One of things that’s struck me lately is how much we rely on “averages”, and how misleading those averages can be. I’d love to get your comments on this (anonymous is fine).
The power of averages is that they can condense huge volumes of data into a single figure. The trouble with averages is that they can condense huge volumes of data into a single figure. In gaining clarity, you lose texture. That texture is where the opportunities lie for smarter pricing and higher profitability.
For example, take a company with $1B in sales and $100M in profit. The CFO and the Director of Pricing both believe that the proverbial “1%” is lying around somewhere, but they don’t know where. They could raise prices by 1% across the board, but this will likely have adverse effects on many sales, where the 1% hits the steep part of the demand curve, and may actually get negated by furious discounting in the field.
Companies typically look at price points and discount rates on average, or on average for a number of high level segments. Within broad categories, however, micro-segments can exhibit dramatically different behavior. One customer within the industrial manufacturing segment might place a premium on timely delivery, to help make its commitments downstream to its customers, while another is less concerned with schedules and more fixated on price. Trying to price both these customers the same way will result in the worst of both worlds. Even worse, if you simply look at averages, you cannot necessarily tell what is happening. Combining the two into one average may show average discounts, average shipping charges, and average shipping costs. “Nothing to see here.” If you average a number of extra-normal values, you can easily end up with something that looks normal.