Editor's note: Jeff Monroe is a Memphis, Tenn., market research manager.
Much has been written about price elasticity, revenue optimization and the impact on profitability. There is little doubt that pricing has the greatest impact on company profits. An often-cited study by McKinsey (“Average economics of 2,500 companies”) demonstrated the impact that various decisions would have on the bottom line: a 1 percent reduction in fixed costs improves profitability by 2.3 percent; a 1 percent increase in volume will result in a 3.3 percent increase in profit; a 1 percent reduction in variable costs will prompt a 7.8 percent rise in profit; but a 1 percent hike in pricing can boost profitability by 11 percent.
The objectives here are to review how revenue optimization is traditionally applied, for single and multiple products and/or services, and describe an improved multivariate approach to revenue optimization.
The multivariate model allows for identification of volatile causative forces of demand in addition to price. This not only improves modeling of short-term volatility but, more importantly, provides an inflection point indicator for short- and longer-term analysis.
To begin, an example of optimal price calculation for a single product is provided. Next, a two-product optimum with constrained supply is reviewed. Finally, a description of multivariate modeling applied to price elasticity is provided; and an approach not too distant from market-mix modeling, albeit more direct for the resource restricted analyst, is proposed.
In an effort to make this method as translatable as possible, assumptions are made to avoid a partial differential equation solution. The benefit of a streamlined process in this technical subject more than offsets the small inaccuracies experienced when applying calculus and linear algebra as opposed to differential equations. A straightforward process lets the anal...