플로리스트 재고·폐기 예측 엔진
식물 구독 리텐션 CRM
플로리스트 주문관리 SaaS
Fresh flowers lose value sharply within 3-7 days of arrival, but small flower shops manually track arrival dates and freshness per inventory item, missing discount windows and facing waste rates of 15-30%. Waste costs frequently exceed 10% of revenue.
Register arrival dates and expected lifespan per flower type to automatically notify when to switch to discounts based on remaining shelf life, and automatically apply discounted prices to connected sales channels. Accumulate waste pattern analysis to recommend optimal order quantities by day of week and season. Input: flower type + arrival date. Output: discount conversion alerts + weekly waste rate report.
| N Novelty | 1-5 | How uncommon the service is in market context. |
| U Urgency | 1-5 | How urgently users need this problem solved now. |
| M Market | 1-5 | Market size and growth potential from proxy indicators. |
| R Realizability | 1-5 | Buildability for a small team with realistic constraints. |
| V Validation | 1-5 | Validation signal quality from competition and demand data. |
| Tech Complexity | / 40 | Difficulty of core implementation stack. |
| Data Availability | / 25 | Practical availability and cost of required data. |
| MVP Timeline | / 20 | Expected time to ship a usable MVP. |
| API Bonus | / 15 | Bonus for viable public API leverage. |
| Competition | / 20 | Signal quality from competitor landscape. |
| Market Demand | / 20 | Demand proxies from search and mention patterns. |
| Timing | / 20 | Fit with current shifts in tech, behavior, and regulation. |
| Revenue Signals | / 15 | Reference evidence for monetization viability. |
| Pick-Axe Fit | / 15 | How well the concept serves participants in a trend. |
| Solo Buildability | / 10 | Practicality for lean-team implementation. |