What Totango does

Totango is a customer success platform built around composable modules called SuccessBLOCs. Each module addresses a CS workflow—onboarding, adoption, renewal, expansion—and includes pre-built health scores, segments, and playbooks.

The core value: configure health scoring dimensions within Totango, set up automation triggers, and run playbooks that guide CSMs through standardized motions. Totango integrates with CRM, billing, and product tools to ingest data, then centralizes customer management within its platform.

Totango is particularly popular with mid-market B2B SaaS companies that want structured CS workflows without the implementation overhead of enterprise platforms like Gainsight.

What Eru does differently

Eru doesn't replace your CS workflows. It solves the data problem underneath them.

Where Totango asks you to configure health scoring within its platform, Eru's AI agent connects directly to your data sources—CRM, billing, product database, support tickets, analytics—and automatically discovers how customer entities relate across all of them.

The result is health scoring that draws on cross-system signals. Not just "usage dropped" from one tool, but "usage dropped AND support sentiment turned negative AND the champion hasn't logged in for two weeks AND there's a failed payment"—correlated from four different systems without manual configuration.

Totango can do multi-source health scoring too, but it requires you to set up each integration, map fields, and maintain the scoring model. Eru discovers the model from your data.

Comparison

Capability Totango Eru
Primary function Customer success workflow management Cross-system data mapping and monitoring
Health scoring Configurable scores within Totango's platform AI-discovered scores across all connected systems
Data sources Integrations you configure and maintain OAuth connections; agent discovers entity mappings
Multi-tool signals Requires manual integration setup per source Automatic cross-system signal correlation
Entity resolution Manual field mapping per integration AI-powered cross-system entity linking
Setup time Weeks to configure modules and integrations Same-day: connect sources, agent maps data
CS Ops requirement Recommended for scoring and workflow maintenance No dedicated CS Ops role needed
NRR forecasting Health-informed retention planning Billing-reconciled account-level NRR forecasts
Best for Teams wanting structured CS playbooks Teams wanting cross-system churn intelligence

Multi-tool health scoring: the core difference

Both Eru and Totango can incorporate data from multiple sources into health scores. The difference is how.

Totango's approach

You connect integrations, map fields to Totango's data model, define scoring dimensions, and assign weights. When you add a new tool to your stack, you add a new integration and update scoring rules. The health score is only as good as your configuration.

Eru's approach

You connect data sources via OAuth. Eru's AI agent explores each source, discovers entities and relationships, and builds a knowledge graph that maps how "customer" in Stripe relates to "company" in HubSpot relates to "workspace" in your product database. Health signals emerge from this unified entity model—automatically.

Watchlist: Cross-System Churn Risk Entity: customer Sources connected: 6 - Product database (usage metrics) - Stripe (billing and payment data) - HubSpot (CRM and engagement) - Intercom (support tickets and sentiment) - Mixpanel (feature adoption) - Slack Connect (communication activity) Signals detected across systems: - Usage: MAU dropped >30% vs 90-day average (product database) - Payment: Failed charge, card not updated in 14 days (Stripe) - Support: 4 tickets in 30 days, negative sentiment (Intercom) - Engagement: No login by primary contact in 21 days (product database) - Adoption: Core feature usage dropped to zero (Mixpanel) - Communication: Slack channel inactive 30+ days (Slack Connect) Scoring: - Each signal: weighted by historical churn correlation - Compound signals: weighted higher than individual Output per account: - Risk score with evidence trail - MRR at risk (from Stripe, reconciled with HubSpot) - Which signals triggered and when - Deep links to investigate in each source system

When you add a seventh data source, Eru discovers its entities and incorporates relevant signals automatically. No re-configuration of scoring rules.

When to use each

Use Totango when:

Use Eru when:

How they can work together

Totango and Eru are not mutually exclusive. They solve different layers of the retention problem.

Eru provides the intelligence layer. It connects to all your data sources, resolves customer entities across systems, and surfaces which accounts are at risk and why—with evidence from every tool in your stack.

Totango provides the action layer. Once you know which accounts need attention, Totango's playbooks guide your CSMs through the right response—standardized outreach, escalation workflows, renewal motions.

Think of it as: Eru tells you who's at risk and shows you the evidence. Totango helps your team act on it consistently.

Also compare

See churn signals your CS platform misses

Eru connects your entire stack and surfaces the compound signals that predict churn—without weeks of setup.