Generative Artificial Intelligence (Gen AI) is, and Will Continue to Be a Gamechanger for Marketers

Artificial Intelligence (AI) is not new; as a concept, it has been around for decades. Alan Turing, the father of computing, published a paper entitled “Computer Machinery and Intelligence” in 1950. The life and works of Turing gained mass recognition when Benedict Cumberbatch dramatised his story in the 2014 film The Imitation Game. Nearly 50 years on, in the 2020’s, AI technology and diction surrounding it has become commonplace. Technologies continuously advance, and today, AI is interwoven into our lives, although we may not recognise it.

In developed countries such as the UK and the USA, AI is already engrained and influential in daily life. AI can be classified as narrow/weak AI or general/strong AI. 

Narrow or weak AI is designed and trained to complete a specific, usually singular task. Siri, your Apple Voice assistant, is an example of narrow AI. Its task is to interpret and respond to a voice command by searching the web for content. Narrow AI is unable to operate outside of its basic function. Other examples include predictive text and chatbots.

General or strong AI can perform tasks comparable to a human’s ability. This form of AI can recognise patterns, understand language, perform multiple tasks, and learn and adapt when given feedback (much like a human). Think ChatGPT. Strong AI today lacks cognition and emotional intelligence.

Superintelligent AI, which surpasses human intelligence in every field, including knowledge, function, social skills, and emotional intelligence, has yet to arrive. It currently exists only in Hollywood.

Typically, Generative AI is a form of strong AI that creates content. It brings many benefits to its users, including inspiration and creativity, and is hugely time-saving. With prompt sophistication and knowledge of how to use the technology, Gen AI can rapidly complete tasks based on learned automation, freeing up time for humans to add more value in other areas of the marketing process.

Let’s take a scenario. A marketing director instructs their marketing manager to review year-on-year brand performance and make recommendations on how to drive growth in the coming year. The Marketing Manager uses Gen AI to analyse a data set and produce a report explaining the current performance of the company’s brands and products. AI quickly interprets the various data points and builds a graphic report.  The AI concludes brand performance is significantly down compared to last year's period. The data is correct, but the AI does not know ‘the why’, whereas the Marketing Manager knows that the company suffered from stock shortages caused by failures in the distribution network, which resulted in lost sales.

She also knows that this was in the peak sales period. Knowing this, the Marketing Manager asks the AI to make some calculations to reflect what the sales would have looked like had this challenge not occurred. The result is that sales would have been up by 3% year-on-year. In this scenario, you can see how the AI output, albeit correct, missed the context and made a blanket statement that, if presented to stakeholders, may have landed and triggered different decisions versus when presented with the contextual overlay. The marketing manager provided additional information, generating a more realistic view of performance while also being able to dimension the impact of the distribution disruption. The Marketing Manager recommends that the business ensure a distribution contingency and then builds a plan off of the sales base that should have been delivered. AI alone was unable to convey the true picture. Without the marketing manager providing background information and asking additional questions, the AI-only output could have led to a different recommendation for how to drive growth in the coming year.

Working with AI, marketers can improve their efficiencies in many areas. Gen AI can help with content creation and repurposing, personalisation, market research and sentiment analysis, eCommerce recovery, and data-driven insights. Let’s explore a few of these further.

Content Creation and Repurposing

Gen AI can create text, visual, audio, and audiovisual content as well as write its own code. That’s right: AI can be used to write AI. Tools like Jasper AI can even create entire marketing campaigns from a clear brief. The AI can produce emails, adverts, press releases, and more and will do so within the brand's guidelines where provided.

Text content creation is probably the most commonly adopted form of Gen AI marketers use. AI's processing speed and analytical abilities can help marketers process huge datasets quickly, helping inform strategy and investment. Tasks here may include creating customer segments by grouping customers based on similar behaviour patterns. This data mining can extract valuable insights that the marketer can use for targeting and predicting consumer preferences, which can lead to more effective marketing strategy development and deployment. A good example is when you see on eCommerce sites that “people who bought this also liked this!” aimed at driving incremental sales weight.

Text generation is commonly used for research, ideation, content suggestion, and copy creation. Marketers can ask AI to scrape the web for information about a certain topic and return research summaries that can inform thinking. AI can be used to give initial ideas for marketing campaigns, digital and social activity and even draft copy, providing a starting point. The marketer then has more time to craft and optimise the content, ensure alignment with the brand and provide an ethnic and moral overlay. 

Image and video generation is advancing rapidly; errors and failure to interpret prompts accurately are still commonplace. Try it for yourself. If you ask Canva Magic to create an image of a lady sitting at a table drinking coffee, the generation will probably return an image of a young, elegant white female. It's also likely that she will have extra or missing limbs.  With all AI, there is an ethical and moral concern. AI needs to be trained to avoid bias. Deepfakes are also a concern. Only last year, images of Taylor Swift naked were circulating on social media. Transparency is key when using  AI in marketing. Businesses that use AI should ensure they follow ethnic and moral codes and best practices. Dove, the personal care brand owned by Unilever, has taken a specific stance against using AI, creating a whole campaign stating that they will never use AI. This aligns with the brand’s positioning of “Celebrating Real Beauty”.

Branding is about the consistency of messaging. Content creation is time-consuming and often expensive. However, using AI, marketers can seamlessly repurpose existing assets, making the content applicable to different marketing channels. This can drive huge omnichannel marketing campaign efficiencies.

Personalisation

Gen AI can use information and data about a customer to generate targeted content. Tools such as RAD AI will use data insights to craft emotionally resonating material that will enhance customer engagement. AI can significantly reduce cost and minimise waste by being so specific in message tailoring.

Market Research and Sentiment Analysis

Using the pure speed of the technology, Gen AI can quickly assimilate data and key insights that can be turned into actionable output for marketers. A great example is product reviews.

Imagine a well-established business with thousands of reviews on platforms such as Feefo and TrustPilot. By entering the links to the right pages on the review site, AI can quickly provide an overview of the sentiment of the content. The AI can identify positive and negative trends across thousands of reviews within minutes, saving the marketer hours of manually reading, interpreting, and collating.

eCommerce Recovery

How often have you gone to buy something online, and then the postage cost or delivery window hasn’t suited you, and you have abandoned the purchase? Probably a few times. AI can help marketers understand where in the eCommerce journey shoppers are being lost and then automatically reach out to those who have not completed purchases with personalised messages and incentives or through retargeting. In this scenario, AI and computer technology work to try and understand the barrier to purchase and overcome it by re-engaging with the prospective customer in an attempt to convert the sale and recover potential lost revenue. The example above could include offering reduced postage or a complimentary upgrade from 3-day to next-day delivery.

These examples are just a few of the ways in which Generative AI is and will continue to be a valuable tool in marketing. Gen AI can do the hard work, freeing the marketer's human brain to address other aspects of marketing delivery.