Microsoft offers a few interesting sample datasets. These datasets, provided by obvience.com, are based on real business data that has been anonymized. One is the Customer Profitability Sample sample.
To run the dataset in the app, type "customer profitability sample" in the "choose a sample dataset" widget.
While company sales are going according to plan, COGS are higher than anticipated and margins are suffering...
Margin variance analysis
See aggregated sales variance or split variance between its components (volume, price, unit costs,..).
...and are 11% under Plan.
This chart morphs into an EBITDA bridge chart if indirect cost info is added to the dataset for the two scenarios. Say our indirect has improved by 250k...
...then our EBITDA bridge will look something like this.
See how a metric, here revenue, interacts along a pair of dimensions, here product and industry.
Multi-tier bar chart
See how a dimension - here product - plays out in the two periods on a metric - here amount.
While sales where essentially flat on plan,..
... the two most important products have lost significant margins vs plan...
...while Sova and Doroga Products managed to keep margins relatively stable.
See how two metrics - here sales and margins - play along a given dimension, here industry.
See how a dimension - here industry - plays out in the two periods on a set of metrics - here amount, COGS and gross margin.
...and seem to be falling more in margins.
Pharma managed to keep margins relatively stable.
The East Region lost marginality...
but maintains its advantage versus the rest of the market.
The top customers seem to be loosing profitability.
Slice variance by dimension.
The customers responsible for the largest Margin losses, lost margin more or less in the same proportion.
Globo-Chem missed the Plan in terms of Margins and of Volumes.
FIRST RESULT COMBINATION
Waterfall chart format
Whenever possible, charts are built in accordance to the IBCS standard. Green is "good", red is "bad", white is "plan", grey is "previous period" and black is "actual".
Not much is going on in terms of Sales, that increased 1%.
Sales in the East Region improved 2%, while Carlos Grillo lost 2% of sales in the North Region for the Core Division. Sales in the South increased around 5% as did sales in the North Region by Andrew Ma in the Growth Division.
The relatively significant (-8.7%) loss of revenues of the Sova Product in the Minor Division was tied to the North and West Region...
...in California and Illinois...
...in the Services Industry.
The first row result of the first waterfall slices by region and picks up the fact that the north region's sales performance is very different when sliced by executive.
SECOND RESULT COMBINATION
Most of the loss of Margins is tied to the Core Division. Excluding Core, the Ho-O Bu lost considerable Margins.
Show small multiple plots
After setting the appropriate filters, set the "Run" widget to "Variable dimension bridge". Set the "Choose dimension for small multiples" widget to brand. Hit the 🚀 Submit button.
The first row result of the second waterfall picks up the fact that the core division - while flat in terms of sales - is the source of most of the fall in margins.
The second row result of the second waterfall shows that, even excluding the core division, the Ho-0 business unit remains a poor performer.
THIRD RESULT COMBINATION
See another angle
This second waterfall shows a few combinations with many nodes that impacted significantly on margin variance.
More specifically, the following combinations lost considerable margins.
The third waterfall flags a few examples of - mostly core division - combinations that have driven margins down.
Every dataset is different
This use case is based on a fake dataset. Test run the app with your data to confirm the advantages of the variable dimension variance approach in your specific use case.
FORTH RESULT COMBINATION
The margin loss was due to higher COGS by unit of product sold, not to volume impact.
Finding patterns in data
'Mparanza uses variable dimension variance to help find patterns in the data that could be missed with a traditional slice and dice approach.
The forth waterfall points to the fact that, for the Ho-0 business unit, the cause of the reduction in margin is higher unit costs, not lower volumes or unit prices.
Consistency of results
Alternative result sets are always consistent. The different sets of results combinations show pretty much the same elements from slightly different angles, potentially helping to unlock insights.
Even excluding the impact of Ho-0, the cause of reduced margin for the Core division is increased unit cost.