• by Laurie Sullivan , Staff Writer, Yesterday

Advertising platforms are by no means identical, but for years, companies have been attempting to give marketers the ability to take their campaign from one company to another without having to change much in terms of size and formats.

Companies that buy digital ads strive for cross-platform compatibility to reduce obstacles that slow processes and can complicate user experiences.

For marketers, that means building a creative asset once and deploying it across Google, Meta, TikTok and programmatic networks with minimal resizing or restructuring.

"While platforms are not identical and may require some level of adaptation, our focus has been on continuously improving the import experience to make it as seamless and effective as possible for advertisers," a Microsoft Advertising spokesperson told MediaPost.

Over the past few months, Microsoft Advertising has introduced several improvements designed to reduce friction and improve outcomes.

Three of these include tailored performance recommendations immediately after each import to optimize campaigns faster; ongoing Performance Max import improvements including capabilities for new customer acquisitions; and the ability to quickly identify and resolve import issues with clear step-by-step guidance.

"Some adjustments advertisers make are strategic rather than technical," the Microsoft Advertising spokesperson said.

Impressions-based remarketing, flexible creative formats and policies, and features that allow brands to differentiate and optimize beyond a direct copy of another platform are some of those strategic reasons.

Adthena has introduced a free migration tool, "AdBridge," that converts Google Ads campaigns into formats accepted by the ChatGPT Ads platform.

The tool analyzes advertisers’ search campaigns to generate keyword lists, negative keywords and competitive insights that can be directly applied to ChatGPT campaigns, and tells brands how and when they serve up specific auctions, how often they appear, and prompts that trigger those placements, at least for now.

Porting or migrating campaigns may not always work because of specific technical details or strategic goals. This depends on what makes the campaign most useful on a specific platform, according to one ad executive.

It's also worth noting that the Interactive Advertising Bureau (IAB) continually updates framework standards to ensure that ad-technology platforms speak the same language.

For example, last week the IAB initiated public comment for its first Campaign Data Standards from Project Eidos, introduced in February.

Campaign Data Standards 1.0 establishes a baseline structure for campaign and placement-level data, presenting an interoperable framework designed to work across existing systems and media types.

Artificial intelligence (AI) in advertising systems has made compatibility easier, and also more difficult. It makes compatibility easier because Microsoft Performance Max can map conversion goals and allow cross-platform bidding strategies. However, it also makes compatibility more challenging because goals and strategies for two distinct systems -- which may reach different audiences -- do not always align. 

Third-party AI systems like software from Skai use AI-driven insights to monitor Google accounts and alert marketers to exactly when a high-performing campaign is ready to be mirrored to Microsoft. Theoretically, the software does it in one click.

Agentic services help with compatibility, in my opinion. This week, "Skai Studio" launched in a move that supports agentic marketing, enabling brands to launch more agents with a unified data foundation across search, social, and commerce media.

Skai Studio launched with managed services to help markets transition to agentic.

Skai wants its platform to become the operating system for agents -- not in the traditional sense like Microsoft Windows.

But Skai executives believe the announcement marks a new chapter for marketing, describing it as a "fundamentally different model" that combines advances in AI with unified data, embedded intelligence, and connected workflows.

The goal is to connect data and systems to execute across channels, and perhaps across platforms.