Brandon de Vries
Pricing & Revenue Growth Strategy | Strategy, Risk & Transactions
London, Ontario, Canada
Artifacts
Deloitte · Manager | Revenue Growth Strategy · October 2025A large end-to-end supply chain management software provider, historically reliant on a SaaS subscription model, engaged our team to lead a transition toward usage-based pricing—specifically evaluating a token-based model. This shift was driven by rising costs tied to rapid product evolution, particularly the integration of AI-based features, and a strategic desire to better align monetization with the value delivered to customers. The client needed subject matter expertise to rigorously pressure-test their pricing hypotheses across hybrid and token-based models, refine their approach, and validate it through comprehensive market research. Over a 10-week engagement, our team provided advisory support to assess and refine the client's existing pricing hypotheses, ensuring alignment with industry best practices and emerging trends in SaaS and AI-driven solutions. My team conducted 10 qualitative interviews with a mix of customers, prospects, and competitors to pressure-test assumptions and gather direct feedback on pricing perceptions and competitive positioning. We also facilitated 5 subject matter expert interviews within Deloitte to deepen our understanding of the evolving supply chain management landscape, and executed a quantitative survey of 105 respondents to benchmark competitive price-to-value, evaluate the proposed pricing framework, and test willingness-to-pay across key segments. We synthesized these findings to optimize the price model, ensuring it met customer needs, reflected market standards, and supported the client's strategic objectives, while also advising on implementation considerations, customer migration, and go-to-market rollout planning. The engagement enabled the client to move forward confidently with a value-aligned, token-based pricing model, supported by both an initial go-to-market launch recommendation and a long-term growth strategy for the transition from adoption to sophisticated revenue capture. Our research identified go-to-market opportunities representing approximately 5% potential revenue uplift, along with evidence that customers were willing to pay a 5% premium for AI capabilities—insights that shaped both near-term rollout tactics and longer-term monetization strategy. The work also strengthened organizational alignment across product, pricing, customer success, and sales functions to support successful implementation. Following delivery, the client's VP of Product Operations & Excellence and Senior Strategic Initiatives Manager presented our findings to executive leadership, who approved the token-based model as part of a phased rollout. Our go-to-market recommendations were incorporated directly into the client's launch plan, translating the engagement's research and analysis into concrete strategic action.
Deloitte · Manager | Revenue Growth Strategy · February 2026A smart mobility and traffic management provider had developed an AI-powered assistant delivering real-time traffic insights, generating strong customer interest thanks to its speed and expanding set of proactive use cases. Ahead of launch, the client needed a clear, scalable monetization strategy to support customer adoption and revenue growth — one grounded in stakeholder input, customer validation, and competitive best practices rather than internal assumptions. Deloitte was engaged to define and market-test this pricing strategy, and I led the workstream responsible for delivering it. As workstream lead, I helped to design the core pricing hypotheses, structured customer validation surveys, and ultimately synthesized findings into a hybrid pricing model recommendation for the client's executive team. I both led and supported in the development and iteration of AI monetization hypotheses, spanning size-based and usage-based pricing, while selectively exploring token-based constructs to support long-term scalability. To ground these hypotheses in real customer sentiment, I conducted 15 targeted customer interviews assessing product needs, perceived value, willingness-to-pay, and reactions to usage- and token-based structures. I also supported the facilitation of collaborative working sessions with the client to refine pricing metrics, model structures, and price bands based on stakeholder input and industry benchmarks, then built preliminary pricing grids and segmentation-specific recommendations grounded in willingness-to-pay data and competitive context. Finally, I provided commercialization guidance to enable a smooth pricing rollout and organizational readiness for launch. To quantify the impact of the recommended hybrid model, we modeled three adoption scenarios against the pricing grid using willingness-to-pay data from the customer interviews, applying adoption uplift ranges of 5% to 15%. This analysis produced an estimated first-year revenue uplift of $380K–$950K. The client's executive team approved the hybrid pricing model for launch, and within 10 weeks of our final presentation, it was formally adopted as the official go-to-market pricing structure — validating both the rigor of the customer research and the commercial viability of the recommended approach.
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Jan 2021 - Present · 5.8 years · Manager | Revenue Growth Strategy, Senior Consultant | Revenue Growth Strategy, Manager | Forensics & Disputes
Feb 2021 - Dec 2021 · 11 months · Loan Analyst
Dec 2017 - Dec 2020 · 3.1 years · Senior Associate | Forensics & Disputes
May 2013 - Nov 2017 · 4.6 years · Supervisor, Senior Accountant, Staff Accountant
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