As the paid search advertising landscape evolves, AI and language modeling tools are gaining traction to streamline and optimize campaign restructuring processes.
Some paid search specialists are no longer content with the status quo. Others argue that our industry has been using AI all along and that the AI revolution is just a change in terminology.
Many favor chatbots for answers, driven by the search for efficiency, creativity and being up-to-date. Julie F. Bacchinimanaging director of the PPC Chat community, emphasized the latter point during her weekly Slack chat on “Diversification in PPC.”
One area where AI shines is streamlining the often tedious and time-consuming process of account restructuring.
Here are six practical ways AI can help PPC managers restructure ad accounts.
1. Find new keyword ideas for accounts
Keywords are the cornerstone of an ad account.
To effectively reorganize your ad accounts, you need to analyze your search term reports and identify new keywords to add to your ad groups or campaigns.
Although necessary, this process can be time-consuming on large accounts. It’s especially confusing for B2B accounts, where it’s key to separate consumer intent from business intent.
This is where AI can help. There are tools on the market to help you:
Analyze datasets: AI can identify new and valuable keywords that might have been overlooked.
Statistically significant surface opportunities: Some search terms need more data before they can be acted upon.
AI tools don’t just identify new terms; they also assess statistical significance. This ensures you focus on terms with a proven track record of converting.
Consider Optmyzr, a popular AI-based tool that integrates seamlessly with platforms like Google Ads and Microsoft Advertising and automatically analyzes your search term report.
Based on its analysis, it recommends relevant keywords to include in existing ad groups or create new ones.
This eliminates the need for manual data downloads, spreadsheet analysis and filtering.
Most importantly, Optmyzr uses statistical significance to ensure its recommendations have impact.
2. Classification of keywords by topic
Effective paid search campaigns are based on well-organized ad groups with thematically related keywords.
Another reason to restructure is if a previous ad manager accidentally added thousands of keywords from auto-recommendations or didn’t understand the importance of keeping ad groups tightly themed.
Grouping keywords by topic ensures that your ads resonate with users’ search terms. This is where great language models like Google’s ChatGPT and Gemini come into play.
Imagine this:
You can effortlessly paste thousands of keywords into a chat box and ask the tool to organize all the keywords by topic.
Manually grouping thousands of keywords by topic can be an overwhelming task.
Enter large language models. By feeding these models your keyword list (even exceeding 2,000 keywords), they can identify and categorize topics with remarkable speed and accuracy.
Checking for duplicates is critical because ad groups often have overlapping keywords. Continue to refine your topic keyword list until it is more manageable to reduce redundancy.
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3. Tailor ads closer to the user’s search query
This results in improved ad relevance, higher Quality Scores and increased campaign effectiveness.
Once you’ve thematically grouped your keywords, writing ad copy becomes a necessity.
You need to tailor your ad copy to the specific theme of each ad group, ensuring a more relevant and impactful message for users.
Restructuring often involves creating new ad groups.
Writing Responsive Search Ads (RSAs) can be difficult because of the many titles and descriptions required. However, AI can help.
Consider ChatGPT and the following message:
“Write me 15 different titles that are 30 characters or less for a Google search ad for my gourmet cookie business.”
Remember that human supervision is still important. Although AI can stimulate creativity, the human factor remains irreplaceable. In the example above, some AI-generated titles may exceed the character limit or lack the desired punch.
This example highlights the importance of human oversight and refinement.
The best approach is to leverage AI for initial brainstorming and then refine the generated content with your unique human touch.
4. Help create images
Google Ads’ AI-powered image generation tool speeds up the testing process and saves time and resources by generating new images.
The reduction in time and costs is especially impactful for brands operating on limited budgets competing on a platform where images are an important tool.
Additionally, there is a theory that platforms could prioritize content created with new features like AI generation. This could temporarily boost your ads while the platform collects data from its tool.
While the exact impact is unclear, some advertisers may benefit from AI-generated imagery.
Take an online-only gourmet soup shop, for example. Unlike clothing with different styles, soup bowls can look similar across brands. This is where the AI shines.
It can create unique and appetizing images for your soup, freeing you from depending on seasonal photo shoots.
These images can provide variety to try on an account that might not otherwise have many images.
Dig Deeper: Building Google Ads Campaigns with Gemini Conversational AI: How Good Is It Really?
5. Predictive modeling in Google’s Performance Planner
Google’s Performance Planner is an example of AI-based predictive modeling.
It allows advertisers to predict how changes to campaigns could affect key metrics such as conversions and clicks.
Leveraging Google Performance Planner before and after account reorganization is a great way to demonstrate scalability.
It helps you make informed decisions about budget adjustments and optimize campaigns for better results.
6. Automatic offer on advertising platforms
When reorganizing ad campaigns and centralizing data, the rationale is often to allow campaigns to leverage larger data sets and automated bidding strategies to perform effectively.
Bidding strategies are not always considered AI, but machine learning.
However, bid strategies use machine learning to automatically adjust bids based on various factors, such as the likelihood of a conversion or the competitiveness of an auction. So, in this sense, bidding strategies can be considered a form of AI.
Dig deeper: How each Google Ads bid strategy impacts campaign success
AI-Driven PPC Optimization: Improve efficiency and strategy
Despite being available for years, AI tools like Optimzr and various platform features are often not recognized as AI, largely because the prevailing perception equates AI with chatbots.
However, AI encompasses more than just chatbots; it involves computers making decisions quickly.
These technologies provide options for advertisers, including everything from keyword research and ad group organization to RSA ad copy writing and image generation.
Predictive modeling and automated bidding strategies improve campaign optimization and save valuable time for paid search managers, fitting neatly within the broad definition of AI tools.
AI integration allows paid search managers to be more effective when creating campaigns and switching accounts. This allows you to transition into a more strategic role, moving beyond mere execution of tasks to shaping strategies and outcomes.
The views expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.
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