Implications of AI on the future of Performance Marketing
Reading time: 6 min
Performance marketing helps luxury brands reach, engage and convert their target audience more effectively. When performance marketing is married with the potential, speed and insights of AI, its importance to luxury brands is only going to increase. We’ll now be able to use data and analytics at every stage of the journey from marketing collateral creation to automated selection of ad types and channels for campaigns.
In this article, we look at four of the most exciting potential implications of AI on performance marketing. Many of the topics we discuss below are already here in an early form but still need a lot of work to reach their full potential. But given the vast sums being invested in AI and the migration of some of the world’s best coders and data analysts to the sector, we don’t think they’re too far around the corner from here.
We look at how:
- Data analysts will become trend architects
- Programmatic advertising results improve through situational intelligence
- Autonomous marketing campaigns starting will start with buyer persona creation and end with ad placements
- Brands can personalise their story for each consumer based on their personalities and interests
Imagine knowing which products to develop and how much of them you need to manufacture in advance. The technology already exists for bots to sift through social media and traditional media to guess with precision what future trends in a particular sector are going to be. These sophisticated systems process vast amounts of data, spotting hidden patterns, enabling them to accurately predict emerging customer trends and preferences.
The impact of this shift is significant, and its implications stretch well beyond tighter inventory, greater efficiency and better supply chain management. Luxury brands could not just react to or predict market trends – they could shape them. Over time, AI will notice subtle shifts in consumer preferences faster and with much greater accuracy, informing not just the marketing teams but the designers and product managers within brands. They can then create and promote products that cater to these newly emerging preferences that consumers can then “discover” and embrace.
Data analysts will become more trend architects. And their insights can be used by product development teams and marketers to de-risk product development, associate their brands with important upcoming styles and drive sales.
Programmatic and situational advertising
Programmatic advertising, at its core, relies on machine learning algorithms to automate the buying, placement and optimization of media inventory which it does in real-time. It learns from patterns in data to determine the ideal time to display an ad, the likely most effective medium and the most engaging format for a particular audience segment. However, AI is set to build on the sophistication already achieved by ML in programmatic advertising as it becomes more situational.
Luxury watchmaker, TAG Heuer, has already made some great strides in this area. It not only identifies customers interested in luxury watches but determines when and where to show the ads. For example, a target consumer may see more of TAG’s ads as an anniversary or birthday approaches. Or they might see them if they’re at a big sporting event where TAG has an association with the sport, like motor racing or golf.
Situational advertising is a move towards fine-tuning the timing and context of the advertisement for maximum engagement. If a customer can be reached when they’re more receptive or connected to a brand’s messages and products, this could drive much greater campaign returns.
Autonomous marketing campaigns
Building upon this situational approach, AI is moving beyond the ability to optimise and personalise ad buying and placement and content creation. We’re seeing the first steps to AI dynamically adjusting complete marketing campaigns for optimal performance with minimal human intervention required.
In these autonomous marketing campaigns, AI would analyse historical and real-time data to find patterns more likely to be successful in campaign generation. It would consider variables like ad type and channel performance, consumer behaviour trends, market dynamics and even analysis of competitor strategies.
With the correct prompts, it could then build marketing collateral that are a perfect fit for the product(s) being offered, the target audience and the brand’s goals. The system would then book space on the platforms it believed would deliver the best engagement and conversion rates. As marketing teams do now, it would analyse the effectiveness of each campaign. The difference here is that it would do so in real time as the AI constantly iterates and fine-tunes campaigns based on these metrics.
Much of this tech already exists on these two platforms:
- Phrasee uses AI to generate and optimise marketing copy for email, social media and the web on an enterprise-level using natural language processing and sentiment analysis to measure and optimise marketing collateral.
- Shutterstock AI offers computer vision and predictive performance solutions for performance marketing. Shutterstock AI recently bought three companies – Pattern89, Datasine and Shotzr – and it’s built a platform to help brands discover, select and personalise digital marketing content. Shutterstock AI also creates and optimises its predictive performance models in real-time.
Different companies are building different parts of what could be autonomous marketing campaign platforms in isolation from each other at the moment. No-one has yet knitted the tech together yet, but it will only be a matter of time. For marketing teams and creatives, their role would evolve to add the human touch to the AI algorithm for a particular client covering everything from product specifications to buyer personas.
But for data analysts, their role would evolve in a more complex direction. As AI handles more of the raw number-crunching and pattern recognition, data analysts can focus on understanding the ‘why’ behind the data. They would interpret nuanced correlations offering more strategic insights to influence the direction of the campaigns. Their role would be to maintain the quality of the data fed to the AI, identifying and rectifying statistical and analytical biases. Their insights and directions would be the human element underpinning how autonomous marketing campaign platforms make their decisions.
AI and ML, the tech that will make autonomous marketing possible, will also enable personalised storytelling. This is for the next stage of the journey where some personal data has been captured on which AI could start to make assumptions.
Large language models, with the right prompts, can be trained to sound like luxury brands – it would use their unique blend of vocabulary, perplexity and burstiness. As the profile of the person was further enriched by interaction and other data to deepen AI’s understanding of that individual, it could then tell unique stories about a brand or a product to that person based upon their needs, wants and desires. AI can do this en masse meaning that 100,000 customers would have 100,000 different spins on the same brand story told to them, each telling a story that person is more likely to identify with.
Imagine how a luxury car brand might use this. There are multiple touchpoints on the customer journey here. An individual could have signed up to a newsletter and AI tracks which articles they respond to so they get an idea of what matters most to them. AI would also track the web pages they visit with first-party cookies, looking at how long they stay on a particular page and which parts of each page they scroll to. This gives the AI further data on which to predict what’s most important to them. Tied into that could be calls made to dealers and online webchats that add further context about each individual consumer.
The more AI learns about this customer, the more the content and style of emails and web chats changes to suit. When they visit the site again, there are personalised content recommendations with insertions of prose on specific areas of interest. The page is written in a style that the customer would use themselves, akin to the mirroring technique which is long established in sales.
Brands will no longer be just linear storytellers – they’re highly adaptive story tellers capable of presenting to end users the brand the client wants them to be.
Building AI into your marketing, creative and placement
At VERB, we have worked with luxury brands for 10+ years now and we’re working on the best ways to marry the automation AI offers with the best of human creativity. To talk to us more about how to include AI and ML in your performance marketing campaigns, click here to get in touch.
We’d love to hear from you!