Welcome to the latest Advertising Initiative newsletter. Welcome from London, England, back from the recent INMA World Congress in New York. 

Whilst in New York recently, I was asked by some INMA members at my Advertising Workshop to explain some more about how AI can assist in media advertising. I had mentioned my Advertising Initiative committee’s AI debate in a recent newsletter, and that prompted members in NYC to ask could I “deeper dive” into the subject, as there was much curiosity around it.

So, how could I refuse?

It’s an area uncharted by many thus far. How can it help us with our advertising clients specifically? So, I thought I would make this newsletter a “AI Special” (part two, if you will?). It’s a topic which deserves its own focus.

AI in advertising: The birth of an ad revolution? 

What are the main “help areas” that can assist with our advertising clients and their campaigns specifically?

AI is becoming increasingly useful in media advertising recently, and it can be used chiefly in three very specific areas to begin with:

• Personalise ads.
• Target audiences more effectively.
• Automate tasks.

Yes, it can assist writing creative briefs, deeper diving, and the researching of clients, but the aforementioned three areas are the main things I would say you should to concentrate resource on firstly.

The three concentration areas (the basics)

1. One of the most common uses of AI in advertising is to personalise ads. This can be done by using machine learning to analyse user data and target ads based on interests, demographics, and other factors. 

2. AI can also be used to target audiences more effectively. This can be done by using machine learning to analyse user data and identify groups of people who are most likely to be interested in a particular product or service. For example, a motor dealer advertiser might use AI to target ads for SUVs to users who live in rural areas. 

3. Finally, AI can be used to automate tasks in advertising. This can free up time for advertisers to focus on other tasks, such as creating creative content and developing marketing strategies. For example, an AI-powered ad platform might automatically create and deliver ads based on user data, as was highlighted by Thomas Schultz-Homburg at KsTA Medien in Germany (from my INMA Advertising Initiative Committee) in my May newsletter. 

Overall, AI is a powerful tool that can be used to improve the effectiveness of client advertising campaigns. By personalising those ads, by targeting audiences better, and by automating laborious/time consuming tasks, AI can help sales teams help their advertising clients reach their target customers and achieve their advertising goals. 

Grasp the above three areas well and you’ll see the benefits of AI in your media business from the start. 

Then, once grasped, we can move into more advanced areas such as:

Ad optimisation: AI can be used to optimise ad placement, format, and messaging based on real-time data on user engagement and performance. This can help advertisers to improve the effectiveness of their campaigns and maximise return on investment. 

Predictive analytics: AI can be used to forecast future trends and behaviours based on historical data. This can help advertisers to make more informed decisions about when and where to advertise, as well as how to allocate their advertising budgets. 

Creative assistance: AI can be used to assist in the creative process by generating ad copy, images, and videos based on input from advertisers. This can help to streamline the creative process and improve the speed and efficiency of ad production. 

However, it’s important to note that the use of AI in advertising also raises ethical and privacy concerns, such as the source verification, the potential for bias in AI algorithms, and the collection and use of personal data. Advertisers must be careful to use AI in a responsible and totally transparent way that respects consumer privacy as well as adhering to ethical/industry standards. 

Overall, the use of AI in advertising has great potential to benefit both advertisers and consumers, but it's important to approach it with care and caution to ensure that it is used wisely.

Here are a few examples of how news media organisations have actually used AI in their advertising today: 

• The New York Times is using AI to improve the effectiveness of their advertising campaigns. They have created a tool called Project Feels, which uses machine learning to analyse the emotional impact of their ads on different audiences. This helps them to create more effective and targeted advertising campaigns. 

• The Washington Post has created a tool called Zeus, which uses AI to optimise their advertising campaigns in real time. Zeus uses machine learning algorithms to analyse user data and behaviour, and adjusts the campaign based on that data to improve performance. 
• The Associated Press is using AI to automate content creation for their news stories. They have created a tool called AP Insights, which uses natural language processing and machine learning to generate insights and analysis from data sets. This tool can be used to create automated news stories, which can then be used for advertising purposes. 
• The Guardian is using AI to personalise their advertising campaigns. They have created a tool called Ophan, which uses machine learning algorithms to analyse user data and behaviour to create personalised ad experiences for individual users.
• CNN is using AI to improve their ad targeting capabilities. They have created a tool called Audience Insight Platform, which uses machine learning algorithms to analyse user data and behaviour, and identify the best-performing ad placements for different audiences. This helps them to optimise their ad spend and improve the effectiveness of their advertising campaigns.

AI SWOT chart

Finally, my good friend and INMA Readers First Initiative Lead Greg Piechota produced an excellent SWOT chart on AI generally for a recent INMA presentation, highlighting the pros and cons of AI, which I share with you below. It’s a great checklist for us to keep in mind (thanks, Greg):

Strengths: 

1. Versatility: Generative AI can be used to create a variety of content types, including plans, research scripts, advertising copy, and others.
2. Speed: It can produce ad content quickly, allowing for faster new customer research, product research, and delivery of new ad campaigns to market.
3. Cost-effective: It can help scale the content creation process, reducing the effort for human marketers, and therefore free up their time and lower production costs.

Weaknesses:

1. Quality control: Generative AI-generated content may not be of the same quality as human-written content, and it requires additional editing or fact-checking.
2. Lack of creativity: AI-generated content may lack the originality and creativity that human-written content possesses. It requires a human touch to add flair and true/relevant creativity.
3. Bias: Generative AI models may perpetuate existing biases in the data used to train them, leading to inappropriate ad content, and requires human supervision of the outputs. 

Opportunities:

1. Personalisation: Generative AI can be used to tailor advertising content to different audiences, increasing relevance and potentially engagement and conversions.
2. Expansion into new markets: AI can be used to create content in multiple languages, possibly opening up new markets and business opportunities.
3. Training: As AI models have been trained on marketing books, academic papers, and articles, they know many established theories and frameworks and can help advertisers apply this knowledge to their daily work.

Threats:

1. Competition: Other news organisations may be able to adopt generative AI as easily as you could, increasing competition in the market and the need to differentiate in the long run.
2. Legal and ethical challenges: Generative AI may potentially raise legal and ethical issues such as: ethical issues related to copyright or biases in the content, fake information, untrustworthy sources, which can be vague at best.
3. Public perception: Negative public perception of AI-generated communications may lead to decreased trust in news organisations or perception of value.

Overall, we must bear in mind the user experience, be it advertiser or end user. AI can assist our efforts, and the pros seem to far outweigh the cons — if we do it well.  I am reminded of the cartoon I found in a corner of the Web (apologies to the creator; there was no source on it to credit), which highlights my user experience point very well:

About this newsletter 

Today’s newsletter is written by Mark Challinor, based in London and lead for the INMA Advertising Initiative. Mark will share research, case studies, and thought leadership on the topic of global news media advertising. Sign up for the newsletter here.

This newsletter is a public face of the Advertising Initiative by INMA, outlined here.

E-mail Mark at Inma.mark@gmail.com with thoughts, suggestions, and questions or follow him on Twitter (@challinor).

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .