisions. - Models Used:
Includes decision trees, support vector machines, logistic regression, and simple neural networks.
Examples:
- Spam Filters:
Email systems that classify messages as spam or not spam. - Recommendation Engines:
Netflix’s movie recommendation system. - Predictive Maintenance:
Systems predicting equipment failure in industrial settings.
Comparison
Objective and Functionality
Generative AI:
Aims to create new content or data that resembles the training data. It can produce novel text, images, audio, and more.
Traditional AI:
Aims to perform specific tasks like classification, regression, and prediction based on the analysis of input data.
Methods and Models
Generative AI:
Uses advanced neural networks like GANs, VAEs, and Transformers that can learn and replicate the underlying data distribution.
Traditional AI:
Uses a variety of machine learning models, including decision trees, support vector machines, and simpler neural networks, to find patterns and make decisions.
Applications
Generative AI:
Suitable for creative applications, data augmentation, and any scenario where generating new, similar data is useful.
Traditional AI:
Suitable for problem-solving, pattern recognition, and decision-making tasks where accuracy and efficiency are critical.
Data Dependency
Generative AI:
Highly dependent on large datasets to learn and generate realistic content. The quality of the output improves with more and better-quality data.
Traditional AI:
Can work with smaller datasets depending on the complexity of the task. The focus is on learning patterns and relationships within the data.
Examples in Digital Marketing
Generative AI:
Used for creating personalised marketing content, generating product descriptions, and designing marketing materials.
Traditional AI:
Used for segmenting customers, predicting customer churn, optimising ad spend, and automating customer service.