Here’s a very crude description of the traditional approach in advertising. First, let your planners identify your target audience and media landscape. Then hand over to your creatives to come up with an idea that resonates with the people you’re trying to reach and is relevant to the platforms they’re using. Your media agency will then ensure it reaches the right people on the appropriate channels.

Sounds fine on the face of it, but upon interrogation there are some significant flaws. Not least that people are vastly different when it comes to what motivates them.

In the past, brands and their agencies will have focused on large, generic groups that they believe will be the audiences with the most to offer in terms of ROI, but it’s been impossible to speak to literally everyone about their specific needs and motivations. The introduction of AI in advertising has changed this, by testing a huge scale of emotional triggers against a broad audience, then creating new audiences based on what people click on.

For example, an audience could be as granular as ‘people who clicked on images of people in motion’, or ‘people who responded to passive language’. Using this methodology makes creative the means and not the end.

AI in pharma 

It’s easy to see how this can apply to pharma. There may be many contrasting reasons why people from all socioeconomic and cultural walks of life decide to use a particular product. For example, the causes of a headache are hugely varied and the benefits of getting rid of that headache are as many as the types of people that have them.

This means that the application of the majority of pharma products will have different benefits for different people. Historically, brands have had to create a proposition that has to be relevant to all people and all of their needs. AI is able to deliver nuanced messages at scale to a huge audience and really understand what they need from a company.

Speaking to the many 

AI works by testing multiple hypotheses in relation to strategy, visuals, audience, tone of voice and targeting timings and creative content in real time across all target audiences.

The game changer here is that while to do this process manually (with a human-led approach) is extremely laborious and ultimately not very cost effective, machine-learning algorithms are able to analyse vast amounts of data from different sources, quickly and at scale. This means more audience groups, more context, more feedback.

With a short amount of time (and feedback), AI will start to identify the most appealing and impactful themes, along with who were the most engaged audiences. This is an important differentiation between the traditional approach, as feedback is received extremely quickly, allowing AI to adapt on the fly, rather than waiting for a slower human-compiled report.

Instead of using generic visuals and copy, AI can create ads from scratch, using existing assets – whether that’s a current ad campaign, stock images, UGC or a combination of a few different sources. It then continuously optimises creative assets in-flight, discovering the best performing strategic themes, visuals, copy, overlay and formats to improve quality as the campaign progresses.

Humans vs machines 

Having said this, there is still a significant role for humans when it comes to AI-led marketing. The sweet spot is the balance between human feel and machine speed and scale.

The key is to let humans do the things they are good at that machines can’t do, and vice versa. AI driven creative can make limitless numbers of ads in minutes, but it creates the problem of knowing which ads to show the audience.

This is where human analysis and strategic thought comes in, by testing human hypotheses around strategy, visuals, tone of voice and format with the audience. This can be done by creating hundreds of ads which test a theory – for example ‘Having an indirect language style and images of three or more people will improve performance’. Ads can be automatically generated and tracked by performance of each one at an asset level, revealing which emotional triggers drive performance for the brand.

No assumptions 

While AI is oft proven from a performance standpoint, another key benefit is the removal of assumptions. It may uncover conversations amongst a relevant audience group that the business was completely unaware of, giving an additional potential target group of consumers.

It also helps marketers understand the semantics of their advertising – what the strategic goal of the ad is, what the visual and written triggers are and how those things are linked. It can tell you why ads work. While providing insights that will reduce advertising costs in the long-term, by helping spot what works and what doesn’t – from an unbiased position.

It’s not a magic wand 

That’s not to say that AI is the infallible answer to pharma’s marketing needs. There are pitfalls to be aware of. Assuming it’s a magic wand that can miraculously do things that humans used to do is one common mistake. The reality is that AI is amazing at doing a huge number of very simple things that would take humans a long time to do.

This means an AI can deliver the workload of hundreds of people in seconds – but only if that workload was very simple. So, the reality is that AI in a marketing context will create new ways of making creative and new skills.

There are also ethical questions to consider. AI opens up so many possibilities for targeting consumers on an individual and personal level, and to do this, it taps into the data available. You need to make sure that the partner or technology you work with is using people’s information in a legal and ethical way.

In practice

Multinational pharma companies have already added AI into their marketing mix. GSK used an AI-led approach to identity and target new audiences with relevant content for its Biotene brand, leading to a sales turnaround in the US market. Likewise, Reckitt Benckiser used AI to create thousands of Facebook ads for Enfamil to investigate which product benefits were most impactful for mums globally. These insights drove down the cost of sale by 47%.

AI has a reputation of being a technologically advanced concept. But this approach isn’t futuristic, or a gamble – in fact it’s the opposite. Using AI will make marketing more relevant and more effective. It’ll uncover insights that you were unaware of. Most of the time, it will probably surprise you. That’s not a bad thing – as much as we humans enjoy being right, it’s impossible to empathise with every audience you’re looking to reach. Particularly with something as personal, and emotive, as healthcare.

Pharma is one of the most advanced industries in the world; it’s time its product marketing reflected that.

Tom Ollerton is the founder of Automated Creative