With current and future benefits, Creatives' dynamic optimization presents itself as a powerful marketing tool - an opportunity to improve campaigns and results that advertisers should definitely take
It's common knowledge by now: Personalization is the most effective way to make your ads stand out from the crowd - especially in today's digital noise. If your ads aren't tailored to the customer's current needs, they'll remain virtually invisible.
That's why dynamic creative optimization has gained traction in the digital advertising industry in recent years - as a powerful tool to improve display campaign performance. With the powerful machine learning available to you today, you can ensure that your ads are continuously adapting to the preferences and browsing habits of individual shoppers.
If you're already using dynamic creatives in your online advertising, it's important to learn the principles of Dynamic Creative Optimization (DCO). This will help you make your creatives even more powerful.
First, let's take a look at the not-always-so-easy-to-understand difference between dynamic creatives and dynamic creative optimization:
"Dynamic creatives" refers to the integration of graphical components into an ad's creative based on buyer-specific data: products viewed, location, time of viewing, etc. The graphical components are defined manually when the campaign is set up; the dynamic portion of the information is added in real time before the creative is delivered to each buyer.
However, "dynamic optimization of creatives" goes the crucial step further. It doesn't just populate a template with personalized data; this technology uses machine learning to determine the most relevant visual components to optimize both the content and the creative - in real time and tailored to the buyer as well as the context.
The goal of such optimization is to improve the performance of a campaign: Click-through rate, conversion rate, sales, etc.
The optimization process is divided into two tasks: First, the engine must select the appropriate graphical components, and second, it must determine the basic design of the creative or components.
There has been a lot of progress recently in the effective selection of graphical components. In a retargeting scenario, for example, the number of products displayed can be optimized for the individual shopper.
This task is extremely complex: therefore, a long-term goal of DCO is to select all creative components in real time, including information that may be relevant to a particular buyer, such as location.
There has been a lot of research and development into optimizing the design of each component; performance has already been significantly improved as a result.
Machine learning can determine, for example, the color palette of the creative, the text of the call-to-action - even the proportions of the components most likely to lead to a conversion.
In the future, even more data will be available to Machine Learning tools; this will enable an even wider range of possible Creative optimizations. For example, we will be able to optimize animations, brand messaging, and more.
Dynamic optimization of creatives offers advertisers many advantages.
First of all: the engine working in the background learns quickly and from each campaign. Which design fits best for a particular demographic? What type of call-to-action yields the best results? Which ad environment offers the strongest performance?
There are a virtually infinite number of such questions; and Creatives' dynamic optimization can help advertisers find the most effective answers.
What's more, DCO enables personalized marketing - as perfectly as is currently possible.
The technology works in a buyer-centric way; therefore, much of the creative is tailored to the specific buyer. Only elements that are relevant for the respective buyer are displayed; and in the best possible form - for example, in size, position and color.
That's why Creatives' dynamic optimization delivers great performance, especially in lower-funnel campaigns such as retargeting.
The optimization process also reduces the time and complexity of the campaign setup without neglecting relevance or brand guidelines. DCO doesn't just provide personalization; it also handles the preparation, analysis and delivery of data at scale. Previously, this work had to be done manually.
This frees up creative staff to do what they do best: develop original content as the basis for marketing campaigns. In turn, these campaigns are based on a continuous stream of relevant, real-time behavioral data collected and processed via machine learning.
Even though DCO is already capable of significantly boosting performance today: this technology is actually still in its infancy. In the future, it will most likely become a central tool of the advertising industry.
Over time, the technology will incorporate more and more aspects in the dynamic optimization of creatives. The end is far from in sight. For more information, check out our whitepaper "Can Machines Be Creative?".
The bottom line is that the performance differences between dynamic and dynamically optimized creatives will continue to grow.
Why Dynamic Creative Optimization MattersFigure 3: Ideas about the parameters for a fully dynamically optimized creative.
Another important aspect of DCO is its focus on the buyer. Creatives are optimized for each individual impression; this keeps marketing communications relevant to buyers on individual publishers' websites; where it is particularly difficult to target buyers due to the widespread use of ad blockers.
Last but not least: Currently, DCO is used primarily in lower-funnel campaigns; however, the technology offers the potential for other areas of use. Whether in other types of campaigns, such as customer acquisition or omnichannel, or in forms of advertising beyond HTML banners (for example, native or video), DCO's potential is far from exhausted.