Use label PDFs, METRC details, COA evidence, product names, and package counts as the first handoff.
New York
New York cannabis loyalty software for brands.
For New York teams, the launch model can stay narrow: send batch materials, add CannaRewards QR to label files, collect scans and feedback, then tune rewards with pilot demand signal. Keep packaging review before print.
Before print
QR-ready output
Batch materials stay under review before launch.
Before print
Batch materials
Label PDFs, METRC details, COA evidence, product names, and package counts.
QR-ready output
Print handoff
Reviewed before launch
- Set scan points
- Choose reward costs
- Confirm feedback prompt
- Report pilot signal
Why start here
Start with the label workflow that can be reviewed before print.
The launch stays practical: share source batch label PDFs, add the QR placement, return print-ready files, and use the first scans to shape points, rewards, and feedback prompts.
Keep the output tied to the source label, reward setup, and first feedback prompt.
Use packaging review before print; product rewards need a participating checkout path.
Scan activity, feedback, reward interest, and repeat behavior guide the next label or reward decision.
Before print
Keep the first New York launch tied to batch-label readiness.
The label workflow starts with source batch label PDFs and ends with a QR-ready batch-label list for print handoff. Before printing, brands can review label, reward, and feedback prompt details through their packaging review process.
Start with the launch materials that matter.
Label PDFs, METRC details, COA evidence, product names, and package counts give the launch enough context to prepare files.
Return a complete batch-label list for print handoff.
CannaRewards adds the QR placement and keeps the output tied to the source label and batch details.
Set points, reward costs, and the first feedback prompt.
The launch stays narrow: scan value, reward menu, feedback prompt, and label run before broader checkout paths.
Use pilot scans and feedback to decide what comes next.
Scans, repeat scans, feedback, and reward interest become the first operating view for the brand team.
Launch readiness
Choose the checkpoint the first label run needs to answer.
A New York pilot stays narrow enough to review before print, direct enough for the first scan, and useful enough to inform the next label or reward decision.
Make the label handoff concrete.
- Brand sends
- Label PDFs, METRC details, COA evidence, product names, and package counts.
- CannaRewards returns
- A QR-ready batch-label list tied to the source label and batch details.
- Review point
- Run the QR-ready label handoff, reward setup, and first feedback prompt through the brand packaging review process.
QR-ready output
Batch-label list
Package counts
Print handoff
- SourceBatch materials stay attached to the label run.
- OutputQR placement is ready for print handoff.
- ScopeNo checkout path is required for the first scan loop.
Keep the customer path visible.
- Scan result
- The app confirms the scan, adds points, captures feedback, and keeps the next reward visible.
- Reward menu
- Start with merch, digital offers, and brand experiences while checkout paths remain out of scope.
- Retail burden
- The package and app carry the customer flow instead of adding a new dispensary workflow.
Scan
Points
added
Turn the first run into the next decision.
- Signals
- Scans, repeat activity, feedback, reward views, saves, and claims become the first operating view.
- Actions
- Promote, inspect, tune, and prepare actions come from the first scan loop.
- Expansion
- Additional label runs and participating checkout paths can follow after the first loop is clear.
Pilot action types
- PromoteFeature the product with visible scan activity.
- InspectReview a batch when feedback shifts.
- TuneMove the reward customers keep saving.
- PrepareAdd QR labels when repeat activity appears.
Keep the page in its lane.
- Page role
- This page is a label-pilot planning path, not a New York rule summary.
- Before print
- Use the brand packaging review process before printing QR-ready labels.
- Separate scope
- Product rewards need a participating retailer checkout path, and published result claims wait for verified customer evidence.
Questions
Answer the questions teams ask before a New York launch.
Use this page to understand what to review before a New York label launch and how the model can add checkout paths after the first scan loop is clear.
Why focus on label readiness in New York?
The page is framed around the operating model a New York team can review before print: batch materials, QR-ready labels, scan points, feedback prompts, and reward direction.
What do teams review before print?
Review the final QR-ready label handoff, reward details, and first feedback prompt through your packaging review process before printing or launching.
Does the product require dispensary retraining?
No. The customer journey starts from packaging and app onboarding, so the product can create value before a brand adds a participating retailer checkout path.
Which reward menu launches first?
Start with merch, digital offers, and brand experiences. Product rewards can be added when a participating retailer checkout path is ready.
What materials start the handoff?
Start with label PDFs, METRC details, COA evidence, product names, and package counts so the QR-ready label handoff has enough context.
Can the model expand after the first label run?
Yes. The launch sequence starts with print-ready QR labels, then layers in rewards, costs, signal reporting, and checkout paths as the program grows.
Launch handoff
Prepare the first QR-ready label run before print.
Send the batch materials first, review the QR-ready label handoff, then use scans and feedback to tune rewards from observed demand.
Before print
QR-ready label run
Batch materials return as packaging-review output.
Label PDFs, METRC details, COA evidence, product names, and package counts.
Use the returned label list for packaging review and print handoff.
Use scans, feedback, reward views, saves, and claims to adjust the next move.