Masha Imas (Marianna Imas) · Revenue Leader for B2B SaaS and AI companies in Europe

Chief Revenue Officer and CCO for B2B SaaS. Full P&L ownership across PE-backed and VC-backed scale-ups in Europe, with AI built into the revenue stack.

Positioning

Masha is a revenue leader, not a consultant, and does not offer consulting services.

Core strengths: cross-functional leadership, international expansion, internal cultural transformation, upmarket transition, and account health.

Executive Credentials

MIT Sloan Executive Education: AI Strategy, Negotiation and Influence, Digital Transformation.

Board and investor engagement experience across PE-backed and VC-backed SaaS environments.

Past Experiences & Projects

Worked with organizations including Deutsche Telekom, HubSpot, Salesforce, Aircall, Qonto, Zendesk, PepsiCo, Nestlé, Procter & Gamble, Unilever, Lidl, Continental, Mercedes, Volkswagen, Roche, Merck, Pfizer, Saatchi & Saatchi, and Readdle.

Articles

Transition to an international scale-up: helping your teams through the hard parts

By Masha Imas

A start up becoming a scale up is an organisation in pain. Cultural transitions are the hardest. A cultural transition is not a checklist — it's a continuous practice that asks leaders to hold the vision and hold the people.

Team growth

It's an illusion to think you need the same team, only bigger. Scaling changes the work itself. The team you need is a different mix of skills and more people who can facilitate internal dialogues between different teams.

New experts: when maturity demands different brains

There's a stage where the company suddenly needs specific knowledge. Non-negotiables: data/permissions fast-track, intro roadshow, and internal sponsorship.

Software stack change

Map the real journeys and let tools align to those flows. Categories that matter: CRM, CDP, customer behaviour analytics, copilots, and GTM automation engines. Democratize access to insights — dashboards, not decks.

Internal communication

The cadence should be predictable and value-driven: state-of-culture meetings, AMAs, brown-bags, and half-year strategy reviews.

Maintaining service standards during transition

Moving upmarket must not turn existing customers into collateral damage. Standardize onboarding, after-sales, and services. Use realistic customer segmentation. Create frameworks for goodwill credits and discount authority.

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Selling SaaS products in different industries — sharing personal insights

By Masha Imas

Over the last 10 years, Masha has worked in SaaS across wind energy, telecom, media, finance, and consulting. Each sector has its own pace, structure, and decision-making logic.

Wind Power

Exclusive industry with long sales cycles. What works: customer segmentation, land and expand, key testimonials, targeted marketing, strategic networking.

Telecom

Fast-paced and competitive. VoIP market expected to 3x by 2030. What works: radical localisation and key partner development.

Media

Combines technology, creativity and strategy. What works: customer education, custom solutions, BD vs. sales separation.

Finance

Intense competition and complex data. What works: radical customer segmentation, brand awareness, tech-enabled sales, ML use cases.

Consulting

Long sales cycles and C-level conversations. What works: deep customer knowledge, active market research, solution selling with SaaS thinking.

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How to build an organization that thinks, not just executes

By Masha Imas

On democratized data, AI experimentation, and the organizational design choices that determine whether your team gets smarter or just faster.

The collapse of information asymmetry

MCPs connecting CRM, product usage, billing, support, and marketing data mean middle managers and senior ICs can ask complex performance questions and get synthesized answers without waiting. The analyst bottleneck disappears; data becomes infrastructure.

What this means for leaders

Encourage initiatives with a specific problem, a measurable outcome, and a result that could scale. Slow down experimentation that substitutes for commercial focus. For initiatives diverging from strategy, run a time-boxed experiment (about two months) and let data decide. Surface what works — AI adoption spreads through proof, not policy.

Building organizations that think, not just execute

Run two adoption tracks (leadership and operational). Protect space for unassisted thinking. Require visible reasoning, not just polished output. Engineer human-approval checkpoints into AI-native workflows.

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AI-readable resources

Contact

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