brand-marketing-strategy-proposal-generator

# Brand Marketing Strategy Proposal Generator > Skill by [ara.so](https://ara.so) — Marketing Skills collection. An agent-based workflow system for generating comprehensive brand marketing strategy proposals. Takes client data through a multi-stage process including needs analysis, research, opportunity identification, outline creation, and slide-by-slide script generation with Word document output. ## What It Does This skill automates the creation of professional marketing strategy proposals by: - Analyzing client background materials and requirements - Defining project problems and objectives - Conducting research and competitive analysis - Identifying strategic opportunities - Building structured presentation outlines - Generating detailed slide-by-slide scripts - Outputting formatted Word documents The workflow includes multiple confirmation checkpoints to ensure accuracy and alignment with client needs. ## Installation Clone or download the project into your workspace: ```bash git clone https://github.com/yuleiwang156-a11y/brand-marketing-strategy-proposal-skill.git cd brand-marketing-strategy-proposal-skill ``` Create the required directory structure: ```bash mkdir -p inputs/客户资料 mkdir -p outputs mkdir -p agent_memory mkdir -p backups ``` ## Project Structure ``` brand-marketing-strategy-proposal-skill/ ├── inputs/ │ └── 客户资料/ │ └── [客户名称]/ # Client-specific folders │ ├── brief.txt # Client brief │ ├── background.pdf # Company background │ └── research.docx # Market research ├── outputs/ # Generated proposals ├── agent_memory/ # Session state (gitignored) ├── backups/ # Workflow backups (gitignored) └── MEMORY.md # Project memory (gitignored) ``` ## Core Workflow The skill follows a six-stage process with three confirmation checkpoints: ### Stage 1: Client Input Place all client materials in `inputs/客户资料/[客户名称]/` ### Stage 2: Checkpoint 1 - Needs Understanding & Problem Definition ```text 请按照本项目 Skill 执行。本次客户资料在 inputs/客户资料/[客户名称]/ 中，请从确认点 1 开始。 ``` The agent will: - Read and analyze all client materials - Extract key business objectives - Define core marketing challenges - Propose initial problem statements - Wait for user confirmation ### Stage 3: Research & Analysis Layer After checkpoint 1 approval, the agent conducts: - Industry landscape analysis - Competitive positioning research - Consumer insights synthesis - Market trends identification - SWOT analysis ### Stage 4: Checkpoint 2 - Opportunity Insights The agent presents: - Strategic opportunity areas - Positioning recommendations - Target audience refinements - Key differentiators - Wait for user confirmation ### Stage 5: Checkpoint 3 - Outline Structure The agent provides: - Client-facing table of contents - Internal analysis structure - Slide sequence and topics - Narrative flow - Wait for user confirmation ### Stage 6: Script Generation & Output The agent generates: - Slide-by-slide content scripts - Visual direction notes - Supporting data and evidence - Quality checks - Final Word document output ## Usage Patterns ### Basic Workflow ```text User: 请为客户"ABC品牌"生成营销战略建议书，资料在 inputs/客户资料/ABC品牌/ 中 Agent: 1. 读取客户资料... 2. 【确认点 1】需求理解与问题定义： - 客户目标：提升品牌年轻化认知 - 核心问题：品牌老化，年轻消费者流失 - 项目范围：品牌重塑战略 是否确认继续？ User: 确认 Agent: 3. 执行研究分析... 4. 【确认点 2】机会点洞察： - 机会1：Z世代文化共鸣 - 机会2：数字化体验升级 - 机会3：可持续发展叙事 是否确认继续？ ``` ### Resuming from Checkpoint ```text 请从确认点 2 继续，使用 agent_memory/session_abc.json 中的状态 ``` ### Regenerating Specific Sections ```text 请重新生成目录结构（确认点 3），保持之前的机会点洞察不变 ``` ### Custom Analysis Depth ```text 请执行深度竞争分析，包含至少 5 个主要竞品的详细对比矩阵 ``` ## Configuration ### Client Data Requirements Organize client materials with clear naming: ``` inputs/客户资料/客户名称/ ├── 01_项目简报.docx # Project brief ├── 02_品牌背景.pdf # Brand background ├── 03_市场数据.xlsx # Market data ├── 04_竞品分析.pptx # Competitive analysis └── 05_消费者调研.pdf # Consumer research ``` ### Memory Management The agent uses `MEMORY.md` and `agent_memory/` to maintain state: ```markdown # MEMORY.md structure ## Current Project: [客户名称] ## Workflow Stage: [确认点 1/2/3 或生成中] ## Key Decisions: - Problem definition: [...] - Approved opportunities: [...] - Outline structure: [...] ``` ### Output Formats Default output structure: ``` outputs/ └── [客户名称]_品牌营销战略建议书_[日期]/ ├── 01_需求定义.md ├── 02_研究分析.md ├── 03_机会洞察.md ├── 04_提案目录.md ├── 05_PPT脚本.md └── 最终建议书.docx ``` ## Code Examples ### Python: Parsing Client Materials ```python import os from pathlib import Path def load_client_data(client_name): """Load all client materials from input directory""" client_dir = Path(f"inputs/客户资料/{client_name}") materials = { 'brief': None, 'background': None, 'research': [], 'data': [] } if not client_dir.exists(): raise ValueError(f"Client directory not found: {client_dir}") for file in client_dir.iterdir(): if file.suffix in ['.txt', '.md']: materials['brief'] = file.read_text(encoding='utf-8') elif file.suffix == '.pdf': materials['background'] = file elif file.suffix in ['.docx', '.doc']: materials['research'].append(file) elif file.suffix in ['.xlsx', '.csv']: materials['data'].append(file) return materials # Usage client_data = load_client_data("ABC品牌") print(f"Found {len(client_data['research'])} research documents") ``` ### Python: Generating Structured Output ```python from dataclasses import dataclass from typing import List from datetime import datetime @dataclass class OpportunityInsight: title: str description: str evidence: List[str] potential_impact: str @dataclass class ProposalOutline: client_name: str sections: List[dict] total_slides: int def to_markdown(self): md = f"# {self.client_name} 品牌营销战略建议书\n\n" md += f"生成时间: {datetime.now().strftime('%Y-%m-%d')}\n\n" md += f"总页数: {self.total_slides}\n\n" for i, section in enumerate(self.sections, 1): md += f"## {i}. {section['title']}\n" md += f"页数: {section['slides']}\n" md += f"内容: {section['description']}\n\n" return md # Usage outline = ProposalOutline( client_name="ABC品牌", sections=[ { 'title': '品牌现状诊断', 'slides': 5, 'description': '市场地位、消费者认知、竞争态势' }, { 'title': '战略机会洞察', 'slides': 8, 'description': 'Z世代文化共鸣、数字化体验升级' } ], total_slides=45 ) output_path = Path(f"outputs/{outline.client_name}_提案目录.md") output_path.write_text(outline.to_markdown(), encoding='utf-8') ``` ### Python: Checkpoint State Management ```python import json from enum import Enum class WorkflowStage(Enum): CHECKPOINT_1 = "needs_understanding" RESEARCH = "research_analysis" CHECKPOINT_2 = "opportunity_insights" CHECKPOINT_3 = "outline_structure" GENERATION = "script_generation" COMPLETE = "complete" class WorkflowState: def __init__(self, client_name): self.client_name = client_name self.stage = WorkflowStage.CHECKPOINT_1 self.data = {} self.approvals = [] def save(self): state_file = Path(f"agent_memory/{self.client_name}_state.json") state_file.write_text(json.dumps({ 'client_name': self.client_name, 'stage': self.stage.value, 'data': self.data, 'approvals': self.approvals }, ensure_ascii=False, indent=2), encoding='utf-8') @classmethod def load(cls, client_name): state_file = Path(f"agent_memory/{client_name}_state.json") if not state_file.exists(): return cls(client_name) state_data = json.loads(state_file.read_text(encoding='utf-8')) instance = cls(state_data['client_name']) instance.stage = WorkflowStage(state_data['stage']) instance.data = state_data['data'] instance.approvals = state_data['approvals'] return instance # Usage state = WorkflowState("ABC品牌") state.data['problem_definition'] = "品牌老化，年轻消费者流失" state.approvals.append({'checkpoint': 1, 'approved': True}) state.save() # Resume later resumed_state = WorkflowState.load("ABC品牌") print(f"Resuming from: {resumed_state.stage.value}") ``` ## Common Patterns ### Pattern 1: Iterative Refinement ```text User: 请生成建议书，资料在 inputs/客户资料/XYZ/ Agent: [生成确认点1] User: 问题定义太宽泛，请聚焦在年轻化转型 Agent: [调整后的问题定义] User: 确认 Agent: [继续到确认点2] ``` ### Pattern 2: Parallel Analysis ```text User: 在执行研究分析时，请同时进行： 1. 5个竞品的详细对比 2. 3个目标人群的深度画像 3. 近3年的市场趋势分析 Agent: [并行执行三项分析任务] ``` ### Pattern 3: Template-Based Generation ```text User: 使用"快消品行业模板"生成建议书框架 Agent: [应用行业特定模板，包含渠道分析、促销策略等章节] ``` ## Troubleshooting ### Issue: Client materials not found ```text Error: Client directory not found: inputs/客户资料/客户名称/ Solution: 1. Verify directory exists and name matches exactly (case-sensitive) 2. Check for special characters or spaces in folder name 3. Ensure materials are not nested in subdirectories ``` ### Issue: Memory state corruption ```text Error: Cannot resume from checkpoint - state file corrupted Solution: # Restore from backup cp backups/[客户名称]_state_backup.json agent_memory/[客户名称]_state.json # Or start fresh rm agent_memory/[客户名称]_state.json ``` ### Issue: Incomplete output generation ```text Problem: Word document missing slides 15-20 Solution: 请从第15页开始重新生成，使用已保存的outline结构： - 保持前14页内容不变 - 从"第三章：战略建议"的第15页继续 - 确保与整体叙事连贯 ``` ### Issue: Generic or shallow insights ```text Problem: Generated insights too generic Solution: 请深化分析，要求： 1. 每个洞察必须引用至少2个具体数据点 2. 包含至少1个真实案例参考 3. 明确说明与客户业务的关联性 4. 提供可量化的预期影响 ``` ## Best Practices 1. **Organize client materials clearly** - Use numbered prefixes and descriptive names 2. **Review each checkpoint carefully** - The quality of later stages depends on early approvals 3. **Save intermediate states** - Use the backup system for complex projects 4. **Provide context at checkpoints** - Give specific feedback to guide refinement 5. **Use environment-specific configurations** - Keep sensitive client data out of version control ## Environment Variables ```bash # Optional: Configure output preferences export PROPOSAL_LANGUAGE="zh-CN" export PROPOSAL_FORMAT="docx" export ANALYSIS_DEPTH="detailed" # standard, detailed, or comprehensive export OUTPUT_DIR="outputs" ``` ## Security Notes - Never commit client materials to version control - Add to `.gitignore`: ``` inputs/客户资料/ outputs/ agent_memory/ backups/ MEMORY.md *.docx *.pdf ``` - Use environment variables for any API keys or credentials - Sanitize client names in logs and error messages