Case studies: RevTech stacks at different growth stages

There is no one-size-fits-all “perfect” RevTech stack. Every company’s stack reflects a unique set of tradeoffs shaped by product maturity, target market, team resources, and strategic priorities. At times, you may assemble a complex ecosystem of tools; other times, a leaner, simpler approach works best. The key is understanding your goals and constraints, then choosing a combination of technologies that propel you forward without overwhelming your capacity to manage them.

In this lesson, we’ll examine eleven case studies—a few for each lifecycle stage—to highlight the differences in stack design. We’ll show you how to read these stack diagrams, what to look for in terms of data flow, and where certain integrations or design choices shine. Along the way, we’ll point out what we love and what we might improve with a magic wand.

How to Read These Stack Diagrams

Each diagram is organized into three phases: Generate, Close, and Report. Within each phase, you’ll see tools grouped by their function—Inbound, Outbound, CRM, Analytics, etc. Arrows indicate how data flows between tools, showing where and how information is enriched, transformed, or moved to the next stage.

As you read, consider:

1. Inbound vs. Outbound Tools: Which tools capture and enrich leads? Which tools push messages or campaigns out?
2. Close Stage Tools: How are leads moved into a CRM or sales platform? How do scheduling, call analysis, or contract tools fit into the sales workflow?
3. Reporting & Analytics: Where does all this data ultimately reside—often in a warehouse—before being analyzed by BI or analytics tools?
4. Center(s) of the Stack: Is the CRM the central hub? Is there a CDP front and center orchestrating audiences? Is data flowing into a Warehouse as the single source of truth, or are all three (CDP, CRM, Warehouse) equally central, each playing a distinct role?

Don’t Ignore the Arrows: Arrows show how data moves. Are they one-directional or two-directional integrations? Are direct integrations (e.g., native connectors) used, or are third-party tools like ETL/Reverse-ETL solutions involved? Notice where data might be redundant or how multiple arrows might complicate the picture. How you route data can significantly influence stack complexity and maintainability.

Pre-PMF Companies

Before product-market fit, the stack should be as lean and flexible as possible. At this stage, companies often benefit from minimal integrations, off-the-shelf tools, and a willingness to experiment, iterate, and discard what doesn’t work. Simplicity is a virtue—every unnecessary arrow or tool can slow you down and create unnecessary complexity.

Example 1: Clarify (Pre-PMF)

Context: Clarify is at a stage where the focus is on quick wins, getting leads in the door, and validating messaging. They prioritize a minimal number of connections and rely on lightweight, modular tools that can be easily replaced as they learn.

Key Tools & Choices:

• Inbound: LinkedIn Ads, SEO/Podcasts, custom webforms feed into a PostHog CDP. Apollo.io, OpenAI-driven enrichment and lead qualification. Slack (via Zapier) provides immediate alerts when a new inbound lead arrives.
• Outbound: Lemlist for email sequences, plus Luma (events) and Kondo (LinkedIn inbox networking) operate without direct integration into the stack, relying on humans to bring leads into the CRM through Calendaring links rather than complex data flows.
• Close: Clarify CRM + Cal.com for scheduling. Thena for CSM/ticketing. Simple and direct.
• Report: AWS Warehouse + Hex analytics for lightweight reporting. No fancy transformations—just a basic ETL/Reverse-ETL setup.

Federation Note: The Clarify stack keeps connections to a minimum. For example, leads sourced from Luma events or Kondo’s networking activities aren’t integrated directly via APIs. Instead, they rely on a user driven step (e.g., someone uses a Cal.com link shared at a Luma event). This reduces tool proliferation and integration points at a stage when clarity and speed matter most. It also means that if tomorrow Clarify wanted to switch out one of these tools, it would require no engineering effort.

Slack Pouncing is Key For Early Stage Start-Ups:"Slack pouncing" means that the moment a lead comes in—say, they fill out a form—Zapier immediately sends a notification to a dedicated Slack channel. A team member can “pounce” on this lead right away, sending a personalized email or scheduling a call within minutes. This hyper-responsive workflow is critical when you’re short on SDRs but still want to engage quickly.

What We Love:

1. Minimal Connections: Early on, the stack is simple, with few integrations. This reduces overhead and confusion.
2. AI-Powered Enrichment: Using OpenAI and Apollo.io to qualify leads and refine ICP is a clever shortcut.
3. Slack Pouncing for Speed: Immediate Slack alerts enable personal, rapid responses that convey attentiveness and agility.
4. Lightweight & Modular: Each tool can be replaced or upgraded without uprooting the entire system.

What We’d Change if We Had a Magic Wand:

This is a tough question because, with unlimited time, money, and resources, there’s so much we’d change. We’d instrument more PostHog events across our front-end and internal applications, optimize our use of Customer.io, implement DBT and HighTouch, invest in a TAM database for marketing, and build a lead assurance database managed by engineering, to name a few.

But for now, the system works for what we need it to do, and that’s the point at this stage. It’s inherently imperfect, but it’s serving its purpose.

Key Takeaway for Pre-PMF:Clarify’s approach—limited connections, focused tools, and minimal overhead—is ideal pre-PMF. Many early teams over-integrate and over-automate, creating complexity before they even know what works. Take a cue from Clarify: less can be more.

Example 2: Volca (Pre-PMF)

Context:

Volca is a four-person startup providing referral marketing tools for home services businesses (think HVAC, plumbing, electrical). They’re in pilot mode with early customers and emphasize in-person relationship-building (trade shows, conferences) over heavy automation. The stack is minimal and scrappy—almost everything is handled manually or via CSV imports into their CRM. Their main priority is learning how customers (and referrers) actually use the product so they can iterate quickly.

Key Tools & Choices

Inbound

• LinkedIn Content + La Growth Machine: Surprisingly effective. Volca posts regularly on LinkedIn, and because their target buyers (home services businesses) rarely see such content, these posts stand out and generate inbound buzz.

Outbound

• Trade Shows & Conferences: This is Volca’s biggest driver of leads. They gather contacts by walking the floor, building personal relationships, and capturing leads in person.
• Apollo.io: Used primarily as a dialer and for highly personalized email sequences.
• Smartlead (Potential): On the shortlist for mass outbound email, but not actively in use.
• Clay: A central hub for data enrichment and cleaning. Volca uploads conference lists or lead dumps into Clay, enriches records, and then exports them into the CRM.

Close

• Clarify CRM: Manages deal flow and day-to-day operations, including email and calendar integrations.
• Grain (Call Recordings) & Common Paper (Contracts): Capabilities added to support the efforts with Clarify.

Report

• AWS: Volca’s engineers have built a scrappy internal dashboard on AWS and Postgres. It captures usage logs (e.g., text messages from homeowners), but there’s no formal BI or amplitude-like tool in place yet.
• LogRocket: They plan to add this capability shortly in an effort to better visualize customer usage.

Federation Note

Like many very early-stage startups, Volca’s tool connections are mostly manual. Trade show leads get pulled into Clay for enrichment, then exported into the CRM (Clarify) via CSV. There’s no direct pipeline or “auto-sync” to speak of—no official CDP or events-tracking solution. As a result, each system stands alone with minimal integration overhead. This keeps things lightweight but does create extra manual steps. With only 4 people, this works for them and is very common, but as they find PMF and scale, they will need to put other systems in place.

“Trade Show Hustle” Is Key for Volca at This Stage

Because the home services industry relies heavily on in-person relationships, Volca focuses on building trust face to face. By attending trade shows in “street clothes,” striking up casual conversations, and socializing at bars and golf outings, they stand out from the crowd. It’s a refreshingly human approach in a world of SaaS automation—and it’s yielded far more leads than any purely digital channel at this stage.

What We Love

• In-Person Relationship Focus: By focusing on real human connections, Volca builds credibility quickly—crucial for a brand-new product in a sometimes tech-averse market.
• Minimal Overhead: At four employees, Volca keeps the toolset lean. No unnecessary integrations or complicated data flows.
• Clay for Enrichment: A single source for data cleaning and enrichment saves time when working with large lead lists from conferences. They can grow with this tool.

What We’d Change if We Had a Magic Wand

• Better Analytics & Session Insights: They’d love to parse text message logs and user flows automatically, but that requires hooking a tool like LogRocket or a more robust analytics platform into their AWS/Postgres environment.
• Less Manual CSV Work: Integrating Clay directly with Clarify CRM (or using Zapier) could remove the need to export/import leads every time.
• Consistent Data Pipeline: If they expand digital inbound (beyond trade shows), a clearer flow from website/intercom/chat to CRM would be crucial.

Key Takeaway for Pre-PMF

Volca’s approach epitomizes early-stage priorities: do the manual things that don’t scale yet, refine your messaging and product through hands-on relationships, and keep the tech stack minimal so you can pivot without sinking time into complex integrations. For a four-person team, focusing on trade shows, direct relationships, and easy enrichment with a single source (Clay) is perfectly aligned with pre-PMF realities. Once they validate the product fully, they can layer in automation and analytics tools—but for now, their lean, relationship-first strategy is exactly what they need.

Example 3: Ravenna (Pre-PMF)

Context

Ravenna is an early-stage startup (six months old, 13-person team) building a modern internal help desk platform—think AI-powered ticketing for employee requests. Their core product is deeply integrated with Slack, targeting mid-market to enterprise (500-1000+ employees). They’ve raised $15M from Khosla Ventures and Madrona, and currently work with multiple design partners. Monetization and heavy outbound motions are still on the horizon; right now, it’s all about refining the product with those design partners and laying basic GTM foundations.

Key Tools & Choices

Inbound:

• SEO + Content: Ahrefs to identify relevant keywords and competitor content. Screen Studio for creating short-form video content for YouTube.
• Website Tracking: RB2B provides real-time website visitor identification, triggering Slack alerts for potential leads (though not yet fully automated into a nurture sequence).
• Demo Scheduling: Cal.com is used to book meetings, which then appear in Clarify (their CRM) thanks to email-sync (no direct integration).

“RB2B is literally just pumping website visitor info into Slack—no fancy automations yet. We know we’ll need a more systematic nurture flow down the road, but for now it’s all about personal, founder-led conversations.”

—Kevin Coleman, Co-founder of Ravenna

Outbound:

• LinkedIn Focus: Dripify for automated LinkedIn outreach and follow-ups. LinkedIn Sales Navigator for manual prospecting, especially targeting IT managers.
• Custom Lists: A third-party consultancy friend provides curated “LinkedIn lists,” which feed into Dripify campaigns.
• Email: Very minimal use—no high-volume cold email sequences yet.

Nurture & Waitlist:

• Loops: A simple email distro/waitlist tool. If someone wants to stay updated, they sign up via Loops.

Close:

• Clarify CRM: Ravenna has opted for a lean CRM approach. No advanced integrations—everything is email-driven. Demo requests flow into Clarify simply because they hit the team’s inbox.

Reporting:

• Native Reporting Only: No formal warehouse or BI stack in place. Ravenna relies on Clarify’s native reporting (plus manual Slack notifs from RB2B). At this pre-revenue stage, advanced analytics and data governance can wait.

What’s Unique or Noteworthy

1. Slack Alerts from RB2B: Instead of feeding leads automatically into the CRM, Ravenna has a Slack “pounce” approach. Once a visitor is identified, the founders manually decide whether to engage.
2. Founder-Led Outbound: Primary outbound is direct LinkedIn outreach from the co-founders to land design partners, keeping the approach personal and high-touch.
3. Ultra-Lean Integrations: Everything funnels into Clarify via email. There’s zero friction if they decide to swap out a tool later—perfect for a pre-PMF team still pivoting on messaging.
4. Planned but Paused Ads: They briefly tested Google, Reddit, and LinkedIn Ads to gauge traffic volume and cost-per-lead. They paused spend to revamp their website and refine messaging before scaling up again.

What We Love

• Minimalist Stack: At six months old, Ravenna’s “less is more” approach avoids the complexity trap.
• SEO from Day One: Betting on content and domain authority early is a strategic move that can pay off long-term.
• Slack Pouncing: Real-time visitor alerts keep them nimble and hands-on with lead qualification.
• Founder-to-Design Partner Hustle: No tool can replicate direct co-founder outreach. This helps them keep an ear to the ground while shaping the product.

What We’d Change if We Had a Magic Wand

• Qualification Layer: With more inbound on the way, they’ll need an automated way to score/qualify leads (e.g., Clearbit or building an internal scoring flow).
• Event Strategy: With big mid-market targets, curated dinners or small gatherings might open new doors and differentiate from the LinkedIn noise.
• Future Nurture Stack: Once the pipeline grows, a more integrated email nurture or marketing automation tool could help keep potential customers engaged post-demo.

Key Takeaway for Pre-PMF

Ravenna’s lean, loosely-coupled approach is exactly what you want when your product and messaging are still evolving. Their founders can pivot quickly, test new channels without breaking a tangle of integrations, and focus on what matters most: building the right product for the right customers. Only once they confirm product-market fit will they invest heavily in more sophisticated RevTech.

Post-PMF Companies

After achieving product-market fit, it’s time to invest in more sophisticated tooling. Companies at this stage often refine lead scoring, introduce a more robust CRM setup, leverage attribution tools, and carefully consider a CDP or data warehouse strategy. They might experiment heavily, adding numerous tools, then later prune what isn’t working. Hopefully, companies aren’t in this phase too long before moving to the growth stage.

Example 4: Thena (Post-PMF)

Context: Thena, a seed-funded customer conversation platform, initially overbuilt their stack, embracing an enterprise-level approach before they had the bandwidth to manage it. After experiencing complexity overload, they trimmed down and learned valuable lessons about balancing experimentation with sustainable architecture.

Key Aspects:

• Generate: Ads, Branch Attribution, Intercom chatbot for inbound; Keyplay, Clay, LinkedIn, Clearbit for outbound.
• Close: HubSpot CRM, UpdateAI (call insights), Accord (deal management), Cacheflow (quote-to-cash), ensuring a smooth end-to-end deal process.
• Report: Mixpanel, Amplitude for product analytics, Dreamdata for multi-touch attribution, and a warehouse for centralizing data.

What We Love:

1. Sophisticated Attribution (Dreamdata): Deep insights into which touches matter most.
2. Integrated Deal Tools: Accord and Cacheflow streamline deal closure.
3. Product Analytics Integration: Using Mixpanel and Amplitude helps tie product usage insights back into marketing and sales.

Pro Tip: It’s okay to experiment with many tools to find what grows your business. Just remember that after trying them out, you need to assess what’s actually useful and cut the rest before scaling.

What We’d Change if We Had a Magic Wand:

1. Unified Product Analytics: Consider consolidating product analytics into one platform to reduce complexity.
2. Stricter Data Governance: A consistent naming schema and data dictionary would prevent future chaos.
3. Better Purchase Management: They just had too many tools for their size. They were a series A $5-10M ARR generation business. They have a sales team, but equipped themselves for a much bigger size/stage.

Full Quote from Thena: "One of the biggest issues we faced when building our stack was overbuilding it. We took an enterprise SaaS approach to building out most of our RevTech stack, even though we didn't have enough people to manage the tools. At first, we felt great because we effectively removed people from the process by adding tools that automated that work for us. But eventually, we had too many tools and it became too hard to manage. In spring of 2024, we started to cut back on how many tools we used."— Brendan Kazanjian, Growth at Thena

Growth Companies

During the Growth stage, companies refine integrations, reduce complexity, and invest in better data governance. They now blend martech (marketing technology) and revtech tools more seamlessly. A hallmark of this stage is the co-existence of a CRM-driven workflow and a CDP-centered marketing engine that feed into each other gracefully.

Context: Ramp’s fintech platform targets both individual users and enterprise accounts, requiring a CDP-like approach for acquisition and a CRM-driven approach for B2B sales. Their stack integrates martech and revtech, showcasing how two distinct ecosystems can harmonize.

Key Aspects:

• Generate: Inbound data enrichment via Clearbit, forms, chatbots; Outbound via LinkedIn, webinars, and email marketing (HubSpot) plus Outreach sequences.
• Close: Salesforce CRM for managing enterprise deals, Chili Piper for scheduling, Gong for call analysis, and a CDP-style toolset feeding into and out of Salesforce.
• Report: A warehouse as the single source of truth, Amplitude for product insights, and Hightouch for consolidating segments and synchronizing audiences back into tools.

What We Love:

1. CDP + CRM in Cohesion: Ramp achieves a powerful synergy where a marketing-oriented CDP stack and a sales-oriented CRM stack feed each other. The CDP refines and segments, the CRM drives deals, and data moves fluidly between them.
2. Audience Consolidation with Hightouch: Instead of segments being scattered across multiple tools, Hightouch acts as a central orchestration point, reducing fragmentation.
3. Regular Stack Audits: Ramp doesn’t let tools pile up. They continuously prune and streamline, ensuring the stack doesn’t become unwieldy.
4. Martech Meets Revtech: The integration of B2C-like inbound funnels with a B2B CRM-driven sales engine is a feat not every growth company can pull off elegantly.

What We’d Change if We Had a Magic Wand:

1. Streamline Opportunity Creation Logic: Consolidate workflows currently split between HubSpot and Salesforce (SFDC) into a single, unified process. Ensure marketing campaign data flows seamlessly through HubSpot into SFDC, including attribution data layered on profiles from Segment. Remove any functionality making HubSpot a redundant CRM and isolate it as a pure marketing email tool.
2. Develop an Internal Scoring System: Replace the third-party tool (Madkudu) with an in-house scoring system.
3. Implement TOFU Alert Monitoring: Set up monitoring for top-of-funnel issues to quickly identify and resolve stack-related problems.
4. Rationalize Tool Connections: Disconnect non-essential connections between HubSpot, Segment, and SFDC. Isolate connections and establish a clear strategy for when, how, and why tools are connected. Move data to the warehouse wherever possible and improve documentation to ensure IT and internal users understand data locations and how to request new data or capabilities.
5. Centralize Clearbit Enrichment: Reduce redundancy by consolidating Clearbit enrichment into a single, internally managed service.

Create Standard Operating Procedures (SOPs): Document how marketing, sales, and other teams approach repeatable tasks, such as list or audience creation. Isolate processes to a single tool to eliminate redundancy and streamline support. Build clear documentation and internal tooling to simplify backfilling data in the systems.

Example 6: Anon-Fin-Tech Company (Growth)

Context

This fast-growing fintech provides APIs and services to businesses of all sizes, driving a mix of self-serve and enterprise deals. Despite thousands of customers and robust revenue streams, their RevTech stack feels patchworked. They have no formal CDP—product-led signups and usage data live in AWS, while marketing/sales data live in Salesforce and Marketo. Early growth from WOM and self-serve, meant they didn't need to invest early in a CDP. Now, as they begin to scale, they are facing issues from delayed implementation. The SDR team is large, split between inbound and outbound pods; a complex lead-scoring approach determines which prospects reach human outreach versus automated nurtures.

Key Tools & Choices

Inbound + Central Generate Tools

• Marketo (Marketing Automation): Leads from web forms, event lists, and content downloads flow here first. Marketo remains the “lead hub,” pushing qualified records into Salesforce.
• Website (Contentful CMS) + Qualified (Chatbot): Qualified (like Drift) captures inbound inquiries, logs them in Marketo, and appends AI-powered chat transcripts.
• Annual Data Provider Bake-Off: They regularly A/B test multiple enrichment vendors (e.g., ZoomInfo, Clearbit, 6Sense and MadKudu) to compare coverage (database size), accuracy, and speed/uptime. Whichever delivers the most robust firmographic data with the best overall score on the three criteria won the contract for the next cycle.
• Lead Scoring Decision Engine: Key to inbound triage. Custom algorithm weighs data provider inputs including user-fit variables and MQL thresholds. High-scoring leads are routed to the “inbound SDR” pool; lower scores trigger an automated nurture.
• Inbound Routing (Custom Salesforce Logic): No standalone “routing tool.” Instead, they built custom code in Salesforce that uses MadKudu’s scores + data enrichment to decide which SDR pod owns the lead. They rely on a round-robin queue to avoid cherry-picking.

Outbound

• Salesloft: Central for outbound sequences (email, call cadences). Some reps go “rogue” in Gmail, but management prefers all outreach flows through Salesloft, which syncs with Salesforce.
• LinkedIn Sales Manager: Critical in fintech verticals. SDRs do account research and cold outreach, then push relevant prospects into Salesloft or Salesforce.
• Reggie.ai (Piloted): They tested AI for automated cold email writing. It pulled basic LinkedIn intel, but reps found purely generative messaging less effective than thoughtful human-curated outreach. They are continuing the pilot, but may remove it.

Close

• Salesforce (CRM): The system of record—Marketo leads become Salesforce records via a bi-directional sync. ZoomInfo data enrichment plugs in at the account level.
• Scratchpad: A quick notepad for SDRs. They jot call highlights, and Scratchpad syncs those notes back to Salesforce so reps aren’t manually updating fields.
• Highspot: Houses pitch decks, playbooks, and other assets. But it’s not deeply integrated—reps manually gather content rather than getting AI-suggested “next slides.”
• Usage-Based Billing: Because the product is usage-driven, they track daily consumption in AWS Redshift. When usage spikes, the account manager renegotiates minimum commits. Conversely, if usage drops, they proactively investigate churn risk. This was also used for upsell. If a user spent over a certain threshold, it triggered an action to move them from a self-serve client to an annual commitment or even multi-year commitment with discounting.
• Cross-Sell Playbooks (All In-House): They’ve built internal “if you have products A and B, you’re likely to need C” models. No Gainsight or specialized tool for expansions—just custom analytics and Salesforce reporting to flag likely next products.

Report

• AWS + Redshift (Homegrown Warehouse): Usage data, partial lead data, and billing details converge here. It’s their “makeshift CDP,” but lacks a non-engineer-friendly interface.
• Mode & Sisense: Mode pulls marketing and CRM data from Redshift for funnel/deal metrics. Sisense (formerly Periscope) is product usage analytics. There’s no single, unified BI tool that seamlessly merges usage analytics with marketing and sales yet.
• Google Analytics: Gauges website traffic and campaign ROI, feeding SEO insights back to Marketo or the data warehouse.

Federation Note

Because product data and marketing data are fully separate, bridging them for lead routing, expansions, and churn mitigation is manual. Product usage must be ported into Salesforce for an AM to see it. Marketing must get CRM data to retarget existing accounts or run cross-sell campaigns. While they’ve built custom pipelines, there’s no turnkey “CDP” bridging all systems. Each team effectively “owns” a slice of data, relying on self-serve dashboards and roundabout Syncs.

What We Love

• Multi-Vendor “Waterfall” for Lead Enrichment: Testing ZoomInfo, Clearbit, MadKudu, 6Sense, etc. each year ensures they maintain coverage accuracy and competitive pricing.
• Sophisticated Scoring & Routing: Inbound leads get triaged by MadKudu + Marketo, then auto-assigned in Salesforce. Humans focus only on the highest-fit or highest-intent leads.
• Usage-Based Upsells: Real-time usage monitoring triggers expansions. This “usage spike → renegotiate” loop systematically drives more revenue.
• Tailored Outbound: LinkedIn for targeted outreach, plus early AI experiments (Reggie.ai) show they’re not afraid to test new personalization approaches—even if they revert to manual methods.

What We’d Change if We Had a Magic Wand

• A True CDP for Unified Data: Even partial unification (product + marketing + CRM) would unlock more personalization, as AMs and SDRs could see usage or CRM signals without custom code.
• Sales Enablement Upgrades: Highspot/Gong remain underutilized. Auto-surfacing slides, competitor callouts, or next-step prompts in Salesforce would streamline rep workflow.
• Dynamic Inbound Thresholds: They tweak MadKudu’s score threshold manually. A more real-time system (e.g., raising the score after a second download or event registration) could route leads at the perfect moment.

Example 7: Hightouch

Hightouch builds a “Reverse ETL” and “Composable CDP” platform, but they also use their own product extensively for internal RevOps. From day one, they embraced a warehouse-first strategy, centralizing all relevant customer, product, and financial data in Snowflake, and pushing curated segments and attributes outward to downstream tools. This approach has kept their ops team lean (just two people until recently) while maintaining a high degree of data integrity and customization in marketing and sales automations.

“We can craft business logic in SQL without bloating Salesforce. It gives us total control over cross-object data and advanced workflows—without hooking up dozens of fields on every contact.” —Nikko Georgantonis , Revenue and Growth Operations at Hightouch

The Warehouse-First Mindset

At Hightouch, Snowflake is the single source of truth. Opportunity data (Salesforce), subscription/payment records (Stripe), and product-usage events all flow in. SQL models then define audiences and logic (e.g. re-engagement campaigns, lead routing rules). Finally, Hightouch reverse-ETL pushes those outputs back into Salesforce, HubSpot, Outreach, etc. The warehouse approach avoids duplicating business logic across many apps, minimizes “bloat” in Salesforce, and allows for easier data governance—so long as you have the technical chops to manage it.

Key Tools & Choices

Inbound:

• HubSpot: Main system for new marketing leads (events, form-fills).
• MadKudu: Lead/account scoring, supplemented by custom warehouse models.
• ZoomInfo + Clay: Additional enrichment for contact data, especially at large target accounts.
• Chili Piper: Calendar and routing for inbound demo requests.

Outbound & Prospecting:

• Outreach: Sequences, multi-touch cadences.
• LinkedIn Sales Navigator: Targeting and prospecting top data-savvy personas.
• Crossbeam: Partner overlap intelligence (co-sell motions).

Close (CRM & Collaboration):

• Salesforce: The CRM of record, but with minimal custom fields thanks to the warehouse logic.
• Gong: Call recording and conversation intelligence.
• Hightouch: Powers automated workflows (e.g. re-engagement campaigns for closed-lost deals, territory assignments) based on SQL logic in Snowflake.

Reporting & Warehouse:

• Snowflake: Comprehensive data repository spanning product usage, self-serve subscriptions, Salesforce opportunities, and marketing events.
• Hightouch: Reverse-ETL for multiple use cases—pushing segmented data and custom attributes to HubSpot, Salesforce, or ad platforms.
• (No dedicated forecasting tool): Hightouch built in-house forecasting logic, leaning on warehouse data.

What’s Unique or Noteworthy

1. Data Orchestration at the Center: Hightouch uses Hightouch—warehouse transformations define every nuance of contact/account logic, keeping the rest of the stack simpler.
2. Lean Ops Team: They’ve run with just two ops folks for years. Combining technical SQL skills with RevOps domain knowledge drastically reduced overhead.
3. Minimal “CRM Bloat”: By pushing only essential fields to Salesforce, they avoid clutter from cross-object roll-ups or archaic fields—everything else lives in Snowflake.
4. Warehouse + Reverse-ETL Over Segment: Rather than employing a standard “CDP,” they build custom SQL logic and let Hightouch distribute it to marketing/sales channels.

What We Love

• Flexible, Code-Driven Logic: SQL-based workflows are far more robust than typical point-and-click fields or roll-ups in Salesforce.
• Steady Stack Hygiene: Hightouch’s ops team runs monthly audits to deprecate old fields or rules. This prevents drift that often plagues fast-scaling startups.
• Seamless “Self-Serve” Meets Enterprise: Stripe data for B2B self-serve merges with Salesforce data for enterprise deals—no complicated multi-object roll-ups needed.

What They’d Evolve Next

• Engineering Bandwidth: A warehouse-first approach needs technical ops or data engineers—this might not suit every org, especially if resources are thin.
• Broader Tool Consolidation: They juggle multiple enrichment providers (ZoomInfo, Clay, etc.). Over time, they may streamline for cost or data consistency.
• Data Flow Visualization: With logic living in SQL, cross-tool flows become invisible to non-technical stakeholders. Clear diagrams or audits ensure everyone stays aligned.

Hightouch’s stack shows how a small but technically strong RevOps group can manage a complex funnel by centralizing logic in the warehouse and letting reverse-ETL push curated insights outward. This drastically reduces the usual pain points of “CRM overload” or messy integration webs. However, it requires the right cultural and technical fit: if you can harness SQL for GTM ops and dedicate resources to continuous stack hygiene, you can enjoy enormous flexibility at scale.

Example 8: dbt

dbt is a fast-growing analytics startup best known for its open-source data transformation framework. Hundreds of data-driven organizations adopt dbt to build, test, and document data pipelines in their modern data stack. Internally, dbt “drinks its own champagne,” leveraging dbt + Snowflake to orchestrate much of their go-to-market data. They’re well beyond initial product-market fit, with a robust sales and success motion but still rapidly refining a multi-layer RevTech stack.

“We’ve built Salesforce so we can report on nearly anything. It’s rare someone comes with a metric we can’t capture.” —James, RevOps & Business Systems Lead at dbt

The Composable CDP

Rather than using a traditional, off-the-shelf CDP to centralize customer data, dbt relies on Snowflake plus dbt transformations to unify and cleanse inbound signals. The result is a “composable” solution, where data from tools like HubSpot, ZoomInfo, Zendesk, etc. land in Snowflake, get enriched and modeled via dbt, then pushed back to Salesforce or other apps as needed. This approach gives dbt full control over data logic—no vendor lock-in or rigid schema constraints—and leverages the in-house data engineering talent they already have.

Key Tools & Choices

Inbound

• HubSpot for marketing automation (email, form captures).
• ZoomInfo & Lucia for account/contact enrichment.
• Distribution Engine (routing tool) to assign leads and manage SDR workflows.
• Common Room to harness community-driven inbound signals.
• Optimizely for website experimentation and conversion optimization.

Outbound

• Clay for list-building and partial enrichment (e.g. event attendee lists).
• Sixth Sense + MadKudu for intent and account scoring—though dbt plans to migrate more logic in-house via dbt.
• LinkedIn Sales Navigator for prospecting (CISOs, analytics leaders, etc.).
• Outreach for multi-touch cadences, especially used by SDRs.

Close (CRM + CPQ + Contracting)

• Salesforce CRM: Heavily customized—dbt avoids the native “Lead” object, using a custom “Touchpoint” object and standard Contacts/Accounts.
• Salesforce CPQ: Quoting/approvals for complex deals.
• Clari: Forecast overlay used by AEs and managers.
• Vivun for solution architects (technical POCs, pre-sales details).
• Dropbox Sign (migrating to DocuSign): eSignature integrated with CPQ.
• Zendesk for ticketing and standard customer support.
• Harvest / Run / RocketLane for professional services resource allocation.
• Delighted for NPS and CSAT (not fully tied back to Salesforce yet).
• Credly + LMS tools for certification programs (some data flows back to Snowflake).

Reporting & Data Warehouse

• Snowflake as the single source of truth—ingesting data from marketing, sales, support, training, etc.
• dbt for transformations—this is the core of their “composable” data approach.
• Looker & Hex (migrating to Tableau) for dashboards and analytics.

What’s Unique or Noteworthy

1. Deep “Composability”: dbt + Snowflake unify inbound signals, effectively doing the job of a CDP without an all-in-one vendor solution.
2. Custom Salesforce Flow: Instead of standard Leads, dbt uses a “Touchpoint” object plus heavy Flow-based logic to manage data consistency.
3. Pro Services & Training Stack: Tools like RocketLane, Credly, and an LMS reflect advanced onboarding/consulting needs for enterprise data teams.
4. Multiple Enrichment & Intent Tools: Clay, ZoomInfo, Lucia, Sixth Sense, MadKudu—there’s some overlap, and they plan to streamline.

What We Love

• Warehouse-First Data Mastery: By owning transformations (dbt), they’re not stuck with any one vendor’s logic or schema for lead/account scoring.
• Highly Customized CRM: Their robust Salesforce instance tracks pipeline changes, snapshots, and custom “Touchpoints,” enabling near-1:1 reporting.
• Tech-Heavy Pre-Sales: With solutions architects deeply integrated (Vivun), dbt acknowledges how critical technical evaluation is for developer-oriented customers.

What They’d Evolve Next

• Tool Consolidation: Multiple data-enrichment and intent products lead to redundant spend; they see potential in bringing more scoring logic in-house.
• Deeper Feedback Loops: Tools like Delighted, Common Room, and certification data could loop back into Salesforce to power renewal/upsell triggers.
• Refined Automation: Clay + Outreach AI-driven outreach is promising, but still in pilot. They aim to scale similar “smart” motions for top-of-funnel.

dbt shows how to build a “composable CDP” using Snowflake + dbt transformations—no single monolithic platform needed. For teams with strong data engineering resources, this approach can provide more agility, deeper customization, and less vendor lock-in. That said, it requires discipline and alignment across RevOps, data engineering, and marketing to keep it all consistent and well-modeled. If you can pull it off, you reap the benefits of real-time data synergy without the rigidity of an off-the-shelf CDP.

Scale Companies

At scale, complexity often increases dramatically. Companies integrate multiple CRMs, advanced analytics, and comprehensive data governance frameworks. They may run parallel B2C and B2B funnels, each with its own set of specialized tools, all feeding into unified data platforms. But, most of all, they likely have an array of custom or home grown tools instead of or to supplement the traditional SaaS tools we’ve been talking about.

Example 9: Anonymous Public Company (Scale)

Context:

We spoke with multiple team members at a large, multinational corporation that spans both B2B and B2C. Within this organization are numerous business units (BUs)—some massive, unicorn-level ventures with their own deeply developed tech stacks, and others smaller, more experimental “start-ups” operating under the corporate umbrella. Each BU has a General Manager (GM) who owns a full P&L and can independently choose which RevTech tools to deploy. Until now, no one had attempted to visualize this entire ecosystem under one diagram, and it was often considered “impossible” due to its complexity. Below is a sanitized glimpse into how it all fits together.

Key Aspects:

Major vs. Minor BUs

• Major BUs tend to have extensive stacks, comparable to those of high-growth tech companies or even more advanced.
• Minor (or ‘Pod’) BUs often share a lightweight, pod-based stack that aligns with smaller, niche goals—or in some cases, they spin up an entirely unique stack if their business model differs substantially from the core.

Syncing (or Lack Thereof)

From a tooling perspective, BUs function almost like independent entities. Ad buying, email campaigns, and outbound motions are rarely coordinated across BUs—each runs its own approach. They do, however, share broader messaging, brand guidelines, and certain top-level compliance requirements. The key tradeoff:

• Flexibility & Speed for each BU to execute quickly in its market without red tape.
• Lost Synergies where centralized data or integrated cross-BU automation might have led to more cohesive campaigns and lower costs.

Centralized Reporting

Despite the autonomy at the Generate (marketing) and Close (CRM) layers, all data eventually flows into a unified data warehouse. This warehouse setup (along with standard analytics tools) is the one mandatory piece across the entire organization. Initially adopted for IPO-readiness and financial compliance, it has since proven invaluable for enabling cross-BU insights. In particular, BUs that want to retarget or define ICPs based on activity outside their own bubble can tap into data from other BUs—provided they have the technical resources to ingest it back into their local systems.

What We Love:

• The Right GTM Motion for Each Business: By allowing each BU to choose its stack, the company ensures each market segment gets the specialized tools and processes it needs. High-growth teams run fast with niche marketing tech, while smaller BUs can stay lean and experiment without corporate overhead.
• A Central Command Center (The Warehouse): Although top-of-funnel and CRM choices vary widely, the warehouse is a single source of truth for performance metrics, revenue reporting, and financial compliance. This shared “command center” accelerates cross-BU data sharing and provides a consistent framework for leadership to make enterprise-wide decisions.

What We’d Change if We Had a Magic Wand:

• Streamlined Top-of-Funnel Functions: Centralizing certain high-level marketing capabilities—like ad-buying platforms or large-scale email nurtures—could reduce redundant contracts and conflicting messaging. Yes, each BU would lose some autonomy, but the efficiency gain (and brand consistency) might be worth it.
• Preferred Vendors: At present, employees moving between BUs often find themselves learning entirely new tool stacks—a significant ramp-up cost. Having a short-list of company-approved vendors or standard “playbooks” could ease transitions and foster internal skill-sharing.
• Deeper Cross-BU Collaboration: While the warehouse creates basic visibility, day-to-day learnings in outbound or sales processes rarely transfer from one BU to another. A more intentional knowledge-sharing framework could unlock hidden synergies across the enterprise.

Example 10: PLG-Stack at a Major Enterprise Company

MegaChip is a global leader in GPUs and AI software. Their Developer Relations program takes a Product-Led Growth (PLG) angle: developers, startups, and academic teams apply via a community portal, receive trial access or discounts, and only then are funnelled to sales reps if data signals high potential. Despite being a massive enterprise, they run this slice of the business almost like a startup—lean, heavily automated, and data-obsessed from the first inbound form to final qualification.

“Our data pipeline classifies each application to decide: does this user just want self-serve dev tools, or do they need deeper sales engagement? We’re effectively running a PLG motion within an enterprise.” —Senior Director of RevOps at MegaChip

PLG at an Enterprise Scale

When applicants land on MegaChip’s developer portal, they fill out an intake form. Behind the scenes:

1. PitchBook enriches basic firmographics (founders, funding, staff size).
2. In-house AI services—nicknamed Nemo and Tagify—classify the user by industry/workload.
3. A final “score” is computed (sub-industry, website validity, hardware interest).
4. If the user is a major prospect or selects advanced product tiers, LeanData flags them for a sales rep. Otherwise, they remain self-serve with community support and tiered discounts.

This approach ensures that high-value leads get immediate AE attention, but smaller dev teams can run fast with frictionless self-serve GPU trials—mirroring typical PLG motions found in SaaS.

Key Tools & Choices

Inbound & Data Enrichment

• Portal + Mulesoft integration: The inbound form feeds Salesforce and triggers AI classification.
• PitchBook: Confirms org details (funding, founding date, founder profiles).
• Tagify on “Nemo” AI: Assigns each applicant an industry subcategory (e.g. healthcare, robotics).

Scoring & Routing

• Homegrown AI checks website validity, a set of “readiness” criteria, and usage signals from the catalog.
• LeanData in Salesforce: Looks for triggers (e.g. specific GPU product selection, enterprise signals) to route accounts to an AE, or keep them self-serve.

Close

• Salesforce CRM: The system of record for all developer program accounts and contacts.
• Internal Catalog: Approved applicants access a benefits catalog—like discounted GPU loans, software trials, or advanced dev toolkits.
• If usage or purchases spike, LeanData re-routes them to sales.

Report

• Tableau for dashboards across dev-rel metrics, self-serve usage, high-touch deals, etc.
• Python Web Scrapers for event data: A separate side project scrapes conference sites to identify new prospects or signals that existing customers may need more advanced GPU solutions.

What’s Special Here

1. True PLG in a Hardware Giant: Rather than forcing all deals into heavy sales motions, MegaChip’s developer portal encourages self-serve exploration—then only escalates leads once data indicates bigger potential.
2. Sophisticated Classification: In-house AI (“Nemo,” “Tagify”) and PitchBook drive immediate scoring, deciding which discount tier or product suite to offer.
3. Conference Scraping: Beyond inbound, a small team mines conference attendee lists for potential new HPC/AI startups—then pre-loads them in Salesforce for future outreach.

What We Love

• Scalable Hybrid Motion: Even at enterprise scale, they let smaller dev teams remain self-serve, preserving precious AE resources for higher-likelihood deals.
• Dynamic Discounting: Tying discount tiers to data signals (industry, funding, possible GPU usage) is an innovative blend of “PLG meets enterprise.”
• Lean, Automated Ops: With just a few business analysts in dev-rel, the entire pipeline is largely driven by internal AI and Mulesoft connectors.

Potential Pitfalls or Next Steps

• Siloed Insights: Complex CPQ and sales processes exist elsewhere at MegaChip—coordinating data across multiple BU stacks is an ongoing challenge.
• AI Oversight: Nemo-based classification is powerful, but continuous retraining is key to avoid funneling the wrong leads into self-serve.
• Demand Overload: Inbound volume is immense. If they ever do need heavier outbound, the portal-based approach might not scale well without more robust call/meeting tooling.

“MegaChip’s” DevRel program proves a large hardware organization can successfully operate a PLG motion—letting developer prospects sign up, get immediate value, and skip “hard” sales. Meanwhile, data-driven triggers escalate big fish to an AE. The lesson? With robust enrichment, AI classification, and flexible routing (e.g. LeanData), you can personalize the buyer’s journey inside a massive enterprise—even if the rest of the company runs old-school sales.

Example 11: A Single Business Unit Empire - Major Cybersecurity Enterprise

Key Tools & Choices

1. Inbound

• Marketo for marketing automation (email, PPC campaigns, inbound lead capture).
• ZoomInfo, HG Insights for lead enrichment, ensuring robust contact data flows directly into Salesforce.
• LeanData to route inbound leads or urgent requests to the correct teams.

Despite strong brand traction, SecuraOne still invests in capturing and qualifying inbound leads, especially for large enterprise prospects needing advanced demos.

2. Outbound

• Salesloft or Outreach for multi-step cadences (emails, calls), primarily for strategic account expansion.
• LinkedIn Sales Navigator for precise targeting of CISOs/IT leads.
• Salesforce Territory Assignment to manage rep coverage across complex geographies.

3. Close (CRM + CPQ + Contracting)

• Salesforce CRM at the core, tracking all opportunities and renewal timelines.
• Initially: Spreadsheet + Custom Integration for highly flexible, deal-by-deal pricing.
• Later: Salesforce CPQ for standardized quotes and compliance, especially as deals scaled in volume and complexity.
• Ironclad or Apttus (CLM) for contract lifecycle management and e-signature workflows.

4. Post-Sales & Retention

SecuraOne’s real focus is delivering “incredible experiences” to retain enterprise clients in a security-critical space.

• Gainsight for onboarding milestones, health scores, QBRs, and renewal cadences.
• Salesforce Service Cloud or Zendesk to handle support tickets.
• PagerDuty for urgent “under attack” escalations; solution engineers can jump on a potential breach in real time, even off-hours.

5. Reporting & Data Warehouse

• In-House Warehouse + BigQuery for comprehensive analytics—tying together marketing data (Marketo), deals (Salesforce), and usage/health metrics (Gainsight).
• Two-Way Sync ensures that enriched data, product usage stats, and other signals flow back to CRM and CS tools, strengthening customer management.

6. Enablement

• Highspot, Showpad, or Aircover integrated with Salesforce for sales and support enablement—arming teams with updated product collateral and breach-response protocols.

What’s Unique or Noteworthy

1. One Flagship Product, Massive Scale: Unlike many large enterprises with multiple lines of business, SecuraOne focuses all efforts on a single, best-of-breed solution—leading to a simpler but very deep sales and support process.
2. Customer Management is the Priority: Trust is vital in cybersecurity, so they invest heavily in post-sales success, real-time alerts, and robust QBR frameworks to prevent churn.
3. Scrappy Origin, Enterprise Now: They managed quotes in spreadsheets well beyond $500M ARR—demonstrating that early-stage “minimalist” solutions can carry a company far.
4. 24/7 Urgency: PagerDuty underscores how critical immediate response is when customers face potential security breaches.

What We Love

• Flexibility Before Formality: Waiting to deploy a heavy CPQ tool allowed them to adapt pricing on the fly until their revenue model stabilized.
• Deep Post-Sales Stack: Gainsight, Zendesk/Service Cloud, and PagerDuty form a powerful safety net for customers—minimizing downtime or trust erosion.
• Security-Centric Alerts: Quick escalation can be the difference between a minor incident and a major breach—demonstrating how product ops can blend into sales ops.

What We’d Change if We Had a Magic Wand

• Salesforce Lock-In: With every major function tied to Salesforce, pivoting to a different CRM or rewriting large pieces of the stack would be extremely painful.
• No Dedicated CDP: While they handle usage data in Gainsight, other teams (e.g. marketing) might benefit from event-level insights. This trade-off keeps the system simpler but may limit advanced personalization or cross-sell triggers.
• Post-Deployment Complexity: Over time, sequences (in Outreach/Salesloft) and custom CPQ rules can become cluttered—regular “stack hygiene” may be needed to keep the system nimble.

When you’re a market-dominant player with a single core product, funnel complexity shifts from top-of-funnel to post-sales retention and responsiveness. SecuraOne’s story shows how early, flexible tool choices (like a spreadsheet-based pricing system) can scale surprisingly far—so long as you eventually replace “scrappy” solutions with more formal infrastructure (CPQ, CLM) once volume and compliance demands peak. Above all, in cybersecurity, trust is everything, so real-time support tooling is just as critical as a best-in-class CRM stack.

The Quick Hits:

• No Perfect Stack: There is no single “right” way to build a RevTech stack. Your stage, goals, and resources dictate your choices.
• Early Simplicity Is Powerful: At Pre-PMF, fewer integrations and minimal complexity reduce overhead. This helps you adapt faster.
• Experiment, Then Refine: Post-PMF, experimentation is good, but remember to prune tools that aren’t delivering value before you scale.
• Martech + Revtech Harmony at Growth: As you grow, blending marketing-focused and revenue-focused tools (CDP + CRM) can unlock powerful, coordinated growth engines.
• Scale Requires Strong Data Foundations: MDM, robust warehouses, and careful data governance become essential as complexity increases.

Use these examples as inspiration. Understand the tradeoffs, read diagrams critically, and remember: the best stack is the one that meets your specific needs—no more, no less.