As AI tools like ChatGPT and Deepseek have gradually become consumers' preferred channels for queries such as "fast food recommendations for commuters" and "weekday lunch options", many fast food brands are facing the dilemma of being "unheard" in AI Q&As: despite having core advantages like fast service and unique flavors, they rarely appear in AI recommendation lists. Even when mentioned, the information is often outdated and disconnected from the brand's selling points.
So, how can fast food brands break through this predicament and seize more exposure in AI responses?
Taking the real practice of a fast food brand as an example, this article breaks down how dtcpack achieves a multiplier increase in brand visibility in AI answers through precise function implementation — from an 8% mention rate to 32%, transforming from a "peripheral player" to a regular in scenario-based recommendations.
I. Project Background and Core Pain Points
A regional fast food brand faced a traffic gap in the AI era in 2026:
- Brand exposure on traditional search engines was stable, but AI Q&A scenarios emerged as a new traffic entry point. The brand's initial mention rate in high-frequency queries such as "weekday lunch recommendations", "fast food options for commuters", and "cost-effective fast food brands" was only 8%;
- In the few scenarios where the brand was mentioned by AI, the information was "outdated/incomplete" (e.g., AI quoted a menu from 2 years ago and failed to mention the core advantage of "5-minute food preparation"), resulting in an information accuracy rate of less than 20%;
- Competitors had occupied the top 2 recommendation positions in similar queries through AI content optimization, leading the brand to be increasingly overlooked by potential customers in AI Q&As.
Based on this, the brand introduced the dtcpack GEO tool and launched the "Targeted Visibility Enhancement Project in AI Answers".
II. Full Optimization Process Driven by dtcpack
(I) Diagnosis Phase: Identify Root Causes with dtcpack
Complete baseline data collection and problem localization through dtcpack's core functions:
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dtcpack-GEO Brand Evaluation:
- Real-time monitoring of brand exposure rate, output of brand baseline data and key competitor data:
- Total mention rate: 8%;
- Competitor comparison: Top competitors achieved a 40% mention rate in similar scenarios.
- Real-time monitoring of brand exposure rate, output of brand baseline data and key competitor data:
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dtcpack-Prompt Evaluation:
- Expand high-frequency fast food-related Prompts and assess the optimization difficulty of Prompts across platforms to formulate reasonable optimization goals and directions.
(II) Execution Phase: Implementation of dtcpack Functions
Targeted optimization based on diagnostic results:
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dtcpack-Prompt Evaluation (Selling Point Binding):
- Conduct "selling point disassembly and binding" for high-potential Prompts:
- For "5-minute pickup fast food for commuters": Bind the brand's advantages of "intelligent food preparation system + pre-prepared meals";
- For "healthy fast food for families with kids": Bind the brand's selling points of "low-sodium formula for children's meals + separate packaging";
- Generate 10 sets of "Prompt - Selling Point" keyword libraries and synchronize them to the brand's content matrix.
- Conduct "selling point disassembly and binding" for high-potential Prompts:
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dtcpack-GEO Real-time Monitor (Full-Calibration):
- Daily crawl fast food-related answers from over 10 mainstream AI platforms and monitor 2 core indicators:
- Brand information exposure position (top 3 entries / end / not mentioned);
- Information completeness (whether it includes selling points and product examples);
- When outdated information quoted by AI is found, synchronize it to the brand's content team in real time, and update the corresponding section on the official website within 24 hours.
- Daily crawl fast food-related answers from over 10 mainstream AI platforms and monitor 2 core indicators:
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dtcpack-LLMs-TXT Generator (Content Adaptation):
- Output "AI-friendly on-site content guidelines" to guide the brand in optimizing 3 types of core content:
- Product pages: Disassemble "5-minute food preparation" into "3 technical supports of the intelligent food preparation system + peak-hour food preparation efficiency data";
- FAQ section: Add AI-easy-to-crawl Q&A content such as "ordering tips for commuters" and "nutritional ratio of children's meals";
- Brand dynamics: Publish a special article on "food preparation efficiency upgrade" and embed keywords from the Prompt keyword library.
- Output "AI-friendly on-site content guidelines" to guide the brand in optimizing 3 types of core content:
(III) Iteration Phase: Refined Optimization Based on dtcpack Data
Adjust optimization strategies through dtcpack's weekly data reports:
- Discovered that the mention rate in the "late-night fast food for overtime workers" scenario improved slowly, so added the brand's selling point of "hot food supply after 22:00" to the keyword library;
- Addressed the lack of "brand store coverage" information in AI answers by using the LLMs-TXT Generator to add content such as "regional store distribution + list of night-operating stores".
III. Project Effect Verification
(I) Core Data Improvement
| Indicator | Initial Value | Post-Optimization Value | Growth Rate |
|---|---|---|---|
| Total mention rate in AI Q&As | 8% | 32% | +300% |
| Information relevance score (10-point scale) | 3.2 | 7.8 | +143.75% |
| Exposure frequency in top 3 recommendation positions | 2 times/week | 18 times/week | +800% |
| Brand information accuracy rate | 20% | 90% | +350% |
(II) Derived Value
- Traffic conversion: Weekly average traffic to the brand's official website guided by AI Q&As increased by 45% month-on-month, with clicks on the "Order Now" button growing by 62%;
- Customer base structure: The proportion of commuter/overtime worker customers aged 18-35 increased from 40% to 55%;
- In-store orders: Weekly average in-store orders associated with AI recommendations grew by 28%, with orders during the night period (20:00-22:00) increasing by 35%.
IV. Case Summary
Through the full-process GEO optimization of "Diagnosis - Execution - Iteration", dtcpack helped the fast food brand transform from an "outsider in AI Q&As" to a "regular in scenario-based recommendations". Its core value lies in: guided by AI's content crawling logic, integrating brand advantages into the AI answer system naturally through precise Prompt matching and dynamic information monitoring.
