Mat Marketing DefinitionDefinition of mat marketing
BottomCount, Tail, Tail, Properties in MDX for MAT (Moving Annual Total), YTD (Year to Date) computation
Just tried to make two computed members (MAT1, MAT2) under the periodic dimensions in my dice and found this one. First I try to tell MAT1 and MAT2. MAT1: The last 12 monthly of the actual monthly number. MAT1 is the monthly from Jul'2010 to Jun'2011 if the actual monthly is Jun'2011.
The last 12 moths beginning with the same mont (given month/current month) of the year before. In the above example, there will be CAT2 from July 2009 to June 2010. IT' YTD1: from January of the year to the actual one. That is, when the actual June of 2011 is. XTD1 is from January 2011 to June 2011.
JTD2: Same month as JTD1, but from year before. By the time the actual month is Jun' 2011, it will be Jan'2010 to Jun'2010 for XTD2. Once I have created MAT1, MAT2, YTD1, QTD2 computed element, it becomes: ), VISIBLE= 1 ; 12, ), VISIBLE= 1; ), VISIBLE= 1; right(Tail([Periodo].[Periodo].[Months].Members). Item (0).Lag(12).properties("Key"),2), Tail([Periodo].[Periodo].[Months].Members).Item(0).Lag(12) VISIBLE= 1; ), VISIBLE= 1; Team Leader at IQVIA (www.iqvia.com).
Calculation of MAT, YTD, RQTR, QTD Marktanteilsberechnung
For each region, I have recurring month-by-month sell dates. Item I want to show a chart of trends in share for each item that is updated to show month/rolling QTR/YTD/MAT/QTD according to the selections ("Parameters"). Enclosed you will find a example database for your ref. These are the main brands that make up the market: PG1, PG2 and PG3.
I also tried to use the date function and the solid function to get this as a computed field myself. In this way I could prevent the table computations and use these computed arrays for further computations.
moving average for the year
This paper considered the Moving Annual Total (MAT) as a way to easily eliminate seasonal effects over a 12-month horizon in order to discover the trends behind a given datarow. MAT works well for flowing positions such as revenue, profit and transport volumes as these can be accumulated over timeframes to obtain significant amounts.
However, we cannot use a MAT for an inventory line such as accounts receivable or number of employees, or for statistics such as gross margin percentage or exchange rate /$, because it does not make much point to add these line across period. Calculation of the Moving Annual Average (MAA) is the answer. But the easiest way to do this is to use a very similar calculation method to that used to compute a MAT - the end score is then split by 12 (or how many cycles we have within our year) to get an annual number.
We can calculate a more precise monthly weighed mean for each period in cases of statistical data such as gross margin percentage (from one flows position split by another flows position) by computing the sum of the last 12 periods for both the meter and the ratios denominator separately and then divide them.
Statistics such as return on capital employed percentage (formed from a cash position divided by a inventory position) require a more complex computation because the value for such a quota is usually shown as an annuity. Therefore, we must compute the sum of the profits of the last 12 moths and split it by the weighting of the capital employed over this 12-month horizon.
MAA transforming is a very versatile process because it can be used on all kinds of elements of data, whether streams, inventories, or statistic. In contrast, you can only calculate the MAT for transaction-types and you must therefore use them with caution.