Tags: COUNTAX

Compute an average for the same day in the past 3 years

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Assume a simple two column dataset with dates in column A and numbers in column B. The dates in column A are from January 1, 2013 to December 31, 2016 and numbers in column B are for the period January 1, 2013 to December 31, 2015 (there are no numbers for January 1, 2016 to December 31, 2016).

The objective is to "Compute an average for each day of calendar year 2016. The average should be for the occurrence of that day in the previous 3 years". Here's an example:

1. January 1, 2016 was a Friday (the first Friday of 2016) and is in cell A1097
2. In cell B1097, the average should be computed as: Average of the "First Friday of each of the previous 3 years"
3. January 8, 2016 was a Friday (the second Friday of 2016) and is in cell A1104
4. In cell B1104, the average should be computed as: Average of the "Second Friday of each of the previous 3 years"

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

Sales data modelling and interactive visualisations

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Visualise Sales Data of a Non-Alcoholic Beverage Company with basic columnar information such as Date of Sale, Time of Sale, Brand, Stock Keeping Unit (SKU), State, City, Quantity sold, Unit Price and Salesman Code.  In this sales dataset, each line item represents one visit for one SKU.  If nothing is sold in a certain visit, then the SKU column displays No Sale.  So effectively there is a line item for each visit whether or not something is sold in that visit.

From this simple Sales dataset, here are a few questions which one may need to find answers to:

1. How did the Company perform (in both years 2013 and 2014) on two of the most critical Key Performance Indicators (KPI's) - Quantity sold and Number of Visits.  Also, what is the month wise break up of these two KPI's.

2. Study and slice the two KPI's from various perspectives such as "Type of Outlet visited", "Type of Visit" - Scheduled or Unscheduled, "Day of week", "Brand", "Sub brand".

3. Over a period of time, how did various SKU's fair on the twin planks of "Effort" i.e. Number of visits YTD and "Business Generated" i.e. Quantity sold YTD.

4. Analyse the performance of the Company on both KPI's:
a. During Festive season/Promotional periods/Events; and
b. During different months of the same year; and
c. During same month of different years; and
d. Quarter to Date

5. "Complimentary Product sold Analysis" - Analysis displayed on online retailers such as Amazon.com - "Customers who bought this also bought this".  So in the Sales dataset referred to above, one may want to know "In this month, outlets which bought this SKU, also bought this much quantity of these other SKU's."

6. "Outlet Rank slippage" - Which are the Top 10 Outlets in 2013 and what rank did they maintain in 2014.  What is the proportion of quantity sold by each of the Top 10 outlets of 2013 to:
a. Total quantity sold by all Top 10 outlets in 2013; and
b. Total quantity sold by all outlets in 2013

7. In any selected month, which new outlets did the Company forge partnerships with

8. Which employees visited their assigned outlets once in two or three weeks instead of visiting them once every week (as required by Management).

9. Which outlets were not visited at all in a particular month

10. Business generated from loyal Customers - Loyal Customers are those who transacted with the Company in a chosen month and in the previous 2 months.

These are only a few of my favourite questions which I needed answers to when I first reviewed this Sales Data.  Using Microsoft Excel's Business Intelligence Tools (Power Query, PowerPivot and Power View), I could answer all questions stated above and a lot more.

You may watch a short video of my solution here

Data slicing and analysis with the Power Pivot

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Visualise an MS Excel file with two worksheets:

  1. Employee headcount – a multi column dataset with information such as Employee code, Date of Joining, Age, Division, Department and Location.  Each row represents data for one employee.  The number of rows on this worksheet is approximately 700.
  2. Training Data - a multi column dataset with information such as Employee code, Training Date from, Training Date to, Training Program Name, Training Program Category (Internal and External), Training Location and Training Service Provider.  Each row represents one training attended by one employee.  The number of rows on this worksheet is approximately 2,600.

Let’s suppose that the training calendar of this company runs from July to June.  Some questions (only few mentioned for illustration purposes) which a Training Manager may need answers to are:

1)   How may unique employees were trained each year; and
a)   Of the unique employees trained, how many were first time trainees and how many were repeat trainees
i)   Of the first time trainees:
(1)    How many joined this year
(2)    How many joined in past years
ii)  Of the first time trainees:
(1)    How many were trained within the first year of joining
(2)    How many were trained in the second year of joining
(3)    How many were trained in the third year of joining
(4)    How many were trained after three years of joining
iii)  Of the repeat trainees:
(1)    What is the average gap (in days) between trainings
(2)    What is the minimum gap (in days) between trainings
(3)    What is the maximum gap (in days) between trainings

Getting answers to the questions mentioned above would entail writing a lot of lookup related formulas, applying filters, copying and pasting and then creating Pivot Tables.  While the example taken above is that of a training database, you may envision “drilling down to and slicing” any dataset – Marketing, Sales, Purchase etc.

You may watch a short video of my solution here

In these two workbooks, you will be able to see the level to which one can drill down and analyse data using the Power Pivot add-in.  When you open this workbook, please go the first worksheet and make the relevant choice of MS Excel version first so that you start looking at the Analysis from the correct worksheet.

1. Analysing Training data of a company; and
2. Analysing Sales data of a company

You will be able to see the analysis in these workbooks only if you are using one of the following versions of MS Office:

1. Excel 2013 Professional Plus; or
2. Excel 2010 with the Power Pivot add-in installed.  Power Pivot is a free add-in from Microsoft which can be downloaded from here.

Lastly, if you are using the Power Pivot add-in in Excel 2010, you will not be able to see the underlying Data Model or the calculated Field formulas because this workbook has been created in Excel 2013 Professional Plus and unfortunately the Power Pivot model is not backward compatible.  However, all the analysis performed in this workbook can be performed in Excel 2010 as well (with the Power Pivot add-in installed).

Count unique values with conditions on large databases

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Given a database of 50,000 rows, counting unique values with conditions using formulas would either adversely effect workbook performance or would not work in the first place at all.

In this workbook, I have shown the technique to count unique values with conditions on a large database

1. Using PowerPivot - Will only work in Excel 2010 and higher versions

2. Using a  normal Pivot Table and SUMPRODUCT() function - Will work for all versions but is not as efficient as the PowerPivot solution.

To count unique values with conditions on small databases, you may refer to the following link