Building Churn Prediction in Snowflake + dbt vs Totango vs ClientSuccess vs Eru: A Data Lead’s Comparison

If you’re a data lead at a B2B SaaS company evaluating churn prediction, you’re probably weighing the same question: should you build custom models in your existing Snowflake + dbt stack, or implement a dedicated solution like Totango, ClientSuccess, or Eru? This comparison breaks down the trade-offs honestly — data reliability, cross-system metric accuracy, maintenance overhead, and time-to-insight — so you can make the right call for your team.

We build Eru, so we have a perspective. But this guide is written for data leads who need to justify their recommendation to engineering, finance, and CS stakeholders. We’ll be straightforward about where each approach wins and where it falls short.

Why Churn Prediction Is a Cross-System Problem

The fundamental challenge of churn prediction in B2B SaaS is that no single system contains all the signals. Churn manifests through a combination of:

• Product usage signals — declining logins, feature abandonment, reduced session depth (lives in your product analytics or warehouse)
• Billing signals — failed payments, downgrades, discount drift (lives in Stripe or Chargebee)
• CRM signals — missed renewals, champion departures, reduced engagement (lives in Salesforce or HubSpot)
• Support signals — escalation patterns, sentiment shifts, unresolved tickets (lives in Intercom or Zendesk)

A churn prediction model that only sees one of these signal categories produces incomplete predictions. The question isn’t just “can I build a model?” — it’s “can I build a model that sees enough of the picture to be accurate?”

Option 1: Build Custom Models in Snowflake + dbt

What This Looks Like

The custom build approach uses your existing data warehouse (Snowflake) and transformation layer (dbt) to create churn prediction models from raw data. A typical implementation involves:

1. ELT pipelines — Fivetran, Airbyte, or custom connectors to ingest data from Stripe, Salesforce, Intercom, and your product database into Snowflake
2. dbt staging models — Clean, deduplicate, and standardise raw data from each source
3. Entity resolution — Match customer records across systems using email, domain, or custom ID mapping in dbt
4. Feature engineering — Build churn-predictive features (rolling usage averages, payment failure rates, support ticket trends) as dbt models
5. Scoring model — A logistic regression, gradient-boosted tree, or heuristic scoring model that combines features into a churn risk score
6. Alerting and dashboards — Downstream consumption in Looker, Metabase, Hex, or Slack alerts via a custom integration

Where It Wins

Advantage	Why It Matters
Full transparency	Every transformation, feature, and weight is visible in your dbt models. You can audit exactly how a churn score was calculated.
No new vendor	You’re building on tools your team already knows. No new procurement, no new data silo.
Custom features	You can incorporate proprietary signals specific to your product (e.g., “days since last report exported”) that no off-the-shelf tool includes.
Data stays in your warehouse	No data leaves Snowflake. This matters for compliance, security reviews, and data governance policies.

Where It Falls Short

Limitation	Impact
Entity resolution is hard	Matching Stripe customer_id to Salesforce Account ID to Intercom user_id requires non-trivial logic. Email-based matching fails when companies use group aliases. Domain-based matching fails with multi-brand accounts. This alone can take 4–8 weeks to build reliably.
3–6 month build time	From pipeline setup to production-quality scores, expect 3–6 months of a data engineer’s time. Your churn problem doesn’t wait.
Ongoing maintenance burden	Schema changes in source systems break dbt models. New data sources require new pipelines. Feature drift degrades model accuracy. Budget 20–30% of a data engineer’s ongoing time.
Cross-system accuracy gaps	dbt models run on batch-loaded data. If Fivetran syncs Salesforce every 6 hours and Stripe every hour, your reconciliation logic operates on stale snapshots, not real-time state. Discrepancies between systems can persist for hours or days before being detected.
Snowflake compute costs	Running feature engineering, entity resolution, and scoring models adds $500–$2,000/month in Snowflake compute on top of your existing warehouse spend.

Option 2: Totango

What It Is

Totango is a customer success platform with pre-built health scoring, journey orchestration, and product usage tracking. It provides a native data model for customer health and offers connectors for CRM, billing, and product data.

Strengths for Churn Prediction

• Pre-built health scoring — Totango provides configurable health score templates that combine usage, engagement, and financial signals without custom model building
• Journey orchestration — Automated playbooks that trigger actions (emails, tasks, escalations) based on health score changes
• Product usage tracking — Native event tracking SDK and integrations with product analytics tools
• Deployment speed — 2–4 weeks to initial deployment with pre-built connectors

Limitations for Data Teams

• Separate data model — Totango maintains its own copy of customer data. When its view diverges from your warehouse, you get conflicting metrics. Data leads frequently report “Totango says X, Snowflake says Y” discrepancies that erode trust in both systems.
• Limited warehouse integration — Totango can ingest data from warehouses, but it’s not designed to use your dbt models as the source of truth. It imports data into its own schema rather than querying yours.
• Black-box scoring — Health score configuration is flexible, but the underlying calculations are not as transparent or auditable as dbt models. Data teams can’t run a dbt docs generate to understand the lineage.
• Another data silo — For data teams that have invested in centralising data in Snowflake, Totango introduces a separate system with its own data model, its own metrics definitions, and its own version of “truth.”

Option 3: ClientSuccess

What It Is

ClientSuccess is a customer success management platform focused on account health tracking, renewal management, and CS team workflows. It’s designed for CS managers who need a unified view of account health without heavy data engineering.

Strengths for Churn Prediction

• CS workflow focus — Built for the daily workflow of CS managers: account reviews, renewal tracking, task management, and customer journey mapping
• Account health scoring — Configurable health scores based on CRM data, support interactions, and manual CS input
• Renewal management — Automated renewal tracking with risk assessment based on account health signals
• CRM integration — Strong Salesforce and HubSpot integrations for bidirectional sync

Limitations for Data Teams

• Limited data warehouse connectivity — ClientSuccess has minimal native integration with Snowflake or other warehouses. If your churn signals live in warehouse-modelled data, ClientSuccess can’t easily access them.
• Narrow signal coverage — Health scores are primarily driven by CRM and support data. Product usage signals require manual import or third-party connectors, and billing reconciliation is not a core capability.
• CS-focused, not data-team-friendly — The platform is designed for CS managers, not data leads. If you need auditable feature engineering, custom scoring models, or warehouse-first architecture, ClientSuccess doesn’t fit the workflow.
• No billing–CRM reconciliation — ClientSuccess does not reconcile billing data against CRM records. Discrepancies between Stripe and Salesforce — one of the most common sources of inaccurate churn metrics — go undetected.

Option 4: Eru

What It Is

Eru is an AI-powered revenue intelligence platform that connects billing, CRM, support, and product data — including Snowflake — to detect churn signals across systems and reconcile revenue data. For a detailed technical walkthrough of how Eru connects to Snowflake and works alongside dbt, see our Snowflake integration guide for churn prediction.

Strengths for Churn Prediction

• Cross-system signal correlation — Eru connects to Stripe, Salesforce, HubSpot, Intercom, Mixpanel, and Snowflake, correlating signals across all sources to produce churn predictions that see the full picture
• AI-powered entity resolution — Automatic matching of customer identities across systems using probabilistic matching (names, emails, domains, custom fields) — the same problem that takes 4–8 weeks to solve in dbt
• Billing–CRM reconciliation — Continuous reconciliation of Stripe billing data against CRM records, detecting discrepancies that distort churn metrics and revenue forecasts
• Time-to-insight — OAuth connection in minutes, first signals within days. No 3–6 month build cycle.

How Eru Integrates with Existing Snowflake + dbt Setups

This is the most common question from data leads considering Eru: “We’ve already invested heavily in Snowflake and dbt. Is Eru going to create another data silo?”

The answer is no — and this is a deliberate architectural choice. Eru connects to your Snowflake warehouse as a data source via OAuth, reading directly from your existing dbt models and tables. Here’s what that means in practice:

• Your dbt models remain the source of truth. If you’ve built MRR calculations, usage aggregations, or cohort definitions in dbt, Eru reads from those models rather than reimporting raw data and recalculating.
• No duplicate data pipelines. Eru doesn’t require you to set up new ELT connectors for data that’s already in your warehouse. It reads what’s there and adds cross-system intelligence on top.
• Additive, not replacement. Eru adds three capabilities that are expensive to build in dbt: cross-system entity resolution, real-time billing–CRM reconciliation, and multi-source signal correlation. Your warehouse handles what it’s good at (batch analytics, custom feature engineering). Eru handles what it’s good at (cross-system connectivity and real-time signals).
• No schema migration. Eru adapts to your existing Snowflake schema. You point it at the relevant tables and it maps the fields. No need to restructure your warehouse to match a vendor’s expected format.

For step-by-step setup instructions including authentication, schema mapping, and dbt model configuration, see the Snowflake + dbt integration guide.

Limitations

• Less customisation than a pure build — If you need highly custom scoring models with proprietary features, a custom dbt build gives you more flexibility. Eru’s scoring is configurable but not as open-ended as writing your own models.
• Newer platform — Eru is a younger product than Totango or ClientSuccess. The ecosystem and community are smaller.
• Vendor dependency — Like any SaaS tool, you’re dependent on Eru’s uptime, roadmap, and pricing. A custom build, while more expensive, gives you full ownership.

Comparison Summary

Capability	Snowflake + dbt	Totango	ClientSuccess	Eru
Cross-system churn signals	Possible (build required)	Partial (own data model)	Limited (CRM + support)	✓
Entity resolution	Custom build (4–8 weeks)	Built-in (limited)	CRM-based only	✓ (AI-powered)
Billing–CRM reconciliation	Custom build required	—	—	✓
Snowflake + dbt integration	Native (it is Snowflake)	Import only	Minimal	✓ (reads dbt models)
Model transparency	✓ (full audit trail)	Configurable, not auditable	Configurable	Configurable, signals visible
Time to first signal	3–6 months	2–4 weeks	2–4 weeks	Days
Ongoing maintenance	High (20–30% of a DE)	Low (managed platform)	Low (managed platform)	Low (managed platform)
Data stays in warehouse	✓	— (copies data)	— (copies data)	Reads from warehouse
Creates a new data silo	—	Yes	Yes	No (additive layer)

Data Reliability: How Each Approach Handles It

Data reliability — the confidence that your churn metrics reflect reality — varies significantly across these approaches:

Snowflake + dbt

Reliability depends entirely on your implementation. If your ELT pipelines are robust, your entity resolution logic is sound, and your dbt tests catch data quality issues, you can achieve high reliability. The risk is that reliability degrades over time as source schemas change, new data sources are added, and the engineer who built the pipelines moves on.

Totango

Totango’s health scores are reliable within the data it has access to. The reliability gap appears when its imported data diverges from your warehouse. If Totango’s last Salesforce sync was 4 hours ago and a renewal was processed since then, the health score is stale. For data leads who need a single source of truth, this creates a “which number is right?” problem.

ClientSuccess

Similar to Totango, ClientSuccess is reliable for the signals it tracks (CRM, support). The reliability gap is narrower because it tracks fewer signals — but that means it’s also missing entire categories of churn indicators (billing discrepancies, product usage decline).

Eru

Eru’s reliability advantage comes from two things: real-time event monitoring (Stripe webhooks, CRM change events) rather than batch sync, and cross-system reconciliation that explicitly flags when data sources disagree. Instead of silently using stale data, Eru surfaces discrepancies as signals.

The “Another Data Silo” Concern

For data teams that have spent months (or years) centralising data in Snowflake, introducing another tool that maintains its own copy of customer data is a real concern. This is the most common objection data leads raise when evaluating customer success platforms:

• Totango and ClientSuccess import data from your systems and maintain their own data model. You now have two (or three) different places claiming to know a customer’s health, MRR, or churn risk. When they disagree — and they will — your team wastes time investigating which number is correct.
• Snowflake + dbt avoids this problem entirely because everything stays in the warehouse. But you pay for it with engineering time and maintenance.
• Eru takes a middle path: it reads from your warehouse and other systems but doesn’t maintain a competing copy of your data. Cross-system signals and reconciliation results are Eru’s output. Your warehouse remains the analytical foundation.

When to Build vs When to Buy: A Decision Framework

Use this framework to match your situation to the right approach:

Build in Snowflake + dbt if:

• You have a dedicated data engineer with 3–6 months of available capacity
• Your churn signals live primarily in 1–2 systems with consistent customer IDs
• You need maximum transparency and auditability for every calculation
• Your compliance requirements prohibit sending data to third-party tools
• You’re comfortable with the ongoing maintenance commitment (20–30% of a DE)

Choose Totango if:

• Your CS team is the primary consumer and they need journey orchestration and automated playbooks
• You’re less concerned about warehouse alignment and more focused on CS workflow automation
• You don’t have a centralised data warehouse and need a standalone health scoring system

Choose ClientSuccess if:

• CS workflow automation (renewals, tasks, account reviews) is the primary requirement
• Your churn signals are mostly CRM and support-driven, not usage or billing-driven
• You need a purpose-built tool for CS managers rather than a data platform

Choose Eru if:

• Your churn signals span 3+ systems (billing, CRM, support, product, warehouse)
• You’ve invested in Snowflake + dbt and want to keep that investment as the foundation
• Billing–CRM reconciliation is a known gap in your current stack
• You need cross-system churn signals without 3–6 months of custom data engineering
• You want to avoid creating another data silo alongside your warehouse

Total Cost of Ownership

Cost Component	Snowflake + dbt	Totango	ClientSuccess	Eru
Initial build / setup	$37K–$100K (3–6 months DE time)	2–4 weeks implementation	2–4 weeks implementation	Days (OAuth setup)
Annual platform cost	$6K–$24K Snowflake compute	$20K–$100K+ (seat-based)	$15K–$60K+ (seat-based)	Contact for pricing
Ongoing engineering	$30K–$60K/year (20–30% of a DE)	Minimal	Minimal	Minimal
Time to first insight	3–6 months	2–4 weeks	2–4 weeks	Days
Cross-system accuracy	Depends on build quality	Limited by data model	Limited signal coverage	Native cross-system

The Bottom Line

Churn prediction is a data connectivity problem. The accuracy of your model depends less on the algorithm and more on how many relevant signals it can see. A perfect logistic regression in dbt that only sees product usage data will produce worse predictions than a simpler model that correlates usage with billing discrepancies, support sentiment, and CRM engagement data.

For data teams with an existing Snowflake + dbt investment, the real question isn’t “build or buy” — it’s “what should we build and what should we buy?” Your warehouse is excellent at batch analytics, custom feature engineering, and historical analysis. It’s expensive at entity resolution, cross-system reconciliation, and real-time signal correlation. The pragmatic path for most data teams is to keep Snowflake + dbt as the analytical foundation and layer a cross-system intelligence tool on top.

Frequently Asked Questions

What are the pros and cons of building custom churn prediction models in Snowflake + dbt versus using Totango or ClientSuccess?

Building in Snowflake + dbt gives you full control, transparency, and keeps data in your warehouse, but requires 3–6 months to build, a dedicated data engineer to maintain, and typically produces single-source predictions. Totango provides pre-built health scoring with faster deployment (2–4 weeks) but creates a separate data model that can diverge from your warehouse. ClientSuccess focuses on CS workflows with CRM and support data but has limited warehouse integration and no billing reconciliation. The right choice depends on your data engineering capacity, number of signal sources, and tolerance for maintaining a separate data model.

How does Eru integrate with an existing Snowflake and dbt setup without creating another data silo?

Eru connects to your Snowflake warehouse via OAuth and reads directly from your existing dbt models and tables. Your dbt-modelled metrics (MRR, usage aggregations, cohort definitions) remain the source of truth. Eru layers cross-system intelligence on top — entity resolution, billing–CRM reconciliation, and multi-source signal correlation — without duplicating your warehouse data. This means you keep your Snowflake + dbt investment as the analytical foundation while adding the cross-system capabilities that are most expensive to build in dbt.

When should a data team build churn prediction in-house versus buying a dedicated platform?

Build in-house if you have a dedicated data engineer, fewer than 3 data sources with consistent customer identifiers, and 3–6 months before you need results. Buy a dedicated platform if you need cross-system signals from 4+ sources, lack dedicated data engineering capacity, or need time-to-insight measured in weeks rather than months. The break-even point is typically 6–12 months: custom builds have lower marginal cost but higher upfront investment. Consider Eru specifically if you want to preserve your Snowflake + dbt investment while adding cross-system intelligence.