The quest to unify the revenue data layer
Clay has grown into a defining force in the market.
The company is said to have crossed the $30M ARR. Daily posts on LinkedIn detail go-to-market plays built using the tool. Companies have followed suit with several startups self-describing themselves as cheaper or simpler versions of Clay.
So, is Clay trailblazing a new category?
CRM’s history is paved with adjacent categories, each arising to work around the rigidity of its lead–contact–account–opportunity model and meet the needs of then-emerging sales/engagement motions.
Marketing Automation Platforms (MAPs) unified leads and contacts into a person-centric model, enabling scalable email campaigns.
Account-Based Platforms (ABX) restructured the data model to support account-driven, coordinated sales and marketing plays.
Customer Success Management (CSM) added subscription and usage data to drive adoption, renewal, and expansion.
Sales Engagement Platforms (SEP) focused on the individual—prospect or buyer, to enable multi-touch, sales-driven, outreach sequences across digital, social, and voice channels.
The short-lived PLG CRM was built around a user-level data model, federating product usage signals to identify key moments in the journey that warranted engagement.
Customer Engagement Platforms (CEP) like Braze are built to ingest and process real-time signals, orchestrating cross-channel outreach across email, messaging, and mobile.
All these categories share core traits: a motion-centric data model, synced back via data replication to the CRM system of record. While the terminology varies, they all feature list builders and campaign tools designed for specific GTM motions and are far easier to use than their CRM counterparts.
Data enrichment and housekeeping typically rely on a single sales intelligence provider, either enriching CRM records directly or supplying lists for use in the above tools, along with utilities for cleansing, normalization, and deduplication within the CRM.
Clay can be seen as the latest iteration of this pattern, designed to support highly-targeted, signal-driven playbooks. It begins with building razor-precise, data-driven Ideal Customer Profiles (ICP) from the analysis of a company’s best customers, enriched with firmographic and technographic data. From there, it assesses the addressable market, identifies lookalikes, and layers in buying signals to surface prospects likely to be in-market. These inputs drive scoring models and trigger targeted sales and marketing plays based on each prospect’s stage in the journey. All of this is powered by a mix of commercial data providers and Clay’s own data, scraped or monitored from the open web.
A few characteristics set Clay apart:
It leverages data from multiple providers instead of relying on a single source
Enrichment and signal usage are credit-based, tied to consumption rather than flat subscriptions
Its spreadsheet-like UI enables building lists and triggering plays by federating all necessary data into tables.
From a data standpoint, Clay provides a staging workspace to upload, enrich, and process data, then pushes lists to downstream systems via no-code workflows.
I’m also looking at this development against the backdrop of how businesses have addressed data fragmentation caused by the proliferation of systems discussed earlier.
Most large and mid-size enterprises have turned to data warehouses to consolidate customer data, primarily for 360-degree views and analytics. While reverse ETL enables campaign activation by pushing data into systems of engagement, its adoption remains limited. Moreover, data warehouses lack the usability and interfaces that non-technical GTM teams need to build and execute campaigns without engineering support.
Customer Data Platforms (CDPs) ingest and unify data from diverse sources, including real-time events and signals, offering GTM teams easier-to-use environments. However, as packaged software, their adoption remains largely focused on e-commerce and a handful of industries. Most also rely on a person-centric data model, a key limitation for businesses with account-based sales motions.
Within the GTM ecosystem, revenue intelligence vendors like Clari, Gong, Outreach, and Salesloft are evolving into revenue orchestration platforms. They’re upgrading their time-series data stores into revenue data marts with two-way sync to enterprise systems (CRM, ERP, email) and integration with sales intelligence providers.
Meanwhile, emerging players like Cargo and Unify create unified repositories across sales and marketing by aggregating data and signals from internal (1st party) and external (3rd party) sources. Their flexible data models and toolsets for GTM teams take the complexity out of managing data and executing coordinated revenue motions.
In this ever-evolving environment, I’ve identified three main architecture styles, each shaping a distinct mini-category:
Multi-system engagement stacks hinge on data orchestration to enrich and sync the various repositories. It represents today’s dominant approach.
Unified customer and prospect data platforms built on modern data technologies let you create a data model tailored to your business that supports all sales and marketing motions while providing toolsets to eliminate the complexity of data warehouses. They effectively act as revenue/GTM data marts.
Clay-like staging workbenches where users upload lists, enrich them with third-party sales intelligence data and signals, and trigger workflows and sales plays through an intuitive, spreadsheet-like interface.
The current landscape remains fluid. It is poised for disruption again by AI conversational interfaces and agents, two breakthrough technologies set to simplify the data layer and streamline workflow execution.
These are my current working assumptions as I update the salestech industry landscape. I welcome your feedback, including alternate or contrarian perspectives.