Volume Decomposition, Part 3: Impact of Pricing

due-to price signpost

This is the third in a series of posts on quantifying the impact of business drivers on sales volume.  Please review this post for an overview of this very useful analytical technique that helps answer the question Why did our volume change?

This post focuses on quantifying the impact of Pricing.  There are many different ways to do this but to keep it relatively simple, I’ll be looking at the impact of a change in base price on volume.

In this case study, we are explaining the 2.5% volume change for Magnificent Muffins, shown in the first line of the table below. I’ll walk through how to determine what the values are in the last 2 columns of the table below based on the change in Price and it’s elasticity. In this previous post I talked about the Distribution driver and in future posts I’ll talk about some of the other drivers.  For now we are focusing only on Price, highlighted in blue in the table below.

due-to price big table

The measure I’ll use for pricing is base price per EQ.  This is the regular or full retail price – what shoppers pay when the product is not on sale.  Take a look at this post to refresh your memory on the various pricing measures available.  Note that I’m using price per EQ and not price per unit.  That’s because this analysis is at the brand level, which aggregates items across different sizes and unit prices.  (The impact of promoted price will be taken into account in the next post on merchandising.)

Here’s the relevant data from the table above:

due-to price vol-basepr

We see that volume sales are up +2.5% and base price is up +2.3%.  As with almost all products, if price goes up, you expect volume to go down.  Since price has increased (which would make volume decrease), then something else is happening to the business to more than compensate for the impact of the higher pricing.  The question is:   How much did the 2.3% increase in price drag down volume?

The most common way to do this is to use the price elasticity.  Once I have the price elasticity and last year’s volume, I can quantify the impact of the price increase.

Price Elasticity and Price-Promo Study

Many companies that have an on-going contract with IRI or Nielsen have their supplier do a Price-Promo study (short for Price-Promotion).  It’s typically paid for out of an “analytics fund” that’s part of the contract.  This type of study is usually updated very year or two, to account for changes in the marketplace for the target business and/or its competitors.  Even if you don’t have an-ongoing contract, you can still have Nielsen or IRI do this study for you as a standalone project.  The key outputs of a Price-Promo study are Base Price Elasticity, Promoted Discount Elasticity, Lift Factors (by merchandising tactic) and Threshold Factors.  There is also a simulator available so that you can play “what-if” games to see what the volume, revenue and profit impacts are likely to be with different pricing strategies.

For this exercise, I’ll use the Base Price Elasticity.  This number is always negative – if price goes up, volume goes down.  It’s usually something between about -2.8 and -0.7.  For the vast majority of businesses I’ve seen, it’s between about -2.4 and -1.2.  If the price elasticity is -1.0, if price goes up by 5% then volume goes down by 5%.  (This is the quick, back-of-the-envelope way to apply price elasticity but is valid for price changes that fall within +/-10% of the original price.)

If a product is “more elastic” it means that it responds more to changes in price and the absolute value of its elasticity is larger.  A product with a price elasticity of -1.9 will see a greater change in volume than another product with an elasticity of -1.2, for the same % change in price.  In general, products with the following characteristics are more elastic:

  • Products less differentiated, commoditized, no secondary benefits
  • More competition/substitutes
  • More switching, fewer loyal consumers
  • Larger size
  • Lots of trade promotion – consumers are trained to wait for a lower price
  • “Simple luxury” – some consumers trade up if price gets low enough

The best way to get the “real” price elasticity is from a Price-Promo study that you usually get from your data supplier, IRI or Nielsen.  (Although it’s possible to calculate an elasticity using information available on your database, I don’t want to endorse doing that since you really need store-level data to get an accurate number.  For the rest of this, I’ll assume that you have an elasticity you can use.)

Calculating The Impact of Pricing on Volume

To calculate the impact of pricing on volume, follow the numbered steps in the following table.

due-to price calc table

The first 4 data columns are facts that are available in most IRI/Nielsen DBs or can be easily calculated from what is available:

  1. Year ago = value during the same period a year ago
  2. Current = value in current year
  3. Abs Chg v. YA = absolute change vs. year ago = Current – Year Ago
  4. % chg vs. YA = % change vs. year ago = (Current – Year ago) / Year Ago = Abs Chg v. YA / Year Ago

(Note that the row for TDPs was calculated in the last post.)  The last 2 columns are “new” measures that I’m calculating and the calculations do happen in this order.  In this example, I’ll assume a price elasticity of -1.8.

  1. Due-to % Chg for Pricing = % chg vs. YA * elasticity = 2.3% * -1.8 = -4.1%.  You can say that the base price increase of over 2% resulted in volume going down about 4%, so other drivers must be more than compensating since volume was actually up +2.5%.
  2. Expected Impact on Volume of Pricing = Due-To % Chg * Year Ago EQ Volume = -4.1% * 182,754,450 = -7,538,185.  You can say that the base price increase of +7 cents resulted in a volume loss of more than 7.5 million LBS.  Fortunately, other good things (like distribution gains) happened on the brand during this period to more than compensate for the volume loss due to pricing.
  3. Expected Impact on Volume of All Other Drivers = Abs Chg in Volume – Expected Impact on Volume of Known Drivers so far.  This bucket will change as you add more drivers to the due-to.  At this point, All Other Drivers means everything else except Distribution and Pricing.  The calculation is 4,556,679 – 5,436,887 – (-7,538,185) = 6,657,977.  Once we take into account pricing, the total impact of all the other drivers has to be a pretty big positive number.
  4. Due-to % Chg for All Other Drivers = Expected Impact on Volume of All Other Drivers / Year Ago EQ Volume = 6,657,977 / 182,754,450 = 3.6%.  Notice that the sum of the Due-To % Chg measures for Distribution, Pricing and All Other Drivers is the same as the % Chg vs. YA for EQ Volume (3.0% – 4.1% + 3.6% = 2.5%).

So to summarize…in this example, I am estimating that a 3.7% increase in distribution was responsible for a 3.0% increase in volume but a 2.3% base price increase resulted in a -4.1% volume decline.  Said another way, if nothing else changed besides pricing, Magnificent Muffin volume would have been down over -4%.  Now that we have the impacts of distribution and pricing, the All Other bucket, including everything BUT those 2 drivers, had a large positive impact on volume. In future posts, I’ll pull some more drivers out of the All Other bucket to shed more light on what might be helping volume. Future posts will add more drivers to the analysis (merchandising and competition) but there will almost always be an All Other bucket, since we do not have data for all possible business drivers.

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