The 10 Best Customer Success Analytics Tools for SaaS (2027)

The best customer success analytics tool for a SaaS company is the one that sees all the data your accounts actually live in: product usage, the CRM renewal book, billing, support and survey feedback. In 2027 that usually means two kinds of tool working together: a customer success platform (or the CRM's own CS workspace) that runs the CSM's day, and an analytics layer that answers the questions crossing all of those systems. This guide compares ten of them, grouped by what they do.

Querri publishes this guide, and Querri is one of the tools listed. We placed it in the category it belongs to, not at #1, and listed its limits the same way as everyone else's. No vendor paid to be included.

How we chose these tools

We looked at the tools that show up most for "customer success software" and "customer success analytics" searches, and at what SaaS CS leaders told us they use. For each one we read the vendor's public product, pricing and documentation pages, release notes from 2026, and published user reviews. We did not run a hands-on trial of every product, and we don't publish prices a vendor hasn't published. We judged each tool on eight things:

1. Signals covered: which of product usage, CRM and renewals, billing, support and survey data it brings together natively, and which arrive by export.
2. How health is scored: whether the rule is visible and editable, or a black box.
3. Shared definitions: whether "active customer", churn and NRR are defined once and reused everywhere.
4. Traceability: whether a CSM or leader can see where a number came from.
5. Alerts and monitoring: what triggers a flag, and on which data.
6. Who can build reports: a CSM on their own, or an admin or analyst.
7. Workflow or analysis: whether it runs playbooks and outreach, answers questions, or both.
8. Pricing transparency: a public price, or quote-only.

The list is grouped by category, not ranked by score: tools that answer questions come first (product analytics, then the analytics layer), then tools that run the CSM's workflow (CRM-native CS, then CS platforms), so a tool's number is its place in that order, not a grade.

Customer success analytics tools at a glance

What a SaaS customer success team needs to see

Most CS leaders ask the same few questions every week: which accounts need attention right now, which renewals are at risk and how much ARR they carry, whether adoption is rising or falling, what customers are saying before the renewal call, and what net and gross retention look like. Those answers sit in different systems. Usage is in the product or the warehouse, renewal dates and ARR in the CRM, invoices in billing, tickets in support and NPS in a survey tool. Product analytics tools are strongest on usage. An analytics layer is for the questions that need several systems at once, and for keeping one definition of each number. CRM-native CS and CS platforms are strongest at turning those signals into a CSM's daily work.

The 10 best customer success analytics tools for SaaS

Product analytics

1. Pendo

Best for: account-level usage data plus in-app guides and NPS, without heavy engineering.

What it measures: page and feature usage by visitor and account. Its Leo AI answers analytics questions in plain language.

Pricing model: free up to 500 monthly active users, then quote-based tiers (Pendo pricing).

Where it breaks: it sees behavior inside your product, not billing or support. Sending Pendo data to a warehouse (Data Sync) sits in its top bundle (Pendo docs).

Bottom line: often the usage signal a health score is built on.

2. Amplitude (or Mixpanel)

Best for: deep usage, retention and cohort analysis, usually owned by the product team and shared with CS.

What it measures: events, funnels, retention and cohorts, with account-level reporting through an Accounts add-on. AI agents and MCP are on every plan (Amplitude pricing). Mixpanel is a close alternative with public pricing and its own always-on Mixpanel Agent (Mixpanel pricing).

Pricing model: free tiers by event volume, then quotes (Amplitude) or published rates (Mixpanel).

Where it breaks: "It's front-end data only," as one October 2026 reviewer put it (G2 review via AWS Marketplace). CRM, billing and support data have to be piped in as events or properties.

Bottom line: the deepest view of usage; not a view of the whole account.

Analytics across your CS stack

3. Querri

Best for: CS leaders who need answers that cross product usage, the CRM renewal book, billing and support, without waiting for a data team.

What it measures: whatever you ask, in plain words, across 125+ connectors and file exports. It connects directly to Vitally, Planhat, Gainsight PX, Pendo, Amplitude, Mixpanel, HubSpot, Salesforce, Stripe, Intercom and the main warehouses. You define "active customer" or NRR once and every answer, dashboard and AI assistant uses it. Click any number to see the query and sources behind it. Monitors check each account against its own history and flag what moved. The Customer health and renewals blueprint starts you with account health, renewal risk and adoption dashboards, graded on a stated rule (seat use under 40% or active users down 25% or more is At risk) (how it works).

Pricing model: free to start with no credit card, then usage-based (Querri pricing).

Where it breaks: Querri doesn't run CS playbooks, tasks or outreach, and it doesn't write back to your CRM. Gainsight CS, ChurnZero and Odie data arrives by export rather than a direct connector. It works from a synced copy of your data on a schedule, not live queries, except for chosen Snowflake and Databricks tables on some plans. A health grade tells a CSM where to look; it doesn't predict churn.

Bottom line: the layer that joins everything else on this list, with one definition of each number.

CRM-native customer success

4. HubSpot Customer Success Workspace

Best for: SaaS companies already on HubSpot that want CSMs working in the CRM with no sync to maintain (HubSpot KB).

What it measures: rule-based health scores from company and contact properties, associated records and logged activity, up to 50 scores (HubSpot KB). The Customer Health Agent (beta) gives a risk grade and summary from CRM activity and call transcripts (HubSpot KB).

Pricing model: public and per seat. The workspace needs Service Hub Professional or Enterprise, and both carry a one-time onboarding fee (HubSpot pricing).

Where it breaks: scores use what's in HubSpot, so product usage and billing have to be synced into HubSpot properties first. "Reporting still feels limited," one reviewer wrote in September 2026 (G2 review via AWS Marketplace).

Bottom line: the simplest start if your customer data already lives in HubSpot.

Customer success platforms

5. Gainsight CS

Best for: larger CS organizations that want health scores, journeys, playbooks, success plans and renewal forecasting in one system, and have an admin to run it.

What it measures: account health through configurable scorecards, built from CRM, usage and other sources that admins bring in with Rules and Data Designer. Snowflake works as a source (Gainsight docs). Gainsight's Renewal Center adds a likelihood-to-renew score on open renewals.

AI: Agent Studio (beta) turns plain-language workflows into reusable skills, and Gainsight's Atlas service runs long-tail renewals with AI agents and people. MCP access is included (Gainsight, Oct 20, 2026).

Pricing model: quote-based today. Gainsight has announced that Gainsight CS moves from per-seat pricing to pricing tied to ARR and customers managed in 2027, with Staircase AI included (same source).

Where it breaks: reports and dashboards work on data that has been loaded into Gainsight's objects, so new outside data waits for an admin. Gainsight documents its own report limits, including a Group By limit of 10, a 30-second report timeout and 30 reports per dashboard (Gainsight docs). One reviewer found "reporting and alerting that seem generic (in many cases)" (G2 review via AWS Marketplace).

Bottom line: the broadest CS platform, best when you have the admin capacity to match.

6. ChurnZero

Best for: mid-market and enterprise SaaS teams that want customer journeys, plays and in-app engagement, with strong support from the vendor.

What it measures: ChurnScores and segments built on accounts, contacts and usage. It imports Snowflake, BigQuery and S3 data, and connects to billing (Stripe, Chargebee, Recurly, ChartMogul, Maxio) and support tools (Zendesk, Freshdesk, Intercom, ServiceNow) (ChurnZero integrations).

AI: 20 AI agents in one flat-price bundle, including Retrospective, which drafts a churn analysis when an account is marked churned (ChurnZero, Aug 12, 2026).

Pricing model: quote-based. One procurement tracker reports an average of about $44,000 a year for smaller customers (SpendHound, third-party data).

Where it breaks: warehouse data is "Import to ChurnZero only", mapped onto existing ChurnZero tables (BigQuery docs), so analysis happens inside ChurnZero's reports.

Bottom line: a strong all-round CS platform that now brings in outside data well, as long as the question fits its records.

7. Vitally

Best for: B2B SaaS teams that want a flexible data model, unlimited automations and observer seats, and docs and projects inside the CSM workspace (Vitally pricing).

What it measures: health and any custom trait you define, with dashboards "built on your CS data, not BI tooling" (Vitally reporting). It imports from PostgreSQL, Redshift, BigQuery, Snowflake and MySQL on a schedule (Vitally docs).

AI: Quin, in public beta since October 2026, answers questions across the book of business and creates tasks (release notes). An MCP server has been in beta since July 2026.

Pricing model: quote-based.

Where it breaks: data lands in Vitally's own objects and analysis happens in Vitally's widgets. One analyst summary notes few prebuilt dashboards and some warehouse sync issues (SelectHub).

Bottom line: among the best mid-market platforms for outside data without engineering.

8. Planhat

Best for: teams that want one configurable customer data model, now sold as CRM, CS and professional services product lines (Planhat pricing).

What it measures: companies, end users, licenses, invoices, NPS, tickets and custom objects, with revenue and license tracking built in. Snowflake and BigQuery sync in both directions (Planhat docs).

AI: an AI platform with a model hub, MCP server and conversational AI, plus an upgraded tier for larger teams (same pricing page).

Pricing model: a platform fee plus a minimum number of core users, billed annually.

Where it breaks: reviewers point to cross-object reporting and metrics built on fixed rolling time periods as the weak spots.

Bottom line: the most configurable model in the group, for teams willing to design it.

9. Odie (formerly Totango)

Best for: enterprise CS teams with complex account hierarchies. Totango merged with Catalyst in 2024 and renamed itself Odie, "the Customer Knowledge Engine for post-sales teams," on September 10, 2026. Existing Totango and Catalyst accounts carry on unchanged (Odie announcement).

What it measures: health across account hierarchies, ROI-based scores from Catalyst, and daily analysis of CRM, usage, email, calls and meetings (heyodie.ai).

AI: Odie agents that interview your teams and expose what they learn to other tools over MCP.

Pricing model: charged on "Curation" and "Draw", not seats or tokens (same source).

Where it breaks: reviewers have called reporting "basic and sometimes limited to just 365 days of history" and noted "limited advanced analytics" (Software Finder, 2024–2025 reviews).

Bottom line: worth a look for large, hierarchical books; check how the new Odie model fits your reporting before you commit.

10. Custify

Best for: SMB and mid-market SaaS teams that want health scores, playbooks and a customer 360 set up in weeks (custify.com).

What it measures: calculated metrics built from multiple sources, including Snowflake, BigQuery and Stripe. CustifyAI adds conversation risk scoring and a chatbot for MRR, tickets and renewals (Custify AI).

Pricing model: quote-based.

Where it breaks: "Reporting gets a bit restrictive once you go beyond the standard dashboards," one 5-star reviewer wrote in August 2026 (Software Finder).

Bottom line: fast to stand up and easy to use; plan on another tool for deeper analysis.

What about BI tools and general AI assistants?

Many CS teams already have a BI tool (Looker, Tableau, Power BI, Metabase) or a general AI assistant. BI tools can join anything once a data team has piped it into a warehouse and modeled it, so the limit is the analyst time to build and maintain that model. General AI assistants now have memory, live dashboards, scheduled tasks and hundreds of connectors, and they're very good at one-off analysis. What they don't keep on their own is one company-wide definition of each metric: context lives with a person or a project, and an answer is worked out fresh each time. Both are worth using. Querri can sit on top of the warehouse your BI tool uses, and works inside the AI assistants your team already has (Querri MCP).

How to choose: match the tool to your stack

• You're on HubSpot, with no CS platform yet: start with HubSpot's Customer Success Workspace, and add an analytics layer when you need usage and billing in the same answer.
• You have a CS platform, but reporting falls short: keep it for the workflow. Add an analytics layer that reads its data (or its export) alongside usage, billing and support.
• You're product-led and usage lives in Amplitude, Mixpanel or Pendo: that's your health signal. Join it to the CRM renewal book before you trust a renewal forecast.
• You run large books with many playbooks and digital journeys: a full CS platform (Gainsight, ChurnZero, Planhat, Vitally) earns its cost.
• You have a staffed data team and a modeled warehouse: build on it with BI, and give the CS team a way to ask questions without joining the queue.

Before you buy: questions to ask in the demo

1. Who maintains the health score and the definitions behind it, and how long does a change take?
2. How does data from outside the tool get in, how often does it refresh, and who sets it up?
3. What happens when a question needs data the tool doesn't hold?
4. Can a CSM see exactly where a number on a dashboard came from?
5. What triggers an alert, and can it compare an account against its own history?
6. What does an export of all our data look like if we leave?

Frequently asked questions

Next step

If the question on your desk this week is which accounts need attention before their renewal, start with the playbook: How to spot at-risk accounts before renewal.

This comparison reflects publicly available information as of November 2026. Product features and pricing change often; check each vendor's site before you buy. Querri publishes this guide and is one of the tools listed.