Cash Actuals & Expected

Dataset Name: Cash Actuals And Expected

Description: This dataset provides a summary of all closed shifts by day, along with each cash drawer’s ‘actuals’ and ‘differences’ by shift.

Unique Composite Keys: account_name, store_id, shift_dateopen

Datetime Last Updated: last_retrieved_timestamp

Incremental Load Logic: Use the column shift_dateopen as data appends per new cash movement action completed.

Pull Previous Day: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(shift_dateopen) = date(date_add(now(), INTERVAL -1 day))"}

Pull Previous Successful Update: Data-Raw Append Example:

 {"sql": "SELECT * FROM table WHERE date(shift_dateopen) >= $stored_last_update_datetime"}

stored_last_update_datetime is your stored DateTime for the last successful data pull

TIP: As a best practice, we recommend that you save the timestamp for each of your dataset pulls as 'stored_last_update_datetime'. Note that this value is not saved in our datasets, but is a suggestion.

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Column Name	Definition	Example Output
shift_dateopen	Datetime Shift Opened	"2021-04-04T15:30:54"
shift_dateclosed	Datetime Shift Closed	"2021-04-04T21:13:50"
is_closed	Is the shift currently open or closed? True or False	true
account_name	Cash Drawer Name	Register1
currency_type	Datetime In Process Started	CASH
expected	Datetime In Process Ended	2705.14
actual	Datetime Packed And Ready Started	2696.40
storeid	Datetime Packed And Ready Ended	420

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Cash Handling Audit Log

Dataset Name: Cash Handling Audit Log

Dataset Description: The cash handling audit log displays a chronological list of any actions that involve money management within your shop. This includes moving funds between cash drawers and accounts, making deposits and cash drops, reimbursing expenses, and more.

Unique Composite Keys: id, storeid

Datetime Dataset Last Updated: last_retrieved_timestamp

Incremental Load Logic: Use the column date as data appends per new cash movement action completed.

Pull Previous Day: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(date) = date(date_add(now(), INTERVAL -1 day)) "}

Pull Previous Successful Update: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(date) >= $stored_last_update_datetime"}

stored_last_update_datetime is your stored datetime for last successful data pull

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Column Name	Definition	Example Output
id	Unique ID of row	1533908e-d528-4a25-87a1-077bd36e09ff
date	Date/time of account activity	2021-01-30T21:02:46
user	ID of user making the account activity	30013
amount	Amount of account activity	1869.66
from_account	Account that activity is coming from	Main Vault
to_account	Account that activity is going to	Register 1
to_account_type	Type of Account	INCOMING_SALES
transaction_type	Transaction Type	CASH TO VAULT
transaction_currency_type	Transaction Currency type	CASH
transaction_notes	Optional - notes related to account activity	Transfer end of shift cash from CD02
storeid	ID of Store	1
last_retrieved_timestamp	the time we last retrieved data from SellTreez	2021-06-09T14:18:30

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Credit Log

Dataset Name: Credit log

Description: The Credit Log displays a chronological list of all actions involving credit usage within your shop. This includes using credits to pay for invoices, returning invoices for distributor credit, and more.

Unique Composite Keys: id, storeid

Incremental Load Logic: Use the column shift_dateopen as data appends per new credit action completed.

Pull Previous Day: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(shift_dateopen) = date(date_add(now(), INTERVAL -1 day)) "}

Pull Previous Successful Update: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(shift_dateopen) >= $stored_last_update_datetime"}

stored_last_update_datetime is your stored DateTime for the last successful data pull

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Column Name	Definition	Example Output
invoice_line_id	Invoice Line Item ID	bcbe583c-5019-11e9-8f30-jou7df09a125
id	Credit Log ID	e795df7a-7687-46c3-bf2e-c3aa7720fc15
invoice_id	Treez Generated Invoice ID	fd34470b-9b65-4bc9-9bb2-6f617f03f9f4
invoice_number	Treez Generated Invoice Number	1500
invoice_external_id	Customer Submitted Invoice ID	HB420
datetime	Time Actions Took Place	2019-11-20T14:58:31
distributor	Distributor Name	HERBL INC.
action	Action taken that impacted Credit	DESTROYED
reason	Customer Generated	‘’
user		SheldonP
amount		12.4
distributor_balance		12.4
current_distributor_balance		85.01
storeid		420
datalake_date		1993-02-19
_BATCH_ID_		200
_BATCH_LAST_RUN_		2021-05-07T11:34:03
last_retrieved_timestamp	the time we last retrieved data from SellTreez	2021-06-09T14:18:30

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Customer

Dataset Name: Customer

Description: Provides an anonymized view of all your registered customers.

Unique Composite Keys: storeid, customer_signup_date, last_updated_at customer_uuid

Datetime Last Updated:last_retrieved_timestamp

Incremental Load Logic: Use the column customer_signup_date as data appends per new customer creation

Pull Previous Day: Data-Raw Append Example:

 {"sql": "SELECT * FROM table WHERE date(customer_signup_date) = date(date_add(now(), INTERVAL -1 day)) "}

Pull Previous Successful Update: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(customer_signup_date) >= $stored_last_update_datetime"}

stored_last_update_datetimeis your stored datetime for the last successful data pull.
Use the column last_updated_at to update rows with new data.

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Column Name	Definition	Example Output
customer_uuid	Customer Universal ID	45a0e197-685e-330a-98c2-711776d5f08b
customer_signup_date	Date Customer Signed Up	2000-01-01T00:00:00
last_updated_at	Date Customer Last Updated	2000-01-01T00:00:00
last_visit	Date Customer Last Visited	2000-01-01T00:00:00
signup_flag	Is the customer signed up? (1 = Yes, 0 = No)	1
gender	Gender (M= Male, F= Female, U= Unknown)	U
age_group	Customer Age Group	60+
age	Customer Age	63
reward_balance	Available Reward Points For Customer	0
customer_source	Customer Source	GOOGLE
Group Names	Customer Group Name	SENIOR
visits	Customer Visit Count	1
notes	Customer Note	Likes Edible Gummies
opted_in	Marketing Opt In (Y=Yes, N = No)	Y
patient_type	Customer Patient Type	ADULTUSE
customer_status	Customer Status	ACTIVE
violations	Violation . Warning , Warning, Ban. (0 = Toggled Off , 1 = Toggled On)	0,0,0
city	Customer City	BERKELEY
state	Customer State	CA
zipcode	Customer Zip Code	94710
longitude	Latitude	0
latitude	Longitude	0
permit_expiration_date	Customer Permit Expiration Date
tz_customer_id	Name Mapping ID	n/a
storeid	Store ID	230

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Discounts

Dataset Name: Discounts

Dataset Description: The discounts dataset displays every discount that may apply to a ticketline. There could be more than 1 discount applied to a single ticketline and therefore the relationship between discount to ticketline is n:1.

Unique Composite Keys: store_id, ticketlineid, discountlogid

Datetime Dataset Last Updated: last_retrieved_timestamp

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Column Name	Definition	Example Output
tz_brandname	Brand name	LOST FARM
tz_brand_id	ID associated with Brand Name	445
customer_uuid	Hashed customer id	0005da4c-1b99-3c8e-9d98-5c12a883c071
discount_date	Date/time the discount was applied	2021-03-03T19:53:56
dateopen	Date that the ticket was created	03/03/2021
timeopen	Time that the ticket was created	18:38:43
dateclosed	Date that the ticket was closed	03/03/2021
timeclosed	Time that the ticket was closed	19:53:56
datetimeclosed	Date/time that ticket was closed	2021-03-03T19:53:56
datelastupdate	Date that ticket was last updated	03/03/2021
timelastupdate	Time that ticket was last updated	19:53:56
ticketalphaid	Visible ticket number that is printed on the receipt	MQOFRG
ticketlineid	ID associated with ticket line, can be used to join to ticket table	b4083885-c5bb-4201-8cba-c45c59d73053
ticketid	ID associated with ticket, can be used to reference tickets within the ticket table	1a840dc1-eb3f-4932-8ed1-323a1d505ed4
gender	Optional - gender	F
customer_city	Optional - city	SANTA MONICA
customer_state	Optional - state	CA
customer_country	Optional - country	USA
Cashier	Cashier name that applied the discount	JOHN
cashierid	ID associated with cashier	3936952f-5185-4f0e-bde9-0f2b801fcae7
producttype	Product Category	FLOWER
productsubtype	Product Sub Category	PREPACK
productname	Product Name	ALIEN OG
priceperunit	Unit price of product	45.00
salestax	Sales tax amount	0.00
discounttype	Type of discount: 0 = Automatic from Discount Module 1 = Custom/Manual 2 = Rewards 3 = Tier Pricing Discount 4 = Round Down 5 = Other	1
discountmethod	PERCENT or DOLLAR discount	PERCENT
discountreason	Optional - if a reason was provided for a custom discount	FIRST TIME
discounts	Actual discount amount	15.25
discountid	ID associated with this discount row	87aae755-e594-4154-9db3-728b7670e6ed
discountlogid	Log ID associated with applying this discount	e053c563-997f-4b3e-bd23-820fd067eb32
customertype	Type of customer	MEDICAL-MMID
customergroup	Optional - group that this customer was part of	SENIOR
weight	Unit amount of product	2
unitofmeasure	Unit of measure for weight	GRAMS
discounttitle	Title of automated or custom discount	20% OFF
skus	Inventory or product barcode associated with product	F3o
receiving_license	Optional - license of retailer	C10-0000000-LIC
customer_mmid_expiration	Optional - Date/time of mmid expiration	2021-07-19 00:00:00
priceperunitafterdiscount	Price per unit of product, post discount	52.82
quantity	Quantity of products that is being discounted	1.00
priceperunitdifference	Difference of price per unit, pre and post discount	4.68
netsales	Total net sales of ticketline	35.10
storeid	ID of retailer	1
distributor	Distributor name of product	CURA
size	Product Size	1G
productbrand	Uncleansed brand name	SELECT
product_id	ID of product	0c5f893f-b359-4483-ac69-b8d00eed86ea
cost	Cost of product	12.70
cost_without_excise	Cost of product, without excise tax	10
last_retrieved_timestamp	2021-06-09T14:18:30	the time we last retrieved data from SellTreez

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Inventory Adjustment Log

Dataset Name: Inventory Adjustment Log

Dataset Description: The inventory adjustment log displays a chronological list of any actions that involve inventory movements within your shop. This includes moving inventory from Back of House to Front of House, destroying inventory, adjusting inventory, selling or returning inventory, and more.

Unique Composite Keys: id, storeid

Datetime Dataset Last Updated: last_retrieved_timestamp

Incremental Load Logic: Use the column adjustment_datetimeas data appends per new inventory adjustment action

Pull Previous Day: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(adjustment_datetime) = date(date_add(now(), INTERVAL -1 day)) "}

Pull Previous Successful Update: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(adjustment_datetime) >= $stored_last_update_datetime"}

stored_last_update_datetime is your stored datetime for last successful data pull

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Column Name	Definition	Example Output
adjustment_datetime	Date/Time that inventory adjustment was made	2020-07-21T20:07:10
product_brand	Brand name of the product	CHOICE
product_name	Product Name	LEMON SWIRL
product_type	Product Category	FLOWER
product_subtype	Product Sub Category	PRE-PACK
product_id	ID of product	f0880425-b44f-4131-9b9d-49f0efea4e6a
package_id	ID of inventory batch	8f2ea4a4-087f-4cfd-b6fd-7ee3c82095b8
state_tracking_id	Visible ID of state traceability	1A405010000A475000000005
batch_id	Visible ID of internal inventory batch	47GRO
action	Action of adjustment	MOVE
reason	Optional - reason of inventory adjustment	Counts Match
original_qty	Original quantity before adjustment	24
adjustment_qty	Adjusted quantity	-24
current_qty	Current quantity after adjustment	0
adjustment_post_excise_cost	Adjustment of inventory in cost, post excise	-284.58
adjustment_pre_excise_cost	Adjustment of inventory in cost, pre excise	-284.58
adjustment_excise_cost	Adjustment of inventory in excise tax	-85
adjustment_user	User ID of the one performing the adjustment	JOHN
location_name	Inventory location name at time of adjustment	FRONT OF HOUSE
inventory_id	ID of inventory batch, can be used to join to inventory table	b47abc95-1aa9-4b0b-93dd-af88be7d3791
invoice_line_id	ID of invoice line, can be used to join to invoice table	2a3209a6-adfc-4cad-bd5b-9adf00868610
credit_note_id	ID of credit note, if applicable
id	Unique ID of row	b47abc95-1aa9-4b0b-93dd-af88be7d3791
storeid	ID of retailer	1
retail_brand	Brand name of Product	CHOICE
last_retrieved_timestamp	the time we last retrieved data from SellTreez	2021-06-09T14:18:30

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Inventory Table

Dataset Name: Inventory

Description: This report shows a snapshot of all your inventory. Inventory is aggregated by the Unique Product ID, Batch ID, and Location, which means it should reflect your inventory as it appears on the Inventory Control page of your portal.

Datetime Last Updated: last_retrieved_timestamp

Unique Composite Keys: storeid, batch_id, location_id, product_id state_tracking_id

Upsert Logic: Replace All

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Column Name	Definition	Example Output
unit_of_measure	Unit of Measure	GRAMS
type	Inventory Type	ALL
storeid	Store ID	230
status	Inventory Status	AVAILABLE
start_date		2020-11-06T16:12:19
source		SELL_TREEZ
sold_units	Units Sold	64
returned_units	Units Returned	0
returned_from_pos	Units Returned from POS	0
reserved_units	Units Reserved	0
productname	Raw Product Name	THC DESIGN \| 24K \| 3.5G FLOWER \| 27.07% THC
productbrand	Raw Brand Name	THC DESIGN
product_id	Product ID	0a1f5bf4-fc23-4f8b-95fc-f2155c00b9ac
packed_and_ready_units	Units Packed And Ready	0
package_id	Package ID	f247d6b7-f466-45e5-bcb8-2c1ff8754938
original_start_date		2020-10-16T14:05:29
location_type	Location Type	LOCATION
location_name	Location Name	FRONT OF HOUSE
location_id	Location ID	2f3df669-a935-11e9-a749-02ba70ae261c
last_user_id	Last User ID	4748ee91-7ca2-4ab0-b2ba-06bea3a4038c
last_reconciliation_id	Reconciliation ID	57ebbc86-fb8f-489a-854f-6cb1a3c92012
last_location_id	Location ID	2f3e1d27-a935-11e9-a749-02ba70ae261c
last_inventory_id	Inventory ID	281d27fd-0340-489a-9c1b-4fb00c485fff
last_action	Last Action	COMPLETE_SALE
is_sellable	Is Sellable? (TRUE = YES, FALSE = NO)	TRUE
invoice_line_id	Invoice Line ID	dde92789-042f-4980-8431-9d79428b50f6
invoice_id	Invoice ID	2c7fcad9-2c27-42d0-902b-fd0cb1be9912
inventory_type	Inventory Type	ALL
id	Inventory ID	0ba4101c-08a5-4c48-88c6-b745a6d9fd27
expiration_date	Inventory Expiration Date
excise_tax_per_unit	Excise Tax per Unit	4.918195
distributor_id	Distributor ID	c7d8b4ec-abf2-4482-a74d-0472bfbd6b3a
destroyed_units	Units Destroyed	0
decision_pending_units	Units Pending Decision	0
cost_per_unit	Cost Per Unit	18.215538
converted_units	Units Converted	0
change_quantity	Quantity Change	1
batch_id	0PAYR-001	Batch ID
available_units	Current Available Units	0
adjusted_units	Units Adjusted	0
producttype	Product Type	FLOWER
state_tracking_id	State Tracking ID	1A40603000067F9000016320
last_retrieved_timestamp	the time we last retrieved data from SellTreez	"2021-06-09T14:18:30"

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Invoice Table

Dataset Name: Invoice

Description: The invoice dataset displays a chronological list of all invoice line items that have been added to your dispensary.

Unique Composite Keys: storeid, invoice_id, product_id, accepted_date

Datetime Last Updated: last_retrieved_timestamp

Incremental Load Logic: Use the column accepted_date as data appends per new invoice line item

Pull Previous Day: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(accepted_date) = date(date_add(now(), INTERVAL -1 day)) "}

Pull Previous Successful Update: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(accepted_date) >= $stored_last_update_datetime"}

stored_last_update_datetime is your stored datetime for the last successful data pull

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Column Name	Definition	Example Output
payment_date	Date Payment Was Done	2020-07-17T18:21:22
payment_user	Employee ID	1
payment_amount	Amount Invoice Paid	13,632.60
payment_notes	Invoice Payment Note	Manually closed without payment before cash handling was enabled
transaction_id	Invoice Payment ID (If Applicable)	7f6cf821-aeb0-11e9-9df1-026d10f0d0f0
invoice_id	Invoice ID	d6962ba7-f864-433a-aada-1dcbf05ac0fa
product_id	Dispensary Product ID	b34192da-ed7f-4dba-852f-dd563d250eca
cost_id	Cost ID	5a944365-75a5-43cf-a860-e0ef62997fe5
invoice_number	Invoice Number	8004
created_date	Date Invoice Created	2020-07-17T18:21:22
due_date	Date Invoice Payment Due	2020-07-17T18:30:31
accepted_date	Date Invoice Accepted	2020-07-17T18:30:31
term_id	Term ID	636bc474-49b4-11e9-8035-774805423a5c
distributor_id	Distributor ID	8ef734ee-8955-468c-8380-1ce6967f3294
inventory_location	Location ID where invoice was sent	b0c09f06-a878-11e9-936d-025c41018ccc
external_invoice_id	External ID
invoice_cost_id	Invoice Cost ID	fbb90ddc-b464-4fb0-aee5-041e00ad7639
productname	Product Name	BLUE DREAM .5G
producttype	Product Type	CARTRIDGE
productsubtype	Product Subtype	POD
unitofmeasure	Unit of Measure	GRAMS
discount	Discount	0
base_cost	Base Cost For Invoice Line	10
qty	Quantity For Invoice Line	200
total_cost	Total Cost For Invoice Line	2540
payment_transaction_type	Payment Transaction Type	CASH
fees_from_lines	Fees From Lines	0
unspread_fees		0
unspread_eligible_fees		0
terms	Terms	COD
storeid	Storeid	230
total_final_cost		47625
productbrand	Product Brand	STIIIZY
vendorname	Vendor Name	JBTB HOLDINGS INC
excise_tax	Tax	540
total_cost_final	Total Cost Final For Invoice	47625
payment_status	Status	OTHER

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Payment Table

Dataset Name: Payments

Description: This dataset displays a chronological list of all payments applied to a ticket. Tickets with multiple payments will see the payment amounts split into multiple rows, however, they will share the same Ticket ID.

Unique Composite Keys: dateclosed, storeid, paymentid

Datetime Last Updated: last_retrieved_timestamp

Incremental Load Logic: Use the columndateclosedas data appends per new payment completed.

Pull Previous Day: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(dateclosed) = date(date_add(now(), INTERVAL -1 day)) "}

Pull Previous Successful Update: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(dateclosed) >= $stored_last_update_datetime"}

Stored_last_update_datetime is your stored DateTime for the last successful data pull

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Column Name	Definition	Example Output
payment_date	Date Payment Was Accepted	2017-11-14T15:47:57
paymentid	Unique Payment ID	ff8081815fba6765015fbceea72f0093
paymentname	Payment Name (Cash, Cashless ATM, Check, Check Sales, Credit Card, Custom, Debit Card, INtegrated ATM, Points, Rewards, cheque, externalapi)	Cash
originalamount	Amount Paid	326.58
changeamount	Change Recieved (Original Amount - Gross Receipts Paid)	0.0
grossreceiptspaid	Ticket Total	326.58
percent_of_ticket	Null	""
ticketid	Unique Ticket ID (Non-customer facing)	ff8081815fba6765015fbceb1695007b
ticketalphanum	Unique Alpha Numumeral ID (Customer Facing)	WE4D20L
dateclosed	Date ticket closed	2017-11-14
timeclosed	Time ticket closed	1970-01-01T15:47:57
paymenttype	Payment Type (Cash, Cash Refund, Cashless ATM, Cashless ATM Refund, Check, Check Refund, Check Sales, Check Sales Refund, Credit Card, Credit Card Refund, Custom, Debit Card, Debit Card Refund, Integrated ATM, Points, Points Refund, Rewards, Rewards Refund, cheque, externalapi)	"Cash" or "Cash Refund"
customertype	Customer Type (MEDICAL-MMID, MEDICAL-REGULAR, ADULT-USE)	MEDICAL-REGULAR
seconds_to_payment	Payment Transaction Time	233.0
customergroups	Customer Group	LOCALS
dateopen	Date ticket was created	2017-11-14T00:00:00
timeopened	Time ticket was created	2019-09-05T15:44:04
datelastmodified	Date ticket was last modified	2019-09-05T00:00:00
timelastmodified	Time ticket was last modified	2019-09-05T02:52:50
datepaymentprocessed	Date ticket was last modified	2017-11-14T00:00:00
timepaymentprocessed	Time ticket was last modified	1970-01-01T15:47:57
authcode	Authorization Code	N/A
refcode	Reference Code	N/A
ticketlineid	Null	""
metrcreceipt	Metrc Sales ID	""
activity	Null	""
cashier	Cashier	Drew
approver	Cashier Approver	Drew
documentid	Document ID	LIC3660826
age	Customer Age	36
city	Customer City	WASHINGTON
zip	Customer Zip	74555
gender	Customer Gender	F
customer_uuid	Customer Unique ID	88deb6b9-5251-3853-b73e-20af025d4528
storeid	Store ID	230
week_year	Year - Week	2017-46
last_retrieved_timestamp	the time we last retrieved data from SellTreez	2021-06-09T14:18:30

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Ticketline Table

Dataset Name: Ticketline

Description: The ticketline dataset displays a chronological list of all ticketline items sold or returned in your shop. Each row contains product details around attributes, state tracking, cost, and revenue.

Unique Composite Keys: dateclosed, storeid ,ticketlineid

Datetime Last Updated: last_retrieved_timestamp

Incremental Load Logic: Use the columndateclosedas data appends per new sale completed

Pull Previous Day: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(dateclosed) = date(date_add(now(), INTERVAL -1 day)) "}

Pull Previous Successful Update: Data-Raw Append Example:

{"sql": "SELECT * FROM table WHERE date(dateclosed) >= $stored_last_update_datetime"}

Stored_last_update_datetime is your stored datetime for the last successful data pull

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Column Name	Definition	Example Output
verification_pending_min	Datetime Verification Started	1/24/2020 13:24
verification_pending_max	Datetime Verification Ended	1/24/2020 13:24
awaiting_processing_min	Datetime Awaiting Processing Started	1/24/2020 13:24
awaiting_processing_max	Datetime Awaiting Processing Ended	1/24/2020 13:24
in_process_min	Datetime In Process Started	1/24/2020 13:24
in_process_max	Datetime In Process Ended	1/24/2020 13:24
packed_ready_min	Datetime Packed And Ready Started	1/24/2020 13:24
packed_ready_max	Datetime Packed And Ready Ended	1/24/2020 13:24
out_for_delivery_min	Datetime Out for Delivery Started	1/24/2020 13:24
out_for_delivery_max	Datetime Out for Delivery Ended	1/24/2020 13:24
channel	Sales Channel	eCommerce
payment_date	Date Payment Was Accepted
pre_paid	Order Prepaid or Not	N
masked_ticketlineid	Masked Ticketline ID	cfbc7aaf1921d1d6bc731c172fb1b46a
masked_ticketid	Masked Ticket ID	d777be50cafc438ba09c1788565eb96b
masked_ticketalphaid	Masked Ticket Alphanumeric ID	53c5f031dd2aa7207da88b364eed248f
masked_storeid	Masked Store ID	c59db769700fc98d0d260730b89e1889
masked_customer_uuid	Masked Customer Universal ID	7164a51fe7042552ff4e50461dc65c0b
retail_brand	Raw Brand Name	NORTH
ticketid	Ticket ID	d7007eae-70de-43ff-be73-ba8ef3163d25
products_remaining	Product Count Remaining	0
register	Cash Drawer Name	CASH DRAWER 3-A
customer_type	Customer Ordering Type (Adult Use vs Medical vs MMID)	MEDICAL-REGULAR
type	Order Type (Sale or Return)	SALE
customer_state	Customer State	MI
customer_city	Customer City	PORTAGE
zip_code	Customer Zip Code	49002
age	Customer Age	34
age_group	Customer Age Group	30-39
cashier	Cashier Name	CTEETERS
approver	Cashier Approver	CTEETERS
producttype	Ticketline Product Type	EXTRACT
productsubtype	Ticketline Product Subtype	BUDDER
productattributes	Ticketline Product Attributes	EDIBLES,CHOCOLATE
tier	Ticketline Product Tier	Top Shelf Tier
weight	Ticletline Product Weight	1
unitofmeasure	Ticketline Unit of Measure	EACH
priceunit	Ticketline Price Per Unit	40
qty	Ticketline Unit Quantity	1
grosssales	Ticketline Gross Sale (Subtotal Before Deductions)	40
discounts	Ticketline Discount	20
returns	Ticketline Return Amount (Will be 0 if it’s a sale)	0
netsales	Ticketline Net Sales (Subtotal After Discount)	20
taxes	Ticketline Tax Amount (Total Tax For Ticketline)	1.2
grossreceipts	Ticketline Gross Receipt (Grand Total Including Deductions and Taxes)	21.2
cost	Ticketline Cost w/ Excise	22
cost_without_excise	Ticketline Cose w/o Excise	22
grossincome	Ticketline Gross Profit (Net Sale - Cost)	-2
avgproductmargin	Avg Product Margin	1945
invoiceid	Ticketline Invoice Number	165
ticketlineid	Ticketline ID	4b2bb4de-8c41-4ac9-a297-b5794320fef3
ticketalphaid	Ticket Alpha Numerical ID	3T8LXD
dateclosed	Ticket Date Closed	1/24/2020
date_time_closed	Ticket Date Time Closed	2020-01-24T13:31:18
date_time_opened	Ticket Date Time Opened	2020-01-24T13:24:11
sku	Product SKU	IOrH,1A4050100001645000008250,oOz
customer_uuid	Customer Universal ID	03176220-e399-3f44-86a0-25b43801417d
customer_signup_date	Customer Profile Creation Date	2020-01-24T16:21:47
reward_balance	Customer Reward Point Balance	36
inventory_location	Inventory Location	FRONT OF HOUSE
ticket_updated_date	Ticket Last Update Date	2020-02-04T07:25:12
line_updated_date	Ticektline Last Update Date	2020-01-24T13:30:06
last_visit	Customer Last Visit Date	1/24/2020 13:24
salestaxamount	Sales Tax Amount	1.2
is_arms_length	Is Ticketline Arm Length or Non Arm's Length (1 = Yes, 0 = No)	1
is_cannabis	Is Ticketline a cannabis product or not (1 = Yes, 0 = No)	1
ticket_type	Ticket Fulfillment Type (POS, EXPRESS, etc)	POS
statetrackingid	State Tracking ID	1A4050100001645000008250
customer_country	Customer Country
customer_groups	Customer Groups
avgproductmarginpreexcise	Avg Product Margin (w/o Excise)	1945
product_id	Dispensary Product ID	c3959cab-0582-11ea-9c46-02a831a0e4d2
inventory_date	Ticketline Inventory Date	2019-11-12T11:29:37
customer_mmid_expiration	Customer Medical Card Expiration Date
ticket_notes	Ticket Note
gender	Gender (M = Male, F = Female, U = Unknown)	M
open_date	Date ticket was opened / created	8/22/2019
tz_brand_id	Brand Mapping ID	10608
tz_product_id	Product Mapping ID	2
tz_productname	Product Mapping Name	~~BOGO~~SOUTH FORK KUSH 29%
tz_brandname	Brand Mapping Name	NO BRAND
storeid	Unique Store ID	230
customer_source	Customer Source	FRIENDS & FAMILY
distributor	Customer Input Distributor Name	TREEZ
first_visit	Is this the customers first visit (1 = yes , 0 = no)	1
repeat_purch	Has this customer purchased this product before (1 = yes, 0 = no)	0
ticket_status	Ticket Status	COMPLETED
custom	Custom Tax
customname	Custom Tax Name
customtaxamount	Custom Tax Amount
dynamic	Dynamic Tax
dynamictaxname	Dynamic Tax Name
dynamicdtaxamount	Dynamic Tax Amount
excise	Excise Tax
excisetaxname	Excise Tax Name
excisetaxamount	Excise Tax Amount
sales	Sales Tax	SALES
salestaxname	Sales Tax Name	SALES TAX
inventory_sku	Inventory SKU
harvest_date	Havest Date
external_id	Ticketline External Product
total_mg_thc	Ticketline Total MG THC
total_mg_cbd	Ticketlient Total MG CBD
extraction_method	Extraction Method
tz_customer_id	Customer ID mapping	n/a
thc_perc	THC Percentage	0
cbd_perc	CBD Percentage	0
package_size	Package Size	SINGLE-UNIT
productbrand	Semi Brand Mapping Name	NO BRAND
anonymized_productbrand	Anonymized Brand Name	NO BRAND
productname	Raw Product Name	LEMON SOUR DIESEL
classification	Product Classificaton	S/I
size	Raw Product Size	1G
thc_ratio	THC Ratio	0
repeat_purch_sku	Has this customer purchased this product before (1 = yes, 0 = no)	0
verification_pending_employee	Employee Who moved order to Verification Pending
awaiting_processing_employee	Employee Who moved order to Awaiting Processing
in_process_employee	Employee Who moved order to In Process
packed_ready_employee	Employee Who moved order to Packed Ready
out_for_delivery_employee	Employee Who moved order to Out For Delivery
completed_employee	Employee who Compled Order
report_last_updated	Report Last Updated
zip	Zip Code	49002
lat	Latitude	42.19737
lng	Longitude	-85.56095
income_household_median	Avg Median Income for County	50771
city	City	PORTAGE
county_name	County	KALAMAZOO
state_name	State Name	MICHIGAN
county_fips		26077
region	Region	MI
store_state	Store State	MI
year	Year	2020
month	Month	1
week_year	Week of Year	2020-4
last_retrieved_timestamp	the time we last retrieved data from SellTreez	"2021-06-09T14:18:30"

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AskTreez: Data Dictionary – Retail

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Cash Actuals & Expected

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Inventory Table

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Articles in this section

Skip to:

Cash Actuals & Expected

Cash Handling Audit Log

Credit Log

Customer

Discounts

Inventory Adjustment Log

Inventory Table

Invoice Table

Payment Table

Ticketline Table

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