Cases & Playbooks

Growth systems y AI workflows que tu equipo puede operar de verdad

Mira cómo Fabrick Media convierte trabajo de marketing disperso en reporting repetible, creative testing y operating systems asistidos por agents.

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Experiencia del founder conectada con equipos y marcas comotrivagoCoIQinneOviva

Growth Loop

Research

Angles

Briefs

Creatives

Landing Pages

Testing

Agent Workflow

Input

AI Agents

Human Approval

Execution

Reporting

Sistemas representativos de clientes

Marketing Operations

Marketing Workflow Operating System

Contexto: Marketing team with recurring reporting, content preparation and campaign coordination.
Punto de partida: Context lived across documents, dashboards and messages, slowing weekly decisions.
Sistema: Agent-assisted reporting and content-prep workflow with QA and human approval.
Implementación: Intake template, prompt library, review checklist, dashboard readout and operating cadence.
Resultado: Reduced weekly reporting assembly by replacing scattered inputs with one reviewed decision readout.
Aprendizaje: The workflow worked best when approval ownership was explicit.
Limitaciones: Not designed as a fully autonomous publishing system.
Periodo / fuente: Pilot pattern; client-specific details anonymized.

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D2C Growth

UGC Growth Loop for D2C

Contexto: D2C team needing more usable creative inputs for paid social testing.
Punto de partida: Creator inflow, briefs and performance feedback were not connected.
Sistema: Creator pipeline, structured briefs, variant production and learning repository.
Implementación: Creator board, briefing templates, naming rules, readout template and weekly cadence.
Resultado: Increased the volume of test-ready creative by connecting creator sourcing, briefs and performance feedback.
Aprendizaje: UGC improves faster when creators receive clear examples and constraints.
Limitaciones: Performance depends on offer, media buying and market conditions.
Periodo / fuente: Representative growth-system pattern; details anonymized.

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Data & Intelligence

Agent-Based Reporting Workflow

Contexto: Team producing recurring reports from multiple data and knowledge sources.
Punto de partida: Manual collection and interpretation delayed decisions.
Sistema: Agents collect inputs, draft summaries, flag anomalies and route output for review.
Implementación: Source list, data checks, anomaly prompts, summary format and human approval gate.
Resultado: Shortened report preparation by routing data collection, anomaly checks and summaries through a reviewed agent workflow.
Aprendizaje: QA prompts need clear thresholds and named review owners.
Limitaciones: Human review remains required for interpretation and final recommendations.
Periodo / fuente: Agent workflow pattern; details anonymized.

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Playbooks

Angle Machine

Objetivo: Prioritize stronger messages
Inputs: Research, reviews, ads data
Pasos: Mine signals, cluster angles, score hypotheses, brief tests
Cadencia: Weekly
KPI: Test velocity and winner rate
Output: Ranked angle backlog
Owner: Growth lead

Landing Page Testing

Objetivo: Improve conversion learning
Inputs: Traffic data, offers, page analytics
Pasos: Map friction, draft variants, launch tests, document readouts
Cadencia: Biweekly
KPI: Conversion signal and learning quality
Output: Page variant roadmap
Owner: Growth/Product

Creator Pipeline

Objetivo: Increase usable UGC supply
Inputs: Creator list, briefs, examples
Pasos: Source, brief, produce, tag, reuse
Cadencia: Weekly
KPI: Usable variants per cycle
Output: Creator production board
Owner: Creative lead

Agent Workflow Blueprint

Objetivo: Design a controlled workflow
Inputs: Process map, tools, risks
Pasos: Map states, assign agents, add QA, define approvals
Cadencia: Project
KPI: Cycle time and error visibility
Output: Workflow spec
Owner: Ops lead

Content Repurposing

Objetivo: Turn one source asset into channel-specific outputs
Inputs: Source content, audience, channels
Pasos: Extract themes, adapt formats, QA, approve
Cadencia: Per asset
KPI: Approved variants per asset
Output: Approved asset set
Owner: Content lead

AI Research System

Objetivo: Speed up market and customer research
Inputs: Sources, questions, constraints
Pasos: Collect, compare, cite, summarize
Cadencia: Per research sprint
KPI: Evidence quality and time saved
Output: Research brief and evidence log
Owner: Strategy lead

Cómo los casos se convierten en sistemas

Empezamos con un bottleneck recurrente de growth, diseñamos el operating system alrededor, lo pilotamos con puntos de aprobación humana y entregamos la cadencia, prompts, checks y readout templates.

Baseline

Document the current workflow, inputs, owners and constraints.

Map

Translate the workflow into a repeatable system with clear handoffs.

Instrument

Add tracking, QA checks, readouts and approval points.

Measure

Review the pilot against the practical success metric.

Handover

Document the operating rhythm and next iteration.

Encuentra el growth workflow que conviene sistematizar primero

Trae un bottleneck de reporting, creative, research o testing. Mapearemos el operating system y el siguiente paso de piloto.

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