List Price Optimization

20 November 2020

Background

A specialized Animation Software company had experienced flat revenue growth over the past three years (See Figure 1). Their main software sold on a simple, two-tiered pricing system, Regular License ($499) and Student License ($0). The subscriber base had remained constant over the past three years despite substantial funding increases for new customer acquisition. Flat revenue, combined with the company’s high variable margin pressured the client to revamp its pricing structure.

THE RML APPROACH

Our approach focused on 4 main areas:

Identify Customer Segments

Do we have customer segments? How many?

Value Creation Identification

What creates value for each customer segment?

Structured Discount Terms

How can we structure our price list to allow customers to self-segment?

Execute, Monitor, Report, Adjust

How do we ensure we can implement changes successfully?

The Challenge

The company was at strategic cross roads in determining how to drive growth, viz. Sales wanted to cut pricing to spur demand, whilst Marketing was focused on selling the value. With flat revenue and high variable margin, the client looked externally for help in developing a new growth driven pricing structure.

1. Identify Customer Segments

Revenue Management Labs employed a mixed-method approach, consisting of both customer/client interviews and transactional cluster analysis. The purpose of a cluster analysis is to group customers based on their similarity in several product-related factors:

# Licenses
Tenure
Customer Service Calls
Installation Support
Customization Support Required

In this case, the cluster analysis (Figure 2), based on 26 factors, found four key customer segments:

Movie Studios
Video Game Manufacturers
Corporate TV/Online Commercials
Students

2. Value Identification Creation

We then analyzed the main factors behind each groups’ purchasing decision. Using MaxDiff Analysis, we uncovered the most important purchase attributes (Figure 3).

Incorporating both the cluster and MaxDiff analysis, we created a unique profile for each customer segment (Figure 4). The four segments were separated based on multiple factors and levels of price sensitivity.

3. Create Price Fences

Previously, the company operated on a two-tiered pricing system (Figure 5). We implemented a four-tier system based on segment, tenure, licenses required, and price sensitivity (Figure 6). This advanced analytics approach to price fences is not only relevant to software but can be applied to other industries.

The Result

+ 100% Sales Increase

Transitioning from a single price point with no conditionality to
multi-level packages resulted in a significant increase in average price realized and total revenue. Through the project, the client came to realize that cutting prices to drive incremental volume was destroying value and adversely impacting profitability. This insight led to a whole new approach to driving growth through price optimization.