DoorDash in AI Answers
Topic-Clustered Competitive Analysis
Executive Summary
- Market position validated: DoorDash leads AI recommendations in 5 of 6 topic categories, consistent with its ~60% real-world market share in food delivery.
- Grocery is the singular competitive gap: 0% win rate in grocery queries — Instacart dominates at 92%. This is the only category where DoorDash does not lead.
- Meta Llama anomaly: One platform (Meta Llama) shows dramatically different behavior — 6.7% DoorDash featured rate vs 60%+ on other platforms. This appears to be a model-specific bias.
- Strong B2B and Value positioning: Restaurant-facing queries (76%) and value/fees queries (72%) show strongest DoorDash positioning.
Topic Cluster Analysis
Win rate represents tests where DoorDash was the primary recommendation. Each cluster contains 5 queries tested across 5 platforms (25 tests per cluster).
Strongest category. DoorDash consistently positioned as the leading platform for restaurant partnerships.
Strong positioning on affordability and fees. DashPass subscription mentioned favorably.
Geographic coverage positioning is landing. DoorDash recognized for suburban and small-town availability.
Competitive but not dominant. Driver satisfaction queries show mixed results across platforms.
General "best delivery app" queries show DoorDash leading but with meaningful competition from Uber Eats and Grubhub.
Singular competitive gap. Instacart dominates grocery queries entirely. DoorDash rarely mentioned and never featured.
Source-Outcome Correlation
Earned media sources that appear more frequently when DoorDash wins vs. loses. Baseline win rate: 52.7%.
Correlated with DoorDash Wins
When these sources are cited, DoorDash is featured more often than baseline
Correlated with DoorDash Losses
When these sources are cited, DoorDash is featured less often than baseline
Overall Competitive Position
Featured platform distribution across all 150 tests. "Featured" means the platform was the primary recommendation in the AI response.
Win Rate by Topic × Competitor
| Topic | DoorDash | Uber Eats | Instacart | Grubhub |
|---|---|---|---|---|
| Restaurant B2B | 76% | 16% | 0% | 8% |
| Value / Fees | 72% | 8% | 8% | 8% |
| Suburban | 60% | 16% | 0% | 16% |
| Driver | 56% | 16% | 4% | 12% |
| Consumer | 52% | 24% | 8% | 20% |
| Grocery | 0% | 0% | 92% | 0% |
AI Platform Performance
DoorDash win rate by AI platform. Each platform was tested with all 30 queries.
Platform Anomaly: Meta Llama
Meta Llama shows dramatically different behavior than other platforms — featuring DoorDash in only 6.7% of tests versus 57-67% on other platforms. This appears to be model-specific rather than topic-specific:
- Meta Llama featured Grubhub most often (43% of tests)
- DoorDash was still mentioned in 80% of Meta Llama responses
- This pattern is consistent across all topic clusters
Implication: If Meta Llama–based products gain market share, DoorDash's AI visibility could be affected. Worth monitoring but not currently actionable.
Interactive Query Appendix
All 150 tests with filtering and sorting. Click column headers to sort.
| ID | Query | Topic | Platform | Featured | Winner |
|---|
Study Parameters & Limitations
Study Parameters
- 30 unique queries across 6 topic clusters
- 5 query variations per topic
- 5 AI platforms tested (ChatGPT, Claude, Perplexity, Gemini, Meta Llama)
- 150 total tests
- 884 citations captured
- Collection date: December 12, 2025
Topic Clusters
- Driver: Gig worker pay, satisfaction, treatment
- Restaurant B2B: Commissions, partnerships, platforms for restaurants
- Grocery: Grocery delivery apps and services
- Consumer: General "best delivery app" queries
- Value: Fees, affordability, cheapest options
- Suburban: Coverage, small towns, availability
What This Shows
- Win rates by topic (where is DoorDash strong/weak?)
- Source authorities per topic (who does AI cite?)
- Source concentration (dominant vs dispersed)
- Platform consistency (do platforms behave differently?)
- Phrasing sensitivity (do results vary within topic?)
What This Does NOT Show
- Whether specific sources cause wins/losses
- Whether pitching a source would change AI outputs
- Individual source win rates with statistical confidence
- Content-level analysis of what sources say
Important Limitations
- Single snapshot: AI responses change over time as models are updated
- Query phrasing matters: Different wording may yield different results
- 5 variations per topic: May not capture full variance within each topic
- Source frequency ≠ influence: Being cited often doesn't mean source determines winner
- Platform algorithms are opaque: Cannot determine why platforms behave as they do
- US-centric queries: Results may differ in other markets
- No content analysis: Cannot assess sentiment or positioning within cited sources
- Meta Llama anomaly: One platform shows dramatically different behavior; cause unknown