Predictive churn tools for individual and parent-child business accounts

Yes. Based on current vendor documentation, Vitally, Totango, and Planhat are the strongest matches for your combination of requirements. ClientSuccess also supports the account hierarchy, but I found less evidence that its churn capability is genuinely predictive rather than health/score based.

Tool	Individual users/contacts	Parent → child business accounts	Churn prediction	Fit
Vitally	Yes — explicit User object with individual activity/adoption	Yes — Organization → multiple Accounts → Users	Yes / AI-assisted — AI surfaces churn risk; lifecycle/churn tracking exists	Strong
Totango + Unison	Yes — contacts and engagement can span hierarchy	Yes — supports multi-level/unlimited account hierarchies and unified contacts	Yes — Unison explicitly uses AI to predict churn	Strong
Planhat	Yes — End Users/Users; sentiment can roll to individual level	Yes — parent/child companies with roll-ups	Yes, but nuanced — predictive/ML scoring is supported conceptually; standard health scoring is configurable/rule-based	Strong
ClientSuccess	Contacts supported, but less evidence of individual predictive modeling	Yes — explicit Account Hierarchy	Partial — SuccessScore/Pulse and risk indicators, but less evidence of ML churn prediction	Possible
Gainsight PX	Yes — user-level product/activity analysis	Account-level capabilities exist, but the exact parent/child business-account model is less clear from the material I found	Risk/health scoring, rather than a clearly documented predictive churn model matching the above	Needs validation

1. Vitally — particularly close to your data model

Vitally explicitly models:

Organization (parent) → Account (child) → User (individual)

For example, one enterprise could be:

Acme Corp (Organization)
├── Acme US (Account)
│   ├── Jane (User)
│   └── John (User)
└── Acme EMEA (Account)
    └── Carlos (User)

Its documentation says organizations can have multiple child accounts, users belong to accounts and roll up to the organization, and individual user activity can be tracked. It also supports organization- and account-level health.docs.vitally.io+3

Vitally's current AI can explicitly surface churn risks from accounts, organizations, conversations, meetings, notes, tasks, and projects.docs.vitally.io+1

Important caveat: Vitally's documentation is clearer about AI-assisted churn-risk detection than about a standalone ML probability such as "Customer has a 73% probability of churning."

2. Totango + Unison — strongest explicit predictive-churn claim

Totango has unusually good support for complex B2B structures. It supports multi-level hierarchies, including structures such as:

Corporate
├── Division
│   ├── Branch
│   └── Branch
└── Division

and says hierarchies can have unlimited levels. Usage can enter at the ultimate-child level and roll up to parents.support.totango.com+1

It also explicitly supports viewing contacts across the hierarchy, avoiding duplicate contact records and allowing engagement with contacts associated with different levels.support.totango.com+1

The separate Unison product is particularly relevant to your "predictive" requirement: Totango says its AI models analyze customer engagement data to predict churn and detect risk. Its current product/pricing material describes standard AI models based on historical/customer engagement data and custom AI models.www.totango.com

So if "predictive" means an actual learned churn model rather than a manually configured health score, I'd put Totango/Unison near the top of the shortlist.

3. Planhat — very flexible data model

Planhat supports parent/child company relationships and explicitly describes rolling up revenue, health, usage, issues, and custom metrics through complex hierarchies.www.planhat.com

Its data model also has an End User entity representing contacts/users with a many-to-one relationship to a Company.help.planhat.com

For churn, there's an important distinction: Planhat's standard Health Scores are configurable and rule-based, although the platform supports predictive/ML approaches and AI-derived signals. Its documentation specifically describes predictive/ML scoring as a model that learns which signal combinations preceded churn, while its current health-score implementation lets teams define the factors and weights themselves.www.planhat.com

It can also aggregate sentiment down to Company, End User, and User levels, which is useful if your real requirement is to identify which individual stakeholders are showing signs of disengagement rather than only predicting account churn.help.planhat.com+1

4. ClientSuccess — hierarchy is a definite yes

ClientSuccess now has an explicit Account Hierarchy feature supporting parent-child client relationships and rolling child ARR/MRR and health into the hierarchy.help.planhat.com+1

The documentation also confirms that both parent and child need separate Client records.help.clientsuccess.com

The uncertainty is the predictive part. Its SuccessScore/Pulse system is clearly designed around customer health and risk, but I didn't find equally strong current documentation establishing an ML churn-probability model comparable to Totango Unison.

My practical shortlist

If your actual requirement is:

"Predict churn for individual users/contacts AND aggregate those signals into a parent company containing multiple child accounts."

I'd investigate these three first:

1. Totango + Unison — strongest documented combination of AI churn prediction + deep B2B hierarchy + contacts across hierarchy.www.totango.com

2. Vitally — strongest match to the explicit Organization → Account → User data model, with AI churn-risk analysis.docs.vitally.io+1

3. Planhat — strongest if you need a highly customizable model combining individual users, contacts, usage, sentiment, and hierarchical account rollups.help.planhat.com+1

One distinction I'd make before choosing: "individual user churn" and "account churn" are not necessarily the same prediction problem. Most CS platforms predict risk at the account/company level while using individual-user behavior as input. If you specifically need something like Jane has a 62% probability of churning from the product, while Acme Corp has a 14% probability of account churn, the shortlist becomes considerably narrower.

If that's your intended architecture, I can do a deeper vendor-by-vendor comparison of the actual prediction granularity, ML model capabilities, hierarchy depth, Salesforce/CRM mapping, and API/data model.