Building an effective modern RevTech stack

There is no one-size-fits-all “perfect” RevTech stack. Each company’s stack will look different depending on their stage, market, and goals. The key is understanding core principles and building a flexible system that can adapt as your company evolves.

In this module, we’ll explore the main stages of a RevTech stack—Generate, Close, Report, and Federation—why flexibility matters, and how to use the FRIC framework (Focus, Redundancy, Interoperability, Coupling) to evaluate and improve your stack. Throughout, we’ll reference real-world examples from Clarify (Pre-PMF), Thena (Post-PMF), and Ramp (Growth), showing how different approaches apply at different stages.

Core RevTech Stack Components

We often conceptualize RevTech stacks by the phases of the customer journey and data flow:

1. Generate (Inbound & Outbound): This phase focuses on lead generation and initial engagement. Inbound includes channels like website forms, chatbots, and CDPs for data collection. Inbound also typically includes the marketing or “MarTech” activities used to drive inbound site traffic. Outbound involves email outreach, targeted B2B ads, events, and other campaigns to attract and qualify new prospects.
2. Close: This phase centers on converting leads into opportunities and then into customers. CRMs typically sit here, managing deal stages, pipeline, and customer interactions. Tools like scheduling software, contract management, and call insights help speed up and streamline the closing process.
3. Report: After closing deals, you need to measure performance. Data warehouses, BI tools, product analytics, and attribution platforms help track metrics and inform future strategy. Reporting is where data converges for analysis, insights, and decision-making.
4. Federation: Federation refers to the systems and integrations that connect all of the above. ETL/Reverse-ETL pipelines, CDPs, and integration platforms move data between Generate, Close, and Report layers. They ensure smooth data flow and unify the stack into a coherent whole.

Why Stack Flexibility Matters

In today’s rapidly changing business environment, stack flexibility is critical. Consider these reasons:

• Handling novel marketing and revenue challenges: As new channels emerge, your stack needs to adapt quickly to support them.
• Quick audience and segment building: The ability to rapidly create and target customer segments is vital for personalized marketing and sales.
• Avoiding tech debt and complexity burden: A flexible stack allows you to add or remove tools without creating a tangled web of dependencies.
• Adapting to changing business needs and tool options: As your company grows, your tech stack should be able to evolve with it.
• Facilitating world-class debugging: When issues arise, a flexible stack makes it easier to isolate and resolve problems.

As Austin says, having a well-structured stack and a data warehouse backbone creates more options for adapting and reconfiguring tools over time. Flexibility isn’t just a luxury—it’s a necessity for sustainable growth.

The FRIC Framework

We devised the FRIC framework to help you assess and improve your stack’s flexibility along four key dimensions:

• Focus
• Redundancy
• Interoperability
• Coupling

Think of these as levers you can adjust to build a healthier, more adaptable RevTech ecosystem.

We’ll dive into each principle, providing multiple examples—good and bad—from real stacks like Ramp and Thena, as well as early-stage companies like Clarify.

Focus

Definition:

Focus means that each tool in your stack has a clear, intentional purpose. Without focus, you risk “shiny tool syndrome” where multiple tools overlap haphazardly. A focused stack ensures each component is well understood, documented, and aligned with a specific function in your RevTech strategy.

What Good Focus Looks Like:

• Minimal overlaps in core functions.
• Clear documentation and architecture diagrams so everyone knows what each tool is for.
• Regular pruning of unused or unnecessary tools.
• Strict data governance and defined roles for tools that do have overlapping functions.

Good Focus Examples:

Ramp’s Data Architecture & Policies:

Ramp faced potential overlap among tools like HubSpot, Salesforce, Segment, Amplitude, and a home-grown CDP. They prevented chaos by setting strict policies: HubSpot only for email marketing (not a CRM), Salesforce as the CRM, Segment for event streaming, and Amplitude for directional product analytics. They documented these decisions so everyone knew what to use each tool for.

Thena's Post-PMF Clean-up

Thena initially overbuilt their stack, adding many automation tools to replicate SDR capacity. While it felt great at first, complexity soared. Eventually, Thena conducted a tool audit, removing unnecessary platforms. This refocus restored clarity and efficiency.

Clarify's Minimalistic Early Stack:

Clarify kept it simple with one CDP, one CRM, and one warehouse. Each tool connected cleanly and served a singular role: the CDP for capturing data, the CRM for managing leads and deals, and the warehouse for storing and analyzing data. This lean approach minimized confusion and made it easy to understand and manage the stack.

What Bad Focus Looks Like:

• Tools added randomly without defined purposes.
• Multiple systems duplicating core capabilities with no clear reason or policy.
• No single source of truth for customer or performance data.

Bad Focus Examples:

Two CRMs without clear differentiation

Imagine if Ramp treated HubSpot and Salesforce both as CRMs without restricting HubSpot’s CRM features. This would create a messy environment—data conflicts, two sources of truth, and confusion. Ramp avoided this by shutting off core CRM functionality in HubSpot. Without such rules, it would have been a case of bad focus.

Multiple CDPs without clear differentiation:

It’s surprisingly easy for teams to accumulate multiple tools that either function as CDPs or provide CDP-like capabilities, leading to overlap and inefficiencies. Take Ramp as an example: they essentially operate with four such tools—a homegrown CDP, their core CDP, Segment, Amplitude (which can act as a CDP), and HighTouch (often used as a CDP). While they’ve mitigated potential chaos through clear delineation of each tool’s role and thorough documentation, there’s still room to simplify for better focus and operational clarity. Ideally, a stack benefits from one central hub for customer data, supported by complementary tools, rather than multiple competing systems trying to perform similar tasks.

Overusing a Multi-Capacity Tool:

Suppose Clarify started using Lemlist not just for outbound email, but also as for upselling, lead scoring and ads management. By blurring lines and forcing Lemlist to do multiple jobs in different parts of the stack, Clarify would lose focus and create confusion in their stack.

Maintaining good focus ensures each tool serves a distinct purpose, reducing overlap and complexity.

Redundancy

Definition:

Redundancy means having overlapping capabilities or data flows. It can be good or bad. Good redundancy provides flexibility and resilience—if one tool fails, another can cover the gap. Bad redundancy leads to confusion, cost, and unnecessary complexity.

Good Redundancy Looks Like:

• Flexibility for discrete, decentralized actions.
• Two tools performing similar functions but each handling a unique segment or stage in the process.
• Clear understanding of why you have overlapping capabilities.

Good Redundancy Examples:

Major Fin-Tech company's lead management setup

At this undisclosed Major Fin-Tech company, they used MadKudu for lead scoring, Salesforce as the CRM, and Marketo for marketing automation. All three touch leads but serve distinct purposes in the lead lifecycle. This gave them coverage if one tool faced issues and ensured each tool’s redundancy had a purpose.

Federation redundancy at Ramp:

Consider adding an extra data flow from Salesforce (CRM) directly to the sales commission tool, even though data already flows through the warehouse. This could be good redundancy or bad redundancy, depending on the use case. Typically, information is sent from a CRM to the Warehouse in batches, meaning there’s latency. The results you see in the warehouse might be accurate as of an hour ago, but not real time. Therefore, without this additional federation, your Sales Commission similarly would not have real-time data. If real-time commission updates matter, this extra arrow is good redundancy—it can provide immediate data directly from the CRM. If not needed, it’s bad redundancy that adds complexity without value. This highlights that redundancy isn’t just about tools, but also about their connections.

Bad Redundancy Looks Like:

• Exact same capabilities causing confusion.
• Two attribution tools or two email marketing platforms with no defined reason.
• Data silos and inconsistent messaging due to too many overlapping solutions.

Bad Redundancy Example:

Two CRMs without distinction (if ramp didn’t set rules):

If Ramp let both Salesforce and HubSpot run as CRMs, they’d create attribute bloat, conflicting data, and two-way sync nightmares. The same applies to having multiple email marketing tools for the exact same campaigns without a strategic reason. If Ramp had HubSpot, MailChimp, Lemlist and Iterable all sending emails to leads, that would be bad redundancy. Finally, if Ramp used both Chili Piper and Calendly for calendar management, that too would be bad redundancy.

Some redundancy provides flexibility. Too much leads to inefficiency and confusion. The key is understanding what redundancy is beneficials and what isn’t.

Interoperability

Definition:

Interoperability measures how easily tools can connect, exchange data, and adapt to changing conditions. It’s about having “optionality”—the ability to rewire your stack when swapping tools or adjusting workflows. High interoperability means you could, if needed, connect any tool to another. Low interoperability (incompatibility) forces brittle integrations and awkward workarounds.

Good Interoperability Looks Like:

• Tools that could handle multiple capabilities if you wanted them to.
• Tools support push, pull, and audience integrations.
• Optionality: you can change flows or temporarily reroute data without massive disruptions.

Good Interoperability Examples:

Ramp’s complete data availability across systems:

Ramp has Segment, a home-grown CDP, Amplitude, High Touch, and a warehouse. Even if they designate a single source of truth for reporting, the fact that all these systems can hold and transfer complete datasets means they could pivot tools if needed without losing access to critical data.

Multiple Email Marketing Tools Available:

Ramp can use Outreach or HubSpot for sending emails. If Outreach fails, they can rely on HubSpot. This optionality is interoperability in action.

Flexible federation options:

Ramp can consider adding a direct connection from the CRM to the sales commission tool if needed. This kind of optionality—whether to go through the warehouse or connect directly—demonstrates strong interoperability, even if Ramp chooses not to use that connection point.

Bad Interoperability (Incompatibility) Looks Like:

• Tools that can’t talk to each other without forcing brittle integrations or kludgy workarounds.
• No easy way to reroute data if a tool is replaced or fails.

Incompatible Integrations Forcing Workarounds Example:

Livestorm at Ramp

Ramp wanted to route all outbound engagement capabilities through Segment (CDP) to Salesforce (CRM). Livestorm couldn’t integrate with Segment as desired. But, to make matters worse, it couldn’t even integrate with Salesforce, forcing them to link it with HubSpot—an intentionally downgraded CRM tool not meant for that role. This added complexity and defeated their ideal architecture and it’s due to incompatibility.

Strong interoperability reduces friction. Even when you must add a suboptimal integration, being aware and documenting decisions can mitigate tech debt.

Coupling

Definition:

Coupling refers to the degree of dependency between systems. Loose coupling makes it easier to replace or remove tools without breaking the stack. Tight coupling means each tool depends heavily on others, making changes risky and complicated.

What Good (Loose) Coupling Looks Like:

• Minimal dependencies between vendors.
• One-way data flows where possible.
• Some tools operate outside the stack’s core without causing issues.

Loose Coupling Examples:

Clarify's one-directional flows and minimal integrations:

Clarify’s tools mostly flow data one way—except for the three major hubs (CDP, CRM and Warehouse)—reducing interdependencies. Also, tools like Luma and Kondo aren’t even integrated directly; they lead users to Cal.com links that then enter the CRM. If Luma disappeared tomorrow, no major rewiring is needed. This is extremely loose coupling.

With fewer bi-directional syncs, Clarify can swap tools in and out without massive stack overhauls.

What Bad (Tight) Coupling Looks Like:

• Vendor lock-in due to complex two-way syncs.
• Removing or changing one tool breaks multiple workflows.

Bi-directional syncs between CRMs at Ramp (hypothetically):

If Ramp had allowed Salesforce and HubSpot to fully sync back and forth, the stack would be tightly coupled, making it hard to remove one system.

Over-reliance on Salesforce integrations

Many RevTech tools integrate most deeply with Salesforce. If your stack is built around hard-coded Salesforce dependencies, removing or replacing Salesforce becomes a painful, high-risk project. At Ramp, we see many capabilities that only flow into Salesforce, indicating that the stack is tightly coupled with this tool.

At scale, some tight coupling may be unavoidable, but you should be intentional. Early on, emulate Clarify’s loose coupling. Later, if you must accept more complexity, understand the tradeoffs and document them.

Balancing immediate needs and future tech debt

While the FRIC framework guides stack flexibility, the optimal approach varies by growth stage. Let's explore how to balance immediate needs and future technical debt across different maturity stages.

1. Pre-product fit stage

At this stage, focus on launching your product and testing value propositions with minimal cost and complexity.

Think of building a company like building a house: First, lay the foundation (raise capital and find product market fit) and get a roof overhead quickly (establish basic functionality). Don't worry about the walls or interior design yet.

Here's how early stage companies typically score across the FRIC framework:

• Focus: Medium to poor Teams acquire tools without defined processes as different members work independently to solve immediate challenges.
• Redundancy: Low Limited tool acquisition at this point.
• Interoperability: Poor - Speed takes priority over best practices when connecting systems.
• Coupling: Tight - Stack connections prioritize short term ease over long term flexibility. Limited documentation and operations expertise lead to quick fix solutions.

Team members often play multiple roles to help the company hit goals and survive. Optimal tool implementation becomes secondary to solving immediate needs.

👍 Good example at this stage: Clarify demonstrates an ideal early stage approach. They started with just two core tools: their own product as a lightweight CRM and Customer.io for email marketing. By using a shared Notion document to log tool decisions and processes, they prevented knowledge silos. Their commitment to native tool connections helped avoid complex custom work that could create future technical debt.

Here’s where you should focus your efforts around your sales tech stack:

• Primary focus: Generalized system understanding (focus on understanding the basic capabilities of the minimum viable amount of tooling)
• Secondary focus: Tool management, capability assessment, and tool procurement (some familiarity with all three, but no need for full competence)

If you have the resources, hiring a technical marketing person can help you ramp up system understanding quickly. Beyond that - focus on trying to document how your stack works and why you made certain choices as you go.

It’s also important to call out what not to do on during this stage:

• Focus too much on architecture vision and org management.
• Let people go off into a silo (creates too many disparate tools/systems).
• Keep mission-critical stack information in someone’s head vs in docs.

👎 Bad example at this stage: Prematurely investing in Salesforce, which is overly complex for your current needs, and adding multiple sales enablement tools without clear use cases. Different team members might use separate tools for similar functions, creating data silos and inconsistencies. There's no documentation of the current stack or decision-making process, and integrations are handled ad-hoc, leading to a tightly coupled system that will be difficult to modify as you grow.

2. Post-product fit stage

Thinking back to our house metaphor, this is the stage where you’re starting to build the inner guts of the house. It’s super chaotic, but the framework is visible and you can see the space take shape.

Here’s how you’re likely scoring across the four focus areas of our framework:

• Focus: Medium to poor - Same issue as during the pre-product fit phase, individual tooling is a bit choose-your-own-adventure and not optimized for function, use case, or spend.
• Redundancy: Medium - You may have started to see overlapping competitive capabilities in your stack.
• Interoperability: Medium - This is likely still poor because you’re still moving fast to solve discrete challenges and you don't have a formalized process for vetting vendors and how you connect them.
• Coupling: Medium - Your stack hasn’t grown too much from early pre-product fit. It may have improved modestly because you’ve hired more people, but more people bring more tools and ways of connecting them.

Your stack is likely starting to take shape as a diagrammed system and there’s knowledge about how things work and who owns what. It’s fertile ground for bystander’s syndrome. This is also the point where siloing of knowledge is most likely to take hold.

👍 Good example at this stage:

Thena’s approach after achieving product-market fit demonstrates good focus and interoperability. They consolidated their user tracking and event collection into a single CDP, ensuring cleaner data flows and easier downstream distribution. Their disciplined pruning of unnecessary tools and strategic vendor selection enabled agile marketing and rapid experimentation, reflecting good coupling. They do have some strange redundancy with their analytic tools, and a bit too much federation they don’t need, but that’s to be expected at this stage.

Here’s where you should focus your efforts around your sales tech stack:

• Primary focus: Tool management and capability assessment (to help you add tools and vendors that support long-term goals)
• Secondary focus: Generalized system understanding and tool procurement

The name of the game for this stage centers around cutting down uncertainty around the stack.

Here’s what not to do on during this stage:

• Focus too much on architecture vision and org management.
• Let people go off into a silo, which creates too many disparate tools/systems.
• Let the team build reliance on a single SME vs build general understanding of the tools.

👎 Bad example at this stage: Prematurely implementing an enterprise-level marketing automation platform like Marketo, along with a complex CRM system, without having the team capacity to fully utilize or manage these tools. A company doing this might create custom integrations between these systems, leading to tight coupling. The marketing and sales teams would use different tools for lead scoring, creating bad redundancy and data inconsistencies.

3. Growth (hyper growth) stage

During rapid growth, your GTM org becomes more complex, serving more individuals and teams.

The “house” you’re building has reached the point of no return. You’ve built final systems and you could move in while you wrap up final work. You can’t make final changes to the fundamentals of the house, its layout, or foundation without significant cost.

Here’s how you’re likely scoring across the four focus areas of our framework:

• Focus: Poor - Until you design a specific function for each tool in your stack and create rituals for periodically pruning them, focus will remain poor as the stack scales.
• Redundancy: High - You likely have overlapping competitive tools. Focus on good redundancy by defining a purpose for each tool, not using tools just because you can. Rip out vendors that aren’t necessary.
• Interoperability: Decent - The newer tools you’ve brought on as you’ve scaled have likely improved interoperability, even if it’s not formally put into practice.
• Coupling: Poor - You’ve spent a few years building a frankenstack and things are connected based on however you set them up. Good teams will at least have documentation and a data diagram. Poorly functioning teams will have it trapped in somebody’s head.

You should have a clear idea of your stack, its architecture, and the set of tooling you’re using to support the company.

👍 Good example at this stage: Ramp’s approach during their growth phase illustrates how to refine focus and interoperability while managing complexity. They consolidated audience segmentation tools into Hightouch, reducing redundancy and enhancing interoperability. Regular tool audits and governance enabled intentional evolution of their stack, avoiding unnecessary complexity. In other words, they are using strong labeling and controls to manage some of the natural tensions companies face at this stage around high redundancy.

To do this, you need competency across all previous focus areas (generalized system understanding, tool management, and capability assessment), as well as three more:

1. Tool procurement to ensure you’re making smart choices as buy vs build decisions will make a lasting impact.
2. Architecture vision to educate more stakeholders about tools and how they work in a scalable way to get buy-in (and save time answering questions).
3. Org management to reduce inefficiency and meet business needs.

Focus on ruthlessly prioritizing and consolidating the tools and capabilities you need in your stack.

Here’s what not to do on during this stage:

• Compensate for a lack of knowledge by blindly hiring more people.
• Lose focus on assigning deliberate ownership of tools and systems.

👎 Bad example at this stage: Rapidly acquiring multiple tools for different aspects of your revenue ops without a clear integration strategy. You’ll end up with separate tools for email marketing, customer support, sales automation, and analytics that don't communicate well with each other. This leads to data silos, poor interoperability, and difficulty in getting a holistic view of the customer journey. The lack of a centralized data strategy results in inconsistent reporting across departments.

4. Scale stage

At this stage, your business is complex but stable. Your GTM stack is likely spread broadly across the organization.

House-wise, you’ve reached the “final” stage of building your house and things feel fresh. Over time, wear and tear creates new opportunities and challenges to address and will require updating.

Here’s how you’re likely scoring across the four focus areas of our framework:

• Focus: Poor – Resourcing isn’t an issue, but inefficiency grows without collaboration and trust. Every department has their own ideas of the “perfect tool” they need to add, and the independent budgets to add it.
• Redundancy: High - You have overlapping competitive tools and need to focus on good redundancy by selecting a purpose for each of your tools and limiting permissions across user groups. Continue to rip out vendors that aren’t necessary.
• Interoperability: Decent - You’re continuing to buy newer and more advanced tooling, which generally means more and better native integration within the ecosystem. That doesn’t mean things are being done in the preferred flow, however.
• Coupling: Poor - Your frankenstack has grown up and connections are running wild. Your team needs to audit your stack and create documentation, but most of the knowledge likely lives in the head of one or two tenured employees.

Resourcing is no longer a major constraint, but better collaboration and trust are essential for solving problems. This requires educating and aligning the teams you work with. At this stage, deeply ingrained technologies can feel difficult to replace, and controlling departmental tendencies to expand the stack can be challenging. Strong leadership buy-in is crucial to centralize stack decisions and actively champion consolidation efforts.

👍 Good example at this stage: We can’t publicly acknowledge the enterprise example we used in the prior lesson, but the key here is being strategic about where you want to centralize - otherwise it can quickly get out of hand. At minimum, centralizing the warehouse and analytics tools is key. To be great, you’d also probably centralize a few key redundant features like email automation and ad buying. It’s also important to make sure you have an enterprise-wide diagram (even if it feels overwhelming to make) because it will help you find redundancies and opportunities to optimize.

At the scale stage, all six competencies need to remain strong, and you’ll need to continue to build proficiency in each as systems become more complex.

The scale stage will push you to slow down to speed up. Lean into intentionality about your work to make dealing with complex tooling and competing needs from stakeholders easier.

Here’s what not to do on during this stage:

• Make decisions quickly and without doing a proper listening/education tour within the org
• Deprioritized the importance of good team leadership

👎 Bad example at this stage: Maintaining separate tech stacks for your self-serve and enterprise sales motions, with little integration between the two. You might have multiple tools performing similar functions across different departments, leading to bad redundancy. Your data warehouse, CRM, and marketing automation platform are tightly coupled through complex custom integrations, making it difficult to replace or upgrade individual components. Your lack of a unified data governance strategy causes inconsistent data definitions across the organization, hampering decision-making and reporting accuracy.

Wrapping up

Building and maintaining an effective RevTech stack is an ongoing process that requires attention to both immediate needs and long-term flexibility. By understanding the historical context of GTM stack evolution and applying the FRIC framework, companies can create more adaptable, efficient, and enduring tech stacks.

Key takeaways:

1. RevTech stacks have evolved from simple sales tech to complex, decentralized systems.
2. Flexibility is crucial for handling changing business needs and technological landscapes.
3. The FRIC framework (Focus, Redundancy, Interoperability, Coupling) provides a structure for evaluating and improving stack flexibility.
4. Stack management strategies should adapt based on the company’s maturity stage.

As we move forward, the challenge lies in creating RevTech stacks that can bend to the changing needs of businesses and GTM teams while providing a solid foundation for scale. This may involve rethinking our approach to CRMs and other core technologies to better manage complexity and build stronger customer relationships.

In the upcoming modules, we'll dive deeper into specific strategies and tactics for implementing these principles in your RevTech stack. We'll explore best practices for data management, tool selection, and integration, as well as strategies for fostering collaboration between marketing, sales, and customer success teams.

Remember, there's no one-size-fits-all solution in RevTech. The key is to remain adaptable, continually assess your needs, and strive for a balance between immediate functionality and long-term flexibility. By doing so, you'll be well-positioned to navigate the complexities of this evolving field and drive success for your organization.