Category: POWERPIVOT

Workaround to the problem of creating a Pivot chart after using “% of row total” calculation in a Pivot Table

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Here is a dashboard created with a Pivot Table, a Pivot chart and slicers (Click to enlarge image).  In the Pivot Table, the % have been computed using "% of row total".

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The Pivot chart shows two columns per month - one for complete and the other for incomplete.  The objective is to show only the Complete column per month in the Pivot chart.  If one filters the Pivot Table only on Complete, then the Pivot chart shows an unexpected result - each column will go upto 100%.  This happens because all calculations in a Pivot Table happen only on the visible cells.  Once the Incomplete column is hidden, the "% of row total" calculation will return 100% which in turn reflects in the Pivot chart.

I have resolved this problem with the help of the PowerPivot.  You may refer to my solution in this workbook.

Perform an “Affinity analysis” to identify co-selling products

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Affinity analysis encompasses a broad set of Analytic techniques aimed at uncovering the associations and connections between specific objects: these might be visitors to a website (customers or audience), products in a store or content items on a media site. Of these, “market basket analysis” is perhaps the most common example. In a market basket analysis, one analyses combinations of products that frequently co-occur in transactions.  For e.g., Of all the shoppers today, how many purchased a school uniform and a school bag.  A retailer can use this information to:

1. Improve the customer shopping experience by rearranging the store layout (place products that co-occur together close to one another); and
2. Run a focused marketing campaign (e.g. target customers who buy a school uniform with offers on school bags, to encourage them to spend more on their shopping basket)

Online retailers and publishers can use this type of analysis to:

1. Inform the placement of content items on their media sites, or products in their catalogue
2. Drive recommendation engines (like Amazon’s customers who bought this product also bought these products…)
3. Deliver targeted marketing (e.g. emailing customers who bought products specific products with other products and offers on those products that are likely to be interesting to them)

Consider a dataset with four columns - Date, Order Numbers, Items sold and Item Description.  To simplify, let's ignore columns 1 and 4 for now.  One may want to know the answer to the following question:

For a particular item sold (say Item X), in how many orders (order frequency) were items X and Item Y were sold together OR in how many orders (order frequency) were items X and Item Z together.

In the Excel workbook (download link below), number of rows in the Sales data sheet is 265,321 (file size is 11 MB) and the number of unique items sold is 23,201.  In Excel, one has to pair each of the 23,201 items with the other 23,200 items to know if there is any affiliation or not.  The sheer size of that dataset would make any formula unusable here.

I have solved this problem with the help of the PowerPivot.  Please note that in the PowerPivot solution worksheet, I have filtered the Row labels on a specific Item number.  This is so because if I attempt to clear the filter criteria to view all Item numbers, then I get a message saying that the number of rows exceeds 1 million.  Also, since the calculations are very intensive, recalculation takes time when you change the criteria in the filter dropdown of the Pivot Table.  Therefore, once you change the filter criteria in the Pivot Table, wait for the Reading data counter (bottom right on the taskbar) to finish processing.

Interpretation

1. Cell C5 - 1,725 orders contained the Item number TNB521711234E.  This figure can be verified via the following steps:
a. On the Sales data worksheet, filter the item number column on TNB521711234E.
b. Copy the resulting rows of the Order number column to another worksheet
c. Select the data so copied and go to Data > Remove Duplicates
d. Count the ensuing rows
2. Cell C6 - Of the 1725 orders containing Item number TNB521711234E, 540 contained Item number TNB52C1
3. Cell C7 - Of the 1725 orders containing Item number TNB521711234E, 446 contained Item number EMT34

You may refer to my solution in this workbook.

Filter the Rank Field in a Pivot Table

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Imagine a two column dataset - Customer Code and Material Number (with alphanumeric data).  The objective is to determine the second highest quantity sold per Customer code.

Since we will first have to determine the Customer wise and Material Number wise quantity sold, a good way to get started is to use a Pivot Table.  One can drag Customer Code and Material Number to the Row labels and Material Number (again) to the Value area section.  We can then sort the numbers in the Value area section in descending order.

Now comes the bit of filtering the Pivot Table to only view the Material Numbers per Customer code which rank second.  To do so, one can try the following steps:

1. Add the Material Number field once again to the Value area section
2. Right click on any cell in the MaterialNumber2 column and choose the "Rank Largest to Smallest" in the "Show Values as" option.  This option is available only in Excel 2010 and higher versions.
3. Click on the Filter drop down of the Row labels heading and select Material Number in the drop down there.  Now go to Value Filters > Equal > Count of Material Number2 > 2

While the steps above sound very logical, the result is incorrect.  The criteria gets applied on the Count column rather than the Rank column.  So the result will be all rows where the Count is 2.

One can overcome this problem by using the PowerPivot.  You may refer to my solution in this workbook.

Quantify combination courses opted by students

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Assume a dataset with two columns which lists down the student names in column A and courses opted for in column B.  Since one student can opt for multiple courses and the same course can be taken up by multiple students, there can be repetitions in both columns.  The objective is to create a matrix like data structure (with courses appearing in both row and column labels) with numbers inside the matrix quantifying the "Number of students who opted for course A and C".  So, for all possible course combinations, one may want to know the number of students who opted for those combinations.

The description above can be extended to cases where buying behavior has to be analysed.  A sore manager may want to know "How many people who buy Brand A also buy Brand B."

Here's a snapshot of the source data and expected result

The number 1 in cell H4 (and cell F6) means that there is only one student who opted for courses B and D.  Likewise, 3 in cell H5 (and cell G6) means that 3 students opted for courses C and D.

You may refer to my Power Query and PowerPivot solution in this workbook.  Power Query has been used for generating a dynamic list of Courses and Power Pivot has been used for writing the DAX formula for quantifying within the matrix.

Identify buy and sell break points

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Assume a two column dataset with Date in the first column and Price in the second one.  The purpose is to identify times to buy and sell - buying would be just after the lowest low is confirmed and sell before or just after the highest high is in place. Confirmation is achieved through crossover of moving averages. This data is being used in back testing buy and sell criteria.

Snapshot of base data

Snapshot of expected result

The Lowest Low is the lowest price that occurs before the next Highest High.  The Highest High is the highest price that occurs before the next Lowest Low..  2.77 is the lowest low after the highest high of 3.69 and 3.23 is highest high after the lowest low of 2.77.

You may refer to my solution in this workbook.

Customise Row/Column appearances in Pivot Tables

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Assume a simple four column dataset with the following columns - User, Month, Leads and Sales.  The dataset shows the user and month wise leads generated and revenue earned.  One may want to analyse this data in a Pivot Table with the User field appearing in the Row labels section, Months field appearing in the Column labels section and the other two Fields appearing in the Value area section.  One can easily create this Pivot Table by dragging field in the quadrant of the Pivot Table Field list pane or in the Pivot Table grid directly.

However, the one customization one may want is to show the Leads generated for all months combined only (not month wise).  The Pivot Table should look as follows:

You may refer to my solution in this workbook.

You may view a video of my solution here

Rank numbers in a range after satisfying conditions

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Assume a five column dataset - ID, Age, Gender, Time and Class.  For chosen ID's, the objective is to:

1. Assign a Rank (in ascending order of time i.e. lowest time will be rank 1 and so on) to each ID
2. Determine the overall place of each ID - Count of unique time entries lesser than equal to the chosen ID' time entry

These can be computed with the VLOOKUP(), RANK(), FREQUENCY(), INDIRECT() functions and array formulas.  You may refer to range H3:K8 of the Sample worksheet.  So far so good.

What adds to the problem is to meet the objectives outlined above after satisfying additional conditions.  For e.g., one may want to give conditions such as Age between 20 and 35 and colours as Orange and Yellow.  Carrying out computations for ranking and Overall place after satisfying these conditions will make the formulas fairly complex.

I have been able to solve this problem with the help of the PowerPivot.  You may download my solution workbook from this link.

Compute year on year growth in a Pivot Table

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Assume a three column dataset which has Year, Company ID and Cash flows.  For each Company, there are cash flows for multiple years.  So for Company ID A001, there are 7 rows, one each for 2010 to 2004 and cash flows appearing in a third column.  Let's assume the number of rows are 750,000.

The task is to compute the year on year growth rate in a fourth column.  While this problem can easily be solved by writing a formula in a fourth column, copying that formula all the way down to 750,000 rows will be time consuming and processor intensive.

I have been able to solve this problem using PowerPivot.  You may download the workbook from here.

You may refer to related questions at this link

1. Computing growth % inside a pivot table
2. Compute Pro rata growth rate within a Pivot Table

Compute month wise pending audits

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Assume a three column dataset showing Audit ID, Date of receipt of audit mandate and Date of audit completion.  There are other columns as well but they are not important for our Analysis.  One may want to compute the following month wise:

1. Which (Audit ID) are the audits pending at the end of every month; and
2. When (Date of receipt of audit mandate) was the mandate for these pending audits received; and
3. Ageing of these pending audits i.e. this would be computed as the last date of the month less Date of receipt of audit mandate

Here's an example:

In January 2014, there are a total of 10 audits reports which were received (Filter "Date of receipt of audit mandate" column on January 2014).  Of these 10 audits, 4 were completed in January 2014 (Filter "Date of audit completion" column on January 2014) itself and therefore there are 6 pending audits.  To this figure of 6, we need to add the audits pending from previous months.  If one filters column "Date of receipt of audit mandate" column on Oct-Dec 2013 and "Date of audit completion" column on dates after January 2014, 8 rows will appear.  This means that there are 8 audits which were received before 1 January 2014 but were completed only after 31 January 2014.  So the total number of pending audits as at 31 January 2014 are 8+6=14.  This task needs to be carried out for all months.

You may refer to my solution in this workbook.

Customise Pivot Table reports

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Assume that someone has created a Pivot Table using the PowerPivot tool.  Now one may want to customize the Pivot Table even further by:

1. Shuffling rows in the Pivot Table; and
2. Recomputing subtotals and Grand Totals after reshuffling rows

A Pivot Table created via the PowerPivot tool can be converted into a normal range via CUBE formulas.  Once each cell carried an individual formula, one can very easily perform the two tasks mentioned above.  You may view my solution in this workbook.

You may watch a short video of my solution here