CPG Consumer Packaged Goods - Case study

An alternative pricing and planning strategy is desired given the failing of volume forecasts for new product introductions and the shortfalls experience in budget plan to actual profitability.

Introduction of new products into the market requires collective efforts on the part of the marketing,  operations/engineering, and finance teams.  There are three primary pieces, Price to Demand elasticity and subsequent demand curve, the direct costs and traceable fixed costs through the process, and the required return (ROI) for the product to be a success.   

As with any model and data garbage in is garbage out. Diligent data planning for collection and analysis of the customer base by the marketing team is required, consistently and thoroughly to arrive at an accurate demand curve for a pricing model.  Since most pricing for a CPG market is represented by a monopolistic competitive environment (economic term) of numerous products all slightly differentiated, a deep understanding of what differentiated characteristics are of value and what that value is worth is needed.  In general, any new product will often incorporate these value driving characteristics and assist in the determination of the demand curve.

In this case, there were two initial scenarios of pricing and marketing campaign planning: 

Scenario One:
Price : $170
Variable Costs :  $35
Fixed Costs : $22 million,
Standard marketing campaign relative to other product lines.

Scenario Two:
​Price :  $190
Variable Costs : $35
Fixed Costs $27 million
Addition $5 million direct funding for marketing campaign for product.

A collaborative effort constructed a demand probability distribution that was anticipated to be the same for each scenario.

These assumptions hinged at how well the marketing team understood their target customer base.  While the right-tailed skew is certainly the most probable distribution, the distribution is also representative of a $170 price with standard marketing AND the $190 price with a focused marketing campaign.  The reliance on this data for the forecasting of volume is just one of the reasons variance shows up in the actuals vs. plan.  Using the distribution can however, in the fact that the visually adds to the discussion of the pricing and asks if the 'correct' data is being collected.

One of the great things about using the variable cost and fixed cost break out is the we can get to the contribution margin and the break even unit volume or dollar amount and also account for any required profit to arrive at the desired ROI.  Further, we also can evaluate the operating leverage and sensitivity of Op Profit to Sales if we want to get an idea of the incremental changes to the P&L. But, that aside, once we arrive at the units required to achieve these goals, we can then use the probability distribution to determine the probability of reaching those units sales.

The unit contribution margin if the difference between the price and the variable costs and because we have a unit distribution, it is simple to stick with units required to achieve break even and required profit.

Scenario One:
UCM = 170 - 35 = $135

Scenario Two:
UCM = 190 - 35 = $155

In order to hit the required ROI, $4 million of profit is needed. As you can see below this equates to an additional 29,630 units in Scenario One and 25,806 units in Scenario Two.

You probably also noticed the probabilities of being greater than or equal to the break evens and the ROI units. In order to arrive at the probabilities, the distribution above is used.  First finding the mean and standard deviation as below.

From the mean and standard deviation we can use the standard normal distribution and the Z-Score to find the cumulative probability.

Using the Z-Score and associated cumulative probability we can see that the probability of selling less than or equal to 150,000 units is 6%.  In other words there is a 94% probability of selling more than 150,000 units according to this demand distribution.  Once we get to 170,000 units, there is a 40% probability of selling less than or equal to this amount, but 60% probability of selling more.  At 180,000 we see this pretty much reverse.  We can also put in an exact number of units to determine the probability which is how we arrived at the probabilities for the break even units and those required to hit the desired ROI.

Now, leaning only on the distribution that we compiled, Scenario One is highly probable to break even at 76% probability whereas Scenario Two is only 50%.  If we are also hoping to hit an ROI target neither of these scenarios is likely.  But we should examine a range of prices and the effects of the marketing spend.

In this case, an understanding of the price elasticity of demand would be helpful in addition to the incremental increase related to the marketing campaign.  Since I do not have that curve, I will try an approximation.  What we are saying here is that we are expecting the customer base to purchase 174,000 units at $170 per unit, but that with an additional $5 million of marketing we can increase the price about 12% to $190 and still achieve an expectation of 174,000 units. What this the suggests is that the marketing spend shifted our price elasticity back to 174,000 units. 

​If you forgot, the equation of the Price Elasticity of Demand is: 

Our marketing spend is a key.  If we say there are identical distribution at both price points, $170 and $190 then our targeted marketing spend is overcoming the quantity change that would occur due to the Price Elasticity of Demand.  If with no marketing we would calculated and expectation of 174,000 units at $170, then at $190 the calculated expectation of units sold will drop.

Let's say we try to represent our marketing spend as a shift in unit demand due to a price increase.  At $5 million / $190 = 26,316 units.  Using this as a basis for our calculation, we would arrive at a Price Elasticity of Demand of approximately -1.47 for our product. 

The Elasticity can be used to do two things, first we can estimate the mean of our distribution for each price point from $170 to $190 in $5 increments, note the Quantity Shift.  The distribution will be the same for each increment, it is simply shifted to each mean and notice that the standard deviation is the same for every price.  The means represent expected sales at the price point if no specific marketing campaign is involved.  

The next thing that we can derive using the Price Elasticity is an approximation of the impact of the marketing on total units.

There are two columns, "Demand w", the demand with the marketing campaign, and Demand w/o, the demand without the marketing campaign.  As you can see from the green boxes, the estimation is fairly close for the initial calculation.  For each subsequent calculation the demand without was input and the price elasticity calculation used it to return the demand with the marketing campaign at each price point.

The Difference column is the approximation of the additional units relative to the marketing campaign and price point.  Below we see the "Without Marketing" and the "With Marketing".

Granted, there are several assumptions within these models.  I am making a leap attempting to calculate a Price Elasticity using a representative measure, the marketing spend that brings an identical distribution.  That said, the marketing must either be reaching a larger pool at the same penetration rate to sell additional units or the marketing is pitching a value proposition that would not be perceived without the campaign and is attracting a higher penetration in the same target customer pool, or there is a combination of the two.  The next assumption is that I can use the Price Elasticity to arrive at expectation of units sold at each price point and that it can be used to calculate out the units sold attributable to the marketing spend.  

Bearing these assumptions in mind, we would have to examine the distribution and the acceptable probability for taking the risk on the product and the selection of the price point.  Using the model as a guide, and the fact that all the price points have the same contribution margin, then we should choose the $170 price point and forego a direct marketing campaign.