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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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Based on our saas pricing book, Price to Scale, the key to segmenting customers for pricing experiments—while avoiding bias—is to use clear, objective criteria that reflect the real needs and behaviors of your customer base. Here are some actionable guidelines derived from the book’s methodology:
• Establish Objective Segmentation Criteria:
– Begin by defining segments based on measurable characteristics such as company size, purchasing behavior, usage frequency, and product needs. For example, rather than relying on assumptions, use data to identify groups like mid-market companies versus enterprise corporations, as discussed in Chapter 1 on segmentation.
• Avoid Pre-Existing Biases:
– Don’t limit your segmentation to only the current or most active customers (e.g., those already visiting your website). Instead, ensure you include a broader cross-section of potential customers, which helps you understand how new pricing strategies might perform in less familiar markets.
– Use random assignment within your identified segments when running experiments. This step is crucial to avoid letting preconceived notions about customer behavior introduce bias into your test results.
• Align Segmentation with Customer Value:
– Understand what drives value for each segment. Our book emphasizes that segmentation should stem from a clear understanding of the customer’s needs and attributes—ensuring that each group is meaningfully different and that your pricing experiments address these specific differences.
• Consistent Testing Framework:
– Use the same segmentation framework consistently across experiments. This consistency allows you to compare results accurately and ensures that any observed differences in pricing effectiveness are due to your pricing variations rather than varying segment compositions.
In summary, the best approach to segment customers for pricing experiments, as detailed in Price to Scale, is to use data-driven, objective segmentation criteria paired with random assignment within those segments. This method minimizes bias and ensures that you obtain actionable insights for refining your pricing strategy across a diverse customer base.