MedFinVentures.org
At some point in your business, you’ll want to add another offering to your core service. This addition can come in the form of a second, distinct offering, or an add-on feature. Let’s understand the difference between the two:
Second offering: one in which there is a distinct market; can be bundled to the core offering, giving the appearance of an “add-on”
Add-on: one in which there isn’t a distinct market; it’s demand is dependent on the core offering
Let’s focus on #2 - that option is the easier of the two from a service-perspective. It is, however, a drag on your profits if you don’t do it right.
Our core offering is a single hour worth of service (say, a healthcare coaching service, or physician resume service). We created a demand curve based on a conjoint analysis of your beachhead customers. Remember this curve and the table from which it is derived (inset)? Use the menu options above to read the exposition on pricing your core offering.
The profit-maximizing pricing strategy occurs at a demand of 40 units (hours) for a price of $251.20 per hour.
Keep the table in your mind: the profit-maximizing price for a single-offering is $251.20 per hour - you can expect a demand of 40 hours based on the data you collected from your conjoint analysis.
Things will change once you decide to add-on a feature: profits will be harder to obtain.
The exact nature of the add-on is not relevant except for the following pertinent facts:
It can be delivered in the same one hour of service you provide
It has a fixed cost of $8000 per year
You conducted a poll (see below) to identify what percentage of your standard demand (Q) would be the add-on demand (Q*)
48% of the demand will be for this new offering (core + add-on) - denoted as Q*. Its price will be denoted P*, and its marginal cost MC*
The add-on costs $8000.00 and you’ve got to pass that cost onto your customer (if you want to have a chance at earning a profit). The marginal cost for customers who choose the add-on feature is:
MC + MC* = $133.85 + MC*
MC* will be the fixed cost of $8000 ÷ number of customers who want the add-on service = $8000 ÷ Q*
Q* is 48% of the total demand (Q), so 0.48Q = Q*
Therefore, MC* = $8000 ÷ 0.48Q = $8000/0.48Q
The price of the add-on offering is P*.
Since the add-on offering can be delivered during your regular service time, the total hourly price is: P + P*
We don’t know what P* is. In our single-offering example, we performed a conjoint analysis to identify the profit-maximizing price (P). You could administer another conjoint analysis, but each conjoint attempt costs time and money. Let’s try another way to identify P*.
Let’s develop a profit equation for both offerings: core and add-on.
Profit for the core offering = P(0.52Q) - MC(0.52Q).
Remember, we identified the profit-maximizing strategy: it happens when P = $251.20 and is predicted to generate a demand of Q = 40 total hours of service.
This Q must be parsed into those who want the core offering, and those who want the add-on. From our poll, 48% want the add-on…so 52% will select the core offering. Hence 0.52Q.
MC = $133.85 per hour.
Profit for the add-on offering = (P + P*)(0.48Q) - (MC + MC*)(0.48Q).
Why P + P*? Because the add-on service is in addition to your core service. The price for the core service is P, the price of the add-on is P*, the total price is P + P*.
Why 0.48Q? We know that 48.2% of all customers will choose the add-on service.
Therefore, our total profit (∏) equation is:
π = PQ + 0.48P*Q - MC(Q) - 0.48MC*(Q)
Let’s take the derivative of this equation and show a relationship between P and P*. What will P* have to be, based on the P that we choose?
∂π /∂Q = 0 = P + 0.48P* - MC - 0.48MC*
That means that the relationship between P and P* is:
P* = (MC - P + 0.48MC*)/0.48
The table below shows the P* (price per hour for add-on service) and the total price per hour based on various demand levels (Q).
P total = P + P* | It represents the hourly price a customer would pay if they select the add-on service instead of the core offering (denoted by P).
Let’s analyze this data. Remember our optimal price (P) for profit-maximization when offering the core service alone was $251.20 at a demand (Q) = 40. If we want to keep that price for customers who want our core offering, we’d have to price the add-on *above* $306.03 per hour in order to generate a profit. In other words, a customer wanting the add-on service would contribute to a profit if they paid more than $557.23 per hour.
At the start, you’d probably guess that expanding your offering portfolio would result in greater profits. The hidden factor that drives profits down is the nature of the cost of the add-on. Costs can be fixed or variable. In this example, the add-on cost is fixed - and therein lies the problem.
Fixed costs are predictable, which is nice, but they are independent of demand. The add-on cost of $8000 doesn’t change if your demand increases from your prediction, or worse, decreases from your prediction. If your actual demand is greater than your predicted, you’ll have a lower marginal cost (MC*) - you can spread out the cost over a larger customer base.
However, if your actual demand is less than predicted, your MC* will increase, as seen in the table above. With a less-than-predicted demand, your MC* will increase, forcing you to charge customers more (higher P + P*) for the add-on service. That action, of course, will decrease subsequent demand (Q*) for the add-on: making the percentage decrease from 48%. Welcome to the death spiral of business.
Even worse, a higher P* could effect both Q* and….Q. If P* effects Q, you’ve got a major problem: an add-on that is eroding demand of the core offering.
There you have it. A quantitative look at how a fixed-cost add-on feature can put you into a death spiral. That is not to say that you should not offer a fixed-cost add-on feature, but that you should understand how much harder profit-generation is when you can’t accurately predict demand.