Financial Management: Volume/Price/Mix

Post 2 in the series on management.

Decomposing Revenue Change

Regardless of whether you are a retailer or an FMCG manufacturer, an airline or a hotel owner, an ad agency CEO or a news publisher, a lawyer or a doctor, if you want your company to make more revenue next year than you made this year, you can do one of two things (or, ideally, both): sell more goods (or services), or make more money per good you sell than you did this year.

Figure 1
  • Similarly, the effect of the increase in price is shown as the yellow rectangle, and equals (P2 — P1) x V1.
  • But there is a third rectangle, shown in green. This shows the interplay of the changes in price and volume, and is equal to (P2 — P1) x (V2 — V1) (though of course, the easiest way to calculate it is to take the total revenue delta and subtract the volume and pricing impacts, as calcualted above). Different people treat this in different ways; the most correct way is to call it out seperately as ‘interplay’, but personally I just attribute it to pricing, for simplicity’s sake. One could equally well attribute it to volume, split it 50/50 across the two, or split it based on the relative contribution of each driver. For example, if the change in volume has led to a $100 increase in revenue, the change in price to a $50 revenue increase, and the interplay to a $25 increase, one could attribute 2/3rds of that $25 to volume ($100 [the volume impact] / $150 [volume + pricing impact]).


Here’s an interview question (slightly paraphrased) I recently asked six different candidates, only one of whom got it right: imagine you run an airline. To keep things simple, suppose you only fly one route, say London to Athens. When looking at revenue change year over year for the month of June, you notice that you sold the same number of tickets both years (to your immense surprise, given COVID); moreover, the price you charged for each seat was also the same both years. Yet your revenue increased. How?

Figure 2
Figure 3
  • Price: (Ticket type price in Y2 — Ticket type price in Y1) x (Ticket type volume in Y2). Note that here I’m following the convention I mentioned earlier of attributing the interplay effect entirely to price, to keep things simple.
  • Mix: (Ticket type volume in Y2 — Ticket type volume in Y1) x (Ticket type price in Y1 — total ticket price in Y1). For example, the formula for the mix effect driven by economy tickets is (80–100) x ($100 — $117); this shows the mix effect due to the decrease in number of economy tickets relative to business class tickets. Note that both factors in this equation are negative, hence the mix effect is positive. This makes sense: selling fewer economy tickets relative to business tickets increases the average ticket price for the airline in Y2.
Figure 4

Multi-layer Mix Analysis — Option 1

The figure below shows how to do the analysis at each level:

Figure 5
  • The price impact calculated at the Level 1 analysis is equal to the mix + price impacts calculated at Level 2. Why? According to the first table, the average ticket price for passengers flying to Athens went up, from $117 in Year 1 to $143 in Y2, causing a $3,200 increase in revenue. This is what is shown as ‘price impact’ in the first table. What the second table shows is what exactly caused this price increase: part of it ($1,200) was driven by actual price hikes, as the price of both business class and economy tickets went up. But another part ($2,000) was thanks to the fact that more passengers chose to fly business in Y2. These two together explain the increase in average price for Athens passengers.
  • Mix means slightly different things when we look at it at the total business level (call this Level 0), at the destination level (L1) and at the ticket type level (L2). At L0 (i.e. the first row in the first table in figure 5), mix refers to the impact of the change in the average ticket price for the total business due to the change in the destinations people travelled to; in the next two rows of the first table, mix does not refer to the change of that destination’s average price due to L2 mix changes — instead, it refers to the impact the change in number of tickets for the destination shown in each row drove for the level above.

Multi-layer Mix Analysis — Option 2

The second option is to create one row for every destination & ticket type combination (or, more generally, one row for every possible combination of product attributes we wish to include in our analysis).

Figure 6

I work at Monzo. Ex Google, P&G