Keyword optimization is a crucial aspect of managing Google Ads campaigns, and one effective method to enhance this process is through the use of Google Ads scripts, specifically for conducting n-gram analyses. This approach allows advertisers to delve deeper into search query performance, providing insights that go beyond the basic reports available in the Google Ads interface. Search query analysis should be a regular task for any search campaign, with frequency depending on traffic volume. While the Google Ads interface offers basic reporting, it can be limiting for in-depth analysis. This is where Google Ads scripts come into play, enabling users to automate tasks and generate more detailed reports. N-grams, which break down search queries into individual words or phrases, can reveal how specific terms perform, helping advertisers make informed decisions about their keyword strategies. To conduct an n-gram analysis, users need to set up a Google Ads script. This involves copying a pre-written script, creating a Google Spreadsheet to store the results, and configuring the script settings, including the date range and the number of words to analyze. The analysis can be tailored to specific campaigns or ad groups, allowing for a focused review of performance. Once the script runs, the results populate the spreadsheet with various tabs for different n-gram levels. This data includes metrics such as cost, clicks, impressions, and conversions, enabling users to filter and analyze performance effectively. For instance, advertisers can identify high-cost queries with no conversions or low-cost queries that yield high conversion rates. This granular insight allows for better decision-making regarding keyword targeting and negative keyword identification. N-gram analysis is particularly valuable for understanding how different terms perform across various levels, such as account, campaign, and ad group. By examining these metrics, advertisers can refine their strategies, optimize their keyword lists, and ultimately improve their campaign performance. Best practices for using n-gram scripts include manually updating the date range for each analysis and ensuring filters are reset after reviewing the data. This ensures that the analysis remains accurate and comprehensive. In summary, leveraging n-gram analysis through Google Ads scripts can significantly enhance keyword optimization efforts. By breaking down search queries into manageable components, advertisers can gain deeper insights into their campaigns, leading to more effective keyword strategies and improved overall performance.

Google Ads data contains a goldmine of information for SEOs. Identify high-converting keywords from ads, then track those in SEO rank tracking tools and optimize organic content for them. Review ad copy performance to identify ways to improve the clickthrough rate of organic pages. Identify long-tail queries to create or optimize existing content. Analyze high-converting PPC landing pages to identify learnings about page structure, CTA, and UX that can be applied to traditional search pages.

This article provides 10 advanced Google Ads strategies, such as separating broad match keywords from phrases and exact match for different bidding strategies. It explains how to use custom segments to target competitor audiences and how to leverage Microsoft Advertising search term reports for keyword optimization. Other tactics include investing in video assets for Performance Max campaigns, using aggressive bidding strategies to speed up the learning phase, and employing flexible budgeting to adapt to market demand.

Reduce your Google Ads spend by leveraging location and time controls, using negative keywords, avoiding keyword bloat, and optimizing ad groups with A/B testing. Examples include setting bid adjustments for high-traffic areas and eliminating ineffective keywords to improve relevance. Conducting A/B tests on ad variations helps determine which ads perform best, reducing overall costs and increasing effectiveness.

This study of over 1 million Google Ads looks at the correlation between Ad Strength and performance, the impact of pinning, and the importance of text formatting. Ads tend to perform best with ‘average' Ad Strength, suggesting it's not something worth optimizing for. Responsive Search Ads and Demand Gen ads both have 40%+ better ROAS when using sentence case text instead of title case. Pinning assets has a negligible impact, while maxing out ad copy character counts tends to hurt performance.

This article dives into the world of negative keywords and how to use them to refine Google Ads strategies. It shares six different implementation strategies including setting keyword thresholds, employing search term reports, and blocking undesirable phrases from the start. Marketers can optimize budget allocation, improve campaign performance, and ensure alignment with their advertising goals by properly using negative keywords.

Google's new tools in Google Merchant Center include product trend insights that highlight popular search and purchase trends. Enhanced AI features now offer summaries of product performance, and conversational queries allow for easier access to tailored data reports. Additionally, Google has improved product availability notes with an automated onboarding process and introduced new customer acquisition goals in Performance Max and Search campaigns.

To avoid wasting ad spend, start a new campaign with select top-performing keywords. Avoid switching all match types to broad match at once to prevent inefficiencies. Monitor performance long-term, as broad match keywords take time to optimize. Conduct daily search term checks to refine targeting and add negative keywords as needed. Use automated bidding strategies like Target CPA or ROAS, and include thematically similar keywords to enhance learning while avoiding Single Keyword Ad Groups.

Optimize existing assets instead of constantly reinventing the wheel by creating new content. Check the last 28 days of your Google Search Console and highlight relevant search queries that rank between #4 to #20. You can optimize these pages by adding new insights to refresh the topics, adding new internal links to recently published articles, replacing or adding new images, updating CTAs, or reviewing outbound links.

GTMetrix provides in-depth page speed insights. AlsoAsked is useful for keyword research and finding long-tail keywords. SEOQuake is an extension that uses Semrush data to provide on-page SEO analysis and findings. Google Keyword Planner within Google Ads offers free keyword research capabilities. Keyworddit extracts keywords from subreddits on Reddit.

A new analysis suggests websites should expect some erosion of traffic from brand-related terms as a result of Google’s Search Generative Experience. These new types of generative results introduce more opportunities for third-party sites and competitors to rank for your brand and product terms. 91.4% of all keywords analyzed had an SGE result, which means virtually all brands and websites will be impacted by this. The study contains additional findings related to links used within SGE results, the impact on PPC results, and more.

Google is piloting AI-generated overviews in search results for complex queries with a subset of U.S. users without requiring opt-in to its Search Generative Experience. This feedback-driven test aims to enhance user experience and may impact site traffic dynamics due to changes in ad placements and organic search visibility.

Choosing the right mix of keywords can increase your reach while also reducing unnecessary ad spend. Google lets you set up to 20,000 target items (such as keywords or customer groups) per ad group, but you should be using less than 2,000 per ad group. The exact number to use depends on your business size, campaign goals, competition, budget, and keyword match types. Over your campaign's lifespan, you should remove keywords that fail to deliver results, test new ones, and continue to track keyword performance.

Google announced plans to test search and shopping ads in the AI summaries. Ads will appear within the AI Overview in a section labeled as ‘sponsored' when they're relevant to both the query and the information in the AI Overview. AI Overview will draw on ads from advertisers' existing campaigns. This means they can neither completely opt out of the experiment nor have to adapt the settings and designs of their ads to appear in the feature.

Google will soon start testing search and shopping ads within AI Overviews for users in the US. The company says it will display ads in AI Overviews when they're relevant to both the query and the information in the AI Overview. Advertisers already running certain campaigns through Google will automatically become eligible to appear in AI Overviews. It is unclear when exactly the change will roll out.

There is a lot of debate surrounding Ad Strength's relevance, with some marketers arguing it’s not useful since campaigns with a 'poor' label often outperform those with an 'excellent' label. Google responded and reinforced its significance as a critical metric. Ad Strength plays a crucial role in assessing campaign assets' breadth and depth, which indicates its potential to generate user engagement across digital channels.

Google assesses the quality of landing pages connected to PPC ads based on visitors' on-page experiences, impacting ad visibility and conversions. It's important to ensure landing pages match ad promises, offer clear navigation, and limit distractions to enhance user experience and boost Quality Scores. This leads to better ad performance and business growth.

A recent survey revealed that a majority of Americans look through more search results and spend more time looking for services now compared to five years ago. The findings reveal a growing dissatisfaction with search engine performance despite advancements in AI technology. Only 12% of respondents found PPC ads relevant, indicating a need for better ad targeting.

Recent changes to Google's user interface have affected the volume and quality of organic website traffic, particularly for top- and bottom-of-funnel keywords. Mid-funnel content can help transition searchers from being problem-aware to solution-aware and improve brand awareness. Examples of mid-funnel content include feature round-ups, solution hijacking, quizzes, niche calculators, scorecards, and alternative assets.

TermSniper identifies search intent for any keyword and suggests content tips for optimizing. SEO.ai offers AI-driven research that can match target keywords to a target audience. Free SEO Keyword Research & SERP Analyzer is a custom GPT on ChatGPT that can scrape any Google search result page and analyze keyword usage, intent, and the target audience.

Google's global search market share bumped up to 91.1% in June while ChatGPT's web visits fell 12%. Rising ad revenue from Google Search helped boost the company's profits by nearly 60% in the first quarter. Google's AI overviews may be helping Google Search grow its footprint. Integrating AI into its products will likely help drive higher usage and increase monetization across Google.

Branded Search campaigns contain branded keywords to capture the demand around your brand, which can be worthwhile even if you own that top organic search position. The benefits include protecting your brand from competitors, dominating the search results, controlling the paid landing page without SEO concerns, paying low CPCs, and preventing pMax campaigns from eating up the branded traffic.

Retailers are facing a significant shift in the ecommerce landscape as Google evolves its search results to resemble Amazon-style category pages. This transformation necessitates that retailers adapt their SEO strategies, placing greater emphasis on optimizing product pages rather than traditional category pages. Over the past year, Google has implemented several updates to its ecommerce search results. These changes include the introduction of product grids that display products directly, the addition of search filters, and the integration of local search elements. Most recently, Google has begun to showcase product results within AI Overviews, further enhancing the visibility of products in search results. Collectively, these updates have created a new ecommerce search experience that prioritizes product visibility. When users search for items like "espresso machines," they are now greeted with product grids at the top of the search results, often before any organic listings. This new layout allows users to refine their searches directly within the results, selecting specific features to narrow down their options. The right-side sidebar provides detailed product information, including images, pricing, and descriptions, all without needing to click through to a retailer's website. This shift indicates that Google is attempting to replicate the traditional category page experience within its search results. By prioritizing product pages, Google is effectively positioning itself as a competitor to Amazon, which has historically dominated product searches. The implications for retailers are profound; as Google continues to enhance its product-focused search results, category pages are losing their prominence. The competition between Google and Amazon for ecommerce market share has intensified, with a notable decline in Google's product search market share over the years. As a result, retailers must now focus on optimizing their product pages to align with Google's evolving search algorithms. This includes updating title tags, H1s, and product data to ensure that key features are highlighted effectively. Moreover, the importance of product data cannot be overstated. Google emphasizes the need for accurate and comprehensive product feeds, which play a crucial role in how products are displayed in search results. Retailers must ensure that their product data is optimized for search engines, reflecting relevant keywords and providing complete information. As Google continues to act as a de facto category page, there is a growing opportunity for brands that focus on long-tail queries. Smaller brands that optimize for specific, less competitive queries can gain visibility in search results that were previously dominated by larger retailers. This shift allows them to compete more effectively in a landscape that is increasingly driven by product-focused search results. However, these changes also pose challenges for affiliate sites, which are finding it harder to compete in a search environment that prioritizes direct product listings. As Google enhances its product features, affiliate sites may struggle to maintain their visibility and traffic. In conclusion, the evolution of Google's search results is reshaping the ecommerce landscape. Retailers must adapt their strategies to prioritize product pages and optimize their product data to remain competitive. As Google continues to refine its search experience to mirror that of a category page, understanding these changes will be crucial for retailers looking to thrive in the digital marketplace.

A long-tail keyword has low-to-medium search volume, high buyer intent, and more characters than regular keywords. To find these, start with 5-10 seed keywords and use tools like Semrush's Keyword Magic Tool with filters like phrase match and low difficulty. Optionally, set a minimum cost per click for conversion-focused keywords. After collecting relevant keywords, input them into tools like ChatGPT for additional suggestions, refining your list to improve SEO.

Google launched a new marketing resource hub called Accelerate with Google aimed at helping advertisers supercharge their campaigns with AI tools. The hub serves as a central destination rooted in Google's AI Essentials resources for ads products like Search and Performance Max. It includes customized skill assessments to identify knowledge gaps, a library of video tutorials and articles tailored to business goals, upcoming digital live streams on AI marketing topics, and more.

Google’s latest core update targets sites that are mass-producing low-quality content. Marketers can still utilize AI responsibly for tasks such as drafting content and FAQS. It’s unclear if Google can actually detect AI-generated content. However, it can identify content that summarizes existing content and websites creating content at an unreasonable scale. The core update also gives paid search ads a boost.

Customizing Google Ads conversion tracking involves understanding its structure, including categories like “Submit lead form” and “Sign-up.” Setting up primary conversions for key actions to optimize, such as lead form submissions and phone calls, while designating actions like page views as secondary, helps avoid skewing data. Regular updates to account default goals and leveraging campaign-specific conversion goals ensure accurate tracking and optimization tailored to each campaign.

Google's new Performance Max for Marketplaces allows sellers to create Google ad campaigns that send traffic to their product listings on third-party e-commerce platforms without needing a website or Merchant Center account. After connecting their Google Ads account to a supported marketplace, like Amazon, brands can use existing marketplace product data (i.e. images and pricing) in ads and will get detailed reporting on campaign performance within Google Ads.

Google launched a closed beta in Search Ads 360 for off-site retail media campaigns. It claims to have created a privacy-centric way for participating brands to use their first-party data to fuel AI-powered Performance Max campaigns. Google said it will expand the beta to more retailers globally over the upcoming months and support additional retail media campaign types, including Microsoft campaigns.

Google is encouraging merchants to enable “conversion annotations” – social proof badges that highlight a product's purchase popularity when displayed in Google Shopping ads. Conversion annotations like “best selling” or “1K shopped here recently” provide visual cues about a product's sales performance directly in the ad unit.