Price-Volume-Mix analysis on Apple

When looking at your revenue/sales/net sales development, you want to understand the drivers behind the changes you are seeing. For example, we can have a look at Apple’s net sales development between 2014 and 2018.

The graph basically tells us that:

• their net sales has shown a positive average annual growth rate between 2014 and 2018;
• between 2014 and 2015, net sales showed a relatively large increase but with a decrease during the following year, and;
• between 2017 and 2018, net sales showed a relatively large increase.

Now you probably have some questions, such as: What drove the major changes in net sales we are seeing? How is the product mix affecting the net sales development (we know they are selling products like iPhone, iPad and Mac)? In regards to the major net sales increases (e.g. between 2014 and 2015), are they driven by price increases or have they sold a higher volume with constant prices? Or maybe a mix? To answer these questions, we will do a Price-Volume-Mix analysis, which is a great way to improve your understanding of your business. It adds another dimension to your business reporting.

Source data

We need the following information in order to perform a Price-Volume-Mix analysis:

• Revenue/sales/net sales figures by product;
• Unit sales for the same products, and;
• Average selling price (“ASP”), calculated as revenue/sales/net sales divided by unit sales by product.

Up until 2018, Apple disclosed unit sales by product (i.e. iPhone, iPad, Mac etc.) in their SEC filings in conjunction to the net sales (in USD) by product. That disclosure let us use Apple for this case study and is the reason for selecting the period 2014-2018. Yearly net sales and unit sales figures for Apple can be retrieved from their 10-K.

Price-Volume-Mix analysis

The Price-Volume-Mix analysis, let us see how individual factors, such as price changes, sales volume changes and product mix changes impact your revenue.

• Price reflects the price of your product as you sell it. It is the main contributor to the growth of margins in your business. Increased price directly translates to improved margins (all else being equal).
• Volume is the number of products you sell. Selling more products at the same price means more revenue. However, volume has little effect on your profit margins. Selling more products at lower prices reduces your profitability if the cost of goods remains unchanged.
• Mix explains how your product mix affects the revenue. For example, are you selling higher value products this year than the last year? Then the mix will be positive. Are you selling more of your less valuable products? That might drag your mix down.

Example 1

I’ve retrieved the following information from Apple’s 10-K filings (except for the ASP by product which I have calculated):

Based on this information, we calculate the price and volume impact by product and year.

The price impact is calculated as: New volume * (New price – Old price)

The volume impact is calculated as: Old price * (New volume – Old volume)

Example:

The price impact in 2015 for iPhone is calculated as: 2015’s volume * (2015’s ASP – 2014’s ASP), in other words: 231,218 * (670,5 – 602,7) = USD 15,682,210k

The volume impact in 2015 for iPhone is calculated as: 2014’s ASP * (2015’s volume – 2014’s ASP), in other words: 602,7 * (231,218 – 169,219) = USD 37,367,790k

Total change: USD 15,682,210k + USD 37,367,790k = USD 53,050,000k ~ USD 53,050m

Consequently, USD 53,050m should equal the change in net sales for iPhone between 2014 (USD 101,991k) and 2015 (USD 155,041k), which it does.

We continue calculating the price impact and volume impact for each product (except for “Services” and “Other Products”) and year. For “Services” and “Other Products” there is no unit sales information available, thus we just calculate the net sales change for each year. When done, we an visualise the information (preferably in a bridge/waterfall chart).

This graph gives us much more insight in the net sales development over the period considered.

For example: The relatively large increase between 2014 and 2015 was mainly driven iPhone sales, which in turn was both volume and price-driven (however to a larger extent volume-driven). This might raise some more questions, such as: What was the drivers behind the significant increased iPhone volumes in 2015? Did they introduce a new model? What drove the price increase? Did they raise the prices on existing models? To answer these questions, we must deep dive into Appel’s 2015 10-K.

Actually, the driver behind the relatively large growth in iPhone net sales and unit sales during 2015 was mainly strong demand for iPhone 6 and 6 Plus which were launched in September 2014 (i.e. just before the end of the financial year 2014. Apple’s financial year is October-September). We have historically seen a massive interest for Apple’s product releases. Generally, when Apple release a new product, it raises the ASP as we assume the newly released product has a higher price than its predecessors.

Example 2

In the above example, we have looked at the price and volume impact by product. If we want to analyse the product portfolio as a whole, it is not as easy as aggregating the price and volume impacts as we then are missing one important dimension, i.e. the mix impact. The mix impact measures the impact in the sales amount resulting from a change in the mix of the quantities sold (e.g. the change in % of iPhone units sold).

This exercise is slightly more complicated than the previous example.  You can download the Excel file used in this example from my GitHub repository.

The below table is a screenshot from the Excel file showing the price, volume and mix impact for 2015. As in Example 1, “Services” and “Other Products” are handled separately as we do not have any units information.

When we have done the above exercise for each year, we can visualise the information (preferably in a bridge/waterfall chart).

To follow up on our findings from Example 1, we see that we have a positive mix impact between 2014 and 2015. The is mainly due to a decreased share of iPad units sold (i.e. relative to total volume), which has a lower ASP than the ASP of the whole product portfolio. This positive impact was partly offset by:

• negative impact from the increased share of iPhone unit sales as it had a lower ASP than the ASP of the whole product portfolio, and;
• negative impact from the decreased share of Mac unit sales as it had a higher ASP than the ASP of the whole product portfolio.

As mentioned, you can download the Excel workbook (including all calculations, tables and graphs) from my GitHub repository.