Industry overview
What is shaping floral
Flowers are bought for occasions, gifting and decoration, through florists, gifting platforms and street vendors. Freshness and delivery reliability are everything.
- Gifting occasions drive demand peaks
- Online gifting platforms are growing
- Growers need better access to markets
Client background
A rose farm, working in Pune. Client identity is withheld to protect confidentiality.
- Organisation
- A rose farm
- Sector
- Floral
- Team engaged
- IT & Development
- Services
- Custom business applications, Mobile app development
Client challenge
The rose farm harvested thousands of stems a day for domestic buyers and exporters, but harvest counts, grading and orders were recorded on paper. Orders were sometimes promised from stock that had already been sold, and the owners could not see wastage by variety or greenhouse.
Research objectives
- Deliver on the brief: tracking harvests and orders for a flower farm.
- Define requirements and success criteria the solution would be tested against.
- Deliver a reliable, secure solution the team can run and extend.
Target audience
Scope: One farm.
Audiences we typically reach in floral
Gift buyersFloristsFlower growersEvent plannersCorporate gifting buyers
Geographic coverage
Coverage: Pune. Market: India.
- India
Research methodology
An iterative delivery approach: discovery, design, build and test in short cycles, with the client reviewing each step.
- 1Brief
- 2Harvest tracking app
- 3Insight
We spent time with harvest and packing teams to understand the daily routine, then built a simple tablet app to record harvest by greenhouse and variety, grading results and packing against orders. Office staff used a web dashboard to see available stock, confirm orders and track wastage.
Custom business applicationsMobile app development
Sampling strategy
- Users and stakeholders from every affected team took part in discovery workshops and acceptance testing.
Data collection
- Requirements were gathered through workshops, process walk-throughs and a review of existing systems and data.
Data quality & validation
- Peer code review on every change
- Automated and manual testing
- User-acceptance testing with the client
- Release checklist and rollback plan
Data analysis
- Usage and performance monitoring after launch
- Defect and support-ticket trends
- Process time before and after
Key findings
- Simple tools designed around the packing shed were adopted quickly and changed daily decisions.
Recommendations
- Build on the core insight: simple tools designed around the packing shed were adopted quickly and changed daily decisions.
- Track adoption and support tickets, and plan the next release from real usage.
Business impact
- Over-promising stopped because sales staff could see real-time stock, wastage fell as slow-moving varieties became visible and the owners used greenhouse data to plan replanting.
Impact is described qualitatively.
Deliverables & next steps
- Cleaned data file and data tables
- Deployed solution with documentation and handover
- Support and maintenance plan
- Repeat the measure after changes are in place to track progress.
- A natural follow-on question: how reliable is delivery?
Client identity is withheld to protect confidentiality.