Offsite marketing is essential in e-commerce, enabling businesses to reach customers through external platforms and drive traffic to retail websites. However, most current offsite marketing content is overly generic, template-based, and poorly aligned with landing pages, limiting its effectiveness. To address these limitations, we propose MarketingFM, a retrieval-augmented system that integrates multiple data sources to generate keyword-specific ad copy with minimal human intervention. We validate MarketingFM via offline human and automated evaluations and large-scale online A/B tests. In one experiment, keyword-focused ad copy outperformed templates, achieving up to 9% higher CTR, 12% more impressions, and 0.38% lower CPC, demonstrating gains in ad ranking and cost efficiency. Despite these gains, human review of generated ads remains costly. To address this, we propose AutoEval-Main, an automated evaluation system that combines rule-based metrics with LLM-as-a-Judge techniques to ensure alignment with marketing principles. In experiments with large-scale human annotations, AutoEval-Main achieved 89.57% agreement with human reviewers. Building on this, we propose AutoEval-Update, a cost-efficient LLM-human collaborative framework to dynamically refine evaluation prompts and adapt to shifting criteria with minimal human input. By selectively sampling representative ads for human review and using a critic LLM to generate alignment reports, AutoEval-Update improves evaluation consistency while reducing manual effort. Experiments show the critic LLM suggests meaningful refinements, improving LLM-human agreement. Nonetheless, human oversight remains essential for setting thresholds and validating refinements before deployment.

Now let's learn more about designing these two types of prompts. The Command-Xlarge model works best when we provide an instruction-based prompt. One way to do this is by using imperative verbs to tell the model what to do, for example: generate, write, list, provide, and other variations. Let's say that we are creating social media ad copy for a wireless earbuds product. We can write the prompt as follows.

In today's digital age, AI has become an increasingly useful tool for a variety of tasks, including writing. Whether you're a novelist, journalist, or student, AI can really help you improve your writing skills. In this article, we will explore some of the ways in which you can use AI to enhance your writing skills and improve the quality of your work. Also, We have made you a list of some Free AI Writing Tools to get you started right away with some tips to keep in mind while using these AI Writing Tools. The term "AI" has become increasingly prevalent in both home and office settings. You may have heard your friends or family members talking about how they are using AI to improve their writing skills, or you may have seen it mentioned in an article or on social media. But what exactly is AI?. In simple terms, AI is a computer program that is designed to learn and improve its performance over time.

OpenAI's flagship natural language processing (NLP) programme, GPT-3, has been available for two years now. Poems, essays, song lyrics, and even comprehensive manifestos could be generated by the AI language tool with just the barest of cues, and its readers were astounded by the quality of the writing produced. OpenAI's GPT-3 is a "foundation model" that was trained using what amounts to the whole of the internet (Wikipedia, Reddit, The New York Times, etc.). In order to determine the most likely responses to each given challenge, it utilises this massive information. Due to the massive scope of this study, only a select few of these basic models exist.

We present DeepGen, a system deployed at web scale for automatically creating sponsored search advertisements (ads) for BingAds customers. We leverage state-of-the-art natural language generation (NLG) models to generate fluent ads from advertiser's web pages in an abstractive fashion and solve practical issues such as factuality and inference speed. In addition, our system creates a customized ad in real-time in response to the user's search query, therefore highlighting different aspects of the same product based on what the user is looking for. To achieve this, our system generates a diverse choice of smaller pieces of the ad ahead of time and, at query time, selects the most relevant ones to be stitched into a complete ad. We improve generation diversity by training a controllable NLG model to generate multiple ads for the same web page highlighting different selling points. Our system design further improves diversity horizontally by first running an ensemble of generation models trained with different objectives and then using a diversity sampling algorithm to pick a diverse subset of generation results for online selection. Experimental results show the effectiveness of our proposed system design. Our system is currently deployed in production, serving ${\sim}4\%$ of global ads served in Bing.

Until now, artificial intelligence has majorly been used to streamline manufacturing, customer support, documentation, and logistics. Business communication has largely been a stronghold of humans. But all that is poised to change now. With AI tools getting more advanced and "human-like" by the day, they are more than ready to handle your internal and external business communication single-handedly. Check out the use cases explained in this post.

If you are reading this article, then you are probably already convinced that AI can have a significant impact on your business's bottom line. It's not a question of'Should I implement AI?', but rather'How can I use AI to stay ahead of others?' You are not alone in your quest. AI is one of the technologies companies like yours are using as a tool to gain a competitive advantage. According to Deloitte, 71% of businesses are planning to increase their investment in AI. It's no wonder, as AI is becoming more accessible for everyone.

Artificial intelligence is no more the future of marketing; it is pretty much at the moment. Consider all the ways AI technology has already started contributing to our everyday lives. Artificial intelligence is progressively becoming a central part of numerous industries and has various use cases, particularly in marketing. All businesses, big or small, have begun using AI to some extent to upgrade their website, products, and customer experience over time. If reports are to be believed, the top-performing organizations are more than 2x likely than their peers to use AI for marketing purposes.

The good news is that there are a lot of pre-existing single-purpose AI/ML platforms for marketing that will snap into your existing platforms right away. Social listening tools will help you design ad campaigns or even write limited ad copy. CRM tools will generate personalised ad copy based on your buyer personas. ESP tools can incorporate dynamic copy and images to improve relevancy, and thus clicks. And end-to-end partners can help you clean up data, solve identity issues in your database and implement machine learning in meaningful ways.

We at SEMrush continuously monitor Google's updates, as the search engine rarely stands still. While testing the updates that are most likely to impact advertisers, we have come to the conclusion that Google is moving in the direction of "autopiloted" campaigns. The message is clear, the more you delegate to Google Ads' artificial mind - bids, placements, banner sizes, etc. - the more benefit you and your customers will get from the campaign. However, it will still be up to you to determine your audience, so it is important to be clear on who your customers are, which search terms they use to describe their query, and which visuals engage and trigger them. This initial input consisting of keywords, ad text, visuals and dozens of other aspects, must be as specific as possible.