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Ways AI is changing ad asset generation
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When Heinz announced it had produced 20,000 ad assets in a single year using artificial intelligence, the advertising industry stopped and took notice. This impressive achievement by one of the world's most recognizable brands shows the seismic shift AI is bringing to marketing. By incorporating next-generation technology, Heinz harnessed computer vision to analyze and enhance creative performance at scale. This truly showed marketers the potential of AI to redefine how they create, evaluate, and optimize advertising in today's digital age.
But this wasn't always the case. In the not-so-distant past, creating ad assets was a labor-intensive process that took significant time. Brands relied on manual efforts, from brainstorming concepts and designing visuals to refining messages and testing effectiveness. Creative teams would spend weeks, if not months, producing a fraction of the content volume Heinz achieved with AI. Even with the introduction of software tools aimed at streamlining these tasks, the process continued to be cumbersome, as they often demanded extensive human oversight and input, leading to bottlenecks and inefficiencies. The result was a slow, expensive, and often repetitive process that could barely keep pace with the demands of the fast-evolving landscape.
Today, ad asset creation has been transformed by integrating AI and advanced software solutions. Brands increasingly rely on these technologies to automate and optimize advertising content production, and AI's role goes beyond simple automation. It allows for a more personalized and data-driven approach to advertising. By analyzing large amounts of data, it can identify patterns and insights that humans might overlook, allowing for customized assets that are tailored to specific audience segments. This level of personalization ensures that marketing messages resonate more effectively with consumers, improving engagement and conversion rates (more on this later).
Why brands need to embrace AI for ad asset creation
Let’s face it, the traditional approach to ad creation has shown serious limitations in today's fast-paced and data-driven market. This process, driven by its heavy reliance on manual labor and linear workflows, faces challenges in efficiency, scalability, and personalization, and creative teams are increasingly frustrated. Let’s take a look at why more brands should embrace AI for ad user creation:
Efficiency and cost reduction: Using manual processes in ad creation is time-consuming. The need for large creative teams to brainstorm, design, execute, and test campaigns can translate into high costs. These methods take many hours, increasing the time to market for campaigns. But the shift towards AI for ad asset creation is a major move to streamline operations and significantly cut costs, allowing your campaigns to launch faster.
Personalization, personalization, personalization: In an era where consumer expectations are shifting towards more tailored and relevant experiences, the generic, one-size-fits-all advertising falls short. AI, on the other hand, uses data to produce highly tailored visuals and messages, meeting the unique preferences of diverse demographics, interests, and behaviors. Some examples below show how a more personalized approach has positively benefited some brands.
Simplify image sourcing: AI effortlessly addresses the complexity of managing various image sources. Brands often juggle stock photos, custom photography, and user-generated content, each with different licensing and quality standards. But AI streamlines this process, ensuring consistency and quality across all sources and removing the manual burden.
Conform to ad platform specifications: Each advertising platform has its own set of specifications for images and content. For example, Google Shopping needs product images to meet certain criteria in terms of size, background, and quality to be accepted. Manually adjusting images to meet these specifications is not only tedious, but also prone to errors, potentially leading to rejected ads or poor performance. AI photo editors like Photoroom API or apps can solve the pain point by effortlessly reformatting different assets to meet the ad asset requirements.
Achieve ROI targets through testing: To meet ROI targets, brands have to constantly test and iterate on their ad creatives. This process can involve creating multiple variants of ads to see which performs best, and the slow creation and revision of assets under traditional methods can really delay testing cycles, making it difficult for brands to optimize performance and adjust strategies quickly. AI accelerates this process, allowing brands to easily identify and scale the most effective ads.
Adapt fast to market dynamics: The changing nature of consumer behaviors and market trends call for agility and flexibility from advertisers. With its lengthy timelines and rigid workflows, the traditional ad creation process limits a brand's ability to adapt quickly. This slow response can result in missed opportunities and a disconnect between the brand's messaging and the current market sentiment or consumer preferences. Again, AI empowers brands to respond faster to changing trends, and this responsiveness ensures that they remain relevant and aligned with current market sentiments.
Enhance collaboration within creative teams: Ultimately AI-powered tools like Photoroom Teams and Canva not only streamline individual tasks but also foster better communication within creative teams. Platforms supporting multiplayer functionalities or team collaboration features allow various users to work on and review projects simultaneously, regardless of their location. This environment is facilitated by AI, which can offer suggestions, automate certain design choices, and even predict team preferences based on past projects. The result is a more cohesive workflow that encourages creativity, reduces the time to market for ad campaigns, and ensures that all team members are aligned with the campaign's goals and aesthetics.
Examples of successful AI-driven campaigns
Aside from Heinz's campaign, here are some examples of successful AI-driven ad campaigns:
Campsider Marketplace and Photoroom
To optimize its advertising strategy and improve efficiency, Campsider decided to integrate the Photoroom API into its workflow. This strategic move was aimed at enhancing the visual quality of their Google Shopping ads through automated image editing and optimization. The outcome of this was significant, leading to a noteworthy improvement in advertising performance metrics.
Nike's AI customized ads
Nike used AI to create personalized ad content for its audience, delivering custom videos to app users. The AI analyzed a user's activity and preferences to generate bespoke videos that featured their name and recommended products tailored to their interests. This use of AI for personalization led to increased engagement rates and boosted brand loyalty among Nike's customer base, showcasing the potential of AI to create deeply personalized marketing experiences.
Spotify's Discover Weekly
Spotify's Discover Weekly is an excellent example of AI-driven content personalization in advertising. By analyzing billions of user data points, Spotify's AI creates personalized playlists for each user, effectively advertising new songs and artists based on individual music tastes. This takes the user experience to another level and serves as a powerful tool for promoting content within the platform, demonstrating the effectiveness of AI in understanding and catering to consumer preferences at scale.
Cosabella and Albert
The fashion brand revamped its marketing strategy by integrating AI into its operations, particularly for ad creation. By using an algorithm named Albert for its paid search and digital marketing efforts, Cosabella saw a 50% increase in its return-on-ad-spend across search and social media, and a 12% decrease in ad spend. The use of AI allowed the brand to automate previously time-consuming tasks, thus improving efficiency.
Optimizing ad asset creation with AI
A recent Deloitte study on generative AI in content marketing shows support for the use of AI-powered tools in ad asset creation. Early adopters are not just meeting but outperforming expectations across several key metrics. Compared to future adopters' projections, they report a 15% gap in content quality, employee productivity, and content volume. In addition, early adopters saw their companies surpass revenue targets by an average of 14% in the past year, compared to just 2% overperformance by companies without generative AI plans. This effectiveness translates into time savings too, with the average content marketer saving more than 11 hours per week.
It's clear that embracing AI through image editing APIs and applications introduces a new era of efficiency and creativity in ad asset creation. By making use of AI-driven tools, marketers can significantly streamline the creative process, making sure content not only stands out but also resonates with the intended audience. Below are key areas where these tools are making a big impact:
AI image editing technology
AI image editing tools have changed marketers' approach to visual content. Automated image enhancement capabilities, such as adjusting brightness, contrast, and saturation, make images pop with minimal effort and give them that little extra edge. Tools like Photoroom offer brands advanced features like background removal, object removal, AI Shadows, and AI Backgrounds, which are now simplified, allowing products to stand out without complex photo editing skills. Additionally, AI can apply artistic touches to images through style transfer techniques, offering unique visuals that capture attention amidst digital advertising noise.
Content personalization
Content personalization through AI can lead to dynamic content creation that resonates with individual user preferences and behaviors, significantly boosting engagement in return. Using tools like Adobe Target and Optimizely allows marketers to perform A/B testing at a large scale, optimizing every aspect of ad content from imagery to copy for various audience segments. In fact, Photoroom conducted an A/B test to evaluate the impact of image backgrounds and quality through ad tests on Facebook. The result? In comparison to the regular, unpolished version of the image, the ad featuring an image edited with Photoroom's AI capabilities improved ad performance by up to 29%.
Predictive analytics
Predictive analytics powered by AI offer insight into creative assets' performance, allowing marketers to make informed creative decisions. Tools such as Google Analytics and SAS Advanced Analytics use historical data to forecast how different elements will resonate with audiences. Additionally, they provide deep insights into audience preferences and emerging trends, leading to more compelling content tailoring and strategic planning.
Benefits of AI image editing for advertising
So, we know that AI image editing tools in advertising have brought about significant change. Here are some of the key benefits broken down:
Increased efficiency and speed: What once took hours can now be accomplished in minutes, thanks to features like automatic background removal, color correction, and object manipulation. It’s clear that AI is changing the game for image editing workflows, which in turn allows marketing teams to focus on strategy and creative direction rather than getting bogged down in manual editing tasks.
Improve ad performance: Using AI image editing tools can significantly boost your campaign performance. Photoroom, for example, can generate new, clean backgrounds for hundreds of product shots at once—cutting CPC by 12% and boosting CTRs by up to 29%.
Cost reduction: By automating repetitive processes, AI reduces the need for extensive manual labor, leading to substantial cost savings for businesses. This democratization of design and editing also means smaller companies can produce high-quality visuals without needing expensive professional services. The starting price for Photoroom's API is $0.02 per image, which is very competitive.
Consistency and brand alignment: AI tools can help maintain visual consistency across all advertising materials, a critical factor in brand recognition and trust. By applying predefined brand guidelines, such as color schemes and logo placement, AI ensures that every piece of content aligns with the brand’s identity, regardless of content volume.
Personalization at scale: One of the most significant advantages of AI in image editing is its ability to personalize content at scale. By analyzing audience preferences and behaviors, AI can customize visuals to match the tastes and interests of different segments, increasing relevance and engagement.
Creative exploration: AI also opens up new possibilities for creative exploration by generating unique images and effects that would be difficult or impossible to achieve manually. This can lead to innovative ad campaigns that capture attention and stand out in a crowded marketplace.
Data-driven insights: Many AI image editing platforms offer analytics and performance tracking, allowing marketers to understand which visuals perform best and why. This data-driven approach to creative decision-making helps optimize ad performance over time, ensuring resources are focused on the most compelling content.
Get started now with Photoroom’s AI photo editing tools. Whether you sell clothing, furniture, beauty products, or jewelry use Photoroom’s web, API and mobile app to elevate your product photos. Choose from a range of AI tools or upgrade to Photoroom Pro to access useful features like Batch Editor, HD quality, Smart Resize, and more.
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