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CLIENT BACKGROUND AND CHALLENGE
Sierra Nevada Brewing Co., a pioneer in the American craft beer movement, has long been a top-of-mind brand for hop-forward brews like their iconic Pale Ale and innovative Hazy IPA line. However, in 2024, the brand faced a new challenge: search behavior was changing fast.
Their SEO strategy, once focused on long-form, keyword-rich pillar content, was becoming obsolete. Google’s new AI Overviews (AIO) and the rise of LLM-powered discovery tools like Chat GPT were rewarding fast, focused answers over traditional dense articles. Despite their strong domain authority, Sierra Nevada’s Hazy IPA content was losing visibility and traction.
They needed to evolve. Fast.
Solutions
Implemented
We didn’t just optimize. We completely re-architected Sierra Nevada’s content strategy based on user intent in an AI-first world.
1. Question-Based Content Mapping
We reverse engineered the way people now search by focusing on questions. Our SEO team used data from “People Also Ask,” Reddit, and actual queries entered into ChatGPT to identify more than 100 real, conversational prompts related to Hazy IPAs (for example, “Is Hazy IPA stronger than regular IPA?” and “Why is Hazy IPA cloudy?”).
This helped us shift focus from broad keywords to specific, answerable queries.
2. From Pillars to Precision
We dismantled Sierra Nevada’s legacy pillar content and rebuilt it as a network of focused, modular Q&A-style landing pages.
Each page was dedicated to a single user question, delivering a clear, concise answer. These pages were designed to function as micro-destinations for both users and search engines.
3. LLM-Ready Technical Implementation
We wrapped each page in structured data using FAQ, HowTo, and Product schema. This made the content easily readable not just for Google, but also for AI models powering tools like ChatGPT and Perplexity.
We also created custom GPT agents to simulate how large language models interpreted and reworded Sierra Nevada’s content. This helped us refine tone, improve clarity, and enhance semantic relevance.
4. Human-Centric Meets Machine-Centric
We struck a balance between conversational language and technical accuracy. The result was content that both educates people and gives machines the confidence to surface Sierra Nevada’s answers at the top of search and AI-driven platforms.
Results Achieved
+490%
+345%
+84%
Want to meet the team involved?
CLIENT FEEDBACK
“We knew SEO was changing, but we didn’t realize how fast. The team helped us not just adapt, but lead. Our Hazy IPA content now shows up exactly where the next generation of beer lovers is searching and asking questions.”
Lessons Learned and Future Plans
Intent Beats Volume
Focusing on specific, high-intent questions outperforms generic, high-volume keywords. Optimizing for how users actually search in an AI-first landscape is more effective than relying on traditional keyword strategies.
Structure Is Strategy
Schema, modular design, and conversational formatting are no longer technical add-ons, they are strategic must-haves. When content is structured clearly, both humans and machines respond better.
Optimizing for AI Requires Intentional Design
AI models prioritize clarity, structure, and relevance. Creating content with explicit intent, modular formatting, and semantic depth is key to becoming discoverable in AI-driven results.
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