Unlocking the Power of AI in Digital Marketing

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.

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.

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.

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.

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.

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.

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).

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.

Comparison

Objective and Functionality

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Methods and Models

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Applications

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Data Dependency

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Examples in Digital Marketing