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The aim of this project is to develop a dashboard that illustrates the high-level performance of Tim Horton’s digital/loyalty program. Currently, executive leadership across Tim Horton’s seeks insights on digital health metrics at a higher frequency than the Digital & Loyalty Analytics team currently provides to them. Furthermore, the existing process to provide these metrics ad hoc requires significant manual intervention by the analytics team. Thus, the dashboard should display key metrics that the digital team already tracks, in one location. The end-users of the dashboard are anticipated to be high-level executives, who will monitor essential business metrics to effectively guide business activities, as well as members of the Digital Loyalty Analytics team and will be displayed on the Digital & Loyalty screens (therefore dashboard layout, perspective & dashboard should align to screen dimensions). However, based on the rate of uptake, users across the broader organization may utilize the dashboard on an ad hoc basis.
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The following outlines details of each of the requested dashboard views (of which there are four c.6 in total), and should inform the development of the underlying data models to power the use case. Note that the analysis should be limited to TH Canada and Canadian Loyalty Guests.
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The first time-series chart, KDS Penetration Over Time, is intended to illustrate, using superimposed line charts, the evolution of KDS penetration throughout the dashboard user’s selected time period, compared to past years over the same time period. Each line represents a different year’s KDS over the user’s selected reporting range. Within this chart, users should have the option to drill up or down on the reporting cadence (e.g., daily, weekly [fiscal], monthly, quarterly, annually). For example, if the user inputted the time range as [Start Date] 2023/01/01 to [End Date] 2023/10/01 and drilled down the reporting cadence to “weekly”, the chart would show weekly KDS penetration overtime for January to October, in 2021, 2022, and 2023.
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Callout Metrics: this first dashboard view should show users the respective sales of each digital ordering or payment method, represented as a percentage of system-wide sales, during the user’s selected reporting range, as defined by [Start Date] and [End Date].
This will include:
Kiosk sales as % of SWS, where
kiosk sales is the sum of sales where service_mode_cd in ('KIOSK', ‘KIOSK TAKEOUT’, ‘KIOSK EATIN’)
Mobile Order & Payment (MO&P) sales as % of SWS, where
Delivery sales (3P and white label),
Catering sales,
Outdoor Digital Menu Board (“ODMB”) sales,
Total digital sales (sum of all digital channels), as well as
Restaurant In-Restaurant and Drive Thru sales (sum of all non-digital sales).
The percentages for each of these would just be computed as the total sales for that service mode within the [Start Date] and [End Date], divided by the total system-wide sales within the [Start Date] and [End Date].
There should also be callouts for Scan & Pay Loyalty Penetration (sum of Scan & Pay sales as a percentage of all loyalty sales) and Scan & Pay Total Penetration (sum Scan & Pay Sales as a percentage of system-wide sales).
Time Series 1 of 3: Digital Ordering Sales Over Time
Note, Sales per Restaurant per Day: cumulative sales made by the specified digital ordering channel within the specified reporting cadence (e.g., for each month, if specified cadence is monthly), divided by number of restaurants that actively operate the digital ordering channel, per day.
The first time series in this view, Digital Ordering Sales Over Time, is intended to illustrate the sales per restaurant per day for each digital ordering channel over the user’s selected time range, as a stacked bar chart, where each portion of the stacked bar corresponds to the average sales per restaurant per day of each digital ordering channel. The sum of all the sales per restaurant per day of each digital ordering channel should amount to the total digital sales per restaurant per day, which is represented by the value of the entire bar. Digital sales as a percentage of system-wide sales is represented as a line (above the bars, on a separate y-axis) also spans the duration of the user’s selected reporting range.
Similar to the other charts, users will have the option to drill up or down on the reporting cadence (e.g., daily, weekly, monthly, quarterly, annually). As well, the chart will feature the average sales per restaurant per day for the user’s entire selected time period in comparison to the average for the same time period in the previous year.
Time Series 2 of 3: Ordering Channel Cheque Comparison Over Time
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MO&P sales is the sum of sales whereservice_mode_cd in ('MOBILE ORDER DRIVE THRU', 'MOBILE ORDER EAT IN', 'MOBILE ORDER TAKE OUT')
Delivery sales (3P and white label) sales as % of SWS, where
delivery sales is the sum of sales whereservice_mode_cd in ('DELIVERY', ‘WHITE LABEL DELIVERY’, ‘THIRD PARTY DELIVERY’)
Catering sales as % of SWS, where
catering sales is the sum of sales wherediningtype = ‘CT’ (from PRODRT.CURATED_TRANS_EVENTS_NEW)
Scan & Pay sales as % of SWS, where
Scan & Pay sales is the sum of sales where SCANANDPAY = ‘TRUE’ (from PRODRT.CURATED_TRANS_EVENTS_NEW)
Total digital ordering sales (sum of all digital channels) as a % of SWS, where
total digital ordering sales is the sum of sales from all the above service modes
In-Restaurant and Drive Thru sales (sum of all non-digital sales) as a % of SWS, where
In-restaurant and Drive Thru sales is the sum of all sales where service_mode_cd in ('TAKEOUT', ‘DRIVETHRU’, ‘EATIN’)
The percentages for each of these would just be computed as the total sales for that service mode within the [Start Date] and [End Date], divided by the total system-wide sales within the [Start Date] and [End Date].
There should also be callouts for Scan & Pay Loyalty Penetration (sum of Scan & Pay sales as a percentage of all loyalty sales), where Scan & Pay sales is the same as the previously-used definition above, and loyalty sales is the sum of all sales where LEFT(LOYALTY_CUSTOMER_ID,3) = '046'.
Time Series 1 of 3: Digital Ordering Channel Sales (% of SWS) Over Time
The first time series in this view, Digital Ordering Channel Sales (% of SWS) Over Time, is intended to illustrate the sales penetration of systemwide sales for each digital ordering channel over the user’s selected time range. This data will be represented as a stacked bar chart, where each portion of the stacked bar corresponds to the average sales as a % of systemwide sales for each digital ordering channel. The sum of the sales as a % of systemwide sales of each digital ordering channel should amount to the total digital sales as a % of systemwide sales, which is represented by the value of the entire bar.
Similar to the other charts, users will have the option to drill up or down on the reporting cadence (e.g., daily, weekly, monthly, quarterly, annually). The x-axis will display the days/weeks/months of the user’s selected reporting range, depending on which reporting cadence that the user drills down to. Average cheque for each ordering channel will be represented by a bar in the cluster. ; average CY YTD data will also be featured in a bar at the end, as pictured in the sample view.
Time Series 2 of 3: Ordering Channel Cheque Comparison Over Time
The second time series, Service Mode Cheque Comparison Over Time, is intended to compare the average cheque amount across all digital ordering channels over time using a clustered bar chart. Similar to the other charts, users will have the option to drill up or down on the reporting cadence (e.g., daily, weekly, monthly, quarterly, annually). The x-axis will display the days/weeks/months of the user’s selected reporting range, depending on which reporting cadence that the user drills down to. Average cheque for each ordering channel will be represented by a bar in the cluster. This chart will also feature a multi-select option, allowing the user to select the specific ordering channels they want to view (from Drive-Thru, In-restaurant, MO&P, Kiosks, Delivery, Catering, ODMB)
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The fourth time series, Scan & Pay First Time Purchasers, is intended to show the evolution of Scan & Pay adoption over time; that is, whether more people are using Scan & Pay for the first time, overtime. Similar to the previous chart, users should have the option to drill up or down on the reporting cadence (e.g., daily, weekly [fiscal], monthly, quarterly, annually). This chart will also feature bars representing the nominal number of Scan & Pay first time purchasers for each cadence that the user drills down to (eg: each month, if they choose “monthly”) across the user’s selected reporting range. For example, if the user inputted their time range as [Start Date] 2023/01/01 to [End Date] 2023/10/01 and drilled down the reporting cadence to “monthly”, the bars will show the nominal number of guests who are using Scan & Pay to make a purchase for the first time ever in each month, from January to October 2023.
View #6: Digital Metrics
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To follow
View #7: Loyalty Metrics
https://rbictg.atlassian.net/wiki/x/aYBmCQE
Technical Specification
To meet the objective of the initiative, Data Engineering will support with the following:
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Project timeline overrun: as a high-priority project, with executive-level interest, adherence to the overall project timeline is the most significant risk of the project.
Inaccurate data: as the dashboard is anticipated to influence TH Digital & Loyalty decision-making, the accuracy of the data is paramount.
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Loyalty Dashboard Wishlist
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Issuance
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Total Points Issued
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Ability to filtered by
any period (day, week, month, campaign period)
province
top cities
restaurant
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Point Type
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Restaurant, FS/Partner, Marketing (split by Hockey Challenge, Word Challenge, Offers, Campaign), Support
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Earning Means
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App, Plastic, Digital Wallet
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Bonus Point Guardrail tracking
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Avg Issuance per guest
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Total Number of Non-Registered Points
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Ability to filtered by
any period (day, week, month, campaign period)
province
top cities
restaurant
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Redemption
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Total Points Burned
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Ability to filtered by
any period (day, week, month, campaign period)
province
top cities
restaurant
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Points Burned by Tier
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Average Redemption Per guest
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Type of Point Burned
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Restaurant, FS/Partner
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Number of points burned as a % of total points in system
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Guest View
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Number of Guests that Earned
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Ability to filtered by
any period (day, week, month, campaign period)
province
top cities
restaurant
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Number of New Guests that Earned (past 30 days)
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Number of Guests that Burned
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Number of New Guests that Burned (P30 days)
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Total Number Non-Registered Guests
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Total Number Non-Registered Guests that Earned
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Total Number of Guests Scanning with Plastic (any plastic)
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Total Number of Guest Scanning with Digital
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Total # of features guest has engaged with (meaning scan, Scan & Pay, MPO, delivery, game, catering,
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Avg cheque
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Ability to filtered by
any period (day, week, month, campaign period)
province
top cities
restaurant
Plastic vs. Digital Earn
New (last 30 days) vs. Ongoing
Frequency/value segments
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Avg visits
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Offer users
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Game interactions (HC, WC, RUTW)
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Ability to filtered by
guests that play in any period (day, week, month, campaign period)
ever played
new players in a certain time
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Scan & Pay Penetration’s
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ability to filter between by guest, by transaction
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Never redeemed
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amount, % of total redeemers, % of all guest
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Balances
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Bankers
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% and number of points
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Set to redeem
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% and number of points
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Expiry/Inactive/Fraud
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Total Points Expiring
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per month
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Number of Guests with Expiring Points
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per month
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Total Points Deactivated (when live)
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per month
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Number of Guests with Inactive Points (when live)
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