CWM — Consumer World Model · Comprensión del consumidor · Núcleo
Comprende comportamientos, revisa señales y descubre audiencias similares mediante el espacio de trabajo, la API o MCP.
500.000 créditos de bienvenida para una nueva cuenta CWM
One member prediction returns next behaviors and personal purchase windows, combining consumer signals with brand demand context when the tenant fusion head is ready.
Interactive example
All states and numbers are synthetic examples, not live predictions or workspace readiness. No customer data is sent.
Behavior predictions remain available. Purchase windows stay unavailable until the brand's fusion head is trained and validated.
One final distribution over this example's four supported events. Next-event probabilities are uncalibrated.
| Next event | Probability |
|---|---|
| Purchase | 37% |
| Portal visit | 28% |
| Email click | 21% |
| Coupon redemption | 14% |
Email sent is an input context event, not a prediction target. Its probability is null. Other unsupported or uncovered events also stay unavailable.
Personal CWM signals + TimesFM brand demand context.
Chance of at least one purchase. These windows are cumulative; do not add them together or to the event distribution.
One member request returns distribution and purchaseWindows together.
curl --request GET \
"https://YOUR_API_HOST/api/v2/cwm/members/YOUR_MEMBER_UUID/next-behaviors" \
--header "Authorization: Bearer YOUR_API_KEY"{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "cwm_predict_next_behaviors",
"arguments": {
"member_id": "YOUR_MEMBER_UUID"
}
}
}Replace the API host, member UUID and API key with your workspace values.
Required access · predictions:read returns behaviors. Also grant metrics:read (or *) to enable purchase windows when the model is ready.
Synthetic excerpt only. Brand-model responses also include the full event catalog and coverage metadata; public-prior references use a separate six-class vocabulary.
{
"predictionKind": "next_event",
"unit": "probability_0_1",
"status": "degraded",
"distribution": {
"status": "ok",
"vocabulary": "member_event",
"normalization": "conditional_on_supported_events",
"calibrated": false,
"rows": [
{
"key": "purchase.completed",
"label": "Purchase",
"probability": 0.37,
"rank": 1,
"availability": "predicted"
},
{
"key": "portal.visited",
"label": "Portal visit",
"probability": 0.28,
"rank": 2,
"availability": "predicted"
},
{
"key": "email.clicked",
"label": "Email clicked",
"probability": 0.21,
"rank": 3,
"availability": "predicted"
},
{
"key": "coupon.redeemed",
"label": "Coupon redeemed",
"probability": 0.14,
"rank": 4,
"availability": "predicted"
},
{
"key": "email.sent",
"label": "Email sent",
"probability": null,
"rank": null,
"availability": "context_only"
}
]
},
"purchaseWindows": {
"unit": "probability_0_1",
"probabilities": {
"7": null,
"30": null,
"90": null
},
"calibrated": false,
"envelope": {
"status": "unavailable",
"backend": "cwm-timesfm-2.5",
"reasonCodes": [
"fusion_not_trained"
]
}
},
"reasonCodes": [
"fusion_not_trained"
]
}Synthetic data · TimesFM 2.5
An example forecast from 128 days of synthetic history. Compare the recent pattern with future daily demand and its quantiles.
Synthetic demand units
| Date (UTC) | Point forecast | q10–q90 |
|---|---|---|
| 2026-10-05 | 22.9 | 22.8–23.0 |
| 2026-10-06 | 21.3 | 21.2–21.4 |
| 2026-10-07 | 18.7 | 18.6–18.8 |
| 2026-10-08 | 17.1 | 17.0–17.1 |
| 2026-10-09 | 17.7 | 17.6–17.7 |
| 2026-10-10 | 20.0 | 20.0–20.1 |
| 2026-10-11 | 22.3 | 22.3–22.4 |
| 2026-10-12 | 22.9 | 22.9–22.9 |
| 2026-10-13 | 21.3 | 21.2–21.3 |
| 2026-10-14 | 18.7 | 18.6–18.7 |
| 2026-10-15 | 17.1 | 17.0–17.2 |
| 2026-10-16 | 17.7 | 17.6–17.7 |
| 2026-10-17 | 20.0 | 19.9–20.1 |
| 2026-10-18 | 22.4 | 22.3–22.4 |
| 2026-10-19 | 22.9 | 22.8–23.0 |
| 2026-10-20 | 21.3 | 21.2–21.4 |
| 2026-10-21 | 18.7 | 18.6–18.7 |
| 2026-10-22 | 17.1 | 17.0–17.1 |
| 2026-10-23 | 17.7 | 17.6–17.7 |
| 2026-10-24 | 20.0 | 19.9–20.0 |
| 2026-10-25 | 22.3 | 22.3–22.4 |
| 2026-10-26 | 22.9 | 22.8–22.9 |
| 2026-10-27 | 21.3 | 21.2–21.3 |
| 2026-10-28 | 18.7 | 18.6–18.7 |
| 2026-10-29 | 17.1 | 17.0–17.2 |
| 2026-10-30 | 17.7 | 17.6–17.7 |
| 2026-10-31 | 20.0 | 20.0–20.1 |
| 2026-11-01 | 22.4 | 22.3–22.4 |
| 2026-11-02 | 22.9 | 22.8–22.9 |
| 2026-11-03 | 21.3 | 21.2–21.3 |
Synthetic example, generated with TimesFM 2.5 weights. The forecast starts on 2026-10-05 (UTC); dates are fixed for the demo. Quantile coverage is uncalibrated. Values do not represent a customer's demand or individual purchase probabilities.
Receive one final probability distribution and ranking. The evaluated brand model takes priority, with up to 23 covered targets. When it is unavailable, internal public-prior models form a single six-class reference. Unsupported and context-only events remain unavailable.
Included automatically in the same member response: a tenant-private lightweight head combines CWM signals with TimesFM brand trends. Ready heads serve directly; missing training or evaluation leaves only the purchase windows unavailable.
Where the TimesFM 2.5 service is configured and daily history is complete, forecast revenue, orders or units sold for up to 90 days, with point estimates and nine quantiles.
Next-event probabilities sum to 100% over supported candidates and are uncalibrated. Purchase windows separately estimate at least one purchase within 7/30/90 days and are cumulative, never normalized with event classes. Elapsed silence is not conditioned in the event vector. The public-prior reference has not earned workspace validation.
Demand quantiles describe aggregate uncertainty; they are not individual purchase intervals and have not been calibrated. Missing data remain unavailable. No accuracy lift is asserted before your own evaluation.
Revisa el comportamiento observado, el estado del consumidor y las señales del modelo estándar sobre compras, inactividad prolongada y eliminación del carrito. Se indican los datos faltantes y los límites de cada señal.
Explora perfiles de comportamiento similares, grupos y anomalías. La similitud ayuda a estudiar una audiencia; no garantiza una tasa de conversión.
Utiliza CWM desde la consola, la API REST o MCP en aplicaciones y clientes de IA. Las claves con permisos limitados controlan el acceso al espacio de trabajo.
El análisis requiere formatos compatibles y suficiente historial de comportamiento. Las señales no disponibles se indican explícitamente. Una cuenta nueva no contiene datos de clientes ni predicciones.
Revisa los análisis en la consola o autoriza a aplicaciones y clientes de IA a consultar CWM. Importa datos compatibles, crea una clave con permisos limitados y consulta las operaciones disponibles en la referencia pública.
Consultar la referencia de API y MCP →Las cuentas nuevas reciben créditos. Las operaciones elegibles los consumen según las tarifas publicadas. Compra créditos adicionales y revisa el saldo y el uso en tu espacio de trabajo.
Regístrate con correo y contraseña o con Google y acepta las condiciones de CWM. La disponibilidad regional se muestra durante el registro.
Probar CWM →El modelado del comportamiento, los gemelos digitales, la validación por marca y las simulaciones se presentan por separado de las operaciones estándar disponibles actualmente en autoservicio.
CWM / Harness / Loop Marketing
Comprensión del consumidor · Núcleo
Consumer World Model
Comprende comportamientos, revisa señales y descubre audiencias similares mediante el espacio de trabajo, la API o MCP.
Explorar CWM →Ejecución de IA · Runtime
SocialHub Harness
Da contexto, habilidades y herramientas a la IA para trabajar dentro de los permisos, presupuestos y reglas de revisión.
Explorar Harness →Resultados de marketing · Aplicación
Agentic Retention Loop
Captura comportamientos, decide, activa los canales y desarrolla fidelidad. Los resultados alimentan el siguiente ciclo.
Explorar Loop Marketing →Los resultados observados orientan el siguiente ciclo. Las actualizaciones del modelo dependen de la calidad y validación de los datos.
CWM
Crea un espacio de trabajo, explora la consola y conecta tus datos. Compra más créditos cuando los necesites.
500.000 créditos de bienvenida para una nueva cuenta CWM
Harness + Loop Marketing
Descubre cómo los tres productos encajan con tus datos, canales y procesos.
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