Partnerships Glossary

Customer stack visibility refers to the level of insight a vendor has into a customer’s SaaS stack — that is, the full set of SaaS tools and technologies a customer uses alongside its own product. Rather than viewing customers in isolation, this approach focuses on understanding the broader software ecosystem in which a product operates, including adjacent applications, integrations and platforms that support the customer’s workflows.

High customer stack visibility is typically achieved by analyzing signals such as integration usage, shared customers with partners, CRM data, implementation details, support interactions and partner ecosystem intelligence. These insights help vendors see which tools are already in place, which integrations are active or missing and where complementary solutions could add value.

For partner ecosystems, customer stack visibility allows for smarter integration prioritization and more effective co-selling. With greater customer stack visibility, vendors can better identify which partners are most relevant for a given account, surface integration recommendations that align with existing tools, and coordinate joint go-to-market motions based on real customer context.

In B2B SaaS environments, improved customer stack visibility supports stronger customer outcomes, faster deal cycles and more relevant partner engagement. When used strategically, it helps vendors align product strategy, ecosystem investments and revenue motions around how customers actually operate.

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The partner influence index is a composite metric measuring the overall impact partners have across the customer lifecycle, from sourced deals to factors like pipeline progression, product adoption, retention and revenue expansion. Rather than focusing solely on partner-attributed leads or closed-won revenue, this index evaluates how partner involvement influences outcomes at multiple stages of growth.

The index is typically built using a weighted scoring model combining signals like partner participation in active opportunities, co-selling engagement, integration usage, customer success touchpoints and expansion activity. Each signal is weighted by its relative importance, producing a single score that reflects a partner’s true business impact.

By capturing both direct and indirect contributions, the partner influence index helps vendors understand which partners consistently accelerate deals, deepen product usage and improve long-term account health — even when they are not the formal source of a lead.

In B2B SaaS ecosystems, this index enables more accurate partner evaluation, smarter incentive design and better investment decisions. When used strategically, it shifts partner measurement from transactional attribution to holistic influence, driving stronger collaboration and more predictable ecosystem-driven growth.

An ecosystem propensity model is a predictive tool that estimates how likely a customer or prospect is to convert, grow or expand with partner involvement. Instead of relying on broad, generic scoring methods, this AI-powered model uses machine learning (ML) to analyze patterns across the partner ecosystem — such as referrals, integration usage, shared customers, co-selling activity, industry fit and past partner-influenced wins.

By comparing these signals against historical outcomes, the model identifies which accounts are most likely to move forward when the right partner is engaged. For example, an account that already uses a complementary integration or frequently appears in a partner’s customer base may receive a higher propensity score, indicating that an ecosystem touchpoint could accelerate the deal.

For vendors, an ecosystem propensity model helps prioritize the right accounts, route opportunities to the best-fit partners and focus ecosystem resources where they will have the strongest impact. When used effectively, it boosts win rates, shortens sales cycles and makes partner-influenced growth more predictable across the customer journey.

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