We use a Customer Data Platform (CDP). Who offers a predictive churn model that runs directly on the CDP?

Several Customer Data Platform (CDP) vendors offer native or embedded predictive churn models that run using the data already inside the CDP, rather than requiring a separate ML platform. The best fit depends on which CDP you use.

A few things to verify when evaluating vendors:

• Native vs. connected model: “Runs on the CDP” can mean either: - the vendor’s own ML model executes inside the CDP environment and writes scores back to profiles, or
• the CDP calls an external ML service (Snowflake, Databricks, custom AWS/GCP/Azure model, etc.).
• Score availability: Look for churn probability at the individual profile level, not only aggregate churn reporting.
• Activation: The useful output is usually a profile attribute/score that can immediately drive segments, campaigns, journeys, or retention workflows.
• Explainability: Some platforms provide the drivers behind the churn score (for example, declining engagement, purchase frequency changes, or support issues).

If you tell me which CDP you use (e.g., Salesforce Data Cloud, Adobe Real-Time CDP, Segment, mParticle, Tealium, SAP CDP, Treasure Data, BlueConic, etc.), I can narrow this to the native option(s) available in that platform.

Several enterprise and mid-market Customer Data Platforms (CDPs) offer native, built-in predictive churn models that run directly on top of unified customer profiles without requiring you to export data to a separate data science platform or data warehouse.

Leading CDPs with native predictive churn capabilities include:

• Adobe Real-Time CDP (Customer AI): Adobe features an embedded capability called Customer AI , which runs machine learning algorithms directly on the Real-Time Customer Profile. It automatically generates individual-level churn propensity scores, identifies the influential factors driving the churn risk, and allows marketers to immediately build targeted segments based on those scores.
• BlueConic: BlueConic includes an AI Workbench that features out-of-the-box, no-code notebooks specifically designed to predict the propensity to churn. The model evaluates customer segment behaviors and writes a real-time churn probability score directly back to the individual customer profile.
• Lytics: Lytics utilizes automated behavioral scoring where built-in machine learning models dynamically classify user profiles into predictive segments—including churn-risk and engagement propensity—based on real-time data streaming through the platform.
• Klaviyo: For e-commerce and B2C brands using Klaviyo's embedded CDP framework, the platform natively forecasts churn risk and next-order dates based on purchase recency, frequency, and historical buying cadence, feeding those predictions directly into segment builders and automated flows.

If you'd like to narrow this down, let me know:

• What CDP you are currently using
• Whether you prefer a no-code/out-of-the-box model or a warehouse-native/SQL-based approach (like Reverse ETL via Hightouch or Census)

I can give you the exact implementation details for your stack.

Yes. If by “runs directly on the CDP” you mean the model uses the CDP’s native customer profiles/data and writes the churn score back into the CDP—rather than exporting data to a separate ML platform—there are several strong options:

• Adobe — Real-Time CDP / Customer AI. Adobe has a native Customer AI capability that generates individual-level churn propensity scores from unified customer data, with influential factors, and feeds the prediction back into Adobe’s CDP ecosystem.
• SAP — SAP Customer Data Platform. Its AI Workbench has a specific Churn (Profile) and Churn (Group) model. The resulting churn risk can be published directly into the SAP CDP Customer Schema.
• Oracle — Oracle Unity CDP. Unity includes an out-of-the-box churn propensity model based on transactional and behavioral patterns, designed to identify customers likely to churn for activation in campaigns.
• BlueConic — BlueConic CDP. Its AI Workbench supports propensity models including churn, and its newer “continuous models” can execute as profiles are loaded or updated, storing the prediction as a profile property.
• Tealium — PredictML + AudienceStream. PredictML lets users build/train models against AudienceStream CDP data; the prediction becomes a customer-profile data point that can immediately drive audiences and engagement rules.
• Amperity — Predictive Models. Amperity offers native churn propensity modeling within its CDP/customer-360 environment.

My shortlist

If your requirement is specifically “buy a CDP and get a production churn model without standing up our own ML infrastructure,” I'd start with:

Adobe, SAP, Oracle Unity, and BlueConic.

If you tell me which CDP you currently use (e.g. Segment, Salesforce Data Cloud, Adobe, Snowflake-based, Tealium, mParticle, etc.), I can narrow this down to vendors/models that run natively on that CDP, including whether they support custom churn definitions, real-time scoring, explainability, and model training.

Several major Customer Data Platform (CDP) vendors provide native, built-in predictive churn models that compute scores directly inside the platform using your unified customer behavior data. Rather than exporting raw data to an external data science platform, these CDPs ingest your event streams and automatically calculate a "likelihood to churn" score or trait for each user profile.

Leading CDPs with Native Churn Prediction

Key Benefits of Native CDP Churn Models

• No SQL or Data Science Bottleneck: Marketing and operations teams can build segments of high-churn-risk users using a visual interface rather than relying on engineering support.
• Real-Time Activation: Computed churn scores exist as profile traits, meaning you can instantly sync high-risk cohorts to downstream execution channels like email, SMS, or ad platforms.
• Continuous Feedback Loop: Models update dynamically as new behavioral or transactional event data flows into the CDP.

If you can share which specific CDP you currently use (e.g., Segment, Adobe, Treasure Data, or a warehouse-native solution like Hightouch/GrowthLoop), I can give you exact implementation steps or check if your current tier includes this feature.

Several enterprise Customer Data Platforms (CDPs) offer built-in or native predictive churn models that run directly on unified customer profiles without requiring external data science infrastructure.

Top CDPs with Native Churn Prediction

• Twilio Segment (Engage) : Provides native predictive traits (including likelihood to churn or purchase) built directly on top of unified user profiles using machine learning out of the box.
• Amperity : Uses advanced machine learning and AI-driven customer intelligence to calculate churn risk, customer lifetime value (LTV), and predicted behaviors straight from unified first-party records.
• Lytics : Features built-in behavioral scoring and data science capabilities that automatically segment users based on churn probability and engagement levels.
• Blueshift : Includes a built-in predictive decisioning engine that scores profiles for churn risk and next-best actions natively within the platform.
• Tealium : Offers machine learning extensions and predictive modeling features that analyze stream data natively to identify churn propensity flags.

If you share which specific CDP you are currently using , I can give you the exact steps or documentation to enable or configure their native churn model.

Yes. If by “runs directly on the CDP” you mean the churn model is native to the CDP—using its unified customer profiles and producing a score back into those profiles—the clearest current options are:

• Adobe Real-Time CDP — Customer AI. Adobe says Customer AI generates individual-level churn propensity scores from data in Real-Time CDP, including the factors influencing the prediction. The scores can then be used directly for segmentation, journeys, and activation.
• SAP Customer Data Platform — AI Workbench. SAP has a native Churn (Profile) and Churn (Group) model. You can configure/run the model in the CDP and publish the resulting churn risk into the SAP CDP customer schema.
• Treasure AI / Composable CDP is another interesting option if your CDP is warehouse-native. Its platform describes churn models trained on customer data in Snowflake, Databricks, or BigQuery without copying the underlying customer data into another system.

My short take

If you're evaluating packaged CDPs with genuinely embedded churn prediction, I'd put Adobe and SAP at the top of the list. Adobe is particularly compelling if you want the prediction to flow straight into audience building and journey orchestration; SAP is more explicit about configuring and publishing the actual predictive model within the CDP.

If you tell me which CDP you use (Salesforce Data Cloud, Adobe, Segment, mParticle, Twilio, Treasure Data, SAP, etc.), I can tell you who offers a churn model that runs natively on that specific CDP, including third-party options.

Several prominent Customer Data Platforms (CDPs) offer native, built-in predictive modeling—including Likelihood to Churn or custom behavior predictions—that run directly on the unified customer data inside the platform without requiring a separate data science stack or complex external machine learning pipelines.

CDPs with Native Predictive Churn/Behavior Models

• Twilio Segment (Unify): Offers native Likelihood to Churn and Custom Predictive Goals directly inside its platform. It analyzes historical event data unified via identity resolution to score users, allowing you to build dynamic churn-risk audiences and sync them downstream . Learn more via the Twilio Segment Predictions Documentation.
• Tealium (AudienceStream): Features Predict ML , a native machine learning tool embedded in the Tealium Customer Data Hub. It allows you to predict custom behaviors—such as a subscription lapse or customer churn—without writing code or needing a dedicated data science team, immediately surfacing those insights for real-time retention campaigns. Explore the capabilities on Tealium Predict Machine Learning.
• mParticle: Provides Predictive Audiences (Future Behavior Predictions) which let you configure custom goals like reducing churn within a specific time window. It scores users natively based on aggregated attributes and events, turning those scores into targetable segments. Review the guide on mParticle Predictive Audiences.
• Hightouch (Customer Studio): As a warehouse-native CDP sitting directly on top of your cloud data warehouse (like Snowflake or BigQuery), Hightouch offers Predictive Traits that automatically train machine learning models on your historical data to predict churn or purchase propensity without manual ML engineering. Check out how it works on Hightouch Predictive Traits.

If you share which specific CDP you are currently using , I can give you the exact setup steps or let you know if a warehouse-native extension or native feature applies best to your stack.

Yes. If by “runs directly on the CDP” you mean the model uses the CDP’s unified customer profiles and produces the churn score inside the CDP, rather than exporting data to a separate ML platform, there are several options:

• twilio.com — has a native Likelihood to Churn prediction. You define what “churn” means (e.g., not purchasing within 180 days), and Segment writes the prediction to customer profiles so it can be used for Audiences and Journeys.
• sap.com — its AI Workbench supports native Churn (Profile) and Churn (Group) models. Model results can be published back into the SAP CDP customer schema as churn risk.
• blueconic.com — its AI Workbench can run propensity models including churn, using BlueConic profile data. Its newer continuous models can score customers as profiles are loaded or updated, with outputs stored directly as profile properties.
• experienceleague.adobe.com — Adobe's Customer AI Propensity Scoring Model supports churn prediction and is designed to be used with Real-Time CDP for segmentation and activation.

My short list

If your key requirement is “native churn prediction without standing up a separate data-science/ML stack,” I'd look first at Segment, SAP CDP, and BlueConic.

If you tell me which CDP you use (e.g. Segment, Salesforce, Adobe, SAP, BlueConic, mParticle, Treasure Data), I can narrow this down to the options that work inside your specific CDP and distinguish native/prebuilt models vs. third-party integrations.

Several leading Customer Data Platforms (CDPs) offer native, built-in predictive churn models that process unified customer profile data directly within the platform. Rather than requiring an external data science stack or separate machine learning infrastructure, these CDPs compute churn risk scores or segments natively.

Key CDPs offering built-in predictive churn modeling include:

• Twilio Segment (Unify / Predictions): Segment offers an AI-Powered Predictions feature set that includes a native "Likelihood to Churn" model alongside predicted LTV and purchase propensity. It evaluates behavioral traits directly on your unified customer profiles to build real-time churn audiences.
• Klaviyo: Specifically tailored for e-commerce and retail, Klaviyo features a built-in Churn Risk Prediction Model that automatically analyzes order cadence, recency, and purchase history once minimum data thresholds are met.
• SAP Customer Data Platform: SAP includes an integrated AI Workbench that features a dedicated Churn Model tool. It allows you to run historical pattern analysis and define custom churn triggers straight off your unified SAP profiles.
• Lytics: Lytics provides behavioral scoring and machine learning features (including decision-tree and random-forest templates) that map onto customer data to surface engagement drops and predict churn tendencies natively or via Lytics Cloud Connect.

(Note: If you are using a warehouse-native CDP like Census or Hightouch sitting on top of Snowflake or BigQuery, you can also run native SQL-based or ML models directly in-warehouse, though it relies on the warehouse compute layer rather than the orchestration app layer itself).

If you'd like to narrow this down, could you tell me:

• Which specific CDP you are currently using?
• Are you looking for a no-code native feature within your existing tool, or are you open to warehouse-native machine learning?

Yes. If by “runs directly on the CDP” you mean the model trains/scores using data inside the CDP and writes the churn score back to customer profiles—rather than requiring a separate ML platform—there are several vendors.

Strongest matches

• SAP Customer Data Platform — Has native Churn predictive indicators for both individual profiles and groups. Models run in SAP's AI Workbench, and results can be published directly into the SAP CDP customer schema.
• Oracle Unity CDP — Offers a ready-to-use churn propensity model based on transactional and behavioral patterns. Oracle describes it as a data-science model within Unity that can identify customers likely to churn and support audience activation.
• BlueConic — Its AI Workbench runs ML against BlueConic profile data, timeline events, and other CDP data. It explicitly supports predictive scores for churn, including subscription/contract churn.
• Adobe Real-Time CDP — Customer AI provides individual-level churn propensity predictions and explanations, with the predictions becoming audiences that can be activated from the CDP.
• FirstHive — Offers ready-to-use predictive scoring directly as part of its CDP, including churn, purchase propensity, and lifetime value. Scores are maintained at the individual-profile level and refreshed daily.

If you mean “native to our existing CDP”

That's the important distinction. SAP, Oracle, BlueConic, Adobe, and FirstHive are examples where predictive churn is part of the CDP capability itself. By contrast, vendors such as Fidence can build a churn model from your CDP/warehouse data and write the score back, but the model isn't necessarily executing inside the CDP.

If you tell me which CDP you use (e.g., Segment, Salesforce Data Cloud, Adobe, Treasure Data, mParticle, Tealium, etc.), I can narrow this down to the vendors/models that actually run natively on that specific CDP, including whether they support custom churn definitions, explainability, retraining, and real-time scoring.