Tags: MIN

Show Balance outstanding everyday even if data for everyday is not available

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In this simple 3 column dataset, there are 2 accounts - Konto 1 and Konto 2.  Each account has a balance outstanding as on a certain date.  However, if you notice carefully, there is no balance for any account on January 4-5,9-10 2020.

The objective is show the balance outstanding every day.  For days which are absent from the dataset, the balance outstanding should be the balance as on the previous day.  So for the Konto 1 account, on January 4-5, the balance should be 400 and on January 9-10, it should be 250.  The same logic applies for the Konto 2 account as well.  The expected result should be

I have solved this problem using Data > Get & Transform and PowerPivot.  You may download my solution workbook from here.

Here's another related question.  Given this 3 column dataset, the objective is to determine the total of the "PDV Combined Total" column for the past "5 billing days" from today - the past 5 working days have to be determined by looking at the "IsABillingDay" column.  So if today is December 4, 2020, then the past 5 business days would be November 30, 2020 to December 4, 2020.  The result should be 14,95,937.  However, if today was December 2, 2020, then the past 5 business days would be November 24, 2020 to December 2, 2020.  The result would be 14,33,545.  You may download the solution in a PBI file from here.  So while this question has been solved using the DAX formula language in PowerBI Desktop, since the same formula language exists in MS Excel as well, this result can be obtained in MS Excel as well.

Analyse membership changes from year to year

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Assume a simple 4 column dataset as shown below.  This data shows which ID had which type of subscription in which year.  So ID A, which started as a "Free" subscriber in 2018 switched to a "Premium" subscriber in 2019 and then churned out in 2020.  Likewise, ID D which started as a "Pro" subscriber in 2018, churned out in 2019 but returned as a "Free" subscriber in 2020.
The objective is to study how subscribers switched from one subscription type to another across year.  So the expected result should look like this


I have solved this question using the PowerPivot.  You may download my MS Excel workbook from here.

Calculate rolling sum for the past week by ignoring blank cells

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Assume a simple dataset as shown in the image below (the input data is in columns A and B only.  The desired outcome is in columns C and D).

The objective is to calculate the 7 days rolling sum and average (as shown in columns C and D) ignoring blank cells.  So in cell C8, the rolling sum is the summation of values from range B2:B8.  In cell C9, it is from B3:B9.  However, in cell C10, it will be from range B3:B9 (not from range B4:B10).  Likewise, in cell C11, the rolling sum will be from range B4:B11.  So the range to be considered for calculating the rolling sum has to roll back automatically until it picks up 7 numeric cells - the blanks have to be ignored.  The rolling average is a simple division - Rolling sum/7.

I have solved this question with Excel formulas here.  This time however, I am sharing a solution by using the DAX formula language available in the PowerPivot and PowerBI Desktop.  You may download my PowerBI Desktop file from here.  The same solution can also be obtained in MS Excel using the PowerPivot as well.

Customer analysis by Country and time period

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Here is a Sales dataset of 8 columns and 29 rows.  It basically details the revenue earned and cash collected by service type, Customer, Country and Period.  For a selected Country and time period, there could be customers availing of both services or of any 1 service.


There are 2 broad questions that one may want to get answers to:

  1. Determine the number of customers who availed of a certain number of services
  2. Determine customers with whom business was forged for the first time and those who churned out

For a chosen country and Year/Month, the first question stated above further sub-divides into:

  1. How may customers availed of both services - Consultancy and Implementation
  2. How may customers availed of only one of the two services

So if a user selects the Country as India and Year/Month as January 2015, then Customers who availed of both services would be 1,3 and 4.  Note that Customer 2 should not be considered (even though he/she availed of both services) because the revenue earned from one of the services (Implementation) was nil.  For the same selection (India and January 2015), the Customers who availed of only 1 service would be Customer 2 - this customer availed of only the Consultancy service (Revenue was earned from this Customer only for this service).  After applying a filter on the source dataset, the rows for India and January 2015 are:

The expected result is shown below in PowerBI desktop software.  If you are not concerned with who those customers are (you just want the count), then you may simply remove the Customer Name field from the visual.

The second question is to determine the number of new and lost customers.  If a customer was not in the database in any prior month, the customer is identified as new.  To clarify, a customer who availed of the Consultancy service in a prior month also availed of the Implementation service for the first time in the current month would not be counted as a new customer.  If a customer ceases to generate revenue in any month, the customer would be counted as lost (churned) in that month.  So when USA is selected in the Country slicer and Year/Month is February 2015, the expected result is:

I have solved this question with the help of the PowerPivot.  You may download my PowerBI desktop solution file from here and source Excel workbook from here.  This problem can also be solved in MS Excel using the PowerPivot.

Flex a Pivot Table to show data for x months ended a certain user defined month

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In this simple 3 column dataset shown below, one can see the month wise demand and energy charge for 2 years - 2017 and 2018.

The objective is to compute the month wise demand charge for x months ended a certain user defined Year and Month.  So, if a user selects the Year as 2018, Month as June and Duration as 9, then the Pivot Table should show month wise demand charge for the 9 months ended June 2018 i.e. from October 2017 to June 2018.  Likewise, if a user selects Year as 2018, Month as May and Duration as 3, then the Pivot Table show should month wise demand charge for the 3 months ended May 2018 i.e. March 2018 to May 2018.

You may download my solution workbook from here.

Show Project wise status in a Pivot Table

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Visualise a simple 6 column Table as shown below - Project Name and the finish date for each of the 5 stages that the projects go through.  Each project goes through 5 stages - Requirement (Req), Development (Dev), UAT, Implement and Warranty.

The objective is to report on the status of each project at the end of each month based on which stage is/was completed in that month.  So, if a given project's requirements are completed in January and development completes some time in March, the one would expect the output of the report to show the project's status in January and February as "Req" and in March as "Dev" respectively.  February should also show "Req" because the next stage was completed only in March (although it may have started in January).  If multiple stages complete in one month, then the report should display only the most recently completed stage.  So, if Project A completed both Requirements and Development stages in January, the report should show only "Dev" as the stage completed in January.

For the data shared above, the expected result is:

You may download my solution workbook from here.

Identify Customers that Organisations can upsell or cross sell their products to

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Here's a simple Sales data of a retail Store which sells Apple Products.  Since a customer can transact multiple times, there will be repetitions in the Cust ID column.  While Cust ID 123 and 782 purchased multiple products from the same Store in one transaction, Cust ID 53 purchased multiple products from different stores (Store ID 165 and 45) [and therefore the Order ID's are also different (Order ID 2 and 6)].

On this small sample, one may want to identify "Cross and up selling opportunities" i.e. one may want to know which are the Customers that can be approached for selling more products to.  So for e.g. one may want to know which Customers have bought only one product so far.  A case in point being the Apple Watch - Customer ID 2442 and 428 bought only this product.  The other Customers who bought the Apple Watch also bought atleast one more product.  Therefore, Cust ID 2442 and 428 could be approached for buying other products as well.

Solving this via conventional Excel formulas and Pivot Tables would prove to be a formidable challenge.  I have solved this problem using a Data visualisation software from Microsoft called PowerBI desktop (it can be downloaded free from the Microsoft website).  This problem can also be solved in MS Excel using Power Query and Power Pivot.

There are 3 sections in the image below - Table at the top (First Table), slicers at the right and another Table at the bottom (Second Table).

Interpretation of First Table

  1. 1 appearing at the intersection of APPLE TV (row labels) and APPLE TV (column labels) represents that there is 1 customer who bought the APPLE TV
    1. 1 appeaing at the intersection of APPLE TV (row labels) and MACBOOK AIR (column labels) represents that the 1 customer who bought the APPLE TV also bought the MACBOOK AIR
  2. 4 appearing at the intersection of APPLE WATCH (row labels) and APPLE WATCH (column labels) represents that there are 4 customers who bought the APPLE WATCH
    1. 1 appearing in other columns of the same row represents other products which those customers bought
    2. When one right click's on APPLE WATCH and selects "Drill down", one will be able to see the Customers who bought the other products as well.
      1. Customer 53 bought the APPLE WATCH, AIRPORT and IPHONE 8S.  Customer 123 bought APPLE WATCH, IPHONE X and IPOD
      2. Customers 2442 and 428 did not buy any other product

Interpretation of Second Table

This table shows a list of Customers (and their transaction details) who bought only and only that one product selected by the user in the filter section (see the red oval selection in the image).  So these two customers could be approached for selling more products to.

You may download my PBI desktop file from here.

Show sales only for corresponding months in prior years

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Refer to this simple Sales dataset

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The objective is to create a simple matrix with months in the row labels, years in the column labels and sales figures in the value area section.  The twist in the question is that for years prior to the current year (2018 in this dataset), sales should only appear till the month for which there is data for the current year.  For e.g., for 2018, data is only till Month 4 and therefore for prior years as well, data should only appear till Month 4.  As and when Sales data gets added below row 17, data for prior years should also go up to that month.

The expected result is

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You may download my PBI file from here. The same solution can be obtained in Excel as well (using Power Query and PowerPivot).

Filtering on 2 date fields within one Table

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This table contains a list of all the inspections created and completed within different time periods.

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The objective is to create two Tables from this single table - one showing the Accounts created within the chosen time period and another showing the those that were closed within the same time period.  Here are screenshots of the expected results.

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You may download my PowerBI desktop solution workbook from here.  The same solution can be obtained in Excel as well (using Power Query and PowerPivot).

Compute transaction fee based on a tiered pricing model

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Consider a simple dataset as shown below:
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For each tier, the tier rate is incrementally applied to the volume within the tier volume range.  Given the following transaction volumes, one may want to compute the transaction fee
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The expected result is shown below
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As one can observe, for a transaction value of 400,000, the fee has been computed as 3% on the first 100,000 and 2.5% on the next 300,000. You may download my solution workbook from here. In the file, I have shared 2 solutions - a conventional formula based one and a PowerPivot solution.

I have also solved a similar question here.