Ad Copy Generation and PPC Landing Page Matching with AI: Case Study

PEMAVOR’s AI-powered PPC solution offers a seamless, scalable approach to ad copy generation, optimized landing page selection, and end-to-end automation, enhancing Google Ads campaigns for maximum relevance and impact.

In partnership with Douglas, Europe’s leading perfume retailer, we developed a streamlined AI-driven PPC automation solution by leveraging Pipedream’s automation pipelines.

Faced with managing ad variations across multiple brands and regions, Douglas needed an efficient, scalable approach to reduce manual tasks and optimize targeting. PEMAVOR’s AI solution empowered Douglas to drive high-relevance PPC management across diverse product categories, enabling data-driven ad placements that align with customer intent. The result? A new level of PPC efficiency and targeting accuracy, setting a benchmark in automated campaign management.

Which problem did we solve with AI?

We identified the essential challenges for achieving scalable PPC relevance and formulated four key questions to address:

• #1: How can we identify the relevant keywords from several gigabytes of search terms? 
• #2: What is the best landing page for each keyword?
• #3: How do we get relevant ad copy?
• #4: How do we automate the entire process?

To bridge the gap between how Douglas organizes products and how customers search, we categorized search terms into structured groups: generic, category-specific, and problem-focused terms. This helped us match the search behavior more closely to Douglas’s product offerings.

Since Google favors high click-through rates (CTR), aligning keywords with landing pages helps prevent users from quickly returning to search results. To achieve that, we created perfectly matched ad texts for each search query to direct customers to the most relevant landing page. As a result, Douglas benefited from higher Quality Scores and conversion rates when ads were maximally relevant.

Let’s explain the whole process in detail.

Building an automated and scalable PPC solution with AI

Step 1: Extract and Enrich Keywords from Search Data

To ensure high-relevance PPC campaigns, we set up a comprehensive filtering and enrichment process for search terms data. This approach simplifies keyword data to match customer intent with Douglas’s extensive product offerings. By enhancing keywords with valuable context, such as brand and product associations, we can direct potential customers to the most relevant ads and landing pages, boosting engagement and click-through rates.

To initiate the process, we extract keyword search terms via Google Sheets from the Google Ads API. The data gathered includes:

• Search Terms
• Clicks
• Impressions

This foundational dataset is then filtered and enriched. The enrichment process leverages the Douglas Product Feed Data stored in an SQLite database, utilizing the FTS5 extension for efficient indexing. We match search terms against the following product attributes:

• Product Title
• Product Description

Once products are matched to search terms, we generate additional data columns to enrich the keywords:

• Top Brands and Products (based on sum and max of reviews)
• Found Brand and Product Counts (based on title and description match)
• Click and Impression Data (from Google Ads)
• Suggested Brands (from Google Autosuggest)
• Custom Search Engine (CSE) Data to confirm matching pages with relevant keywords.
• Avg Search Volume, the average search volume for the keyword (from Google Ads Keyword Planner).

The enriched data is stored in Google Sheets for the next phase.

Step 2: Automated Selection of High-Performing PPC Landing Pages

In our AI-powered PPC solution, we focus on the automated selection of high-performing landing pages to align each keyword with the most relevant content, improving Quality Scores and engagement rates.

1. Equal Links Sheet: We filter terms where the landing and category page links from the Custom Search Engine (CSE) are identical, storing them for further analysis.
2. Ad Group and Campaign Sheet: We generate names for ad groups and campaigns based on CSE link URLs, structuring data for seamless ad placement.

This structured data set ensures keywords are linked with the most relevant landing pages for maximum engagement.

By considering PPC landing page optimization in our selection process, we ensure that each ad leads to a high-quality destination aligned with user expectations.

Automated selection of PPC landing pages:

• Our AI system analyzes and selects high-relevance landing pages based on keyword match and user intent.
• Landing pages are chosen to maximize Quality Scores, ensuring alignment with ad content and search intent.
• Data-driven landing page selection enhances engagement, ensuring each PPC landing page matches the user’s expectations.

Step 3: AI-Driven Ad Copy Generation for PPC Campaigns

Our solution acts as an AI-powered ad copy generator, producing targeted ad copy for Google Ads that maximizes relevance and engagement in each PPC campaign.

To craft effective ad copy, we draw on our domain knowledge into the process as below:

• Top Product Title and Descriptions for each search terms
• Website Content from the target URL via Custom Search Engine

Using OpenAI’s Assistant, we apply Retrieval-Augmented Generation (RAG) to create responsive ad copy. The system functions as a versatile AI ad copy generator, generating optimized Google Ads descriptions and headlines for higher PPC performance. It leverages content from Custom Search Engine (CSE) URLs or when those lack meaningful information, selects top product titles and descriptions from our enriched data (step 1) to ensure the most relevant and impactful results:

• 15 Headlines and 4 Descriptions for each term

Benefits of PEMAVOR’s AI Ad Copy Generation Process:

• Creates highly relevant, engaging Google Ads copy.
• Automates ad copy generation to align with targeted keywords.
• Enhances Quality Scores and user engagement through tailored content.

For those interested in implementing an AI-powered solution, our AI Ad Copy Cost Calculator provides a quick estimate for scaling ad copy generation with OpenAI models.

Step 4: Automate the entire process with Pipedream

To streamline the workflow, we use Pipedream, a low-code automation platform, which enables seamless integration across APIs and custom workflows. This automation includes:

• Pipeline 1: Calls the Cloud Function to generate enriched keyword data. This triggers filtering and enrichment function.

• Pipeline 2: Generates the Keyword-Campaign-Adgroup-Ad Copy sheet. This pipeline automates the process of creating ad copies tailored to each search term using OpenAI.

With Pipedream’s extensive integrations—Google Sheets, Slack, ClickUp, and more—it’s the backbone of our automation, coordinating processes from data extraction to final ad copy export. Try Pipedream’s versatile integrations here to explore workflow automation for your own projects.

Results: A streamlined, scalable solution for Douglas

Considering the thousands of the products Douglas offers, this automated system helped them reduce manual work and speed up ad copy creation. Creating relevant ad copy and optimizing landing pages also led to better engagement. 

This partnership with Douglas highlights how PEMAVOR is setting new standards in PPC automation. With technologies like Pipedream, Google Cloud, OpenAI, Python-based custom codes, Google Ads API and much more, we’re providing efficient, scalable digital solutions for leading brands.

Contact us to learn how PEMAVOR’s sophisticated solutions and PPC automation can elevate your Google Ads performance.