Case Study: How Spoil Reached 70% Visibility Across 5 AI Engines in 9 Months

Key Takeaways

• Spoil, a gifting platform, went from 30% AI visibility with inconsistent coverage to 70% visibility across 5 major AI engines in 9 months, with every engine now recommending them.
• The core challenge was platform inconsistency: Spoil showed up on 2 of 6 engines at baseline but was completely invisible on the other 4, meaning most AI users never heard of them.
• A platform-by-platform strategy addressed the specific retrieval preferences of ChatGPT, Perplexity, Gemini, Claude, Grok, and Copilot individually rather than treating AI search as a single channel.
• AI referral traffic now accounts for 31% of Spoil's new user signups, converting at 4.4x the rate of paid social, with a 22% lower cost per acquisition.
• The 9-month timeline broke into 3 phases: foundation (months 1 to 3), platform targeting (months 4 to 6), and consistency optimization (months 7 to 9).

The Challenge: Showing Up on 2 Engines but Missing from 4

Spoil is a gifting platform that helps people send personalized gifts to friends, family, and colleagues. The app handles everything from gift selection to delivery tracking, and had built a strong user base through paid social and word of mouth. By early 2025, Spoil had over 200,000 registered users and was processing $3.8M in annual gift transactions.

But their growth team noticed a trend they could not ignore. An increasing number of users were arriving at Spoil after asking AI search engines questions like "best gifting app," "how to send a gift to someone long distance," and "top gift delivery services." The problem was that this traffic was inconsistent and far smaller than it should have been.

When Spoil ran a comprehensive AI visibility audit using GRRO across 25 gifting-related queries on 5 major AI engines, the results revealed a fragmented picture:

Spoil's overall AI Recommendation Score was 27 out of 100. They were being recommended on Perplexity and occasionally on Gemini, but were almost completely invisible on ChatGPT, Claude, Grok, and Copilot. That meant 4 out of 6 engines, representing roughly 65% of the 800M+ weekly AI search queries, had no idea Spoil existed.

Meanwhile, two competitors with smaller product catalogs and fewer users were being recommended across 4 and 5 engines respectively.

The Diagnosis: Why Spoil Was Strong on 2 Engines and Invisible on 4

GRRO's audit identified the specific reasons behind Spoil's platform-by-platform inconsistency. Each AI engine retrieves information differently, and Spoil's existing digital footprint aligned with some retrieval methods but not others.

Where Spoil Was Strong

Perplexity (52% visibility): Perplexity's retrieval pipeline relies heavily on Brave and Bing search, and it favors Reddit as a source. Spoil had an organic Reddit presence. Users in r/gifts, r/giftideas, and r/LongDistance regularly mentioned the app in recommendation threads. Perplexity's 48 to 72 hour freshness window meant these recent discussions kept Spoil in the retrieval pool.

Gemini (41% visibility): Gemini pulls primarily from Google search results and weighs Quora as a secondary source. Spoil ranked on Google's first page for 18 of their target keywords and had a handful of Quora answers mentioning the platform. This was enough for moderate Gemini visibility on some queries.

Where Spoil Was Invisible

ChatGPT (8% visibility): ChatGPT's retrieval relies on Bing search, and it heavily favors Wikipedia and LinkedIn as authoritative sources. Spoil had no Wikipedia page, a minimal LinkedIn presence (company page with 340 followers, no published content), and weak Bing rankings compared to their Google rankings. Without these signals, ChatGPT had almost no basis for recommending Spoil.

Claude (6% visibility): Claude draws from a broad web crawl and favors well-structured, in-depth content from authoritative domains. Spoil's website had thin content: a homepage, 4 feature pages, a basic FAQ, and 6 blog posts that were primarily product announcements. There was not enough substantive content for Claude's retrieval system to associate Spoil with gifting expertise.

Grok (0% visibility): Grok sources primarily from X/Twitter and prioritizes content less than 24 hours old. Spoil's X/Twitter account had 1,200 followers with sporadic posting (2 to 3 tweets per month, mostly promotional). Zero Grok visibility was the direct result of zero meaningful X/Twitter presence.

Copilot (3% visibility): Microsoft Copilot relies on Bing search results. Spoil's Bing rankings were significantly weaker than their Google rankings because they had never optimized for Bing specifically. Their site lacked the structured data and Bing Webmaster Tools verification that would have improved Copilot retrieval.

The diagnosis was clear: Spoil's AI visibility was a platform-specific problem that required platform-specific solutions. A generic "create more content" approach would have improved Perplexity and Gemini slightly while leaving the other 4 engines untouched.

The Strategy: A Platform-by-Platform Approach Over 9 Months

Spoil's growth team, working with GRRO's platform intelligence data, designed a 3-phase strategy targeting each AI engine's specific retrieval preferences.

Phase 1: Foundation (Months 1 to 3)

The first phase focused on the baseline work that would improve visibility across all platforms simultaneously.

Schema Markup Implementation (Month 1)

Spoil's developer spent 2 weeks implementing comprehensive structured data:

• Organization schema on the homepage with consistent brand description, founding date, social profiles, and app store links
• Product schema on every gift category page with pricing ranges, availability, and aggregate ratings
• FAQ schema on 30+ pages with 150 question-answer pairs covering gifting use cases, shipping, pricing, and platform features
• Article schema on all blog content with named authors, publication dates, and modification dates
• BreadcrumbList schema across the site for clear hierarchy signals

The standardized brand description, "Spoil is a gifting platform that lets you send personalized gifts to anyone, anywhere, with curated selections, real-time tracking, and scheduled delivery for birthdays, holidays, and everyday moments," was pushed to every profile and directory listing.

Answer-First Content Library (Months 1 to 3)

Spoil identified the top 40 questions their potential users ask about gifting through GRRO's query analysis, customer support data, and manual AI engine research. They created comprehensive guides for each:

• "Best Gift Delivery Apps in 2026" (comparison with honest competitor analysis)
• "How to Send a Last-Minute Gift Anywhere in the US"
• "Best Birthday Gift Ideas for Someone Who Has Everything"
• "How to Send a Gift When You Do Not Know Their Address"
• 36 additional guides covering holiday gifting, corporate gifts, long-distance relationships, sympathy gifts, and more

Each guide followed answer-first structure: the first sentence directly answered the query, comparison tables with specific data followed, and FAQ sections with schema markup closed out each page. Publishing pace was 3 to 4 guides per week.

Wikipedia Page Creation (Month 2 to 3)

This was one of the highest-impact single actions. Spoil worked with a Wikipedia-experienced editor to create a properly sourced article about the platform. The page included founding history, product description, funding information, and citations from 8 independent media sources that had covered Spoil. The page went live in month 3 after community review.

This single action had an outsized impact on ChatGPT visibility because ChatGPT weights Wikipedia as one of its most trusted sources (Wikipedia appears in 47.9% of ChatGPT's cited sources according to GRRO's platform research).

Phase 2: Platform Targeting (Months 4 to 6)

With the foundation in place, Spoil executed targeted strategies for each underperforming platform.

ChatGPT Strategy: Bing + LinkedIn + Wikipedia

• Verified site in Bing Webmaster Tools and submitted an optimized XML sitemap
• Published 12 LinkedIn articles from Spoil's CEO on gifting trends, consumer behavior, and the future of personalized commerce
• The CEO committed to 4 LinkedIn posts per week, building from 340 to 2,800 followers over 3 months
• Monitored Wikipedia page for vandalism and kept citations current
• Created 3 Bing-optimized landing pages targeting high-volume gifting queries

Result at month 6: ChatGPT visibility rose from 8% to 54%.

Grok Strategy: X/Twitter Presence

• Hired a part-time social media manager focused exclusively on X/Twitter
• Published 3 to 5 posts daily: a mix of gifting tips, user stories (with permission), trending gift ideas, and replies to gifting-related conversations
• Engaged authentically in gift-related threads and holiday planning discussions
• Built X/Twitter following from 1,200 to 8,400 over 3 months
• Published real-time gift trend data during major holidays (Valentine's Day, Mother's Day) that got reshared by lifestyle accounts

Result at month 6: Grok visibility rose from 0% to 38%.

Claude Strategy: Broad Web Content Depth

• Published 20 additional long-form guides (1,500 to 3,000 words each) on gifting topics, establishing Spoil as a topical authority
• Secured guest articles in 4 lifestyle and ecommerce publications, creating independent web sources that associated Spoil with gifting expertise
• Built detailed "how it works" and "gifting etiquette" content sections that gave Claude's retrieval system substantive pages to index

Result at month 6: Claude visibility rose from 6% to 48%.

Copilot Strategy: Bing Optimization

• Optimized all existing content for Bing ranking factors (structured data, clear meta descriptions, Bing-verified site)
• Created Bing Places listing and ensured NAP consistency across directories
• The Bing Webmaster Tools verification and sitemap submission from the ChatGPT strategy also directly benefited Copilot visibility

Result at month 6: Copilot visibility rose from 3% to 42%.

Phase 3: Consistency and Optimization (Months 7 to 9)

The final phase focused on maintaining all 5 platforms simultaneously and closing remaining gaps.

Review Growth Campaign

• Implemented post-purchase review requests at 7 and 21 days after gift delivery
• Grew Trustpilot reviews from 89 to 430 with a 4.6 average
• Grew App Store ratings from 3,200 to 5,100 at 4.7 stars
• These review signals strengthened sentiment across all 5 platforms

Content Refresh Cycles

• Established a 30-day update cycle for all comparison and "best of" content
• Refreshed statistics, pricing data, and product information monthly
• Added new FAQ pairs based on emerging gifting queries tracked through GRRO
• Perplexity's 48 to 72 hour freshness preference meant frequent updates kept Spoil consistently in its retrieval pool

Cross-Platform Monitoring

• Used GRRO to track visibility scores daily across all 5 engines
• Identified and addressed visibility dips within 48 hours
• When Grok visibility briefly dropped in month 8 due to reduced X/Twitter posting during a team vacation, they corrected course within a week

The Results: 9-Month Transformation

Visibility by Platform Over Time

Spoil's GRRO AI Recommendation Score rose from 27 to 81 over the 9-month period. Every one of the 6 major AI engines now recommends Spoil for relevant gifting queries.

Traffic and Acquisition Metrics

Referral Traffic by Platform (Month 9)

AI referral users converted to paying customers at 4.4x the rate of paid social users (14.2% vs. 3.2%), confirming the pattern we see across industries: users who arrive through an AI recommendation have higher intent because the AI has already vetted and endorsed the product.

Revenue Impact

Over the full 9 months, Spoil attributed $524,000 in revenue to AI search referrals:

• Months 1 to 3: $31,000 (foundation work indexing, initial improvements on Perplexity and Gemini)
• Months 4 to 6: $128,000 (platform targeting strategies driving ChatGPT, Grok, Claude, and Copilot growth)
• Months 7 to 9: $365,000 (all 5 engines recommending, compounding visibility driving accelerating traffic)

The growth curve accelerated sharply in months 7 to 9 because AI visibility compounds. As more engines recommended Spoil, more users searched for the brand by name, which strengthened entity signals, which led to even more recommendations. This compounding effect is why 97% of brands that are invisible to AI fall further behind every month they wait.

The Cross-Platform Playbook: What Spoil Learned About Each Engine

Spoil's 9-month journey produced a detailed understanding of what each AI engine prefers when deciding what to recommend. This playbook reflects what they observed through GRRO's tracking data.

ChatGPT retrieves primarily through Bing and gives heavy weight to Wikipedia (47.9% of cited sources) and LinkedIn. If you are invisible on Bing, you are invisible on ChatGPT. A Wikipedia page and consistent LinkedIn publishing were the two actions that moved the needle most.

Perplexity uses Brave and Bing search with a strong preference for Reddit (46.7% of cited sources) and a 48 to 72 hour freshness window. Spoil's organic Reddit mentions were their strongest asset from day one. Keeping content fresh and maintaining authentic Reddit presence sustained Perplexity visibility.

Gemini pulls from Google search and favors Quora (14.3% of cited sources) and Google Featured Snippets. Strong Google rankings were necessary but not sufficient. The structured data and FAQ schema that triggered Featured Snippets were what pushed Gemini visibility from moderate to strong.

Claude draws from a broad web crawl and rewards depth, substantive content, and presence across multiple authoritative domains. Thin content is the main reason brands are invisible to Claude. Spoil needed 60+ well-structured pages and independent media coverage before Claude consistently recommended them.

Grok sources from X/Twitter and prioritizes content less than 24 hours old. There is no shortcut: if you are not actively posting on X/Twitter, Grok will not recommend you. Spoil went from 0% to 62% visibility on Grok purely through consistent daily X/Twitter activity.

Copilot relies on Bing search results. Bing Webmaster Tools verification, an optimized XML sitemap, and structured data are the baseline requirements. The strategies that improve ChatGPT visibility (Bing optimization) also improve Copilot visibility.

For a deeper breakdown of how each engine decides what to recommend, see our guide on how AI decides what to recommend.

FAQ

Why did Spoil start at 30% instead of 0%?

Spoil had an existing digital footprint that partially aligned with 2 of 6 AI engines. Their organic Reddit presence gave them Perplexity visibility, and their Google rankings gave them partial Gemini visibility. Most brands we audit start at 0% to 15%. Spoil's 30% baseline was above average but still meant 4 of 6 engines were not recommending them at all.

What was the total investment over 9 months?

Spoil allocated approximately 2 full-time equivalent team members to the project over 9 months: their content lead (full-time on AI visibility), a part-time social media manager (hired in month 4 for X/Twitter), and their developer (approximately 25% time for structured data and Bing optimization). Out-of-pocket costs included the GRRO subscription ($79/month for 9 months = $711), the part-time social media manager, and the Wikipedia editor consultation. Total investment including team time was approximately $180,000. Against $524,000 in attributed revenue, the ROI was 191% in the first 9 months, with the compounding nature of AI visibility suggesting increasing returns going forward.

Can this cross-platform approach work for B2B companies?

Yes. The platform-specific retrieval preferences apply regardless of industry. A B2B company would prioritize LinkedIn content more heavily for ChatGPT visibility, target G2 and Capterra for review signals instead of Trustpilot and App Store, and focus long-form content on industry-specific publications. The framework of diagnosing per-platform gaps and addressing each engine's retrieval preferences independently is universal. See our B2B SaaS case study for a detailed example.

How does Spoil maintain visibility across all 5 engines now?

Maintaining 6-engine visibility requires approximately 30 hours per week of ongoing effort: publishing 3 new content pieces per week, updating 4 to 5 existing pages per month, posting daily on X/Twitter, publishing weekly on LinkedIn, continuing the review collection program, and monitoring GRRO dashboards for visibility dips on any platform. The content refresh cycle is especially important for Perplexity, which favors content updated within the last 48 to 72 hours.

What is an AI Recommendation Score?

The AI Recommendation Score is a 0 to 100 score that measures how likely AI search engines are to recommend your brand when users ask relevant questions. It factors in mention rate, position in recommendations, sentiment, and consistency across platforms. Spoil went from a score of 27 to 81 over 9 months.

Conclusion

Spoil's journey from 30% to 70% AI visibility across all 5 engines demonstrates that cross-platform AI visibility is achievable when you treat each engine as a distinct channel with its own retrieval preferences. The generic approach of "create good content and hope AI finds it" leaves gaps that competitors will fill. Spoil's platform-by-platform strategy, from Wikipedia for ChatGPT to daily X/Twitter posting for Grok, addressed each engine's specific requirements and produced 5 out of 6 engine coverage within 9 months.

With AI search processing 800M+ queries weekly and growing at 527% year over year, the brands that build cross-platform AI visibility now will capture the users that competitors cannot reach. Every month of invisibility on even one engine is a month of lost recommendations that compound against you.

Start with a free scan at grro.io to see your current AI visibility across all 5 engines.