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CRM Valuation Multiples

Explore private market CRM valuation multiples with benchmarks structured by stage and region. Updated quarterly.

Coverage
EV/Sales; EV/EBITDA
Valuation Multiples
Private Market
Benchmarks
Stage & Region
Adjustment
Quarterly
Updated
Sector Profile

CRM

Customer Relationship Management platforms centralise sales, marketing, and support data — enabling businesses to manage customer lifecycles at scale. Private market CRM valuations reflect the premium placed on high switching costs, seat-based expansion dynamics, and strong net revenue retention across enterprise accounts.

The category spans sales automation, marketing platforms, customer success tools, and revenue operations suites. DealMatrix tracks valuation dynamics across 7 funding stages and all major global regions, updated every quarter.

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High Switching Costs
Deep CRM integrations across sales, marketing, and support create significant migration friction — driving long-term retention.
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Seat-Based Expansion
Per-user pricing allows CRM platforms to grow revenue organically as customer organisations scale their teams.
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Revenue Operations Hub
Modern CRMs extend beyond sales into marketing automation and customer success — increasing wallet share per account.
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AI Enhancement
AI-driven lead scoring, forecasting, and next-best-action recommendations are becoming key valuation differentiators.

Sector

CRM

Software & Data

Sector tracked since

2000

25+ years of data

EV/SALES & EV/EBITDA ACROSS

6 Regions · 7 Stages

Modelled independently via proprietary econometric approach

UPDATE FREQUENCY

Quarterly

Data updates & model improvement

Private Market

CRM Valuation Multiples

Select a region and funding stage to preview how CRM companies are valued in private markets. Full data available on the platform.

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Multiples
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Methodology

The Venionaire DealMatrix Multiples Model

DealMatrix multiples are derived through a five-step model combining public capital market comparables, proprietary VC/PE/M&A transaction data, and macroeconomic indicators.

The model produces three components: The reported public multiple, the model-predicted multiple, and the lower bound predicted multiple averaged into the DealMatrix Composite, then adjusted for region and funding stage. The methodology follows the IPEV Guidelines 2025.

Read full Methodology
Model Architecture
Step 1: Data Acquisition
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Step 2: Data Cleaning
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Step 3: Econometric Modelling
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Step 4: Multiple Averaging
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Step 5: Region/Stage Adjustment
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Final DealMatrix Multiples
Following IPEV Guidelines 2025
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IoT

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Machine Learning

Machine Learning companies build platforms and applications that enable systems to learn from data and improve over time – powering automation, prediction, and intelligent decision-making at scale. Valuations are driven by training data quality, MLOps maturity, and the ability to translate model performance into reliable production deployments.
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COMPANY VALUATION

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