Documentation
Our Method for Average per Unit Analysis
Average per Unit Analysis:
How we Calculate
Our approach can be summed up with the idea "what would happen to the Overall Average Per Unit if we changed one product alone?".
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So for example:
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The Overall Budget Average Revenue per Unit is $100.
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If we substitute the Budget volume and revenue for Product #1 with the Actual volume and revenue for Product #1 and recalculate the new overall average...
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The New Average per Unit increases slightly to $105.
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The difference between $100 and $105 = $5.
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The $5 is the result of the analysis for Product #1.
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This is repeated for every product for ranking and sorting.
How to Interpret the Average per Unit Results
Sometimes the results can be surprising (and therefore insightful). Here are some tips on understanding why some situations have a greater impact on the average:
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A high-volume-share product has a big change in price
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For example, Product A's share of volume stays at 50%, but its price had increased from $100 to $125.​
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It makes sense it would have a large impact on the overall average, because its share of volume is high.
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A low-volume-share product slightly changes its share, and its price is nowhere near average.
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For example, Product B's volume share changes from 3% to 5%.
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Its price stays stable at $650 (vs an overall average of $90).​
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Because Product B's price of $650 is a long way from the average of $90, any small deviation in volume share is going to have an large impact on the average.
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Why Does the Sum of the Results Not Equal the Overall Change in "Average"?
The Budgeted Average Revenue per Unit may be $100, and the Actual Average Revenue per Unit is $120.
But the sum of the results from above, across Product #1 through to the last product, might be $17. There is $3 of unexplained variance. Why?
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The reason is related little green box that we saw in the Price Volume Analysis. The reason is "Mix", where the new price and new volume interact with each other.
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Instead of using complicated formulas to apportion the $3 above, we keep it simple and focus on the overall relative impact on average price.
We believe that it is more important to identify and communicate that Products #1, #8 and #16 had the biggest impacts on "average", rather than confusing your stakeholders with mathematical minutiae.