Jira initiative | TBC | Document
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Due Date | 23 Nov | |||||||||||||||||||||||||||||||||||||||||
Document Owner |
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Digital Loyalty | |
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Data Engineering |
🤝 Introduction
Through our Digital App, Tim Hortons (“TH”) gathers a significant amount of data on loyalty guests, including purchasing and traffic information. From a data analytics perspective, this data is predominantly utilized by the Digital Loyalty Analytics team. When examining the performance of new products following introduction to market, the Category Management team typically makes use of system-wide data and, using this, is able to analyze performance metrics based on these macro performance metrics. However, the lack of guest-level data prevents the wider organization, including Category Management, from gaining a deeper understanding of uptake of the new product, as guest level data is relatively inaccessible to these teams. For example, teams are unable to examine the critical measure of rates of repurchase of a product, as they are unable to track guest-level purchases. The availability of this data would allow Category to track guest level purchases, and importantly, repurchase behaviour.
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Title | Description | Priority | Notes | ||||||
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Analyze performance of new products following launch to market | A user selects: a product (level 4)menu item name, an analysis start date, and an end date. The user is able to filter the entire dashboard based on Region and Restaurant(s). |
| This product should be the equivalent of levelmenu_4item_platformname from the loyalty.stg.derived_master_table_new table | ||||||
A user is shown high level call-out metrics for the performance of the product during this date range chosen, and is shown the uptake of the product by loyalty guests. |
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A user is shown a weekly, time-series chart of loyalty guest: (i) cheque-level data, (ii) product purchases, and (iii) product repurchases. |
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A user is shown the proportion of product mixes at the start and end dates chosen for both guests who purchased the product, as well as overall loyalty. |
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[Start Date] A feature to allow the user to select a start date of the analysis, which is a fiscal week. This will represent the start date of the date range being examined by the user.
[End Date] A feature to allow the user to select an end date of the analysis, which is a fiscal week. This will represent the end date of the date range being examined by the user.
[Product Category] Four Five features representing product categories 1 to 4, and menu item name. This will represent the product being analyzed by the user on the dashboard. For this use case, the end user should only be able to select product category level 4 menu item name (“levelmenu_4item_platformname”), with product category levels 1, 2, 3 & 3 4 being display only.
A.2. Filters
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The behaviour of guest cheque when purchasing the product [Product Category].
The behaviour of guest cheque who purchased the product, and guest cheque who purchased the category (but not necessarily the provideproduct). Providing insights on whether guests are trading within category. This, in combination with other metrics, provides insights on product & category cannibalization.
The behaviour of guest cheque when purchasing the product [Product Category] as compared to the overall guest base. This provides insights on whether the new product has an influence on guest cheque.
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