Glossary of Digital & Loyalty Metrics
\uD83D\uDCD8 Metrics List
Metric and Definition | Query | Comments and Business Use |
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Known Diner Sales (“KDS”) All sales that are made by recognizable guests, i.e. guests that have registered through the app (identified by their registered account id beginning with ‘us-east’) | SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, SUM(AMOUNT) AS SALES, COUNT(DISTINCT TICKET_ID) AS TRXNS, COUNT(DISTINCT REGISTERED_ACCOUNT_ID) AS GUESTS FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN '<START DATE>' AND '<END DATE>' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = 'CANADA' AND LEFT(REGISTERED_ACCOUNT_ID,7) = 'us-east' GROUP BY 1,2 | This metric is used as a baseline to see loyalty sales penetration against system wide sales. |
System Wide Sales (“SWS”) Total sales across TH | SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, COUNT(DISTINCT TICKET_ID) AS TRXNS, SUM(AMOUNT) AS SWS FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN '<START DATE>' AND '<END DATE>' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = 'CANADA' GROUP BY 1,2 | Note: Some forecasts may use Hyperion sales interchangeable in lieu of SWS sourced from STG.DERIVED_MASTER_TABLE_NEW. |
Cheque Average sales value ($) of an individual transaction | SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, COUNT(DISTINCT TICKET_ID) AS TRXNS, SUM(AMOUNT) AS SWS, SUM(AMOUNT)/COUNT(DISTINCT TICKET_ID) AS CHEQUE FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN '<START DATE>' AND '<END DATE>' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = 'CANADA' GROUP BY 1,2 | Can calculate this on a system level or down to an individual guest level. Can filter on loyalty or non-loyalty. |
Frequency Average loyalty guest visits in a given time period | SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, COUNT(DISTINCT TICKET_ID) AS TRXNS, COUNT(DISTINCT REGISTERED_ACCOUNT_ID) AS GUESTS, COUNT(DISTINCT TICKET_ID)/COUNT(DISTINCT T1.REGISTERED_ACCOUNT_ID) AS FREQUENCY FROM STG.DERIVED_MASTER_TABLE_NEW T1 WHERE PARTITION_DATE_KEY BETWEEN '<START DATE>' AND '<END DATE>' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = 'CANADA' AND LEFT(REGISTERED_ACCOUNT_ID,7) = 'us-east' GROUP BY 1,2 | Usually look at this metric for a week, month, or year. |
White Label Delivery (“WL”) Delivery sales initiated from the TH mobile app | SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, SUM(AMOUNT) AS SALES FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN '$START_DATE' AND 'END_DATE' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = "CANADA" AND SERVICE_MODE_CD IN ('WHITE LABEL DELIVERY','DELIVERY') AND LEFT(REGISTERED_ACCOUNT_ID,7) = 'us-east' GROUP BY 1,2 | Included in known diner sales & digital sales. Sales of our internal app delivery platform. |
Mobile Order & Pay (“MO&P”) | SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, SUM(AMOUNT) AS SALES FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN 'START_DATE' AND 'END_DATE' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = "CANADA" AND SERVICE_MODE_CD IN ('MOBILE ORDER DRIVE THRU', 'MOBILE ORDER EAT IN', 'MOBILE ORDER TAKE OUT') AND LEFT(REGISTERED_ACCOUNT_ID,7) = 'us-east' GROUP BY 1,2 | Included in known diner sales & digital sales. |
Loyalty Scans Sales made by known diners and includes eat-in, takeout & drive thru | SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, SUM(AMOUNT) AS SALES FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN 'START_DATE' AND 'END_DATE' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = "CANADA" AND SERVICE_MODE_CD IN ('TAKEOUT', 'EATIN','DRIVETHRU') AND LEFT(REGISTERED_ACCOUNT_ID,7) = 'us-east' GROUP BY 1,2 | Included in known diner sales & digital sales. |
3P Delivery Delivery sales initiated and fulfilled by third-party delivery providers, such as UberEats, SkipTheDishes and DoorDash | SELECT A.PARTITION_DATE_KEY, SUM(A.AMOUNT) AS SALES FROM STG.DERIVED_MASTER_TABLE_NEW A INNER JOIN TLOG.TLOG_SALE_TICKET_TENDERS B ON A.TICKET_ID = B.TICKET_ID WHERE A.PARTITION_DATE_KEY BETWEEN 'START_DATE' AND 'END_DATE' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = 'CANADA' AND TENDER_NAME IN ("UBER EATS CREDIT","Credit Uber Eats","Uber Eats Credit","CRÉDIT UBER EATS","SKIP CREDIT","Skip Credit","CRÉDIT de DoorDash","CRÉDIT DE DOORDASH","DoorDash Credit","DOORDASH CREDIT","Crédit de DoorDash","Doordash") GROUP BY 1 | Included in digital sales |
Kiosk Sales via Kiosk; can be split into registered and un-registered Kiosk sales using | SELECT PARTITION_DATE_KEY, SUM(AMOUNT) AS SALES FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN 'START_DATE' AND 'END_DATE' AND SERVICE_MODE_CD IN ('KIOSK', 'KIOSK TAKEOUT', 'KIOSK EATIN') AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = "CANADA" GROUP BY 1 | Included in digital sales -- Registered Kiosk AND LEFT(COALESCE(REGISTERED_ACCOUNT_ID,''),7) = 'us-east' -- Un-Registered Kiosk AND LEFT(COALESCE(REGISTERED_ACCOUNT_ID,''),7) != 'us-east' |
Catering Catering sales | SELECT PARTITION_DATE_KEY, SUM(ITEMTOTALPRICE)AS SALES, FROM PRODRT.CURATED_TRANS_EVENTS_NEW WHERE PARTITION_DATE_KEY BETWEEN 'START_DATE' AND 'END_DATE' AND COUNTRY_NM = 'CANADA' AND IS_PASS_THROUGH = 0 AND LEFT(REGISTEREDACCOUNTID,7) = 'us-east' AND DININGTYPE = 'CT' GROUP BY 1 | Included in digital sales |
Restaurants Reporting Any Menu Item (“RRAMI“) | WITH RESTAURANTS AS ( SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, PARTITION_DATE_KEY, COUNT(DISTINCT REST_NO) AS RESTAURANTS FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN 'START_DATE' AND 'END_DATE' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM = 'CANADA' GROUP BY 1,2,3 ORDER BY 1) SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, SUM(RESTAURANTS) AS RRAMI FROM RESTAURANTS GROUP BY 1,2 | |
Scan & Pay (“S&P”) Transactions where Scan & Pay feature was used | SELECT STOREDATE, COUNT(DISTINCT TRANSACTIONID) AS TICKETS, SUM(ITEMTOTALPRICE) AS SALES FROM PRODRT.CURATED_TRANS_EVENTS_NEW WHERE STOREDATE BETWEEN 'START_DATE' AND 'END_DATE' AND IS_PASS_THROUGH = 0 AND COUNTRY_NM IN ('CANADA') AND LEFT(REGISTEREDACCOUNTID,7) = 'us-east' AND SCANANDPAY IS TRUE GROUP BY 1 | Often tracked as a % of registered transactions Scan & Pay Penetration Formula: (Scan & Pay Transactions) / (Registered Transactions)
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Loyalty Redemptions Products that were redeemed using loyalty points | CACHE TABLE DAILY_REDEMPTIONS AS WITH TRANSACTIONS_WITH_OFFER_ID AS ( SELECT LEFT(PERIOD_DT, 10) AS PERIOD_DT, REGISTEREDACCOUNTID, TRANSACTIONID, EXPLODE(APPLIEDOFFERS) AS EXPLODED_OFFERS FROM PRODRT.CURATED_TRANS_EVENTS_NEW WHERE DATE_KEY >= '20230606' AND COALESCE(REGISTEREDACCOUNTID,'') IS NOT NULL AND APPLIEDOFFERS IS NOT NULL AND COUNTRY_NM = 'CANADA') SELECT PERIOD_DT, COUNT(DISTINCT TRANSACTIONID) AS TRXNS FROM TRANSACTIONS_WITH_OFFER_ID AS A INNER JOIN (SELECT DISTINCT OFFERID FROM DYDB.WEEKLYOFFERS UNION ALL SELECT DISTINCT OFFERID FROM DYDB.OFFERS WHERE CONTAINS(description, 'LOYALTY') AND DESCRIPTION LIKE 'CA L%' AND DESCRIPTION NOT IN ('CA LR Registered Default (same as L1)-LOYALTY', 'CA LU Unregistered (same as 102) ONE-TIME-LOYALTY') ) B ON A.EXPLODED_OFFERS = B.OFFERID LEFT JOIN DYDB.OFFERS AS C ON A.EXPLODED_OFFERS=C.OFFERID WHERE EXPLODED_OFFERS IS NOT NULL AND C.OFFERID IS NOT NULL GROUP BY 1 | Used for tracking how many free items we’re giving away and the value of them. Often viewed as a percentage of SWS or KDS. Loyalty drag formula = $ value of redeemed items / SWS $ |
Monthly Active Users (“MAU”) Number of guests that visited the app each month | WITH EVENTS AS ( SELECT DATE , _TIMHORTONS.INTERACTION.ELEMENT.NAME , _TIMHORTONS.LOYALTY.ID AS GUEST_ID FROM loyalty.events.adobe_app_events WHERE EVENTTYPE IN ('app_open','app_launch') AND _TIMHORTONS.PLATFORM IN ('app') AND LEFT(_TIMHORTONS.LOYALTY.ID,7) = 'us-east' AND DATE <= CURRENT_DATE ) SELECT LAST_DAY(EVENTS.DATE) AS DTE , COUNT(DISTINCT EVENTS.GUEST_ID) AS ACTIVE_USER FROM EVENTS GROUP BY 1 ORDER BY 1 DESC | Used to track the unique number of guests who opened or launched the app per month. Limitation of the dataset is: (1) data only exists from Nov 1st, 2023, and (2) Prior to mm/dd/yyyy (TBD), dataset did not contain guests using app versions prior to 7.1.187. |
Digital Sales | Pulled using the individual queries listed above. | Consists of WL, MO&P, Loyalty Scans, 3P Delivery, Kiosk & Catering. |
Weekly Offer Redemptions | WITH REDEMPTION_SUMMARY AS ( WITH OFFER_REDEMPTION_TRXNS AS ( WITH TRANSACTIONS_WITH_OFFER_ID AS ( SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, CAST(WEEK_START_DT AS DATE) AS WEEK_START_DT, DATE_FORMAT( CAST( UNIX_TIMESTAMP(LEFT(WEEK_START_DT, 10), 'yyyy-MM-dd') AS TIMESTAMP ), 'yyyyMMdd' ) AS WEEK_START, CAST(PERIOD_DT AS DATE) AS PERIOD_DT, REGISTEREDACCOUNTID, TRANSACTIONID, EXPLODE(APPLIEDOFFERS) AS EXPLODED_OFFERS FROM PRODRT.CURATED_TRANS_EVENTS_NEW WHERE PARTITION_DATE_KEY BETWEEN 'START DATE' AND 'END DATE' AND REGISTEREDACCOUNTID LIKE 'us-east%' AND COUNTRY_NM = 'CANADA' ) SELECT DISTINCT A.TH_FISCAL_YEAR, A.TH_FISCAL_WEEK, A.WEEK_START_DT, A.PERIOD_DT, A.REGISTEREDACCOUNTID, A.TRANSACTIONID, C.NAME, C.DESCRIPTION, A.EXPLODED_OFFERS, 1 AS VOLUME FROM TRANSACTIONS_WITH_OFFER_ID A INNER JOIN DYDB.WEEKLYOFFERS B ON A.EXPLODED_OFFERS = B.OFFERID LEFT JOIN DYDB.OFFERS C ON A.EXPLODED_OFFERS = C.OFFERID ) SELECT WEEK_START_DT, A.DESCRIPTION AS OFFER_DESCRIPTION, A.EXPLODED_OFFERS AS OFFERID, SUM(VOLUME) AS REDEMPTION_VOLUME FROM OFFER_REDEMPTION_TRXNS A GROUP BY 1, 2, 3 ) SELECT a.WEEK_START_DT AS WKDT, OFFER_DESCRIPTION, OFFERID, SUM(REDEMPTION_VOLUME) FROM REDEMPTION_SUMMARY A GROUP BY 1,2,3 | Used to track the volume of redemptions for each offer in a given week. |
Offer Challenge | with TRXN_OFFER_CHALLENGES_REDEMPTIONS as ( WITH EXPLODED AS ( SELECT TH_FISCAL_YEAR, TH_FISCAL_WEEK, DATE_FORMAT( CAST( UNIX_TIMESTAMP(LEFT(WEEK_START_DT, 10), 'yyyy-MM-dd') AS TIMESTAMP ), 'yyyyMMdd' ) AS WEEK_START_MAPPING, CAST(PERIOD_DT AS DATE) AS PERIOD_DT, REGISTEREDACCOUNTID, loyaltyCustomerId, TRANSACTIONID, EXPLODE(APPLIEDOFFERS) AS EXPLODED_OFFERS, REST_TYP_NM, OPS_DIV_NM FROM PRODRT.CURATED_TRANS_EVENTS_NEW -- 1. Challenge duration: 26/09 to 02/10 WHERE DATE_KEY BETWEEN '${myapplication.Offer_Start_Date}' AND '${myapplication.Offer_End_Date}' AND COUNTRY_NM = 'CANADA' AND REGISTEREDACCOUNTID IS NOT NULL AND TRANSACTIONID IS NOT NULL -- AND REST_TYP_NM = "STANDARD" ) SELECT DISTINCT A.TH_FISCAL_YEAR, A.TH_FISCAL_WEEK, A.WEEK_START_MAPPING, A.PERIOD_DT, A.REGISTEREDACCOUNTID, A.loyaltyCustomerId, A.TRANSACTIONID, A.REST_TYP_NM, A.OPS_DIV_NM, B.DESCRIPTION, 1 AS REDEMPTIONS FROM EXPLODED AS A LEFT JOIN DYDB.OFFERS AS B ON A.EXPLODED_OFFERS = B.OFFERID WHERE EXPLODED_OFFERS = '76290e2e-1783-442f-8ff6-a2b16900a34e' ), points_issued as ( Select *, WEEKOFYEAR(to_date(partition_date_key, "yyyyMMdd")) AS WEEK_START, regexp_extract(barcode, '(\\d+)|(\\d+)', 0) as lid From prodrt.curated_points_events as points where partition_date_key BETWEEN '${myapplication.Offer_Start_Date}' AND '${myapplication.Offer_End_Date}' and tag = 'PRODUCT_CHALLENGED_COMPLETED' ), completed as( Select a.TH_FISCAL_YEAR as TH_FISCAL_YEAR, a.TH_FISCAL_WEEK as TH_FISCAL_WEEK, a.WEEK_START_MAPPING as WEEK_START_MAPPING, a.PERIOD_DT as PERIOD_DT, DATE_FORMAT( CAST( UNIX_TIMESTAMP(LEFT(PERIOD_DT, 10), 'yyyy-MM-dd') AS TIMESTAMP ), 'yyyyMMdd' ) as transaction_dt, a.REGISTEREDACCOUNTID as REGISTEREDACCOUNTID, a.loyaltyCustomerId as loyaltyCustomerId, a.TRANSACTIONID as TRANSACTIONID, a.REST_TYP_NM as REST_TYP_NM, a.OPS_DIV_NM as OPS_DIV_NM, a.DESCRIPTION as DESCRIPTION, a.REDEMPTIONS as REDEMPTIONS, b.transactionID as completion_transactionID, b.pointsEarned as pointsEarned, b.restaurant as restaurant, b.partition_date_key as offer_completion_dt, b.WEEK_START as offer_completion_week, b.lid as lid from TRXN_OFFER_CHALLENGES_REDEMPTIONS a left join points_issued b on a.loyaltyCustomerId = b.lid and b.WEEK_START = a.TH_FISCAL_WEEK ) Select TH_FISCAL_YEAR, TH_FISCAL_WEEK, WEEK_START_MAPPING, REGISTEREDACCOUNTID, loyaltyCustomerId, offer_completion_week, count( distinct( case when offer_completion_dt is not NULL and transaction_dt <= offer_completion_dt then TRANSACTIONID end ) ) as trnx_before_completion, count(distinct(TRANSACTIONID)) as total_trnx, count( distinct ( case when lid is not Null then lid end ) ) as completed from completed group by 1, 2, 3, 4, 5, 6 | Count of guests who completed a specific Offer Challenge |
Games NHL Hockey Challenge & Tims Word Challenge | SELECT YEAR(TIMESTAMP) AS YR , MONTH(TIMESTAMP) AS MTH , COUNT(DISTINCT GUESTS) AS GUESTS FROM ( -- WORD CHALLENEGE PLAYERS SELECT DISTINCT TIMESTAMP, _TIMHORTONS.LOYALTY.ID AS GUESTS FROM loyalty.events.adobe_app_events WHERE TRIM(_TIMHORTONS.INTERACTION.PATH) IN ('/timswordchallenge') AND TRIM(_TIMHORTONS.INTERACTION.ELEMENT.NAME) = 'play' AND _TIMHORTONS.PLATFORM IN ('app') AND LEFT(_TIMHORTONS.LOYALTY.ID,7) = 'us-east' AND DATE >= DATE '2023-11-01' UNION -- HOCKEY PLAYERS SELECT DISTINCT TIMESTAMP,_TIMHORTONS.LOYALTY.ID AS GUESTS FROM loyalty.events.adobe_app_events WHERE TRIM(_TIMHORTONS.INTERACTION.PATH) = '/hockey_challenge' AND TRIM(_TIMHORTONS.INTERACTION.ELEMENT.NAME) IN ('submit_picks') AND _TIMHORTONS.PLATFORM IN ('app') AND LEFT(_TIMHORTONS.LOYALTY.ID,7) = 'us-east' AND DATE >= DATE '2023-11-01' ) GROUP BY 1,2 ORDER BY 1 DESC, 2 | Count of guests who played Games (NHL Hockey Challenge and Tims Word Challenge). Limitation: Data only available from Nov 1, 2023. |
Total Sales and Ticket Count by Tender Type | WITH LOYALTY_TENDERS AS ( WITH TENDERS AS ( SELECT REPLACE(UPPER(TRIM(TENDER_NAME)), '.', '') AS TENDER_NAME, TICKET_ID FROM loyalty.tlog.tlog_sale_ticket_tenders WHERE PARTITION_DATE_KEY BETWEEN 20230101 AND 20230731 AND LEFT(REST_NO,2) = 10 ), LOYALTY AS ( SELECT TICKET_ID AS TICKET_ID FROM STG.DERIVED_MASTER_TABLE_NEW WHERE PARTITION_DATE_KEY BETWEEN 20230101 AND 20230731 AND COUNTRY_NM = 'CANADA' AND IS_PASS_THROUGH = 0 AND LEFT(REGISTERED_ACCOUNT_ID,7) = 'us-east' ) SELECT A.* FROM TENDERS A INNER JOIN LOYALTY B ON A.TICKET_ID = B.TICKET_ID ) SELECT CASE WHEN UPPER(TENDER_NAME) IN ('CASH', 'COMPTANT', 'EFECTIVO', 'US CASH', 'CANADIAN CASH', 'CDN CASH', 'ROUNDED CASH', 'ROUNDED COMPTANT') THEN 'CASH' WHEN UPPER(TENDER_NAME) IN ('DEBIT CARD', 'DEBIT', 'CARTE DEBIT', 'DEBITO', 'DÉBIT', 'DO DEBIT') THEN 'DEBIT' WHEN UPPER(TENDER_NAME) IN ('VISA', 'MASTERCARD', 'MASTER CARD', 'AMEX', 'AMERICAN EXPRESS', 'CREDIT CARD', 'CREDIT CARDS', 'DISCOVER', 'M/C', 'DISCOVER CARD', 'DIGITAL AMEX', 'DIGITAL DISCOVER', 'DIGITAL MASTER CARD', 'DIGITAL MASTERCARD', 'DIGITAL AMERICAN EXPRESS', 'DIGITAL VISA', 'DO VISA', 'DO MASTERCARD') THEN 'CREDIT' WHEN UPPER(TENDER_NAME) IN ('TIM CARD', 'DIGITAL TIM CARD', 'CARTE TIM', 'MOBILE TIM CARD', 'DIGITAL CARTE TIM', 'TIM CARTE', 'TIMS GIFT CARD', 'DIGITAL TIMS GIFT CARD', 'CARTE-CADEAU TIM', 'DIGITAL CARTE-CADEAU TIM') THEN 'TIMCARD' WHEN UPPER(TENDER_NAME) LIKE '%SKIP%' THEN 'SKIP' WHEN UPPER(TENDER_NAME) LIKE '%UBER%' THEN 'UBER' WHEN UPPER(TENDER_NAME) LIKE '%DOOR%' THEN 'DOORDASH' WHEN UPPER(TENDER_NAME) IN ('SCAN AND PAY VISA', 'SCAN AND PAY MASTERCARD', 'SCAN AND PAY TIMCARD', 'SCAN AND PAY AMEX', 'SCAN AND PAY DISCOVER', 'SCAN AND PAY TIM CARD', 'NUMERISEZ ET PAYEZ – VISA', 'SCANTOPAY', 'NUMERISEZ ET PAYEZ – MASTERCARD', 'NUMERISEZ ET PAYEZ – CARTE TIM', 'NUMÉRISEZ ET PAYEZ – VISA', 'NUMÉRISEZ ET PAYEZ – MASTERCARD', 'NUMÉRISEZ ET PAYEZ – CARTE TIM', 'NUMERISEZ ET PAYEZ – AMEX', 'NUMÉRISEZ ET PAYEZ – AMEX', 'SCAN AND PAY TIMS GIFT CARD') THEN 'SCANANDPAY' WHEN UPPER(TENDER_NAME) IN ('HST', 'HST1', 'TVQ', 'TPS', 'GST', 'TAX', 'PST', 'HST TAXABLE SALES', 'SALES TAX' 'HST 1', 'H.S.T.1', 'H.S.T', 'HST 13% TAXABLE SALES', 'GST TAXABLE SALES', 'TAX 1', 'HST 1', 'GST# 75696 6891 RT0001', 'HST # 897258141', 'HST5%', 'HST8%', 'QST', 'SALES TAX', 'MEAL PLAN CARD - PREPAID TAX', 'CARTE PLAN REPAS - TAX PREPAYEE', 'GST # 121071781RT0001', 'TVH', 'TVH1', 'VAF', 'H.S.T.') THEN 'TAX' WHEN ((UPPER(TENDER_NAME) LIKE '%ROUND%') OR (UPPER(TENDER_NAME) LIKE '%ARRONDIS %')) THEN 'ROUND UP' ELSE 'OTHER' END AS TENDER, COUNT(DISTINCT TICKET_ID) AS TRXNS FROM LOYALTY_TENDERS GROUP BY 1 | Total sales $ and ticket count organized by tender type (debit, credit, cash, Tims Card, tax) for a given restaurant |