Revenue Optimization
Author: Anete Jodzevica 16 minute read
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What is Dynamic Creative Optimization (DCO)?

Dynamic creative optimization (DCO) ensures that each user receives the most relevant and engaging ad, enhancing the overall effectiveness of digital ad campaigns. The main difference between a DCO tool and other types of adtech is its use of data and the creation of dynamic ads in real-time.

Want to learn more? In this article, we’ll explain what DCO is, how it works, and how it enhances programmatic advertising!

What is Dynamic Creative Optimization (DCO)?

Dynamic creative optimization is a process that automatically generates personalized ad creatives based on real-time data. The software behind this is known as a DCO tool or platform–a component of the whole adtech ecosystem that creates, serves, and measures relevant ads for users. 

Algorithms help the DCO tool to customize elements such as images, text, and CTA buttons to match individual users’ preferences, behaviors, and demographics. 

The main goal is to deliver a more relevant and effective ad experience and improve engagement and conversion rates.

The evolution of DCO in digital marketing

DCO has evolved significantly since its inception. Initially, digital advertising relied heavily on static banners and predetermined creatives, limiting personalization. As data collection and processing capabilities improved, the need for more personalized and effective ads grew too. 

DCO emerged as a solution, using real-time data to tailor ad creatives for individual users. Machine learning and AI further enhanced DCO’s capabilities, allowing for more accurate customization. 

Today, DCO is a key component of programmatic advertising, enabling marketers to deliver highly targeted and engaging ads at scale.

How does DCO differ from traditional advertising?

Here are 4 key differences between dynamic creative optimization and traditional advertising: 

  1. Traditional advertising uses static creatives that are the same for all viewers, while DCO dynamically personalizes ads based on individual user data.
  1. DCO adjusts ad content in real-time based on user behavior, context, and other data points. Whereas traditional ads remain unchanged.
  1. DCO relies heavily on data analytics and algorithms to inform creative decisions, whereas traditional advertising often relies on pre-campaign research and creative intuition.
  1. DCO can efficiently create thousands of ad variations, targeting different audience segments, whereas traditional methods require more manual effort to create and manage multiple ads.

Benefits of DCO in Modern Advertising Strategies

DCO enhances user engagement and experience through personalized, contextually relevant ads. It offers scalability and efficiency in ad production by automating the creation of numerous ad variations, saving time and resources on manual tasks.

Increased ad relevance and personalization

As mentioned, DCO analyzes factors like user behavior, demographics, and contextual information to create highly relevant and personalized ads. Thus, each user receives content that’s suited to their interests and needs.

Improved campaign performance and ROI

Personalized ads generate higher engagement rates, leading to increased CTRs and conversions. 

This heightened performance translates into a better ROI for advertisers, as resources are utilized more efficiently to achieve the desired outcomes.

Enhanced user engagement and experience

With DCO, ads are more likely to capture users’ attention and interest, leading to a more interactive and engaging experience. This fosters positive brand perceptions and encourages users to take further actions (e.g., a purchase, signing up, etc.)

Scalability and efficiency in ad production

DCO automates creating and delivering personalized ad variations, generating numerous variations without manual intervention. This automation reduces the time and effort required for ad production, allowing marketers to scale their campaigns quickly and effectively.

How Does DCO Work?

Dynamic creative optimization platforms use data from various sources. A centralized DMP manages the gathered data. Then, the data is analyzed to segment the audience into distinct groups based on shared characteristics and behaviors. 

Machine learning algorithms, the backbone of DCO, also play an important role in determining the best combination of real-time ad elements for each user. Based on the user’s profile and current context, the platform selects the most relevant elements from the available templates and data points to create a personalized ad.

With the help of a CMP, DCO platforms develop modular ad components such as images, headlines, and CTAs that can be mixed and matched. 

Note: DCO platforms use:

  • Performance data to refine ad creatives.
  • Decision-making algorithms to learn from historical data and improve predictive capabilities.

These algorithms accurately decide which ad variation to serve based on the user’s profile, behavior, and current context. Once the optimal ad variation is determined, DCO delivers the personalized ad to the user.

Tracking DCO performance

Performance tracking is an integral part of DCO. It involves continuous monitoring of ad performance and helps to understand which ad elements and combinations are most effective. 

Additionally, DCO employs A/B and multivariate testing to evaluate different combinations of ad elements. Performance metrics and user data are analyzed to refine and optimize ad compositions, ensuring maximum effectiveness.

Note: Advertisers can request additional analysis and insights from the DCO platform to understand why certain creatives perform better than others. 

Additionally, your DCO provider should enable you to connect data sources, such as Google Analytics and Facebook’s performance insights, to track the buyer journey, manage the ad budget, and get recommendations to improve your campaign’s performance.

DCO within programmatic advertising 

Here are 8 steps that simplify how the DCO tool works within programmatic advertising:

  1. The DCO tool integrates with existing adtech platforms, including demand-side platforms (DSP) and ad exchanges. It utilizes data feeds and machine-learning algorithms to gather and analyze information about users.
  1. When a user visits a website, an SSP sends a bid request to an ad exchange.
  1. The ad exchange forwards the bid request to all integrated DSPs.
  1. Each DSP evaluates the user information against the targeting criteria of their campaigns and returns a bid response.
  1. The ad exchange selects the highest bid and determines the winning bidder.

This is where the DCO tool comes to help:

  1. Before displaying the ad, the DSP sends an ad call to the DCO tool.
  1. The DCO tool creates a hyper-relevant ad tailored to the user’s data and context.
  1. The personalized ad is delivered and displayed to the user on the publisher’s website. This entire process (from the user’s visit to the ad display) occurs in less than 100 milliseconds.
How DCO Works Within Programmatic Advertising

Key Components of DCO

There are 3 key components of dynamic creative optimization: 

  • Data collection. This means gathering user behavior, demographics, location, and browsing history data to personalize ads and tailor content to user preferences.
  • Creative assets. Consists of elements like images, videos, headlines, copy, and CTAs. These creatives are designed to be flexible and interchangeable, which allows to create personalized ad combinations for different users.
  • Algorithms and machine learning. These functionalities analyze the collected data to determine each user’s optimal combination of creative assets. They continuously learn from interactions and performance to optimize ads in real-time.

Dynamic Ad Creation

Dynamic ad creation begins with designing flexible templates with creative elements (e.g., images, headlines, and CTAs.) These templates are designed to easily insert and arrange different components.

Note: Despite the variability in ad elements, it’s crucial to maintain brand consistency across all ad variations. This means adhering to brand guidelines, such as color schemes, fonts, and logos, ensuring that each ad reflects the brand’s identity and messaging.

Personalization and customization

Personalized ads might include different product recommendations based on past browsing history, localized offers based on geographical data, or time-sensitive messages aligned with the user’s browsing patterns. 

  • For example, a travel ad might show destinations based on previous searches, while a retail ad might highlight items left in an abandoned cart.

Real-time ad generation and delivery

DCO platforms use real-time data to adjust ad delivery, ensuring that each user sees the most relevant ad based on their current context and behavior. This maximizes engagement and conversion potential.

Ensuring that ads are delivered to the right audience at the right time is crucial! 

Once generated, ads are delivered through integrated ad networks and exchanges. This integration ensures that ads reach a wide audience across various platforms and devices.

Performance Tracking and Analytics

Real-time Reporting

Real-time reporting involves using various tools and metrics to monitor campaign performance continuously, for example: 

  • Dashboards that give marketers a comprehensive view of their campaigns, enabling real-time tracking of key metrics. 
  • Analytics tools integrated within DCO platforms collect and analyze data points such as CTRs, conversions, and user engagement.
  • Data visualization techniques, including graphs, charts, and heatmaps, that provide a clear visual representation of data trends and performance insights.

Iterative Optimization

Iterative optimization is the process of continuously refining and improving marketing strategies through data-driven experimentation and adaptation. 

This can include: 

  • Performance analysis–collecting and analyzing data to understand which ad elements and strategies are performing well.
  • Insight application–applying insights gained from performance data to refine ad components, targeting, and overall campaign strategy.
  • A/B testing–comparing two versions of an ad (e.g., different headlines or images) to determine which performs better.
  • Multivariate testing–involves testing multiple elements (e.g., images, headlines, and CTAs) simultaneously to see which combination yields the best results. This approach is particularly useful for optimizing complex ad variations.
  • Real-time adjustments–using real-time data to make immediate changes to ad content and delivery strategies, ensuring that ads remain relevant and effective.
  • Feedback loops–continuously feed performance data into the DCO system to refine algorithms and improve ad personalization.
Key performance indicators (KPIs) specific to DCO
Click-through rate (CTR)Measures the number of clicks an ad receives relative to its impressions, indicating its effectiveness in capturing attention.
Conversion rateTracks the percentage of users who take a desired action after interacting with an ad.
Engagement rateMonitors user interactions with the ad, such as likes, shares, and comments.
Return on investment (ROI)Assesses the financial return generated from the DCO campaign relative to its cost.
Ad relevance scoreEvaluate how well the ad content matches the interests and behaviors of the target audience.

What are Creative Management Platforms?

CMPs provide design tools and refinement insights to help marketers create, test, and improve ad creatives. Their engines produce and control the design versions of ads required for a DCO campaign.

Essentially, CMPs handle the creative design aspect, ensuring that the ad content is compelling and effective. 

The difference between a DCO and a CMP

DCOs and CMPs both play important roles in digital advertising but serve different purposes. CMPs are centered on creative production, while DCOs are dedicated to optimizing and delivering these creatives to achieve the best performance.

In short, a CMP is responsible for the creative part, but a DCO is for the ad testing and serving part.

FunctionalityAutomates the creation of personalized ad creatives in real-time using data insights and optimizes the ad content for each user interaction.Provides tools for designing, managing, and deploying ad creatives across various channels. CMP is more about creating and managing ads rather than real-time optimization.
AutomationHighly automated, using algorithms to adjust ad elements dynamically based on real-time data.Offers workflow and creative production automation but doesn’t optimize creatives in real-time for individual users.
Use caseBest suited for programmatic advertising campaigns that require high levels of personalization and dynamic content adjustment.Ideal for teams needing a centralized platform to manage the end-to-end creative process across multiple campaigns and channels, from design to deployment.

DCO Best Practices

1. Analyze and segment your audience

Understand and cater to different audience needs and preferences by personalizing creative content. Even basic demographic information like location or language can significantly enhance engagement.

Then, identify specific customer segments and tailor your messaging to their unique needs. 

For example, personalize content for different customer relationships, e.g., cross-selling to loyal customers or upselling to recent buyers.

2. Contextualize personalization

Enhance personalization by incorporating context such as time, location, and weather. 

This approach, like showing breakfast foods in the morning and dinner foods at night, can significantly increase engagement.

3. Experiment, measure, and learn

Continuously test different creative elements to understand what resonates with your audience. Use these insights to refine your strategy, ensuring you adapt to changing preferences and avoid creative fatigue. 

How to build your own DCO tool?

Building your own DCO tool involves 3 key phases: 

1. Scoping and planning

  • Collect both technical and business requirements.
  • Choose other adtech platforms to integrate with, such as ad servers and DSPs.
  • Determine the main integration methods (e.g., APIs) and review their documentation.
  • Identify the features you want the DCO tool to have.
  • Select the project’s technology stack and infrastructure architecture.
  • Create a plan for the MVP development phase.

2. MVP development and launch

MVP stands for minimum viable product. This development approach involves building and launching a product with the minimum set of features necessary to function to gather feedback from initial users. 

  • Develop the MVP of your DCO tool, which includes frontend, backend, and infrastructure development, UX/UI design, and QA/testing.
  • Launch the MVP to initial users or stakeholders.
  • Collect feedback for the post-MVP development phase.

3. Ongoing maintenance and support or project handover

  • Use an agile development process (2-week sprints) to continuously develop the platform, aiming to provide new or improved features at the end of each sprint.
  • Offer ongoing support and maintenance for the software and infrastructure, subject to a separate SLA.
  • Alternatively, hand over the DCO tool to your internal team and give them access to the project’s repositories, codebase, tools, and systems.

The Future of Dynamic Creative Optimization

As AI and machine learning continue to advance, the future of DCO looks increasingly promising across all marketing sectors. These technologies will enhance DCO platforms’ ability to understand user preferences and predict behaviors.

  • Enhanced personalization. AI and machine learning will allow for deeper insights into user data, facilitating the creation of super-personalized ads that resonate more with individual users.
  • Adaptation to privacy regulations. With the decline of third-party cookies and the implementation of privacy policies like App Tracking Transparency (ATT), DCO will shift towards using first-party data. This shift will require marketers to develop robust data collection strategies prioritizing user consent and privacy.
  • Overcoming signal loss. As traditional tracking methods become less reliable, DCO platforms must innovate new ways to target audiences effectively. This may include leveraging contextual targeting and aggregated data insights to maintain ad relevance.
  • Integration with ad tech ecosystems. Successful DCO platforms must integrate seamlessly with both the buy and sell sides of the advertising ecosystem. This integration will help create closed-circuit measurement loops that provide accurate performance metrics.
  • Privacy-conscious strategies. Transparency in data practices will be crucial. DCO platforms must prioritize user privacy while delivering personalized ad experiences to maintain trust and compliance with regulations.
  • Advantage of walled gardens. Platforms like Google, Meta, and Amazon, which possess vast amounts of first-party data, will be well-positioned to continue offering effective DCO solutions. These platforms can provide consistent and reliable targeting and measurement capabilities.
  • Human and machine collaboration. Marketers will need to blend human decision-making with machine-led optimization. This collaboration will ensure that strategic insights and creative expertise complement DCO’s automated processes, leading to more effective campaigns.
  • Broader application. Beyond app marketing, DCO will continue to be valuable in e-commerce, travel, financial services, and other industries where personalized advertising can significantly impact customer engagement and conversion rates.


With the help of machine learning and data, DCO dynamically tailors ads to each user in real-time. We know that personalization is essential in advertising. DCO is especially important for marketers who want to remain competitive as the world becomes cookieless. DCO leverages contextual signals and weather-based targeting to deliver tailored ads without feeling too invasive. 

Typically, platforms that employ DCO offer additional functionalities for adtech. Thus, embracing DCO will help marketers to connect more meaningfully with their audiences and achieve greater advertising success.


What is Dynamic Creative Optimization (DCO)?

DCO is a digital advertising technology that automatically creates and delivers personalized ads by using data to tailor creative elements in real-time.

How does DCO improve advertising performance?

DCO improves advertising performance by delivering highly relevant and personalized ads to target audiences, resulting in higher engagement, conversion rates, and return on investment.

What are the main types of DCO?

The main types of DCO include rule-based DCO, which uses predefined rules to customize ads, and machine learning-based DCO, which uses algorithms to optimize ad creatives based on performance data.

How do you implement DCO in a campaign?

To implement DCO in a campaign, integrate DCO technology with your ad platform, define your creative elements and rules or algorithms, and continuously monitor and optimize performance based on data insights.

What are the challenges associated with DCO?

Challenges with DCO include the need for high-quality data, complex integration processes, maintaining creative consistency, and managing privacy concerns related to personalized advertising.

What is the future outlook for DCO?

The future outlook for DCO is promising, with advancements in AI and machine learning driving greater personalization, efficiency, and effectiveness in digital advertising.

About Anete Jodzevica
Anete is a content marketing specialist at Setupad. In addition to writing articles, she works at gathering information, verifying data, and explaining complex concepts to others. Anete believes that simplicity is the key to brilliance.

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