Mad Men Reinvented: Dynamic Creative Optimisation Is Reimagining Advertising In The AI Era
Photo by Johny Vino for Unsplash So far the role of artificial intelligence has only been that of an assistant to human creativity. Today, new and improved AI-based tools like those of Adobe, are invigorating artists by providing a platform to visualise every idea that inspires them by allowing them to draw, erase and redraw. Programmatic advertising, for instance, has grown significantly over the years for several reasons — it eliminates the human work involved in media placements, has pricing efficiencies due to real-time bidding and uses data to more precisely target individuals. The inclusion of AI in this domain has made marketing more personalised and real-time. A typical ad campaign can be categorised as follows: Geo-targeting based Feed-based Demographic-based Behavioural-based However, these approaches miss targets at a granular level and this is where personalised dynamic creative technology, which is adept at tailoring and delivering customised content and ads to consumers, comes into play. What is DCO? Dynamic creative optimisation (DCO), is one of the best variants of programmatic advertising that uses statistical approaches like multivariate testing. The optimisation can be based on any trivial user action such as click or install. Optimisation of this objective is usually carried out using some form of discrete or combinatorial optimisation. DCO is based on display ads, which can produce tons of ads that are optimised depending on multiple rules and variables (sourced from various data feeds). These variables and data may include physical components like: Colour Font Day of the week Time of day Geographical location Weather The ad creative will change to one that is the best fit for a specific consumer, and it will also be served at the most appropriate time and place. At the end of the day, the conversion rate will be higher as the served ad will have all the elements as per the want or need of the consumer. Though the rise of DCO can be credited to its knack of being flexible with content and context, the traditional DCO approaches can fall short of delivering on real-time. The definition of real-time has changed with the advent of the internet. People cannot tolerate a video that buffers a few seconds or a Google search result that doesn’t show up within a fraction of a second. The role of AI, in this situation, almost becomes obvious. With the immense power of data handling combined with flexible deployment through cloud services, AI-powered campaigns are almost underway. Role Of AI In Creative Optimisation Today, AI is being used to help choose the elements that go into an ad, as requiring people to instruct the machines on what is and isn’t relevant so they can start to learn how to make decisions. Machine learning algorithms can process huge, constantly changing historical data sets and predict the products a user wants to see next. Ideally, there will be far fewer ads that follow a user around the internet showing a product they don’t want to purchase. With the flourish of recommendation systems, many profitable ventures have surfaced in the past couple of years. Whereas, companies like Netflix and Amazon have grown into giants with the incorporation of algorithmic driven solutions. DCO with AI makes it possible to achieve hyper-relevant ads for true 1:1 offers, content, and ads. AI-powered product recommendation ads help consumers discover new products they most likely to want to buy by leveraging predictive learning models. Users Can Accomplish the Following With AI-powered DCO: Automate the creative muse. Have an integrated system that unifies customer profiles Adjust multiple components of the content to match the target audience. Make Ad delivery system and audience data can work together instead of in isolation. Mobilise Real-time content and obtain faster results. There are companies like Criteo that have taken hyper-relevant ad campaigning to the next level by investing heavily into AI labs. Whereas, Adobe lists its own set of advantages that it looks to bring on board with DCO: Streamlining dynamic ad creation and trafficking tools for designers Real-time optimisation of creative asset selection at product and placement levels with machine learning. Delivering rich ad targeting across device types (display, mobile and tablet) Target users with relevant ads on any device. Utilising either cookie data or server to server integrations to assure the users are targeted with the most relevant creative. Utilising heat mapping over impressions time to see when and where on the ad the users click. There is no doubt that the creative discipline has transitioned from a single creative expression to leveraging data to craft a one-to-one kind of conversation with an audience. Adding in AI with DCO certainly gives the marketing companies the much-needed edge of personalised campaigns, which is almost the norm in this age of information abundance.
So far the role of artificial intelligence has only been that of an assistant to human creativity. Today, new and improved AI-based tools like those of Adobe, are invigorating artists by providing a platform to visualise every idea that inspires them by allowing them to draw, erase and redraw. Programmatic advertising, for instance, has grown […]
DCO with AI makes it possible to achieve hyper-relevant ads for content, and ads. AI-powered product recommendation ads help consumers discover new products
["Advertising", "Marketing"]
["Go", "machine learning", "artificial intelligence", "programming_languages:R", "AI", "Advertising", "ML", "recommendation systems", "RAG", "ViT", "R", "Marketing"]
["AI", "artificial intelligence", "machine learning", "ML", "RAG", "recommendation systems", "R", "Go", "ViT", "programming_languages:R"]
I have a master's degree in Robotics and I write about machine learning advancements.