Scaling SEM (Search Engine Marketing) Ads Generation using GenAI
Problem Statement
The client's business model depended on volume, but their margins were being eroded by the manual labor of campaign setup. The hours required for each analyst to research, write, and launch ads for a new attraction made it increasingly unprofitable to serve the small-to-mid-sized clients that formed the core of their business. They were working harder for less return.
Client Info
A large digital marketing agency specializing in the travel and tourism sector. Their business model is built on efficiently managing campaigns for hundreds of mid-sized attractions, from regional theme parks to city museums.
Outcomes
Reduced time to create SEM Ads by 97%
Click-Through Rate (CTR) of the SEM Ads increased by 37%
How did BeautifulCode do it?
BeautifulCode started by shadowing the client's Business Analysts to deeply understand the creative and strategic process that went into creating a successful SEM ad. We identified that the core challenge was codifying their domain expertise and automating the research and generation process. To address this, we developed a human-in-the-loop AI agent to act as a powerful assistant for the analysts.
Our solution streamlined the entire ad creation workflow into a simple, interactive application:
Capturing Domain Knowledge
We collaborated closely with the analysts to distill their expert insights on ad copy, keyword strategy, and calls-to-action into a comprehensive, sophisticated prompt that would guide the AI's creative process.
Building an Empowered AI Agent
We constructed a single, powerful agent using LangChain. This agent was armed with our expertly crafted prompt and equipped with specialized tools: the Serper Search API for real-time web research and a custom Python tool to integrate directly with the Demand-Side Platform (DSP) API. The entire experience was delivered through a user-friendly Streamlit web application.
Automated Research and Ad Generation
When an analyst entered the name of a tourist attraction into the app, the agent immediately used the Serper tool to search the web for current details, highlights, and unique selling points about the property. It then synthesized these search results with the best practices embedded in its core prompt to generate a variety of high-quality SEM ad options, which were instantly displayed to the user.
Analyst-in-the-Loop for Approval
The Business Analyst could then review the AI-generated ads, selecting the most promising options with a single click. Once they gave their final approval, the agent automatically used its "Create in DSP" tool to push the ads live, completing the process seamlessly.
Continuous Improvement via Feedback
To ensure the agent grew smarter over time, we implemented a feedback loop. We collected data on the ads that analysts rejected and used this information to continuously refine and improve the master prompt, making the agent's future suggestions even more accurate and effective.
Technologies Used
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