How We Grew Private Label MFG's AI Search Visibility from 1% to 20% in 6 Months

In this case study, we’re sharing exactly how we grew Private Label MFG’s AI search visibility from 1% to being recommended more than 20% of the time for their target prompts in just 6 months. The increased visibility grew AI conversions from 0.5% of total sales to 5% — a 10x increase in just a few months.

We’ve used these same techniques on other brands to achieve similar results. By the end of this case study, you’ll have a clear picture of how to apply the same AI SEO approach to your business.

About Private Label MFG

Private Label MFG (PLM) is a San Francisco-based eCommerce store that has been selling high-performance aftermarket car parts since 2005. Exhaust systems, engine components, suspension upgrades, and cold air intakes built for Hondas, Subarus, BMWs, Toyotas, and more.

They came to us because AI search wasn’t doing anything for them. Their post-purchase surveys showed only 0.5% of customers found PLM through ChatGPT or another AI assistant. For a brand selling to car enthusiasts who are constantly researching mods online, that number should have been a lot higher.

• Website: privatelabelmfg.com
• Products: Performance aftermarket car parts (exhaust, engine, suspension, interior)
• Platform: Shopify
• Location: San Francisco, CA
• Timeline: September 2024 to March 2025 (6 months)

The Strategy

The plan we put together had four main pillars:

1. AI Search Foundations — Optimizing the website to be more LLM-friendly so their key pages get cited more often.
2. Content Creation — Creating content that AI assistants love to cite, shaping the conversations around their products.
3. Brand Mentions — Getting their products mentioned in the exact articles AI assistants use to recommend products.
4. Reddit Marketing — Engaging in relevant Reddit threads that AI assistants rely on for product recommendations.

Step 1 — AI Search Foundations

Before publishing a single piece of content, we made sure the foundational elements were in place. There were four main things we focused on.

Prompt Research

Just like keyword research is the foundation of traditional SEO, prompt research is the foundation for AI search optimization. We started with Google keyword data and worked backwards:

• “cold air intake” gets 29,000 monthly searches in Google. Someone searching for this is probably asking ChatGPT “What’s the best cold air intake for my car?”
• “Motor mount” with 8,000 monthly searches maps to “What are the best replacement motor mounts?”

Using Google search volume data is more grounded than guessing prompts, because at least we know there’s actual search demand behind the prompts we’re targeting. We built a list of 100 prompts and loaded them into our prompt tracker for monitoring across ChatGPT, Perplexity, and Gemini.

On-Page Optimization

We reviewed PLM’s product pages and noticed most of them were thin on the exact information LLMs need to confidently recommend products.

For example, when someone asks “Which traction bars work best for reducing wheel hop on a Subaru WRX?”, the AI is looking for pages that clearly state important information like specs, materials, compatibility, unique selling points, and use cases.

We assessed PLM’s most important products based on the following criteria:

• Specifications (dimensions, materials, weight, compatibility)
• Unique selling points
• Use cases (daily driver vs. track build vs. racing)
• FAQs specific to the product

Creating a Dedicated FAQ Page

We created a consolidated FAQ page covering the high-level questions customers ask about PLM — shipping, returns, warranty, checkout, coupon codes. LLMs are more likely to confidently recommend a brand when they have complete, easy-to-find information about how that brand operates.

Content Audit

Both traditional search engines and AI search engines favor sites that are tightly focused on a single topic. We scored every article on topical relevance and flagged the ones that were diluting the site’s core focus. Removing less relevant content sent a clearer signal about what PLM specializes in.

Step 2 — Content Creation

We noticed ChatGPT and other AI assistants were consistently citing “Best of” articles and competitor comparison posts. We reverse engineered the AI responses to identify the exact topics that would have the best chance of being cited.

”Best Of” Articles

We created “best of” articles with comparison tables showing which products are best for which builds, what the key specs are, and who each option is right for. PLM’s own products were included as the primary recommendations, with full specs, pricing, and application notes in the tables.

Competitor Comparisons

We also published articles that directly compared PLM to its main competitors. These types of topics map directly to the kinds of questions people ask AI assistants when they’re close to making a purchase.

Content Optimization for AI Citation

Beyond format, we optimized the content itself to improve citation rate. Many of our optimization recommendations come from a Princeton study on what gets content cited more often:

• Including expert quotes increases citation likelihood by ~30%
• Incorporating quantitative statistics improves citation frequency by ~27%
• Using simple, clear language (Wikipedia-style) improves it by ~22%
• Citing sources within the content helps by ~20%

Structuring content in an FAQ format with questions for subheadings also positively impacts citation rate.

Step 3 — Brand Mentions

With AI SEO, the currency isn’t backlinks. It’s brand mentions. Ahrefs ran a study of 75,000 brands and found that more brand mentions in more places on the web (with positive context) equates to more recommendations by AI assistants.

We built PLM’s brand mention footprint through four strategies:

1. Press Release

PLM was making their first appearance at the SEMA trade show in Las Vegas. We wrote a press release about the event and distributed it to major media outlets. The press release earned 138 pickups, including Yahoo Finance, MarketWatch, Morningstar, Benzinga, and dozens of regional outlets. That’s 138 new branded web mentions they didn’t have before.

2. Pitching Product Roundups

Getting included in third-party roundup articles that AI assistants are already citing is one of the most impactful ways to improve AI visibility. Using our prompt tracking tools, we could see exactly which external articles were being cited most across our 100 tracked prompts. We pitched PLM for inclusion in the most cited roundups.

3. Guest Posts and Sponsored Posts

To further increase the number of brand mentions, we secured guest posts and sponsored articles on relevant automotive sites.

4. Scholarship Campaign

We created a $1,000 scholarship for university students and promoted it to universities and financial aid websites. Every university that picked up the scholarship resulted in another brand mention.

Step 4 — Reddit Marketing

Reddit gets its own section because the data on this is hard to ignore. Across the 100 prompts we were tracking, reddit.com was the #1 most cited domain.

If you’ve been wondering why everyone in AI SEO keeps talking about Reddit, that’s the reason. For PLM, there were active conversations happening where enthusiasts were asking exactly the questions we were tracking. Over the course of 6 months, we published 129 Reddit comments from aged, high-karma accounts.

6-Month Summary

Here’s what the campaign delivered:

• Optimized top category and product pages for AI search
• Cut less relevant articles to tighten topical focus
• Published 20 blog posts in AI-friendly formats
• Built 154 brand mentions from the press release, roundups, guest posts, and scholarship campaign
• Posted 129 Reddit comments in relevant subreddits

Results

Revenue: +344% AI Referral Revenue

GA4 showed AI referral revenue up 344% over the course of the campaign. ChatGPT accounted for 94.6% of AI referral sessions, with Perplexity and Gemini making up the rest.

Note that GA4 only captures customers who clicked a link directly from an AI tool. Post-purchase surveys give a much better picture of actual AI search conversions.

Post-Purchase Surveys: 0.5% to 5%

Submissions citing “ChatGPT, Perplexity, or another AI assistant” went from 0.5% to 5% over the campaign. A 10x increase. AI search went from a rounding error to a meaningful revenue channel in a few months.

AI Visibility: 1% to 20%

PLM started the campaign being recommended only 1% of the time for the AI prompts they were targeting. By the end, they were being recommended in more than 20% of their 100 tracked prompts.

Citation Rate: 7% to 18.4%

PLM’s citation rate went from 7% to 18.4%, which put them ahead of all their direct competitors. The articles doing the heavy lifting were the ones we had created, all of which were published within the previous 4 months.

Spillover to Traditional SEO

Even though we weren’t actively focused on traditional SEO, PLM’s organic rankings jumped up anyway. Many high-volume keywords went from completely unranked to page 1, and in some cases position 1.

Key Takeaways

• Prompt research is the AI SEO equivalent of keyword research. Start by turning your existing Google keyword data into likely ChatGPT prompts. It’s more grounded than guessing.
• Thin product pages don’t get recommended. AI assistants need specs, USPs, compatibility, use cases, and FAQs to confidently recommend a product.
• Fresh content gets cited quickly. AI assistants have a strong recency bias. New, well-structured content can drive AI citations within weeks of publishing.
• Brand mentions are the #1 driver of AI recommendations. Get your brand mentioned in more places with positive context.
• A single press release can generate 100+ brand mentions. A trade show appearance is enough of a hook.
• Reddit is one of the most cited domains in ChatGPT. If your brand isn’t there, you’re leaving AI visibility on the table.