Canadianoutside787
New Member
- Joined
- Aug 26, 2020
- Messages
- 3
- Office Version
- 365
- Platform
- Windows
Hello Excel Community,
I'm hoping you can help me with building some calculations into my data model in Power Pivot. For some context: I work for a retail company and I am looking to build the dashboard that lets me analyze how our stores and their products have been performing. The data set is too large to build pivot tables straight off of which is why I originally wanted to use power query but I am slowly learning that there are a lot of other advantages. However, I feel like I'm in over my head right now and the world is spinning. For a first step, I would just like to bring in YoY performances for our stores.
Ideally, I will build 4 different pivot tables that show me the following with their year over year growth/decline: Last week performances , Last month, season to date, and year to date. For an example, for a last week calculation I would want to see the growth between the "calendar year/week" column for 34.2020 and 34.2019...we go by fiscal weeks at our company.
I have included an example data set (for confidentiality reasons) which is very similar to what I'm working with but with only a couple lines for last year sales, this year sales and current inventory in the stores.
All that being said....could someone help me with a formula that calculates the year over year performance of this data? I've also attached an image of what the power pivot table looks like right now at a basic level.
thanks a bunch to all of you for your words of wisdom!
I'm hoping you can help me with building some calculations into my data model in Power Pivot. For some context: I work for a retail company and I am looking to build the dashboard that lets me analyze how our stores and their products have been performing. The data set is too large to build pivot tables straight off of which is why I originally wanted to use power query but I am slowly learning that there are a lot of other advantages. However, I feel like I'm in over my head right now and the world is spinning. For a first step, I would just like to bring in YoY performances for our stores.
Ideally, I will build 4 different pivot tables that show me the following with their year over year growth/decline: Last week performances , Last month, season to date, and year to date. For an example, for a last week calculation I would want to see the growth between the "calendar year/week" column for 34.2020 and 34.2019...we go by fiscal weeks at our company.
I have included an example data set (for confidentiality reasons) which is very similar to what I'm working with but with only a couple lines for last year sales, this year sales and current inventory in the stores.
All that being said....could someone help me with a formula that calculates the year over year performance of this data? I've also attached an image of what the power pivot table looks like right now at a basic level.
thanks a bunch to all of you for your words of wisdom!
Example data table for data model.xlsx | ||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | |||
3 | Retail / E-Com | DTC Shop ID | DTC Format | Prod. hier. level 1 | Prod. hier. level 2 | Product Type | Gender | Material # | Article Name | Calendar Year/Week | Week | Year | Inv Gross Sales | Inv Net Sales | Inv Net Units | Inv Gross Units | Returns value | Returns units | Stock in Store | Stock in Transit | ||
4 | RETAIL | Shop 1 | BS | C1 helmets | Alp. Helmets.ON | Helmet | F | 38400 | HELMET 1 | 03.2020 | 03 | 2020 | 115.7 | 115.7 | 1 | 1 | ||||||
5 | RETAIL | Shop 2 | BS | C1 Jackets | Alp. Jackets.ON | Helmet | M | 37300 | Jacket 2 | 51.2019 | 51 | 2019 | 140.5 | 140.5 | 1 | 1 | ||||||
6 | RETAIL | Shop 1 | BS | C1 Jackets | APP Active Wear | Midlayer Jacket | F | 55800 | midlayer 1 | 34 | 2020 | 7 | 0 | |||||||||
7 | RETAIL | Shop 1 | BS | C1 Jackets | APP Essentials | Down Jacket | F | 89900 | Down jacket 1 | 34 | 2020 | 7 | 0 | |||||||||
8 | RETAIL | Shop 2 | BS | C1 Jackets | APP Essentials | Down Jacket | F | 90000 | Down jacket 2 | 34 | 2020 | 5 | 0 | |||||||||
9 | RETAIL | Shop 1 | PS | C1 Socks | Running socks | Socks | U | 4700 | Socks 1 | 33.2020 | 33 | 2020 | 72.72 | 72.72 | 4 | 4 | ||||||
10 | RETAIL | Shop 1 | PS | C1 Socks | Running socks | Socks | U | 4700 | Socks 1 | 34.2020 | 34 | 2020 | 36.36 | 36.36 | 2 | 2 | ||||||
11 | RETAIL | Shop 2 | PS | C1 Socks | Running socks | Socks | U | 4700 | Socks 1 | 32.2020 | 32 | 2020 | 12.34 | 12.34 | 1 | 1 | ||||||
Sheet1 |
Cell Formulas | ||
---|---|---|
Range | Formula | |
L4:L5,L9:L11 | L4 | =LEFT([@[Calendar Year/Week]],2) |
M4:M5,M9:M11 | M4 | =RIGHT([@[Calendar Year/Week]],4) |