Wedding Cake Chart for Analyzing Performance of Multi-Pack Configurations


Many consumer goods products are sold in cases or multi-pack configurations. For example, a multi-pack of beer might be composed of a 6 or 12 pack  while a case of screws might come 12 or 100 packs per case. Analyzing performance across multiple package/case configurations and understanding the performance at the case/package level (wholesale selling product) and unit level (individual items inside), is challenging, but the Wedding Cake chart is a great way to understand and visualize this level of detail.


In the upper chart (Wedding Cake Chart), the x axis represents time, the width of the bar and color represent the “units per case”, and the y axis represents the sum of cases/packages or units.

In the lower chart, the x axis represents time,  the size of the bubbles and color represent the “Units per Case”, and the y axis represents the % change vs the previous year (in cases/packages or units). The black bubbles represent the total.





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In the sheet "Case Configuration":

  • Looking at cases, the total performance (black bubbles) started to perform worse than the previous year around May/June of Year 3.
  • The package configuration with 100 units per case, was introduced around May/June of Year 3, and around the same time, the performance of the 24-Pack, started declining significantly. There seems to be cannibalization taking place.
  • Towards the end of Year 3, the 100-Pack configuration represents about 6-7% of total case/package sales, but around 40-45% of total unit sales.

What other insights can you generate from this sheet and the other ones? Leave comments below to share your additional insights.


  • Q: Should I analyze my business looking at cases/packages or units?
    • A: The answer is both. The performance, insights, and decisions taken might differ substantially when looking at cases/packages vs units, so both need to be considered.  In this example, the overall category is down in cases/packages, but up in units. For example, if the overall profitability of the 100-pack item is similar to the other package configurations, then situation I fine. However, if the profitability of the 100-pack configuration is considerably lower than the other configurations (that were cannibalized), then actions should be implemented to shift sales back to lower pack configurations.
  • Q: Why not use the traditional pie charts?
    • A: Traditional pie charts are considered to be a poor way of visualizing information… unfortunately, pie charts are one of the most common types of charts. We won’t get into all the details about why they are poor, as Stephen Few (one of the Gurus of data visualization) clearly explains it here:
  • Q: Why is the chart called a “Wedding Cake” chart?
    • A: We are not aware of this chart being named before, and it looks like a “Wedding Cake”.

Want so share your own visualization for this data? Use the comments section below.