The walled gardens of search and social advertising channels have served an important purpose for performance marketers for many years. Meta’s and Google’s owned channels have been the default for brands to drive conversions, but cost increases and stiff competition are changing that. Open web advertising has emerged as a vastly scalable option, with high-intent audiences visiting publishers and other media sites.
Advertising on the open web is incredibly different from those search and social channels. In particular, campaign set up and optimization can be manual and disjointed, taking up way too much time for performance marketing teams. AI-driven technology has matured to the point where it can serve as a workflow copilot, helping teams launch open web campaigns more quickly and easily than with traditional channels. AI capabilities now inhabit every step of the campaign setup process, from generating creative to doing predictive audience discovery and targeting.
The Evolution of Campaign Set Up: Enter AI and Automation
Newer AI-driven campaign setup tools can’t come soon enough to help boost performance marketing scale. The era of tedious, manual campaign configuration is finally ending as AI automation platforms mature. These modern platforms use conversational AI and LLMs, so marketers can type goals in plain text or simply speak instructions to start the AI automatically building media plans and targeting parameters.
The power of AI for campaign set up is not only that it saves time, but that users get results. Realize’s Abby generative AI tool, for example, works to increase advertiser productivity and pull in trusted data to support campaigns. These types of performance platforms can walk an advertiser through every step of setting up and managing a campaign, including AI ad optimization, targeting the right audience, testing and optimizing creative, and setting budgets correctly.
When you’re creating a new campaign on the open web, the scale is massive compared to walled gardens, so you have to make sure you get foundational tracking and signals set up correctly and do robust AI training for a solid start.
How Do Advertisers Execute a Full Campaign Set Up in 7 Steps
1. Defining Your Performance Objectives and KPIs
AI can do a lot of the execution and analysis, but the human directing the performance marketing campaign has to define goals up front, to ensure the AI’s success. Make it clear whether the campaign goals are related to lead generation, sales, ROAS, brand awareness, or other options. This first step trains the AI models on the specific conversion actions they should optimize for during the learning phase.
2. Configuring Conversion Tracking and Data Signals
The conversion tracking configuration plays a key role in successful AI campaign usage. This step involves setting up server-to-server (S2S) tracking and/or conversion pixels as well as post-back URLs. These data signals serve as the fuel for AI algorithms to optimize ad delivery and identify winning patterns, then continue learning from that information going forward.
3. AI-Powered Audience Discovery and Targeting
Static demographic segments have become stale, as predictive audience targeting gains ground and becomes easier with help from AI tools. Machine learning and AI can analyze contextual signals, semantic data, and predictive purchase behaviors on the open web. This works even in privacy-first, cookie-less environments, where so many marketers are operating.
4. Structuring Campaigns for Machine Learning Success
Ad campaign architecture needs to provide a solid foundation for AI and ML advertising campaigns. Make sure to consolidate ad groups and avoid over-segmenting — these tactics allow AI algorithms to gather sufficient data density and shorten the learning phase.
Broad Targeting vs. Niche Segmentation
AI algorithms for advertising campaigns perform better with more data, so setting up wider initial targeting parameters makes the most sense. This trade-off between narrow targeting and broader reach generally pays off down the road, once the AI has learned from the data.
Funding the AI Learning Phase
While it may seem counterintuitive to allocate budget for this initial AI launch and learn phase, it’s absolutely necessary to give the AI algorithm a sufficient base of data. Make sure to allocate enough budget in this stage so there are enough conversion events feeding data to the algorithm, so it can optimize effectively.
5. Leveraging Generative AI for Creative Asset Production
Generative AI creatives can bring the power of a video studio and design team to even the smallest business. Many modern ad platforms offer built-in gen AI tools to create hundreds of ad variations, headlines, and images, all based on as little as a site URL. Beyond saving teams time and money, these tools make it easy to quickly do the A/B testing that performance marketing needs to succeed.
6. Automated Bidding and Predictive Budget Allocation
AI-driven media buying is another emerging feature of modern performance platforms that can save tons of time. Automated bidding strategies powered by AI focus on a target metric, like cost per acquisition (CPA) or return on ad spend (ROAS). With that goal, predictive analytics can adjust bids in real time across thousands of publisher sites, based on the likelihood of a user converting. This adds precision to bidding, so you can spend the right amount of money in the right place at the right time.
7. Launch, Monitor, and Optimize
Before you click “launch” on this new campaign, always do a final quality assurance process. AI is ideal for micro-optimizations, real-time bidding, and creating new ad variations, but the human advertising team has to monitor high-level metrics, work against creative fatigue, and guide the overall strategy.
Overcoming Measurement Challenges on the Open Web
Tracking user journeys outside the closed walled-garden ecosystems brings more complexity to an advertising campaign. The right tools can simplify and automate a large part of the tracking process to take full advantage of the open web’s conversion potential. Performance marketers have to move beyond last-click attribution and incorporate AI-based predictive modeling to get full-funnel visibility and cross-channel attribution and tracking.
Key Takeaways
Open web success needs a solid campaign foundation and best practices of steering AI toward business goals, instead of manual micromanagement. Getting an advantage in the digital ecosystem today requires speed and the right tools to help you automate the bulk of digital campaign execution. Make sure to prioritize data signals, generative tools, and automated bidding to stay ahead of competitors on the open web.
Frequently Asked Questions (FAQs)
How does AI improve the campaign setup process on the open web?
AI can improve the entire campaign setup process by making it much simpler, replacing repetitive, manual tasks with automation. AI tools bring hyper-personalized targeting and can immediately generate ad creatives from a URL, as well as suggesting optimal budget allocations, automating bidding strategies, and using ML to build high-intent audience segments. Many modern tools can ingest plain text or natural language goals, so advertising teams can focus on strategy rather than manual set up tasks. AI tools can reduce production time for campaigns dramatically.
Can I achieve the same ROAS on the open web as I do on search and social?
Yes, it’s possible to achieve the same or better ROAS with open web campaigns, since AI bidding algorithms and predictive targeting can identify users with high purchase intent in real time. This can save budget as well, since marketers are accessing premium ad inventory that’s less saturated than search and social channels may be.
What is the best bidding strategy for a newly launched open web campaign?
For a newly launched open web campaign, start with an automated strategy that focuses on maximizing conversions. This feeds initial data to the algorithm, which it needs to produce top-notch, accurate results later. Once there is sufficient conversion density, you can switch to target CPA or target ROAS so the AI can optimize for profitability.
Do I still need third-party cookies to target audiences effectively?
No, you don’t need third-party cookies for targeting any longer. Now, advanced ad platforms use AI to analyze real-time behavioral signals, such as contextual and semantic. This allows for precise, persona-based targeting that’s independent of third-party cookies.
