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2. [End Date] A feature to allow the user to select an end date of the analysis, expressed in the format yyyy/mm/dd. This will represent the end date of the date range being examined by the user.

This date range - along with all others across the dashboard views - should always default to YTD, unless specifically noted otherwise.

Callout Metrics: this first dashboard view should present users with a series of callout metrics that summarize the performance & health of Tim Hortons Digital & Loyalty during users' selected reporting ranges, as defined by [Start Date] and [End Date]:

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In addition to callout metrics, this view should also feature an option for users to filter for one specific metric and display it across each province in a map visualization. For example, if the user selects Known Diner Sales Penetration, the map should populate each province with a percentage representing the proportion of Known Diner Sales that each province accounts for (e.g., Ontario accounts for 32.3% of Canada’s KDS). This is done by taking the selected metric’s value for each province and dividing it by the value for Canada (e.g., Ontario KDS / Canada KDS).

...

2. [End Date] A feature to allow the user to select an end date of the analysis, expressed in the format yyyy/mm/dd. This will represent the end date of the date range being examined by the user.

This date range - along with all others across the dashboard views - should always default to YTD, unless specifically noted otherwise.

Callout Metrics: this first dashboard view should present users with a series of callout metrics that summarize the performance of the Tim Hortons loyalty program and guest engagement during the user’s selected reporting range, as defined by [Start Date] and [End Date]:

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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.

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Similar to the previous chart, users should have the option to drill up or down on the reporting cadence (e.g., daily, weekly, monthly, quarterly). Total guests, comprising all three types of guests, for each cadence will form one bar in the bar chart. 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”, comping guests in January 2023 would be the number of guests who made a purchase both in January 2022 and January 2023; non-comping guests would be the number of guests who did not make a purchase in January 2022 but did make one in January 2023; non-returning guests would be the number of guests who made a purchase in January 2022 but did not make one in January 2023. The count of comping and non-comping guests would accumulate to form the positive portion of the bar for January, while non-returning guests would be represented as the negative portion of the January bar. This would be repeated to create a bar for each month from January to October 2023. Similar to the previous chart, this chart will also feature the average of comping, non-comping, and non-returning guests for January to October 2023 in comparison to the average of comping, non-comping, and non-returning guests for the same time period in the previous year.

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2. [End Date] A feature to allow the user to select an end date of the analysis, expressed in the format yyyy/mm/dd. This will represent the end date of the date range being examined by the user.

This date range - along with all others across the dashboard views - should always default to YTD, unless specifically noted otherwise.

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, Mobile Order & Payment (MO&P) sales, delivery sales, catering sales, Outdoor Digital Menu Board (“ODMB”) sales, Scan & Pay sales, total digital sales (sum of all digital channels), as well as restaurant POS 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 / payment method within the [Start Date] and [End Date], divided by the total system-wide sales within the [Start Date] and [End Date].

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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.

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2. [End Date] A feature to allow the user to select an end date of the analysis, expressed in the format yyyy/mm/dd. This will represent the end date of the date range being examined by the user.

This date range - along with all others across the dashboard views - should always default to YTD, unless specifically noted otherwise.

Callout Metrics: this first dashboard view should show users series of callout metrics that summarize the performance of the Tim Hortons offers and rewards program during the user’s selected reporting range, as defined by [Start Date] and [End Date]:

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• Discounting views

  • Points Issued per KDS $ spent

  • Net discounting

Data Engineering will

• Translate requirements into a plan with engineering activities to meet due date.

• Complete the data governance requirements for the project (such as availing metadata such as a data dictionary for users)

• Provide support and ongoing maintenance of the data in Databricks to ensure the data continues to meet the requirements defined herein.

Risks

1. Project timeline overrun.