The eras tour: GTM stack edition

Over the past two decades, the Go-to-Market (GTM) technology landscape has undergone profound changes. We’ve progressed from sales-driven tools operating in silos to interconnected, data-rich ecosystems that power every stage of the customer journey. Understanding this history isn’t just a trip down memory lane—it’s a practical lens for evaluating the stack you have today and guiding the improvements you’ll make tomorrow.

Pre-modern context: the on-premise and early cloud days

Before the “four eras” truly began, businesses managed customer relationships using fragmented, on-premise databases. Information was locked behind departmental boundaries, and CRM technology—if you could call it that—was often hard to maintain, costly, and barely integrated.

The breakthrough came when Salesforce introduced cloud-based CRM in the early 2000s. Suddenly, data could flow freely, accessible from anywhere. This foundational shift democratized CRM technology, making it scalable, affordable, and accessible to businesses of all sizes. It also set the stage for the Sales Tech revolution of 2015, as companies began to build centralized systems for managing their go-to-market (GTM) operations.

Era 1 (circa 2015): the sales tech core

By 2015, the introduction of cloud-based CRMs like Salesforce had redefined how businesses approached customer management. These platforms provided a central repository for sales data and streamlined processes for lead management and pipeline tracking.

At Branch in 2015, Austin and Mike Molinet built their first RevTech stack around Salesforce, using a simple yet effective configuration designed to boost sales efficiency. While this approach solved many of the challenges of the on-premise era, the stacks were still narrowly focused on sales, with minimal integration across teams.

The OG stack diagram at Branch from 2015

Typical stack components (Branch, 2015):

• CRM as the “Center of Gravity” (e.g., Salesforce)
  • Stored lead and customer information
  • Provided basic pipeline management and reporting
• Outbounding tool (e.g., Outreach, Yesware)
  • Automated prospecting and email cadences
  • Allowed for some personalization, but data often lived in separate systems
• Email marketing tool (e.g., Mailchimp, Marketo for basic nurture)
  • Handled simple nurturing campaigns
  • Usually not deeply integrated, resulting in fragmented insights
• Data enrichment tool (e.g., Clearbit)
  • Augmented lead/contact records with firmographic details
  • Improved targeting but often operated as a bolt-on solution
• Deduplication/Quality management tool
  • Ensured cleaner CRM data
  • Addressed issues caused by multiple siloed tools and partial integrations

Remnants you might find of stacks from this era:

• Sales-centric: Focused on optimizing sales processes and lead management.
• Limited integration: Tools operated in isolation, creating data silos.

Basic reporting: CRM reports supported pipeline management but lacked deeper insights into customer journeys.

Era 2 (circa 2017): the B2C martech explosion

Context: By 2017, consumer behavior and marketing priorities had evolved, leading to an explosion in marketing technology. B2C companies needed richer, more detailed customer insights to deliver personalized experiences at scale. The CRM was no longer the center of gravity; instead, the stack revolved around Customer Data Platforms (CDPs) and data warehouses, which enabled advanced attribution, personalization, and multichannel engagement.

Core tech anchors:

• Data warehouse (e.g., Snowflake, BigQuery)
  • Centralized all customer-related data for advanced analytics and downstream use.
• CDP (e.g., Segment)
  • Acted as a front-end hub to collect, clean, and route data to various applications.

Typical additional components:

• Analytics and attribution tools (e.g., Mixpanel, AppsFlyer)
  • Enabled detailed measurement of both paid and organic performance.
• Channel marketing tools (e.g., Braze, Iterable)
  • Facilitated omnichannel outreach through email, push, and SMS campaigns.
• Data management and governance
  • Managed schema definitions, privacy, and data quality across the stack.
• Marketing acquisition tools (e.g., Google Ads, Facebook Ads)
  • Enhanced SEO, SEM, and affiliate marketing efforts while providing attribution clarity.

Remnants you might find of stacks from this era:

• Data-driven: Anchored around CDPs and warehouses for advanced insights.
• Scalable personalization: Stacks enabled hyper-targeted campaigns but introduced complexity.

Technical marketing: Marketers often relied on engineers to manage data-heavy tools. Toward the end of this era, some engineers became marketers in disguise to meet the needs of the business.

Era 3 (2022–now): the B2B2C and product-led alignment

Context: As companies like Notion, Figma, and Canva rose to prominence, the distinction between B2B and B2C blurred. These hybrid models required stacks that could handle individual users as well as entire teams. Product-led growth (PLG) strategies made it essential to unify data from CRMs, CDPs, and product analytics tools. RevOps emerged as a key function to align sales, marketing, and product teams around shared goals.

Typical stack evolution:

• Unified CRM and CDP integration
  • Bi-directional data flows ensured consistent, synchronized customer profiles.
• Product analytics tools (e.g., Amplitude, Heap)
  • Tracked in-app behavior and identified Product-Qualified Leads (PQLs).
• Marketing and sales automation
  • Unified inbound (MQLs) and outbound motions, blending traditional sales with PLG strategies.
• Customer success platforms (e.g., Gainsight, Catalyst)
  • Focused on retention, upselling, and reducing churn.

Remnants you might find of stacks from this era:

• Lifecycle focus: Supported customer journeys from acquisition through retention and expansion.
• Unified teams: RevOps ensured cross-functional alignment across sales, marketing, and product.

Product-led motions: Stacks reflected PLG’s self-serve onboarding and enterprise upsell strategies.

Era 4 (today and beyond): decentralized, composable, and cost-conscious, with a twist of AI

Today’s GTM landscape is defined by composability—the ability to build a modular stack using best-in-class tools for specific needs, rather than relying on one monolithic platform. This flexibility is enabled by modern tools that offer broad functionality, allowing businesses to use them for multiple purposes beyond their original designations. At the same time, advancements in data federation and integration platforms have made connecting tools easier than ever before.

Tools are no longer constrained by their original definitions; instead, their functionality is often far broader than their label suggests. For example:

• Hightouch, known as an rETL (reverse Extract, Transform, Load) tool for syncing data from warehouses back into operational tools like CRMs, analytics platforms or marketing system, can also serve as a CDP or even an analytics platform.
• HubSpot, a CRM, is often used exclusively for its marketing automation features, while another CRM (e.g., Salesforce) handles sales.
• Some organizations operate multiple CRMs: one for their B2B business and another for B2C or hybrid B2B2C models.

What is a composable stack? A composable stack allows you to select and integrate the best tools for each specific function without being locked into a single vendor or solution. This flexibility is possible because modern tools are built with APIs, connectors, and integration platforms that simplify data sharing between systems.

For example, you might build a “CDP alternative” by combining a warehouse, ETL, and rETL tools, replicating the functionality of a traditional CDP. A CDP is a tool that tracks event data, then lets you manipulate, analyze, and selectively distribute that data to other tools. However, the same functionality can be achieved using other tools:

• Tracking event data can be handled by an ETL.
• Storage of event data happens in a warehouse.

Manipulation, analysis, and distribution of the data can be performed by an rETL.

Using Snowflake (warehouse) + Fivetran (ETL) + Hightouch (rETL) provides functionality similar to a traditional CDP but offers greater control and flexibility. Together, these three tools create a feedback loop that allows for real-time personalization and reporting without the need for a standalone CDP.

Instead of relying on a bundled CRM and marketing automation suite, you could pair standalone tools to meet your specific needs:

• Example: Use Marketo as a marketing automation tool alongside a lightweight CRM.
• HubSpot can function as a CRM, a marketing automation tool, an email marketing tool, or any combination of these depending on how you configure it.

• Hightouch can be used as an rETL, a CDP, or even an analytics tool.

The role of data federation and integration

In earlier eras, connecting tools and managing data flow between them was complex and often required custom engineering. Today, modern integration platforms like Zapier, Merge, Workato, and Tray.io have democratized data federation, making it easier to connect disparate tools with minimal technical effort.

Why does this matter? Composable stacks let you adapt your tech to your unique business needs, but they also introduce complexity. Knowing when to prioritize simplicity over modularity is critical for RevOps teams.

Challenges in today’s era:

• Tool overload: Because it’s so easy to add tools, and there are so many out there, without governance, overlapping features can create redundancy and unnecessary costs.
• Future-proofing: Building a stack that can adapt to new needs without becoming overly rigid is critical. It’s much easier to add a tool than to remove one or swap one out.

Key features of this era:

• Composable architecture: Modular, flexible stacks allow companies to adapt to changing needs.
• Interoperability at scale: Improved API integration tools make cross-platform data sharing seamless.

Strategic versatility: Tools are selected for their full potential, not just their primary marketing label.

The bottom line:

Your current stack likely reflects a combination of past eras—legacy CRM configurations from Era 1, layered marketing tools from Era 2, or product-led analytics from Era 3. By understanding these historical shifts, you can identify inefficiencies, leverage modern tools more effectively, and design a stack intentionally tailored to your business goals. The lessons of the past empower you to navigate today’s GTM landscape with clarity and purpose.