On Wings: Revenue Optimization through Pricing Strategy

Rather than being driven from a strategic perspective, the basis for most price increases is an internally driven response to cost increases and a subsequent attempt to pass those costs on to customers in hopes of maintaining profit margins.

The Opportunity or Threat:

I was invited to a meeting with the CEO, CFO and finance department of an organization to examine the budget assumptions and cost-revenue projections. As the result of increased spending on marketing and investment into upgrading product quality and key managers, the budget was projected to run an unacceptable deficit unless a series of price increases could be designed and effectively passed on to our customers. (In other words, expenses were already up with the hopes that sales would increase. However, the current price structure did not sufficiently cover expenses on a unit basis and could not provide a return on this investment.)
I was asked to examine the sales forecast, operational capacity and capability, price structure and the revenue forecast –and- develop a pricing structure and revised revenue forecast.

The Insight:
My insights and strategy came from understanding dynamic models for predicting the interrelationships between price, units sold and total net revenue. You are probably familiar with the first dynamic model which is the basic price elasticity curve from micro-economics. In essence, I needed to develop pricing that walked the prices upward pushing downward on demand to approximately the sweet spot on the curve where net revenue (profit) is optimized.


The other model I used is similar to the basics of powered flight in an airplane. We had made improvements which were beginning to show up as increased demand but were projecting even greater increased demand, which is similar to fuel for the engines.

Figures below from a slideshow I've published on Cost and Revenue at SlideShare.

When we were able to convert demand into a sale it converted fuel into engine thrust and pushed the plane forward. Although we had excess capacity most of the time, we were losing sales at peak demand periods due to lack of capacity.

So as long as we captured sufficient sales, we had achieved lift and had a net gross profit (Gross Sales – Gross Expenses). Given lift and altitude, the plane is in flight. When the plane is in flight, it can use an

 increase in demand to either speed up (increase sales), increase altitude (increase profit margins) or a combination of the two. However, too much of a price increase will quickly lead to a loss in demand and the plane will stall.

Just as a pilot, adjusts the flaps to optimize while flying, I developed a series of price increases that took into account the product mix available for sale, past demand patterns and current capacities.

The optimal flap adjustments will vary across the product-mix segments. Essentially there are three important segments to understand when pricing:

1) What is Vanilla? What is the highest volume or typical product offering? This will be the bar as it is not possible to successfully raise prices if you can not maintain price and volume in this segment simply due to the proportional size of this segment. In this case, the Vanilla was about 55-60% of the volume in both capacity and revenue.

2) What is your entry-point product or Economy Offer? Unless demand outstrips capacity, there will be little room for price increases here that do not reduce sales volume. A smaller price increase combined with off-peak pricing to shift demand away from cyclical peeks can be most effective here in maintaining volume.

3) What is your deluxe product or Premium Offer? This segment of your market is least sensitive to price but may be very sensitive to quality and performance. If there is strong demand relative to capacity for this segment already, it can absorb significant price increase without a loss in sales volume.

The Action:

Our goal was to grow sales volume by 8% and increase prices by 10% with an understanding that there would be some trade-off between the two.

My description here is simplified for examples purposes. The percentages quoted are on an annualized average basis and represent a weighted average of units expected to be sold at each price. The actual number of distinct product offerings was eleven and the total number of offerings by price was 44 based upon two types of peak and off-peak demand.

Based upon the analysis, we increased the Economy product price by 4% on average but in-fact lowered the price by 4% during our strongest off-peak periods where we had excess capacity.
The Vanilla product price was increase by 10% on average with a 5% increase during off-peak periods. We also began to lay the groundwork to further segment vanilla with both an upgraded and a value option that took two years to fully implement due to needed upgrades to our enterprise software.
The Premium product line was historically the most segmented from a product offering perspective. It also had a generally healthy demand with periods of over-capacity demand due to our limited capacity. Here we engineered a price increase that averaged 16% but ranged between 10% and 22% and planned future expansion.

We were successful in our efforts to simultaneously grow demand which we did by 14% and revenue by 26% year over year. In essence we maintained our 10% price increase on top of 14% unit sales growth. Given our strong growth in demand and the modeling we did, we were able to engineer an even more aggressive strategic price increase the following year as we filled to capacity and achieved a record for net operating income.

In an upcoming article, I will go into greater detail as to how to build a revenue projection model and cover the use of Bayesian probability or analysis. This method allows you to estimate the sum total of a sequence of events, if you can estimate the probability of individual future events. (This is the same branch of statistics that Google uses to determine what you likely to be looking for when you enter a specific key word sequence.)

To see how our work benefitted a client related to this method, visit our case studies at Metamorphosis Management Group (http://www.metamg.com/).