Bridge Analysis Example -- CoffeeCo 1
In analyzing financial statements, you will often encounter a need to explain the difference between two numbers provided by management. For example, you may want to compare the sales revenues of the company from one year to the next or the actual results to a forecast number. One technique you can use to do this is ‘bridge analysis,’ so called because you create a ‘bridge’ from one number to another by breaking down the differences between the two numbers into various components (you may also see this referred to as Volume-Price-Mix analysis). The main purpose of conducting this type of analysis is to obtain a better understanding of why the numbers are changing (or are different than expected, depending on your analysis). Breaking down the changes into different components allows you to better see the reasons for the differences. In the end, the main purpose of this analysis is to gain an understanding of how the company is changing so that you have better inputs into your forecasting model. I will illustrate how to use this technique by examining the sales revenue of a small fictitious coffee store chain – CoffeeCo. Assume you are an analyst covering CoffeeCo and notice a press release with the following headline:
“CoffeeCo reports 2011 results: Sales increase from $25 million in 2010 to $30 million in 2011.”
With this information you can perform the most simplistic, and least informative, one component bridge analysis:
This is an extremely obvious analysis and you do not learn much. You know that sales grew by $5 million (or 20%), which may be a nice growth rate, but you really do not know why the company grew.
So to find out more information, you open the press release and note that CoffeeCo provides details about the number of units sold and the average selling price per unit, as follows:
(Units and Revenues are reported in 000s)
This case was developed for Business 30130 by Michael Minnis.
From this additional information you now know how many units of product they sold and the average selling price. In particular, you note that the number of units increased by 2.5 million and the average price decreased by $0.10 per unit. Just by a quick perusal of the data, then, we know that total revenues increased because of higher volumes, but that this was partially offset by a slight decrease in the average selling price per unit. To understand the size of these effects, you can use the unit volume and pricing data detail to breakdown the revenue change into two components, volume and price, as follows: Analysis of Change
Change in total volume
Old average price
New total volume
Change in average price
So now you can expand the one component bridge analysis that you performed previously into two components: 2010 Revenue
+ $6.25 million
- $1.25 million
This analysis is somewhat more informative because now you may begin to think that CoffeeCo is driving volume growth by lowering prices. This is likely useful information for your forecasting model because you may question how long CoffeeCo can sustain its growth if it is achieving it by lowering prices. Moreover, the lower prices may impact the gross margin percentage you will want to assume in your model. So with this additional detail, you now have a bit more understanding of the current position and, more importantly, potential future performance of CoffeeCo.
However, being a savvy analyst, you know that CoffeeCo actually sells two different products: drip coffee and lattes. Therefore, you know that the units