Client: Large payment processor with a two-part pricing model: monthly subscriptions and transaction fees.

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Problem: The client was facing high churn, especially among smaller customers. The existing model lacked flexibility for different customer types.

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What We Did:

Analyzed customer data to segment users based on behavior, size, and price sensitivity.
Adjusted the mix of subscription vs. transaction fees for each segment and created tailored pricing for each group:
- Lower fixed fees for small customers.
- Scaled pricing for larger customers based on usage.
- Hybrid models for growing businesses.

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Results:

Churn reduced by 35%.
Revenue increased overall, even though some price points were lowered.

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Takeaway:
Treating all customers the same was driving cancellations. Understanding the customer types better and segmenting users before adjusting pricing accordingly improved retention and revenue.

Client: Communications and conferencing software company

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Problem: The sales-led model was costly and unsustainable at scale. Shifting to product-led growth (PLG) required major changes across Pricing, UX, Engineering and Go-To-Market operations. Cross-functional coordination was a major challenge.

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What We Did:

Introduced a low-risk entry plan to open the top of the funnel and drive conversions to premium plans.
Adjusted pricing structure to allow for incremental feature discovery and purchase.
Facilitated aligned decision-making, and buy-in through separate and joint stakeholder sessions. Led efforts across Product, Sales, UX, Marketing, and Finance

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Results:

Self-service adoption increased significantly, the new structure supports PLG with better UX and reduced friction.

$3.6M annual sales uplift.

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Takeaway:

Successful pricing changes require not just the right analysis, but also strong cross-team coordination and operational follow-through.

Client: Unified Communications (UCaaS) provider

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Problem: The company offered too many plans, add-ons, and feature combinations. Customers, especially in the self-service channel, were overwhelmed. Even enterprise deals were taking longer due to pricing complexity.

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What We Did:

Audited all existing plans and add-ons, identified core features that drove customer value and usage.
Designed a clear Good–Better–Best pricing structure based on the importance of different functionalities. Bundled popular add-ons into main plans, reducing optional extras
Streamlined online purchase flow and simplified sales proposals.

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Results:

Online conversion rates improved. Enterprise sales cycle shortened
Revenue grew 5% year-over-year, driven by better clarity and faster decision-making.

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Takeaway:

Too much choice can reduce revenue. Simplifying pricing helps both self-service and enterprise sales teams close more deals, faster.

Client: Document processing and management software provider

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Problem: The company’s usage-based pricing model had worked well during the adoption phase, but growth stalled once most customer organizations reached full internal adoption. With usage flat, so was revenue.

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What We Did:

Assessed the most impactful value drivers across the product. Shifted pricing from usage-based to feature-based, using those strong price drivers.
Switched to monetization of access to advanced and innovative features instead of volume. Aligned pricing with functionality adoption, not just usage levels

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Results:

Revenue growth resumed. Customers saw clearer value differentiation across plans
Product roadmap aligned more closely with monetizable innovation, instead of following what the competition is launching. 

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Takeaway:

Pricing models must evolve with the product and market. What works in one phase can stall in the next. Reassessment every 2–3 years is essential to maintain alignment with the customer expectations and quickly changing competitive landscape.

Client: Large enterprise software provider with a broad portfolio in IT security and communications

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Problem: Core features became commoditized and the offering was no longer competitive. Conversion rates were low, and sales teams struggled to compete on value.

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What We Did:

Identified commoditized features and moved them into a free entry-level tier (Freemium). Introduced a premium paid package focused on differentiated features. Shifted most of price-points down. Revenue growth was to come from drastic volume increase.
Forecasted revenue and volume impact using data models (using Conjoint and MaxDiff studies to quantify preferences and price sensitivity).
This allowed for building internal alignment by showing the expected results and improved acquisition metrics
Developed go-to-market plans and pricing operations playbooks for high-volume conversion

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Results:

Fast rollout and testing due to robust forecasting (fewer pricing permutations needing to be tested). It allowed for the entire Pricing, Billing, Quote-To-Cash redesign to be finished in 11 months.

Drastic increase in top-of-funnel conversions.

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Takeaway:

Accurate forecasting of pricing impact helps secure buy-in, reduces internal resistance, and speeds up implementation of new pricing strategies.

Client: U.S.-based web hosting and design provider

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Problem: The product performed well in the U.S. but failed to gain traction abroad. Prior attempts to expand internationally were unsuccessful, leaving the team discouraged and unclear on how to adapt pricing and packaging for other markets.

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What We Did:

Quantified feature preferences, purchase behaviors, and willingness to pay across target markets
Adjusted pricing and packaging to fit local expectations and competitive conditions
Updated commerce systems, accounting logic, and online purchase flows to support localized pricing

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Results:

Successful expansion across Europe and South America

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Takeaway:

Product localization isn’t just about language, it requires pricing and packaging aligned to local value perception and buying behavior.

Client: Enterprise security software provider

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Problem: Enterprise sales teams lacked clear pricing guidance, leading to inconsistent and excessive discounting. Discounts varied widely and were often given without a clear rationale or value exchange.

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What We Did:

Analyzed past deals by rep, customer type, region, use case, and deal size. Identified patterns showing discount levels were strongly influenced by who sold the deal and who was buying
- Created structured discounting guidelines based on customer attributes and deal context. Designed a give-to-get framework: discounts required a defined concession or commitment from the customer
- Delivered training for sales teams and introduced pricing tools for quick decision-making
- Established a Deal Desk to support reps with context and help structure better deals, not to block discounts, but to make them smarter.

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Results:

Average discount rate reduced by 7 pp. Sales teams became more confident in pushing back on unjustified discount requests
Deal cycles shortened due to more clarity and support

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Takeaway:

Sales teams don’t need stricter rule, they need data, structure, and support to price deals with confidence and discipline.

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