Pricing your AI product: Lessons from 400+ companies and 50 unicorns

Madhavan Ramanujam, Senior Partner at Simon-Kucher & Partners and author of Monetizing Innovation, is a leading pricing strategist who has worked with hundreds of tech companies and many unicorns. The conversation covers willingness-to-pay research, segmentation, pricing strategy and models, behavioral pricing, and how to price during downturns, with practical methods for product teams.

01Key learnings

• Start willingness-to-pay conversations early, before building, because pricing is a measure of value and you can't avoid the pricing question, only choose when to answer it.
• Frame pricing questions relatively, benchmarking against a known product like Salesforce, since people give more meaningful answers on relative value than on absolute dollar figures.
• Use acceptable, expensive, and prohibitively expensive price probes to find psychological thresholds, which often show steep demand cliffs just above certain price points.
• Prioritize the roadmap by what drives willingness to pay, since roughly 20% of features often drive 80% of willingness to pay; use most/least exercises to find them.
• Segment by needs, what customers value, and willingness to pay rather than demographics or personas, and make sure you can productize differently for each segment.
• Choose the pricing model before the price: usage, subscription, or hybrid should depend on predictability of usage, value pattern, and whether you can reliably track the metric.
• Pitch benefits rather than features on pricing pages and sales materials, since customers buy outcomes and features alone rarely communicate value.
• During a downturn, avoid simple discounting; instead create a de-featured lower tier, use non-price concessions like longer terms or payment terms, or shift to a usage-based model.

02Frameworks mentioned

Summary and takeaways written by PM Atlas; quotes are short excerpts. © the original author.