Gartner defines atomic content as “dynamically, and often in real-time, combining different content ‘atoms’ to create a more relevant marketing asset and experience that specifically meets the needs of the recipient based on where they are on the customer journey.”
With AI and Machine-learning fueling media and creative optimization, now is the right time to leverage this thinking. Think of each part of the atomic ad as customizable and reusable. These elements could include the images you use, the content your distributing, the calls-to-action, and the copy of the ad. These elements can be versioned for different audiences across the customer journey, mixing and matching these elements to create more relevant brand experiences which, in turn, should drive higher response and conversion. Once deployed, the various versions start optimizing, delivering rich data based on how you’ve set-up the campaigns and the different pieces you’ve created and how each version of the atomic ad performs.
So, where do you start? Think customer journey. Barriers to address. Audiences segments. Content (to address barriers). Touchpoints. If you want to dip your foot in the water, maybe start with Paid Search. With your campaign structure set-up, you will want to give the system as much data as possible (using a combination of match types, upper/mid/lower funnel keywords, etc.). Then, layer in automation through the use of Smart Bidding and RLSA to auto optimize, including the use of Dynamic and Responsive Ads. Attribution is key. Leverage non-last click attribution models to feed machine learning algorithm with better data. This exact approach can be taken to programmatic media. We’ll address this on a follow-up article.