Sauce opportunity is not a single trending flavour. It lives in the relationship among dish use, staple texture, regional preference and the way consumers describe taste. The brand needed more than a trend list: an evidence chain explaining what to develop first, for which context and why it mattered.
Every step reproducible
- 01TrendAI
Assemble real restaurant-market performance
TrendAI assembled noodle and rice-dish restaurant data using rules for dish, price point, category and city tier, creating a comparable demand base.
- 02ezData
Filter to dishes worth studying
Recommendation performance and growth identified star dishes, emerging challengers and high-value cash cows. Low-contribution dishes were removed so analysis could focus on the opportunity pool that carried business value.
- 03ezSummary
Decode flavour in AI-coded layers
AI first coded primary flavour and regional preference, then added secondary flavour, ingredients, staple texture, usage and sauce texture — translating disparate dishes into structured data in one shared language.
- 04
Converge on developable combinations
Crossing “usage × staple × flavour” produced high-concentration, non-fragmented opportunity combinations with representative dishes, pairing logic and development priority.
- 05TrendAI
Use social voices to complete value drivers
Social discussion around representative dishes added consumers’ language for taste, texture and satisfaction, connecting the data map back to product and consumer language.
- 78,005条restaurant-data records entered opportunity screening
- 17,006道dishes formed the analyzable opportunity pool
- 4,607条priority flavour × priority province records received deep AI coding
- 用途 × 主食 × 风味formed prioritizable development combinations and a flavour-demand map