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Retail analysis models often include a competing outlet indicator to improve the quality of the model. The indicator does this by allowing the user to manage the effect that is associated with partial trading periods. For example, the true / false indicator shows whether the store has traded for all available weeks in a month. This post looks at how the competing store indicator can be implemented using powerpivot and DAX.
The Competing Store Definition
There are a few definitions of competitive stores (and perhaps another post to follow). In this post, the definition of competitive is a store that trades for every week in the month. If the store has not traded for all weeks, it is not considered competitive and this permits the identification of sales that occurred when;
The Model
The underlying model considers a store dimension, date dimension, and sales data. The grain for sales is { store x day } à sales amount. Although, this implementation treats the sales relation {store x day} as unique, there is no requirement to do so. The dates table follows standard structure of a date dimension table (grain of day) with the fiscal calendar attributes aligned along a 445 Calender. The fields fiscal_fullweek_key has the format YYYYWW and fiscal_fullmonth_key has the format YYYYMM. These are standard implementations where YYYY refers to the century, WW refers to the week of year and MM refers to the month of year.
The store table is a type 1 dimension (showing the ‘current’ state of the store). There is a strong argument to show the [competing store] as an attribute of the store dimension however, this would require a type 2 store dimension and intensive ETL. This is outside the management of the end user who consumes available data as it would require more intensive resources and relational constructs.
Method
The approach to determine whether the sale is competing or not is based on the comparison of the stores actual trading weeks in a month compared to the available trading weeks. When the two values are the same the sale is competitive, otherwise it is not. Additionally, note that this formula is applicable to each row of the sales fact because the sales fact is the only table that combines both store and date data and can therefore be used as the auto-exists cross-join of stores and dates.
For each row in the sales fact, we must determine;
In order to improve readability of the formula the sales table has been extended to include a field full_month_key (formula = RELATED(fiscal_fullmonth_key)) and full_week_key (formula = RELATED(fiscal_fullweek_key))
Formula 1 – Available Trading Weeks in Current Month (weeks_in_month)
The available weeks in the current month are the distinct count of weeks for the related month. We can achieve this by removing the filter context of the dates table and reapplying it so that the row sales month equals the dates month (below)
| CALCULATE( DISTINCTCOUNT(Dates[fiscal_fullweek_key]) , filter(all(Dates), Sales[fullmonth_key]=Dates[fiscal_fullmonth_key]) ) |
If the sales table did not include the fullmonth_key field, we could use the related function directly in the formula.
| CALCULATE( DISTINCTCOUNT(Dates[fiscal_weeks_key]) , filter(all(dates), RELATED(Dates[fiscal_fullmonth_key])=Dates[fiscal_fullmonth_key]) ) |
Alternatively, we can remove the row filter context of the dates table to the extent that it only includes the current month. This is done with the ALLEXCEPT function so that the dates table filters are removed except for the listed column restrictions (as below)
| CALCULATE( DISTINCTCOUNT(Dates[fiscal_fullweek_key]) , ALLEXCEPT(Dates,Dates[fiscal_fullmonth_key]) ) |
Formula 2 – Weeks Traded in the Current Month (weeks_sales_in_month)
To determine the number of weeks that a store traded for (in the current month), we use a similar restriction but apply it to the sales table. That is, count distinct weeks after we remove the current row filter context and reapply it based on the current month of the rows sale date.
| CALCULATE( DISTINCTCOUNT(sales[week_key]) , ALLEXCEPT(sales,sales[store_key], sales[fullmonth_key]) ) |
Formula 3 – Continuing Sales
The final (and only visible formula) is a simple if function that compares the trading weeks to those available for a true/false output.
=if([weeks_in_month]=[weeks_sales_in_month], “Yes”, “No”)
Final Model
The final schema for the model is shown below. The measure [Sales Amount] is simply the sum of the sales_amount field.
In a previous post I looked at the built in DAX Total to date functions (TotalYTD, TotalMTD, TotalQTD) which sum calendar periods to an end date. The YTD function is somewhat flexible because it allows a year end date to be passed as an optional argument in the function. However, the end date is static in the formula, that is, the same year end date is applied must be applied to all years (also indicating the start of the next year is the day after the prior year ends). The 445 calendar contravenes this requirement because the yearend date (and year start date) is not consistent between years.
The 445 Calendar
| The 445 calendar (and variants) are a commonly used to standardise months so that there are an even number of weeks in each month.One of the side effects of this is that the year does not start and end on the same day each year (note 2010 starting on 3-Jan and 2-Jan in 2011). This means that functions that rely on dates cannot be used to aggregate yearly totals. |  |
The Formula
The calculation works by simply removing the current date filter context and then reapplying it so that the year is restricted to the current fiscal year and the date is less than the maximum date for any filter (ie row, column, filter axis) that is applied. The use of max for the fiscal_year_key is simply to return the current year as a scalar.
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Using the totalytd function, the YTD value for December 2010 is effectively calculated as the YTD value for a date which ends on for 1-Jan-2011 (1 day).This does not occur when the filter context is reapplied because the calculation includes all data for the current year that is below the last date of the current filtered year (that is, all dates up to and including 28-Nov-2010). |
An alternative approach that deals with text running balances can be found at Javier Gullen‘s blog.
DAX has three periods to date functions which easily allow the creation of common the common measures (Month To Date, Quarter To Date and Year To Day). This post looks at period to date calculations and member sorting in the BISM tabular model.
Sorting
| By default the tabular model sorts members by name. For example, clicking on the month field shows the alphabetical listing of months. Although these values are shown in the project model they are displayed in the same order when the project is deployed.The order of these members can be changed and sorted by an additional column by using the sort by column function (menu path: Column > Sort > Sort By Column).When this is done, we simply specify the sort column in the popup (below). |
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After sorting, members appear as expected in both the project design and deployed project.
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Period to Date Calculations
Period to date can be easily created using the DAX functions TOTALYTD, TOTALMTD, TOTALQTD. Note that these functions are not dependent on sort order but on a related date field.
| For example, a date field in the related DimTime table.Note that there is a primary relationship between FactResellerSales and DimTime and the inclusion of the TimeDate (which is a date data type) column in DimTime. |
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The general format of the function is TOTALPPP ( scalar_function , time_column) where the time_column is a primary related date field. To show YTD Values for SalesAmount we use the formula:
Sales YTD:=TOTALYTD(sum(FactResellerSales[SalesAmount]), DimTime[TimeDate])
Also note that measures (as they would be thought of in SSAS) must be added to the ‘Measure Grid’. If they are not, measures are not seen in the cube. The table FactResellerSales has columns hidden from client tools (so that no dimension members are created) and contains the measures [Sales] and [Sales YTD] (below).
Browsing the cube shows data (sorted) with YTD values as expected. Unlike SSAS, the tabular model will show the [YTD] measure defaulting to the last year when the (related) time table is not used.
MISC
The technet reference for these functions can be found at YTD , QTD , MTD .
