Scroll through your social feeds or browse your favourite websites and you will be bombarded by hundreds of advertisements. Most of them blend into the background because they are generic. The problem is not that there are too few adverts – the issue is that they rarely speak to the individual viewer. Consumers are exposed to thousands of marketing messages each day and have learnt to ignore anything that feels irrelevant . Static creative – a single design served to everyone – cannot cut through this noise and often underperforms. According to research by Codedesign, campaigns optimised by artificial intelligence reduce customer acquisition costs by around 30 %, deliver conversion rates 40 % higher than non‑AI campaigns and achieve click‑through rates up to 257 % higher because they constantly refine and vary their creative combinations . These gains illustrate why agencies increasingly turn to technology to solve the creative fatigue problem.
Enter Dynamic Creative Optimisation (DCO) – an AI‑powered technique that assembles adverts on the fly using modular creative assets and real‑time audience data. Instead of designing dozens of separate banners, marketers upload a library of images, headlines, calls to action and offers. Machine‑learning algorithms then analyse first‑party and contextual signals (such as browsing behaviour, location, device and time of day) and build the most relevant combination for each impression . The result is hyper‑personalised creative delivered at scale. A Yahoo survey of more than 300 brands found that 82 % of advertisers were committed to using DCO in 2024, with a third planning to increase spending . For agencies striving to offer differentiated services, DCO is quickly becoming essential.
This article explores what dynamic creative optimisation is, why it matters for an AI marketing agency, and how to implement it successfully. We will examine core benefits, practical applications, real‑world case studies, best practices and common pitfalls, before looking at how this technology is likely to evolve.
What Is Dynamic Creative Optimisation?
Dynamic Creative Optimisation is a technique within programmatic advertising that uses machine learning to tailor advertisements to individual viewers in real time. Rather than creating a single static ad, DCO treats an ad as a collection of interchangeable components – images, headlines, copy blocks, calls to action and prices – that can be swapped and rearranged. When a user is eligible for an impression, the DCO engine evaluates numerous data points (demographics, browsing history, location, device type, time of day, weather and more) and assembles the combination that is statistically most likely to resonate . Each ad impression therefore becomes a miniature test, feeding data back into the algorithm and enabling continuous optimisation.
The concept emerged in the early 2010s as programmatic buying grew, but only recently have AI and machine‑learning models matured enough to assemble creatives and decide winners in milliseconds . Research shows that personalised creative delivers a 50 % higher brand lift than generic messaging . Cross‑channel DCO campaigns – where the same user is reached across display, video, social and digital out‑of‑home (DOOH) – achieve engagement rates up to 300 % higher than single‑channel campaigns . Early adopters of predictive DCO, which uses historical performance to anticipate optimal combinations, report conversion rates 25 % higher than with non‑predictive approaches . Video‑based dynamic ads can produce 83 % higher emotional engagement than static video, according to consumer research by Unruly .
In practice, DCO platforms (such as Google Studio, Adobe Target, Hunch or Smartly.io) integrate with demand‑side platforms (DSPs) and ad servers. Advertisers upload a pool of creative assets and a product feed. A tracking pixel or SDK captures user interactions with ads and websites, populating a behavioural profile. When an ad request is triggered, the platform identifies the viewer’s characteristics, pulls matching product data and assembles the creative accordingly . It then serves the ad across display, video, native or DOOH placements and measures performance. Winning combinations are scaled up while underperforming elements are retired. Because each impression is customised, the system continuously tests and learns without human intervention.
Core Benefits for Agencies
DCO offers a range of advantages that appeal to media agencies and their clients:
- Hyper‑personalisation at scale: Modern consumers expect personalised experiences; 81 % say they are more likely to engage with brands that offer personalised interactions . DCO turns this expectation into reality by tailoring the creative to each viewer. Retailers can show products based on browsing history; travel brands can display the destination and price relevant to a user’s search; streaming services can recommend shows aligned with viewing habits. Because the system assembles assets automatically, an agency can deliver personalised ads for thousands of products or audience segments without the time cost of manually producing each variant.
- Improved performance metrics: The combination of relevance and continuous testing translates into higher engagement and conversion rates. Personalised ads achieve higher brand lift and emotional connection . Marketers using dynamic creatives report 50 % higher brand lift and up to 83 % more emotional engagement than static ads . A Yahoo‑sponsored survey found that 82 % of advertisers use DCO in display campaigns, 90 % use it for video and 87 % for native campaigns . With more relevant creative, DCO campaigns often deliver click‑through rates up to 257 % higher and conversion rates 40 % higher than non‑AI campaigns . These improvements contribute to higher return on ad spend (ROAS): early adopters of DCO achieve ROI improvements of 10–30 % , while e‑commerce advertisers implementing AI‑driven bidding and creative optimisation see ROAS rise by 28 % .
- Cost efficiency and resource savings: Automated creative assembly reduces the manual workload involved in producing multiple ad versions. Once a library of assets is prepared, the AI handles assembly, distribution and testing. Codedesign’s analysis shows that AI‑optimised campaigns reduce customer acquisition costs by roughly 30 % . By identifying and promoting high‑performing combinations, DCO also minimises wasted ad spend and ensures budgets are concentrated on the creative elements that deliver results . For agencies, this means delivering better performance without increasing headcount.
- Real‑time optimisation and insights: Traditional A/B testing allows marketers to compare creative variations, but it is inherently slow and reactive. DCO platforms test thousands of permutations in real time. Each impression informs the algorithm, which updates creative selection on the fly . Advertisers can therefore respond instantly to changes in audience behaviour, market conditions or inventory levels. For example, weather‑triggered campaigns in DOOH produce up to 2.5× higher ad recall compared with static creatives ; dynamic dayparting yields engagement increases of 20–35 % . Because the system reports performance at the element level (e.g. which headline drives the highest click‑through rate), creative teams gain granular insights that inform future asset production.
- Scalability across channels and regions: DCO supports omnichannel strategies. The Yahoo study reported that 56 % of advertisers consider running across multiple formats important to their DCO strategy . By connecting product feeds and data sources, agencies can launch thousands of ads across display, video, native, social and DOOH with minimal incremental effort. The technology also accommodates localisation – dynamic templates can insert local store details or translate copy based on the viewer’s language and region.
Key Applications
Dynamic creative optimisation can be applied to almost any digital advertising format, but several use‑cases have emerged as particularly valuable:
Retail and E‑commerce
Retailers leverage DCO to show consumers products that align with their browsing and purchase history. For instance, a fashion brand might display dresses or shoes previously viewed and highlight a limited‑time discount. The system automatically pulls price, size and availability from the product feed and assembles a compelling offer. In the Fashion & Friends campaign run by Hunch, dynamic ads lowered cost per acquisition (CPA) by 50 % and lifted purchases by 72 %, while boosting website purchase conversion by 62 % and increasing ROAS by 73 % . Such results illustrate the revenue‑generating potential of personalised product feeds.
Travel and Hospitality
Travel marketers use DCO to tailor imagery and offers to a user’s travel intent. When a prospect searches for flights to Paris, the system dynamically inserts the exact route, price and dates into the creative. For Air Serbia, dynamic ads showing recent destinations helped automate promotion for 73 flights across 54 destinations; the campaign generated positive recall, increased new site visitors and boosted conversions . Dynamic DOOH ads triggered by weather (sunny days prompt iced drink ads) or time of day (commuters see breakfast promotions) can also improve results: weather‑triggered campaigns achieve 2.5× higher ad recall and dayparted creatives show 20–35 % higher engagement than all‑day ads .
Streaming Media and Entertainment
Streaming services and media platforms use DCO to recommend content tailored to viewing histories. For example, a viewer who watched a romantic comedy might see a creative for a similar film, including a contextual headline like “Your next feel‑good watch.” DCO can insert real‑time information such as a countdown to a show’s premiere or a personalised thumbnail. Research by Unruly shows that personalised video ads drive 83 % higher emotional engagement , making dynamic video creative a powerful tool for subscription platforms aiming to deepen user loyalty.
Out‑of‑Home and Digital Signage
Dynamic creative also extends to programmatic DOOH (pDOOH), where ads on digital billboards change according to real‑time conditions. The Neuron notes that location‑based triggers – for example, showing sunscreen ads only near beaches – generate three times more engagement than generic placements . Dynamic creatives combined with contextual triggers (like weather or stock availability) deliver engagement lifts of up to 40 % and improve conversion when paired with mobile retargeting . For AI marketing agencies, pDOOH is an emerging channel where dynamic creative can differentiate campaigns and demonstrate measurable results.
Real‑World Examples
GS Shop’s 4 000 % ROAS with Moloco
Korean home‑shopping company GS Shop wanted to expand its mobile app and turned to Moloco’s dynamic creative solution. Within three days of launching the first campaign, the company hit its target cost per install (CPI) and then reduced it further. After twelve weeks, daily installs exceeded 1 000 while maintaining the CPI . The company synchronised its creative across mobile ads and live TV, delivering product‑specific adverts to the right audiences. Over a two‑month span, the dynamic creative campaign generated more than 2 000 % return on ad spend, and a retargeting campaign achieved weekly ROAS above 4 000 % . These results came without increasing creative production resources, underscoring how DCO scales performance.
Fashion & Friends and Air Serbia with Hunch
Hunch, a creative automation platform, produced several notable case studies. Fashion & Friends, a fashion retailer, used dynamic product ads for Valentine’s Day promotions. By employing conditional layers and automated A/B testing within dynamic templates, the brand achieved a 50 % reduction in CPA, a 72 % increase in purchases, a 62 % lift in website purchase conversion, and a 73 % boost in ROAS . In another case, Air Serbia personalised flight adverts based on users’ recently searched destinations. The airline automated promotion of 73 flight routes and 54 destinations, resulting in positive recall and increased bookings . These examples show how structured feeds and creative automation allow brands to serve relevant offers and capture demand at scale.
Telenor’s 3× ROAS on Paid Social
Telecommunications provider Telenor sought to advertise a diverse range of plans without overwhelming its creative team. By adopting Hunch’s creative automation and DCO, Telenor created personalised customer journeys that delivered a three‑fold return on paid social advertising . The campaign matched phone models and service plans to audience interests and ensured on‑brand designs. This case highlights how data‑driven creative can transform performance even in commoditised industries.
E‑Commerce Advertisers and AI Bidding
Beyond specific brand examples, aggregated studies illustrate the impact of AI‑driven creative optimisation. Codedesign’s analysis found that e‑commerce advertisers using AI‑powered bidding and creative technologies saw 28 % higher ROAS compared to advertisers relying on manual setups . AI‑optimised campaigns delivered a 30 % reduction in customer acquisition cost, 40 % higher conversion rates and 257 % higher click‑through rates, while remaining effective three times longer because creative variants are refreshed constantly .
Best Practices for Implementing DCO
While DCO promises substantial rewards, success depends on careful planning and execution. Agencies should consider the following guidelines:
Build a Robust Asset Library
The AI cannot invent quality if you do not supply it. Prepare a large pool of high‑resolution images, videos, headlines, body copy and calls to action. Each component should be adaptable to different audiences and contexts. Include seasonal and evergreen variations so the algorithm can respond to shifting trends. Investing in an asset library up front prevents creative fatigue and allows the system to rotate content without repeating itself.
Maintain Brand Consistency
Personalisation must not compromise brand identity. Establish clear rules around colour palettes, typography, tone of voice and logo placement. Use templates to ensure that dynamic combinations stay on brand. For example, set parameters for maximum headline length or ensure the logo always appears in a specific corner. A consistent look builds recognition and trust even as the message changes.
Segment Audiences Thoughtfully
DCO is most effective when fuelled by accurate data. Go beyond broad demographics by incorporating behavioural, psychographic and contextual signals. Segment users based on purchase history, browsing patterns, content consumption, funnel stage and location . The more precise the segments, the more relevant the creative combinations. However, avoid over‑segmentation that fragments your audience into segments too small to learn from.
Define Clear KPIs and Testing Protocols
Before launching a DCO campaign, decide which metrics matter: click‑through rate, conversion rate, ROAS, customer lifetime value or something else. Use these KPIs to evaluate combinations and instruct the algorithm. Implement guardrails such as minimum impression thresholds before judging an element’s performance. This prevents premature decisions and ensures statistical validity. Continuous A/B testing within the dynamic environment remains important – for instance, test different offers or creative themes to feed winners into the asset pool.
Monitor Performance and Optimise Inputs
DCO is not “set and forget.” Analyse element‑level performance reports to identify which images, headlines or offers resonate. Retire underperforming assets, create new variants inspired by high performers and update product feeds. Monitor creative fatigue; if click‑through rates decline, refresh assets or rotate messaging. Remember that nearly 60 % of advertisers surveyed by Yahoo said they need better tools to measure and contextualise DCO results , so invest in analytics that provide granular insights.
Balance Automation with Human Oversight
AI can assemble and optimise creatives, but humans must set the strategy. Review automated combinations to ensure they are not tone‑deaf, insensitive or off‑brand. Over‑personalisation can feel intrusive; for example, recommending pregnancy products to someone who browsed baby clothes could be seen as presumptive. Establish thresholds for how specific messaging should be and include universal fallback creatives for uncertain situations. A human‑in‑the‑loop approach combines data‑driven precision with judgement.
Respect Data Privacy and Compliance
DCO relies on data, so agencies must follow data‑protection regulations like GDPR and the UK’s Data Protection Act. Use consent‑based data wherever possible and be transparent about how information is collected and used. Work with legal teams to ensure that third‑party data sources comply with regulatory requirements. Remember that users’ tolerance for personalised advertising varies by age and culture – eMarketer research shows that while many internet users under 50 appreciate personalised ads, the majority over 50 do not . Sensitivity to these differences helps avoid backlash.
Common Pitfalls and How to Avoid Them
Inconsistent Messaging
The power of DCO can become a weakness if creative elements are mismatched. Without strict templates and brand rules, the algorithm may assemble combinations that feel inconsistent or confusing. Ensure that every asset within the library fits together logically and adheres to brand guidelines.
Over‑Personalisation and Intrusion
Showing highly specific ads can cross the line from helpful to creepy. For example, referencing a user’s location or recent purchase in a way that seems like surveillance may put them off. Use personalisation to enhance relevance, not to reveal how much data you have. Provide clear privacy notices and allow users to opt out of personalised ads.
Poor Creative Inputs
DCO cannot compensate for low‑quality assets. If the images, headlines or offers are weak, the algorithm will still produce poor‑performing combinations. Invest in professional design and copywriting to create compelling creative components. Follow the best practice recommendations from Hunch and DecenterAds: keep messaging simple, use high‑quality assets, align creatives with data signals and limit complex animations or overly elaborate designs .
Data Gaps and Targeting Errors
If the data feeding your DCO system is incomplete or inaccurate, the algorithm will mis‑align ads. Regularly audit data sources and ensure that first‑party data (from CRM and site analytics) is integrated properly. When personalisation relies heavily on third‑party cookies, consider alternatives like context signals, consented CRM data and clean‑room partnerships to maintain performance in a cookieless environment.
Measurement Complexity
Because each ad impression may be unique, measuring DCO performance can be challenging. Set up consistent reporting frameworks and attribution models that account for element‑level variation. Use lift studies or holdout groups to compare personalised campaigns against control groups. Invest in measurement tools; as noted, nearly 60 % of advertisers feel they lack adequate measurement .
Future Outlook: Beyond Assembly to Creation
Dynamic creative optimisation is still evolving. As AI models become more sophisticated and privacy regulations reshape data flows, several trends will shape the next generation of DCO:
- Predictive and Pre‑emptive Personalisation: Current systems react to user data; future platforms will anticipate intent. By combining predictive models with real‑time signals, DCO will serve creative that addresses a need before the user explicitly expresses it. For example, an airline might promote ski holidays to users whose browsing patterns suggest they will be planning winter travel, increasing conversion windows. Early adopters of predictive DCO already see 25 % higher conversion rates .
- Generative AI for Asset Creation: Today, DCO assembles assets created by humans. Advances in generative AI will allow systems to generate bespoke imagery, copy and video on the fly that adhere to brand guidelines. This could further reduce production time and enable unlimited creative diversity. However, agencies will need robust guardrails to ensure generated content aligns with legal and brand standards.
- Integration with Neural Networks and Deep Learning: Neural networks can analyse subtle patterns in how different combinations perform across micro‑segments, automatically refining creative strategy . When combined with reinforcement learning, DCO engines will continuously explore and exploit creative options, maximising returns.
- Omnichannel and DOOH Expansion: With the demise of third‑party cookies, contextual and first‑party data will become paramount. DCO will extend beyond display and video to connected TV, voice assistants, augmented reality and out‑of‑home screens. Brands will deliver seamless experiences across channels, triggered by location, weather, local events and real‑time behaviours. Already, combining DOOH with mobile retargeting yields 48 % higher conversion rates than standalone DOOH .
- Ethical and Privacy‑First Design: As personalised advertising becomes more invasive, regulators and consumers will demand greater transparency and control. Future DCO systems will incorporate privacy by design, anonymising data, providing clear opt‑out options and adhering to emerging standards like the EU’s Digital Services Act. Ethical frameworks will also guide how and when personalisation is appropriate – for instance, avoiding sensitive topics or protected attributes.
Conclusion
Digital advertising is at an inflection point. Audiences ignore generic ads, while brands struggle to keep up with the demand for personalised content. Dynamic Creative Optimisation offers a solution by harnessing AI to assemble tailor‑made creatives in real time. The benefits are compelling: higher engagement, improved conversion rates, lower acquisition costs and scalable personalisation . Real‑world case studies such as GS Shop’s 4 000 % ROAS and Fashion & Friends’ 72 % increase in purchases demonstrate the potential.
For AI marketing agencies, DCO represents both an opportunity and a responsibility. The opportunity lies in delivering bespoke experiences that drive measurable results and differentiate your service offering. The responsibility comes from ensuring that automation enhances – rather than replaces – creativity and that personalisation is executed ethically and with respect for user privacy. By combining a robust creative library, clear strategy and rigorous measurement with the power of machine learning, agencies can transform programmatic campaigns into precision marketing engines. As DCO evolves to include predictive targeting and generative creation, those who master these tools will be best placed to lead the next era of digital advertising.