Debunking AI in Marketing & Advertising

In an increasingly tech-heavy landscape, the AI jargon is adding up–but here’s how you can keep ahead of the conversation

Picture this: you just walked into your weekly marketing and advertising meeting after missing the whole AI-volution of 2023. The conversations start to feel surprisingly alien and everybody is talking about ‘hallucinations’, ‘machine learning’ and ‘natural language processing’. You begin to wonder if you walked in on the wrong meeting or if you just stepped through a time portal leading to 2050? Yet fortunately it’s neither of these: it’s the right year, you’re in the right room and there’s just a bit of catching up on AI–and how this is transforming the marketing and advertising landscape–to be done.

You dive into conversations about the role of AI in marketing and advertising: where every message and piece of creative is tailor-made for you, where efficiency meets personalisation, and where data is the new monarch. And then it finally sinks in: the rise of AI is not just a dream but a vivid reality. 

Let’s start by looking at frequently used AI-related terms needed to stay ahead of the conversations in 2024.


What is Artificial Intelligence?

Before diving headfirst into AI-advertising wonderland, we need to take a moment to decode general Artificial Intelligence. Here are some key terms that may need a quick explanation depending on your level of familiarity: 

Artificial Intelligence (AI): an umbrella term for the field of computer science which is focussed on creating intelligent machines; incorporating new technologies and learning methodologies.

Artificial General Intelligence (AGI): an AI system which matches human-level intelligence and can perform intellectual tasks that humans are able to do.

Big Data: these are wide-ranging and complex datasets–that require specialised tools and techniques–and are often used in AI and machine learning.

Chatbot: an AI-powered software application which stimulates human conversation and is able to support online customers with queries.

Deep Learning: a branch of machine learning which optimises artificial neural networks and is particularly efficient for image and speech recognition.

Generative AI: a form of AI that learns from patterns, structures and existing data to autonomously produce content without direct human input.

Machine Learning (ML): this designates a subgroup of AI where systems are able to learn and become empirically better without explicit programming. 

Neural Networks: these are used in several AI applications–such as deep learning–and are based on computational models inspired by the human brain.

Natural Language Processing (NLP): the field of AI focused on enabling computers to understand, interpret, and generate human language.

Responsible AI & AI Ethics: the ethical considerations and guidelines in the development and use of AI technologies.


How is AI being used in advertising?

Technology has been stridently shaking at the advertising world since its advent–it has mostly been a repetitive question of how ads can be made ‘more effective’, ‘personalised’, and ‘data-driven’. And the latest AI solutions mean that this is finally more achievable than ever before–something that most marketers and advertisers know all too well. In fact, Hubspot reported that marketers haven’t wasted any time adopting automation, with 44% of them already using at least some form of AI, such as DALL-E, to generate visuals. 

But the use of AI isn’t limited to marketing teams. There’s a myriad of advertising professionals riding this disruptive wave upstream–far beyond content generation–and reimagining how AI can future-proof advertising strategies to deliver more relevant and effective content for a better targeted consumer demographic. Creative analytics and optimisation are no longer just an afterthought or added bonus for ad performance; they merit just as much attention, if not more, as the creative generation process and form part of the new marketing and advertising flywheel.  

The value that AI solutions can provide creative marketers and advertisers with is limitless–but some key advice before shifting to any creative solution is to make sure it encompasses the full arsenal that you are going to need to convert data into insights and conversions. Because the future of advertising is going to be fuelled by masses of otherwise unfathomable data. Kirk McDonald, CEO at GroupM, commented in an interview with AdWeek

“You have to have a dependency on AI to turn the data into insights at the speed it needs to turn so you can make decisions and activate in near-real time”

In the context of advertising and post-2023 marketing strategies (and for the sake of a fun analogy), ignoring floods of data and putting ineffective ad content out in the wild for a non-targeted demographic and on an unsuitable platforms makes as much sense as putting steamed vegetables in your cat bowl and then hiding it away in the cupboard. The food looks good enough to you and you know where to find it–but you’re not the one who has to sit down in the cupboard and eat it. Wrong product. Wrong audience. Wrong place.

And AI finally provides us with the tools to avoid this (or alternatively it can show us where improvement is required and suggest preventative actions). But, how? Well, this is where it gets a little more complex. There are lots of ways that AI can intervene and help us achieve better results, including ad targeting, ad personalization, ad optimisation, forecasting, attribution, creative scoring and testing, engagement and customer journey analysis–and that’s just scratching the surface. But knowing when and how to dive deeper into each of these starts with goal setting. Orzen Ortioni, CEO of AI2 quipped:

“To say that AI will start doing what it wants for its own purposes is like saying a calculator will start making its own calculations.”

And this is why human intervention is essential. AI results are only as good as what we ask for and so as marketers we need to make sure we are being consistent and purposeful. This requires some intense noodling on content strategy and desired outcomes in the following areas: 

Real-time bidding and retargeting: RTB is a programmatic advertising method where ad impressions are bought and sold in real-time auctions using AI algorithms to determine bid prices. AI helps identify users who have previously engaged with an ad or website and targets them with relevant ads to encourage conversions.

Ad Targeting: automation can be used in ad targeting to identify and reach the most relevant audiences for campaigns (also considering factors like demographics, interests and online behaviour).

Ad Personalisation: technical software can adapt content to individual users based on their browsing history, preferences etc., improving click-through rates and conversions to drive higher ROAS.

Ad Optimization: advanced AI algorithms are able to analyse and adjust campaigns continually and in real-time, maximising performance, click-through rates, conversion rates and ROAS. It can also select the most effective placement and website formats to boost conversions.

Dynamic ads and creative testing: AI can A/B test ad creatives to determine which versions are most effective in terms of engagement and conversion. Dynamic ads can change in real-time based on user data, such as location, time of day, weather, browsing history, behaviour etc.

Creative generation: automated tools generate ad creatives, including images and copy, to match specific target audiences and campaign goals.

Creative scoring: AI can also evaluate the quality and relevance of content to help create more engaging and compliant advertisements.

Engagement and click prediction: automated models predict the likelihood of a user clicking on a specific ad, aiding in bid optimisation and budget allocation. They can also measure user interactions with ads, such as clicks, views, and conversions, to provide performance insights.



What are the use cases for AI in Marketing?

The adoption of AI in marketing has grown substantially and according to Hubspot’s ‘The State of AI’ report, 90% of marketers who use AI say that it’s effective for content creation; whether it’s writing marketing emails, creating images or churning out ideas for content. The chances are that you’ve already had some form of AI-generated content in most media feeds. And what’s interesting is the positive impact that this is having on outputs such as emailing. For example, using an AI-optimised email frequency, Hotel Chocolat experienced a 40% decrease in unsubscribe rates and a 25% increase in revenue. This is rather impressive for an automated customer churn prediction model. 

So, savvy marketers aren’t only using out-of-the-hat ChatGPT commands to create quick-and-easy social media content and email blurbs: they are taking it to the next level and baking AI into the foundations of their marketing strategy. Because tech enablement in marketing is pivotal for success. Salesforce’s recent ‘Enterprise Technology Trends’ report stated that 83% of IT leaders say AI and machine learning are transforming customer engagement, while 69% confirmed that it is transforming their business. And he same notion is echoed in the creative space: 

69% of creative and marketing leaders agree that AI is enhancing their team’s creativity, and 75% consider it an essential part of their creative toolkit—Canva

What it all comes down to is driving efficiency. Artificial Intelligence is a marketing catalyst for personalisation, data analysis and customer relationship management. So, here’s what it looks like in action: 


1) Personalisation and engagement

The art of making your brand stand out has practically evolved into a science, and personalisation is on the textbook cover. By optimising AI-driven solutions like chatbots and recommendation systems, brands can now take customer interaction to the next level. Think of it as your front-line customer support team. These systems work in tandem to offer instant assistance, drive engagement and analyse customer behaviour; serving up content and products which are tailored to each individual to delight potential customers. 

Beyond text-based interactions, AI is also transforming how we search for information and products. For example, voice search optimisation enables customers to effortlessly connect with product offerings and voice search strategies are how brands can anticipate and respond to consumer questions such as: “Hey Alexa, where are the best burgers in town?” or “Siri, can you find me a white dress for my friend’s wedding?” (the latter being a big no-go). This new and unchartered territory is a unique opportunity for brands to think out of the box and stand out with their responses. 

Social media listening and emotion recognition also take personalisation to another level by tapping into the emotions and sentiments of a brand’s consumer demographics. These AI-driven tools allow you to not only understand what people are saying but also how they feel. By being in sync with an audience’s emotions, you can tailor your marketing efforts to resonate on a deeper level.


2) Data analysis and optimisation

Data is at the heart of modern marketing and customer segmentation, predictive analytics, and marketing attribution are the key components. Customer segmentation harnesses AI’s power to divide your audience into distinct groups based on shared characteristics, making it easier to target your marketing campaigns. Predictive analytics, on the other hand, takes the guesswork out of the equation and—using historical data and AI algorithms—predicts future outcomes to ensure campaigns hit the mark each time. 

Attribution helps you understand which parts of your marketing efforts are bringing the most revenue by highlighting the most effective consumer touchpoints. With AI, you can track user interactions and analyse performance, allowing for more accurate allocation of resources and optimisation. 

Additionally, AI marketing analytics provide insights that not only fuel your strategy but also fine-tune your tactics, helping you understand what’s working and what’s not.


3) Customer relationship and communication:

AI-powered Customer Relationship Management (CRM) tools provide insights into your customer base, enabling you to manage leads and streamline your customer interactions. Marketing automation takes care of the more repetitive tasks, like email campaigns and lead nurturing, so your team can focus on creating more meaningful connections. And A/B testing lets you experiment with different strategies and continuously refine your approach for maximum impact.

Ensuring that your marketing messages reach the right people and the right time, AI-assisted ad targeting optimises ad placements to achieve higher engagement levels. And at the same time SEO, another vital component, helps to optimise your online content for search engines, making your brand more discoverable. 


The intersection between technology, data and creativity

Technology, data and creativity are the new advertising trinity. When each of these elements are strategically aligned and integrated through AI, they create a seamless and personalised customer journey designed to improve ad effectiveness and foster consumer loyalty. And the potential of this master design can also be viewed through the lens of investment. Driven by a promise to revolutionise the way marketing departments operate, AI in advertising is growing expeditiously and by 2028 the market is expected to reach a staggering $107 billion. That’s a solid CAGR of 31.6% from 2021–2028.

As technology continues to progress, we can expect personalisation, customer engagement and data optimisation to remain at the forefront of marketing and advertising strategies. And so embracing the evolving landscape–as well as AI-driven tools–will be the key to launching more engaging ad campaigns and connecting with the right audiences to deliver tangible results. 

SmartAssets is an AI-enabled platform which transforms the way that brands manage their creative content–intelligently tagging assets, predicting campaign performance and optimising creative content. We use computer vision to categorise the visual elements within an asset, and then make both predictive and prescriptive analyses based on the performance and sum of creative tags. Leveraging visual insights and historical data, our software  generates creative adaptations and tailors content for the right consumer demographic and ad platform–making sure brands hit the mark every time.


Written by: Daniel Purnell, Marketing Manager

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