Most travel sellers are leaving between 5 and 12% of their revenue on the table – not because they lack the right content or airline relationships, but because of how they price it.
In the first instalment of the TPConnects Interview Series, Paul Ryumugabe draws on 14 years of revenue management experience to break down where the leakage comes from, what smarter pricing looks like at each stage of maturity, and how the gap between negotiated fares and captured margin can be closed. From private fares and NDC to dynamic pricing, sub-agency networks, and the role of AI in demand forecasting, the conversation covers the full picture of what it takes to price strategically in a competitive market.
Whether you’re still working from a spreadsheet or already exploring dynamic pricing, this is a practical conversation about what the next step actually lookslike.
About Paul Ryumugabe
Paul has spent 16 years in the travel industry, with a decade leading revenue and pricing teams. His career includes a period as Interim Chief Revenue Officer at ODIGEO group, one of the largest online travel companies in Europe. He now leads Revenue Management and Data at TPConnects, where his work sits at the intersection of distribution, fare strategy, and commercial operations.
What the interview covers
1. The real cost of static pricing.
Agencies spend months securing private fares with airlines – negotiating by route, cabin, season. Then many apply a flat markup across the board. Paul walks through exactly where the loss occurs in that process and why it tends to go unnoticed until someone runs the numbers.
2. Private fares and the NDC opportunity.
NDC changes the distribution model, but its impact on pricing is less often discussed. Paul explains how the shift affects ancillary revenue, why the economics look different for sellers than for airlines, and what it takes to actually capture the value of deals that are already in place.
3. The build vs. buy dilemma.
Building an in-house pricing engine is a decision many agencies consider and most underestimate. Paul covers why it typically takes longer and costs more than projected, and where AI is likely to have the most practical impact on travel pricing over the next three to five years.
4. How smart pricing works in practice.
Rule-based engines, dynamic pricing, machine learning – the interview moves through each level of sophistication with concrete examples of what they look like in operation, and where human judgment still outperforms the algorithm.
The interview is available on demand. If you’re a travel seller, consolidator or a professional, working with pricing decisions day to day, and want a clearer picture of what a more structured approach could look like, this interview is for you.
