Pricing decisions may be straightforward if they can be based on reliable market information such as competitive pricing and your known costs. Often, however, decisions are complex due to uncertainty. In these cases of uncertainty, methods that embrace uncertainty to better estimate results (e.g. the Monte Carlo method) can be employed as discussed in the real-world example below.
A Real-World Example
Leadership mandated an after-sale support program that would help ensure high product reliability. The challenge was that the customer base had been established in prior years and that each customer instance was different. In some instances, customers had a large number of products with both high and low use while in other instances customers had a low number of products. This use distribution made a “fixed” price offering difficult. The goal was to price the service such that overall program profitability was achieved even if some number of individual participating customers generated a loss.
Existing customer sites were examined to understand the scope of service coverage, including the in-place product volume that would need to be covered by the support program. Customers included both direct sales and sales through independent distributors which required careful confirmation to avoid possible distortion in the information gathered.
The examination of existing customers confirmed a wide range of product purchases and utilization. In some situations, customers had a large installed base of product that was in regular use while in other situations customers had a small installed base of products with highly variable utilization.
Failure rates based on utilization were gleaned from service records. Costs related to support were identified, including shipping replacement parts to customers and receiving parts requiring service from customers who are distributed across the United States.
A simple Monte Carlo model was created in Excel that generated one-thousand instances of revenues and profits for each run. Ranges for installed product and utilization were set based upon customer information, and the model generated anticipated failures and associated support costs. Price proposals were adjusted until overall program profitability was achieved.
Using the insight gained from the analysis described above, pricing was confidently set and offered by the sales organization.