In the high-volume environment of online and kiosk ordering, data integrity is the backbone of a premium customer experience. When a customer interacts with our brand, we want to recognize them instantly. Without a standardized approach to data entry, we risk creating "fragmented identities"—multiple profiles for a single person—which leads to diluted loyalty points, inconsistent marketing, and a cluttered database.
To maintain a "Single Source of Truth" for every shopper, our system utilizes a specific lookup hierarchy and a "Staging State" to ensure every transaction is attached to the correct individual.
Treez Lookup Logic
Our platform is designed to find an existing match by moving from the most unique identifiers to personal identifiers:
Email Address: This is the primary key. The system first scans for an exact email match.
Phone Number: If the email doesn't yield a result, the system searches by phone number.
The ID Tie-Breaker: In the event that a phone number is attached to two different profiles, the system is programmed to automatically select the customer with the lower Membership ID (the older account).
The Identity Fail-Safe (Name + DOB): If neither the email nor the phone number matches an existing record, the system enters the preliminary "Verification Pending" state.
Pro-Tip: Resolving Phone Number Conflicts
If you find that an order is consistently pulling into the "wrong" profile despite a phone number match, it is likely because that number exists on multiple accounts. You can verify and fix this manually:
Navigate to Customer > Customer Management.
Use the Search Phone tool to see every profile associated with that number.
Identify the duplicate and remove the phone number from the profile that should no longer be active.
This ensures that future orders correctly "silently match" to the intended primary account.
Third-Party Lookup Logic
While our internal system follows a strict hierarchy, each third-party integration is unique in how it utilizes our Customer Lookup options. Our partners prioritize data differently based on their specific platform requirements:
Jane: Employs a broad search starting with First/Last Name, followed by Email, and then Phone Number.
Dutchie: Prioritizes the Phone Number first, using Email as the secondary identifier.
Dispense: Follows a "mobile-first" approach, looking for a Phone Number match before checking for an Email.
Tymber: Focuses on the digital account first by searching Email, then falling back to the Phone Number.
Rank Really High (RRH): Prioritizes Email as the primary key, with Phone Number as the secondary lookup.
Weedmaps: Operates on a Phone Number only logic for its initial customer identification.
Understanding the "Verification Pending" & Draft State
Because these platforms have different "starting points," it is common for a profile to arrive in your Verification Pending queue—for example, if a customer uses a new email on Jane that doesn't match the phone number in our system.
When a profile is in this state, the system doesn't instantly "write" a permanent new record. Instead, it acts as a "Look-Before-You-Leap" fail-safe:
Verify Customer: Once a staff member selects Verify Customer for a pending profile, the system performs a final scan for a Legal Name and Date of Birth match.
The Patient Merge Prompt: If the system sees any possible duplicate matches during this verification step, it will prompt that first. You will see a "Patient Merge" window displaying the potential match’s Name, Member ID, and DOB.
Committing the Data: Only after an operator selects the correct match and clicks "Approve match" does the data move from a "Draft" to a permanent "Saved" status. If it truly is a new customer, you can then finalize the new profile.
Why the "Merge" Matters
This manual intervention is the final line of defense for a clean database. Merging ensures:
Loyalty Consolidation: Customers keep all their points in one "basket," even if they use a new email or ordering platform.
Unified History: Staff can see a complete purchase history, allowing for better recommendations and service.
Database Health: We avoid "Ghost Profiles" that skew analytics and marketing efforts.
Best Practices
To ensure this system works at peak efficiency, it is vital that the following data is collected accurately:
Use Legal Names: Avoid nicknames (e.g., "Mike" vs. "Michael") so the Name/DOB check can find its match.
Verify DOB: Since the Date of Birth is the primary factor in the duplicate check, accuracy here is essential.
Review "Verification Pending" Regularly: Keeping up with pending verifications ensures that customer profiles are merged in real-time, preventing a backlog of unlinked orders.