Best NRR Forecasting Software for B2B SaaS in 2026

You’ve raised your Series B. You’re at $15M ARR with 150–300 accounts. Your board is asking for NRR forecasts you can defend — not a trailing twelve-month retention number presented as a prediction, but a forward-looking model that accounts for expansion, contraction, and churn risk at the account level.

If you’re the VP of RevOps tasked with making this happen, this guide is for you. We’ll cover the evaluation criteria that actually matter, an honest comparison of the tools in this category, and a step-by-step implementation framework you can start this quarter.

We build Eru, so we have a perspective. But we’ll be honest about where each tool excels and where it falls short.

Why NRR Forecasting Matters More at Series B

At Series A, retention is tracked in a spreadsheet and defended with anecdotes. At Series B, three things change:

• Board expectations sharpen. Investors at the B round expect cohort-level retention analysis, not aggregate numbers. They want to see how NRR varies by segment, tenure, and ACV — and whether you understand the drivers.
• Account volume outgrows intuition. With 150+ accounts across enterprise and mid-market segments, no one person can assess renewal risk by feel. You need a systematic approach.
• Data integrity becomes a liability. If your Stripe MRR and Salesforce ARR diverge by 6% (common at this stage), every NRR forecast built on either number is wrong. The error compounds quarterly.

The first problem isn’t choosing a tool. It’s getting your revenue data accurate enough to forecast from. That’s why data source breadth and reconciliation capability should top your evaluation criteria.

Four Evaluation Criteria for NRR Forecasting Tools

After working with dozens of Series A–C RevOps teams, these are the four criteria that separate tools that produce defensible NRR forecasts from tools that produce dashboards:

1. Data Source Breadth

NRR is driven by retention, expansion, and contraction — and the leading indicators for each live in different systems. A tool that only reads billing data can tell you what happened. A tool that connects billing, CRM, support, and product usage can tell you what’s about to happen.

What to look for: Native integrations with your billing system (Stripe, Chargebee), CRM (Salesforce, HubSpot), support platform (Intercom, Zendesk), and product analytics (Segment, Mixpanel). The more signals feeding the model, the earlier you see risk.

2. Forecast Accuracy

The difference between a useful NRR forecast and a vanity metric is the methodology behind it. Flat-rate forecasting (applying last quarter’s NRR to next quarter’s cohort) misses segment dynamics and account-level risk.

What to look for: Account-level risk scoring, segment-aware modelling, and the ability to apply different retention/expansion assumptions to different risk tiers. Bonus: confidence intervals on the forecast number.

3. RevOps Integration

An NRR forecasting tool that creates another data silo is worse than no tool at all. The platform needs to reconcile data across your existing systems, not sit alongside them.

What to look for: Automatic billing–CRM reconciliation (especially Stripe–Salesforce), entity resolution across systems, and the ability to surface discrepancies rather than silently inherit them.

4. Board-Readiness

Your board doesn’t want a dashboard login. They want a defensible NRR number, the methodology behind it, and the drivers moving it up or down.

What to look for: Exportable metrics with confidence intervals, driver attribution (which accounts and signals are moving NRR), and cohort-level breakdowns that answer the follow-up questions investors always ask.

The Six Tools Compared

These are the platforms that appear most frequently when RevOps leaders search for NRR forecasting software. They solve overlapping but distinct problems.

Tool	Primary Focus	NRR Forecasting Approach	Best For
Eru	Revenue Intelligence	Cross-system account-level risk scoring using billing, CRM, support, and product data. Segments renewal cohorts into risk tiers with differentiated retention and expansion assumptions.	Series A–B SaaS ($5M–$30M ARR) without a data team
Clari	Revenue Operations	Pipeline-focused revenue forecasting using CRM, email, and calendar signals. Primarily predicts new-business bookings. Expansion/contraction NRR is secondary.	Sales-led organisations with complex pipelines
Baremetrics	Subscription Analytics	Calculates NRR directly from billing data. Shows historical MRR movements (expansion, contraction, churn) with subscription-level detail. No predictive forecasting.	Self-serve SaaS with billing as single source of truth
ChurnZero	Churn Prevention	Health scoring and segment-level retention tracking. Focused on churn reduction through CS workflows and in-app engagement rather than NRR forecasting specifically.	Mid-market SaaS with active CS teams (5–15 CSMs)
Totango	Customer Success	Modular health scoring through SuccessBLOCs with journey orchestration. Provides retention insights but NRR forecasting requires significant configuration and data engineering.	Mid-market to enterprise SaaS with a CS operations function
Gainsight	Enterprise Customer Success	Comprehensive health scoring based on configurable rules (usage, support, surveys). NRR visibility through retention and renewal reporting. Requires extensive implementation.	Enterprise SaaS with 20+ CSMs and dedicated CS Ops

Detailed Comparison by Evaluation Criteria

Data Source Breadth

Tool	Billing	CRM	Support	Product Usage	Auto-Reconciliation
Eru	Stripe, Chargebee	Salesforce, HubSpot	Intercom	Via Segment, Snowflake	Yes — Stripe–Salesforce
Clari	No native	Salesforce	No	No	No
Baremetrics	Stripe, Chargebee, Braintree	No	No	No	No (single source)
ChurnZero	Limited	Salesforce, HubSpot	Zendesk, Intercom	Custom API	No
Totango	Limited	Salesforce, HubSpot	Zendesk	Custom API, Segment	No
Gainsight	Via integration	Salesforce (deep)	Zendesk, Intercom	Via PX module	No native

Forecast Accuracy Methodology

Tool	Methodology	Account-Level Scoring	Confidence Intervals
Eru	AI-powered risk scoring across all connected data sources. Segments renewal cohorts by risk tier with differentiated assumptions.	Yes	Yes
Clari	AI deal scoring and pipeline analytics. Strong for predicting bookings. Expansion NRR requires CRM data enrichment.	Deal-level (not retention-focused)	For pipeline
Baremetrics	Historical billing trends. No predictive modelling — extrapolates from past MRR movements.	No	No
ChurnZero	Health score rules engine. Predicts churn risk per account but does not model expansion or contraction probabilities for NRR.	Churn risk only	No
Totango	Configurable health scoring with SuccessBLOCs. Retention insights require significant configuration. No native NRR forecast model.	Health-based	No
Gainsight	Configurable health scores and renewal likelihood. NRR forecasting possible but requires CS Ops to build and maintain the model.	Yes (configurable)	Limited

Setup and Time to Value

Tool	Setup Time	Engineering Required	Time to First NRR Forecast
Eru	5 minutes per integration (OAuth)	None	Same day
Clari	2–4 weeks	CRM admin	2–4 weeks
Baremetrics	Minutes	None	Same day (historical only)
ChurnZero	2–4 weeks	Light engineering for product data	3–6 weeks
Totango	2–6 weeks	Data engineering for full integration	4–8 weeks
Gainsight	6–12 weeks	Dedicated CS Ops or implementation partner	2–3 months

Eru: Cross-System NRR Forecasting for Series B

Eru is an AI revenue intelligence platform built for Series A–B SaaS companies that need NRR forecasting without building a data warehouse or hiring a data team.

How Eru Forecasts NRR

Eru’s NRR methodology differs from the tools above in three ways:

1. Cross-system data foundation. Eru connects to your billing system (Stripe), CRM (Salesforce, HubSpot), support platform (Intercom), and data warehouse (Snowflake) via OAuth. Its AI automatically resolves customer entities across systems — matching Stripe customer_id to Salesforce Account ID without manual mapping.
2. Automatic reconciliation before forecasting. Before producing a forecast, Eru reconciles billing and CRM data at the account level. This step is critical: most Series B companies have 3–8% variance between Stripe and Salesforce, and any forecast built on unreconciled data inherits that error. Eru flags discrepancies (missed cancellations, price changes not reflected in CRM, billing cycle misalignments) so your starting ARR is accurate.
3. Account-level risk scoring from leading indicators. Eru scores each account using signals from every connected system: usage decline patterns, support sentiment shifts, payment anomalies, champion departures, and engagement quality changes. Each renewal cohort is segmented by risk tier, and different retention, expansion, and contraction probabilities are applied to each tier — producing a forecast that reflects actual account dynamics rather than a flat historical rate.

Board-Ready Output

Eru produces NRR forecasts with three components your board expects: the headline number, confidence intervals (e.g., 108–114% NRR at 80% confidence), and driver attribution showing which accounts and signals are moving the forecast up or down. No data team required to produce or present these metrics.

Where Eru Falls Short

Eru is a newer entrant with a smaller community than Gainsight or ChurnZero. It does not offer in-app engagement tools or CS workflow automation. If your primary need is CS team productivity (task management, playbook execution, email sequences), Eru is not the right fit. It’s purpose-built for revenue intelligence and NRR forecasting, not customer success operations.

Clari: Pipeline-Focused Revenue Forecasting

Clari is a revenue operations platform built for sales-led organisations that need deal-level revenue prediction. Its core strength is pipeline forecasting — predicting whether deals will close, when, and at what value.

NRR relevance: Clari can surface expansion opportunities through CRM data, but its forecasting engine is optimised for new-business bookings. Retention-based NRR — the combination of churn, contraction, and expansion across your existing customer base — is not its primary use case. If your revenue risk is in pipeline conversion, Clari is excellent. If your revenue risk is in retention and expansion of existing accounts, it leaves gaps.

Data limitation: Clari reads from CRM, email, and calendar data. It does not natively connect to billing systems, support platforms, or product analytics — which means the cross-system signals that drive NRR outcomes are invisible to it.

Baremetrics: Billing-Native Subscription Analytics

Baremetrics connects directly to your billing system (Stripe, Chargebee, Braintree) and provides clean, instant SaaS metrics: MRR, churn rate, NRR, LTV, and cohort analysis. It’s fast to set up and excellent at showing what happened.

NRR relevance: Baremetrics calculates historical NRR accurately from billing data. However, it cannot forecast NRR because it has no access to the leading indicators that predict future retention outcomes. It shows that NRR was 112% last quarter but cannot tell you whether it will be 108% or 115% next quarter.

Data limitation: Billing-only. No CRM, support, or product usage integration. If your billing system is your single source of truth and you don’t need predictive forecasting, Baremetrics is simple and effective. If you need to reconcile billing against CRM or forecast from account-level signals, it won’t get you there.

ChurnZero: CS-Workflow-Focused Churn Prevention

ChurnZero is a customer success platform built for mid-market SaaS companies with active CS teams. Its strengths are real-time health scoring, in-app engagement (walkthroughs, announcements, surveys), and CS workflow automation.

NRR relevance: ChurnZero’s health scores can identify accounts at churn risk, which is one input to NRR forecasting. However, it does not model expansion or contraction dynamics, does not reconcile billing data, and does not produce a composite NRR forecast. It helps reduce churn, but forecasting NRR as a complete metric requires stitching together ChurnZero health data with billing and CRM data externally.

Best fit: If your primary goal is equipping CSMs with real-time account visibility and in-app engagement tools, ChurnZero is strong. If your primary goal is producing a defensible NRR forecast for the board, it’s an incomplete solution.

Totango: Modular Customer Success with SuccessBLOCs

Totango offers a modular customer success platform with pre-built SuccessBLOCs for onboarding, adoption, renewal, and expansion. It’s positioned as a more configurable alternative to Gainsight for mid-market companies.

NRR relevance: Totango provides health scoring and retention analytics that can inform NRR conversations. However, building a reliable NRR forecast in Totango requires significant configuration — connecting data sources, defining health score weights, and building custom reporting. It provides the building blocks but not an out-of-the-box NRR forecasting model.

Data limitation: Totango supports CRM and support integrations but has limited native billing reconciliation. Product usage data requires custom API integration, which means engineering involvement.

Gainsight: Enterprise Customer Success Platform

Gainsight is the incumbent enterprise customer success platform. It offers the most comprehensive feature set: health scoring, CS workflow automation, journey orchestration, surveys (via Gainsight PX), and retention reporting.

NRR relevance: Gainsight can produce renewal likelihood scores and retention forecasts, but building a reliable NRR model requires a dedicated CS Ops role to configure health scores, integrate data sources, and maintain the forecasting logic. The platform is powerful but not self-serve for NRR forecasting.

Implementation reality: Gainsight implementations typically take 6–12 weeks with a dedicated implementation partner. For a Series B company at $15M ARR, the implementation cost and time can be difficult to justify unless you already have a CS Ops function and 20+ CSMs who will use the platform daily.

Implementation Best Practices: NRR Forecasting at Series B

Regardless of which tool you choose, the methodology for implementing NRR forecasting at the Series B stage follows the same five steps. Here’s the framework VP RevOps teams are using successfully:

Step 1: Audit and Reconcile Your Revenue Data

Before you can forecast NRR, you need an accurate starting point. Reconcile your billing system against your CRM at the account level.

• Pull ARR from Stripe (or your billing system) and Salesforce (or your CRM) for every active account.
• Identify discrepancies: missed cancellations in CRM, price changes not reflected, subscription modifications that bypassed Salesforce, billing cycle misalignments.
• Resolve discrepancies before building any forecast. Most Series B companies discover 3–8% variance between billing and CRM. That error compounds in every forecast built on top of it.

Pro tip: Eru automates this reconciliation continuously. If you’re doing it manually, budget 2–5 days per month for a finance team member.

Step 2: Define Your Renewal Cohort and Segmentation

Group accounts renewing in each quarter by meaningful segments:

• Contract value tier: <$10K, $10K–$50K, $50K+ ACV
• Customer segment: Enterprise vs mid-market vs SMB
• Tenure: First renewal vs second+ renewal (first renewals are typically higher risk)
• Product line: If you have multiple products with different retention profiles

At $15M ARR with 150–300 accounts, 4–6 segments typically capture meaningful behavioural differences without over-fitting to small sample sizes.

Step 3: Build Account-Level Risk Scores from Cross-System Signals

This is where tool choice matters most. Score each account using leading indicators from multiple data sources:

• Product usage: Login frequency trends, feature adoption depth, active user count relative to contract
• Support signals: Ticket volume trends, escalation frequency, sentiment patterns
• Billing health: Payment timeliness, discount history, proration frequency
• Relationship indicators: Champion presence, QBR attendance, stakeholder engagement level

Weight each signal based on its historical correlation with actual churn, expansion, and contraction outcomes in your business. Every SaaS company’s signal weights are different.

Step 4: Apply Segmented Retention and Expansion Assumptions

Instead of applying a flat historical NRR rate to your entire renewal cohort, assign different probabilities to each risk tier within each segment:

• High-risk accounts: 60–70% retention probability, minimal expansion, potential 20–30% contraction
• Medium-risk accounts: 85–90% retention probability, 5–10% expansion potential
• Low-risk accounts: 95%+ retention probability, 15–25% expansion potential

This segmented approach typically produces forecasts within 2–4 percentage points of actuals, compared to 8–15 points with flat-rate methods.

Step 5: Validate, Recalibrate, and Present to the Board

After each quarter, compare your forecast against actuals:

• Which risk tiers were most accurately predicted? Which signals were most predictive?
• Recalibrate signal weights based on what actually drove churn, expansion, and contraction.
• Present NRR to the board with three components: the headline number, confidence intervals (e.g., 108–114% NRR at 80% confidence), and driver attribution showing what’s moving the number.

Boards at Series B expect this level of rigour. A single NRR number without methodology, confidence bounds, or driver attribution will not survive due diligence.

Which Tool Should You Choose?

Choose Eru if:

• You’re a Series A–B SaaS company at $5M–$30M ARR
• Your Stripe and Salesforce numbers don’t match and nobody knows by how much
• You need NRR forecasting based on cross-system signals, not just billing data
• You want board-ready NRR metrics without building a data warehouse
• You don’t have a data team or CS Ops function to configure and maintain complex tooling

Choose Clari if:

• You’re sales-led and pipeline forecasting is your primary revenue challenge
• Your CRO needs deal-level revenue prediction, not retention-based NRR
• The revenue risk in your business is in new-business conversion, not existing-account retention

Choose Baremetrics if:

• You need fast, clean SaaS metrics from billing data
• Your billing system is your single source of truth (no CRM reconciliation needed)
• Historical NRR reporting is sufficient — you don’t need predictive forecasting

Choose ChurnZero if:

• You have an active CS team of 5–15 CSMs who need real-time health scoring
• In-app engagement and customer communication are central to your retention strategy
• You want a lighter alternative to Gainsight with faster time to value

Choose Totango if:

• You want modular, configurable CS tooling without the Gainsight implementation overhead
• You have some data engineering capacity to connect custom data sources
• Journey orchestration across the customer lifecycle is a priority

Choose Gainsight if:

• You have 20+ CSMs and a dedicated CS Ops function
• You’re enterprise-scale and can invest in a 3-month implementation
• CS workflow automation and team productivity are your primary goals (not data connectivity)

The Gap That Matters Most

Most NRR forecasting failures aren’t about the tool. They’re about the data. If your billing system says MRR is $412K and your CRM says $389K, no forecasting model will produce a number your board can trust.

The most impactful first step isn’t choosing a forecasting tool. It’s reconciling your revenue data across systems so you’re forecasting from an accurate starting point.

That’s the specific problem Eru was built to solve: connect the systems, reconcile the data, and then forecast NRR using the full cross-system picture — not just billing data, not just CRM data, but the correlated view across your entire revenue stack.

Frequently Asked Questions

What is the best NRR forecasting software for SaaS?

The best NRR forecasting software for B2B SaaS depends on your company stage and data maturity. For Series B companies around $15M ARR without a data team, Eru provides cross-system NRR forecasting by connecting billing, CRM, support, and product data with automatic Stripe–Salesforce reconciliation. Clari is best for sales-led organisations that need pipeline-focused revenue forecasting. Baremetrics suits billing-centric companies that want fast MRR and churn dashboards. ChurnZero and Totango are stronger for mid-market companies with active CS teams. Gainsight is the enterprise choice for organisations with 20+ CSMs.

What are the best practices for implementing NRR forecasting at a Series B SaaS company with $15M ARR?

Implementing NRR forecasting at Series B requires five steps: (1) audit and reconcile your revenue data across billing and CRM systems, (2) segment your renewal cohort by risk tier using account-level signals, (3) apply different retention, expansion, and contraction assumptions to each segment, (4) validate forecasts against actuals quarterly and recalibrate, and (5) present board-ready NRR with confidence intervals and driver attribution. The most common mistake is skipping step 1 — forecasting from unreconciled data produces numbers that don’t hold up under scrutiny.

How does NRR forecasting software differ from billing analytics tools?

Billing analytics tools like Baremetrics and ChartMogul calculate NRR from billing data alone — they show what happened but cannot predict what will happen. NRR forecasting software like Eru combines billing data with CRM, support, and product usage signals to build account-level risk scores that predict retention, expansion, and contraction outcomes before they materialise. The key difference is data source breadth and predictive capability.

What evaluation criteria should a VP of RevOps use when selecting NRR forecasting tools?

Four criteria: (1) data source breadth — does the tool connect billing, CRM, support, and product data? (2) forecast accuracy — does it use account-level risk scoring or flat historical rates? (3) RevOps integration — does it reconcile data across systems automatically? (4) board-readiness — does it produce metrics with confidence intervals that survive investor scrutiny? Also consider implementation time, engineering requirements, and fit for your current team size.

Related: NRR Forecasting for Series B SaaS — how Eru calculates NRR, who it’s best for, pricing transparency, and a direct comparison with Gainsight’s approach.