Free AI Google Ads Copy Generator

Generate high-quality, compelling copy variations for your Google ads that entice clicks and conversions.

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Available for all paid subscribers while in beta.

Use cases of Ahrefs’ Google Ads Copy Generator

  • Digital advertising agencies. Ahrefs’ Google Ads Copy Generator is an essential tool for digital advertising agencies. It assists in creating persuasive and effective ad copy for Google Ads campaigns. By inputting relevant information such as keywords, target audience, and campaign goals, this tool generates compelling ad copy that grabs the attention of users and encourages them to click. It saves time for advertising professionals, allowing them to focus on strategic campaign planning and optimization.
  • Small business owners. Small business owners who manage their own Google Ads campaigns can benefit from Ahrefs’ Google Ads Copy Generator. It provides them with ready-to-use ad copy ideas that are tailored to their specific business and target audience. This tool assists in crafting engaging and impactful ad text that effectively promotes their products or services, driving traffic and conversions. Small business owners can leverage this tool to improve the performance of their Google Ads campaigns and maximize their advertising budget.
  • PPC marketers. Pay-per-click (PPC) marketers can utilize Ahrefs’ Google Ads Copy Generator to enhance their ad copywriting process. This tool generates multiple variations of ad copy based on the given inputs, allowing marketers to experiment and test different messaging approaches. It helps in creating compelling and relevant ad text that increases click-through rates and drives conversions. PPC marketers can rely on this tool to optimize their Google Ads campaigns and achieve better results by delivering captivating and persuasive ad copy.

The technology behind Ahrefs’ Google Ads Copy Generator

Ahrefs’ Google Ads Copy Generator uses a language model that learns patterns, grammar, and vocabulary from large amounts of text data – then uses that knowledge to generate human-like text based on a given prompt or input. The generated text combines both the model's learned information and its understanding of the input.

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