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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
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,
Outdoor Digital Menu Board (“ODMB”) sales,
Total digital sales (sum of all digital channels), as well as
Restaurant 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) and , where Scan & Pay Total Penetration (sum Scan & Pay Sales as a percentage of system-wide sales)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 Sales Over Time
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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 per restaurant per day 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 per restaurant per day of as a % of systemwide sales for each digital ordering channel. The sum of all the sales per restaurant per day as a % of systemwide sales 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% 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). 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; 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
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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
To follow
View #7: Loyalty Metrics
https://rbictg.atlassian.net/wiki/x/aYBmCQE
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