Strategic Planning research

An Actor that scrapes real-world strategic planning implementations from business journals, corporate sites, and case studies, then transforms them into execution-ready assets: OKRs/KPIs, Balanced Scorecard maps, EOS components (Rocks, Scorecard, L10 meetings), RACI matrices, project timelines, and OCAI culture assessments. Outputs include executive briefs, structured data, and actionable implementation templates.

Input Fields

Search & Discovery

• searchQuery (text): e.g., "ADKAR digital transformation healthcare"
• frameworkType (multi-select):
  • Strategic Planning
  • Strategic Thinking
  • VUCA / VUCA-Prime
  • McKinsey 7S Model
  • ADKAR Change Model
  • Kotter's 8-Step Process
  • Blue Ocean Strategy
  • EOS/Traction
  • Balanced Scorecard
• sourceType (multi-select): Business journals, Corporate websites, Case study databases, Industry reports, News articles
• dateRange (date picker): From / To
• industry (dropdown): Technology, Healthcare, Finance, Manufacturing, Public Sector, Education, Retail, etc.
• companySize (dropdown): Startup, SMB, Enterprise
• region (dropdown): Global, North America, Europe, APAC, MENA, etc.
• languages (multi-select): e.g., ["en", "ar", "es", "fr"]

Processing Options

• includePdf (boolean): Fetch and parse PDF documents
• followDepth (integer): Link crawl depth (0-2)
• maxResults (integer): Maximum results to return
• dedupe (boolean): Enable hash-based deduplication
• summaryWords (integer, default 150): Executive summary length

Execution Artifacts

• executionArtifacts (multi-select):
  • OKRs (Objectives & Key Results)
  • KPIs (Key Performance Indicators)
  • BSC (Balanced Scorecard)
  • EOS Components (Rocks, Scorecard, L10)
  • Project Management Plan
  • RACI Matrix
  • OCAI Culture Profile
  • Executive Report
• okrHorizon (dropdown): Quarter, Half-Year, Annual
• bscPerspectives (multi-select): Financial, Customer, Internal Process, Learning & Growth
• eosQuarter (text): e.g., "Q1-2025"
• pmMethodology (dropdown): Agile, Waterfall, Hybrid, PRINCE2, PMI/PMBOK
• teamRoster (array): [{name, role, email, department}] for RACI automation
• meetingCadence (object): {weeklyL10: true, monthlyOps: true, quarterlyPlanning: true}

Filtering & Export

• allowDomains (array): Whitelist specific domains
• blockDomains (array): Blacklist specific domains
• export (multi-select): JSON, CSV, Excel, Markdown

Output Data Points

Core Evidence & Context

• title (text): Case study or article title
• companyName (text): Organization name
• industry (text): Industry classification
• frameworkUsed (array): e.g., ["ADKAR", "7S", "EOS"]
• implementationDate (date): When implementation occurred
• challengeAddressed (text): Primary problem solved
• implementationTimeline (text): Duration and phases
• keySuccessFactors (array): Critical success elements
• lessonsLearned (text): Key takeaways
• successMetrics (object):
  • revenueImpact (percentage)
  • efficiencyGains (percentage)
  • marketShareChange (percentage)
  • employeeEngagementScore (0-1)
  • adoptionRate (0-1)
  • customerSatisfaction (0-1) 4:56
• roiImpactSummary (text): Quantified results summary

Strategic Framework Mappings

• frameworkMappings (object):
  • adkar: {awareness, desire, knowledge, ability, reinforcement}
  • kotter: ["urgency", "coalition", "vision", "communication", "empowerment", "wins", "consolidation", "anchoring"]
  • sevenS: {strategy, structure, systems, skills, style, staff, sharedValues}
  • vucaPrime: {vision, understanding, clarity, agility}
  • blueOcean: {valueInnovation, eliminate[], reduce[], raise[], create[]}

Execution Artifacts

OKRs & KPIs

• okrs (array):

json { "objective": "Transform digital customer experience", "keyResults": [ {"kr": "Increase NPS to 70+", "target": 70, "owner": "CXO", "due": "2025-03-31"}, {"kr": "Reduce support tickets by 40%", "target": -0.40, "owner": "Support Lead", "due": "2025-03-31"} ], "confidence": 0.75 }

• kpis (array): {name, definition, formula, target, frequency, owner, sourceSystem}

Balanced Scorecard

• bscMap (object):
  • financial: Revenue growth, cost reduction, ROI metrics
  • customer: Satisfaction, retention, acquisition metrics
  • internalProcess: Efficiency, quality, innovation metrics
  • learningAndGrowth: Skills, culture, technology metrics

EOS/Traction Components

• eos (object):
  • rocks: Quarterly priorities with owners
  • scorecardMetrics: Weekly measurables
  • issuesList: Current challenges to solve
  • l10Agenda: Meeting structure and cadence

Project Management

• pmPlan (object):
  • workstreams: Major work tracks
  • milestones: Key deliverables with dates
  • risks: Risk register with mitigation plans
  • dependencies: Cross-team dependencies
  • timeline: Gantt chart data

RACI Matrix

• raci (array): {task, responsible, accountable, consulted[], informed[]}

Culture Assessment

• ocai (object):
  • currentProfile: {clan, adhocracy, market, hierarchy} percentages
  • desiredProfile: Target culture state
  • gapAnalysis: Key culture shifts needed

Metadata & Documentation

• evidenceQuotes (array): Key supporting quotes with citations
• url (URL): Source link
• domain (text): Source domain
• pdfAssets (array): [{filename, downloadUrl}]
• authorOrganization (text): Publishing entity
• publicationDate (date): When published
• language (text): Content language
• sentiment (number): 0-1 sentiment score
• confidence (number): 0-1 confidence score
• summary150 (text): Executive summary (~150 words)
• dedupeId (text): Unique content hash
• scrapedAt (timestamp): Processing timestamp

Example Output

{
  "title": "Digital Transformation Success: ADKAR + EOS Implementation",
  "companyName": "TechCorp Solutions",
  "industry": "Technology",
  "frameworkUsed": ["ADKAR", "EOS", "Balanced Scorecard"],
  "challengeAddressed": "Low digital adoption and misaligned priorities",
  "successMetrics": {
    "adoptionRate": 0.92,
    "efficiencyGains": 0.35,
    "employeeEngagementScore": 0.84
  },
  "okrs": [
    {
      "objective": "Achieve company-wide digital transformation",
      "keyResults": [
        {"kr": "90% teams using new platform daily", "target": 0.90, "owner": "CDO", "due": "2025-06-30"},
        {"kr": "Reduce process time by 35%", "target": -0.35, "owner": "COO", "due": "2025-06-30"}
      ]
    }
  ],
  "eos": {
    "rocks": [
      {"name": "Complete platform migration", "owner": "CTO", "quarter": "Q2-2025"},
      {"name": "Train all departments", "owner": "HR Director", "quarter": "Q2-2025"}
    ],
    "scorecardMetrics": ["Weekly Active Users", "Process Cycle Time", "Training Completion Rate"]
  },
  "raci": [
    {"task": "Platform rollout", "responsible": "IT Team", "accountable": "CTO", "consulted": ["Department Heads"], "informed": ["All Staff"]}
  ],
4:56
"summary150": "TechCorp achieved 92% adoption of their digital transformation initiative within 6 months using ADKAR change management combined with EOS rocks for accountability. Key success factors included executive sponsorship, department champions, and weekly L10 meetings tracking progress. The implementation reduced process time by 35% and increased employee engagement to 84%. Lessons learned emphasize the importance of combining strategic frameworks with tactical execution tools and maintaining consistent communication cadence throughout the transformation journey."
}

## Use Cases
- **Consultants**: Build evidence-based proposals with proven implementation patterns
- **Executives**: Transform case studies into actionable execution plans
- **PMOs**: Generate project templates from successful implementations
- **Change Managers**: Create RACI matrices and communication plans from similar transformations
- **Strategy Teams**: Benchmark against industry success stories and extract best practices

## Arabic Support (نسخة عربية)

This comprehensive Actor would provide immense value for organizations looking to learn from others' strategic implementations and quickly transform those learnings into their own execution plans.

*Please elaborate on your use case for the Actor*
Here's a **"red-team" review** of your Actor spec, followed by a **re-fined version** that plugs the gaps and makes it Apify-store ready.

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1. What is missing / weak
A. **Role & Persona** – never stated who the Actor "is" (consultant, strategist, PMO).  
B. **Tech & Deployment** – no mention of Apify SDK, Puppeteer/Playwright, memory limits, dataset schema, or pricing tier.  
C. **Scope OUT** – not clear what the Actor will *not* do (e.g., no custom consulting calls, no legal advice).  
D. **Non-Functional Requirements** – no SLA, privacy/GDPR stance, rate-limiting, or cost ceiling.  
E. **Deliverable Format** – says "JSON, CSV, Excel, Markdown" but not *how* they are exposed (Apify dataset? KV store? S3?).  
F. **Test / Review Strategy** – zero mention of CI, sample runs, or validation.  
G. **Arabic UX** – only tagged on at the end; RTL layout, font, and locale handling undefined.

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2. Re-fined Actor prompt (plug-and-play for Apify)

[1] ROLE 
You are a senior strategy-transformation consultant and certified Apify Actor developer. 
Your mission is to curate, analyse, and re-package real-world strategic-implementations into execution-ready artefacts.
[2] GOAL 
Deliver an Apify Actor that: 
- Scrapes evidence of successful strategy implementations (ADKAR, EOS, BSC, 7S, etc.) 
- Generates OKRs, KPIs, Balanced Scorecards, EOS Rocks, RACI, OCAI culture profiles, and executive briefs. 
- Exposes the artefacts via Apify datasets + optional Google-Sheets/Notion/Slack integrations.
[3] SCOPE IN 
- Search: Google, Bing, PubMed, SSRN, Emerald, McKinsey Insights, Harvard Business Review, company blogs, PDF case studies. 
- NLP extraction: framework used, timeline, ROI, metrics, lessons learned. 
- Output artefacts: JSON, CSV, XLSX, Markdown, Google-Sheets append, Notion DB update. 
- Multi-language: English + Arabic (RTL-ready). 
- Deduplication & confidence scoring. 
- Rate-limit respectful (max 30 req/min per source). 
- GDPR-compliant (no PII storage).
[4] SCOPE OUT 
- No live consulting calls. 
- No custom legal or financial advice. 
- No scraping behind paywalls or login-only content.
[5] TECH & CONSTRAINTS 
- Node 20 + Apify SDK 3.x. 
- Crawler: PlaywrightCrawler, headless=true, memory=4 GB max. 
- LLM: OpenAI gpt-4-turbo for summarisation & mapping. 
- Max runtime: 15 min (default), extendable by user. 
- Pricing tier: Starter (≈ $0.25 / 100 pages). 
- Storage: default Apify dataset + optional Key-Value store for PDFs. 
- Privacy: strip e-mails, phone numbers, GDPR “right to be forgotten” flag.
[6] ACCEPTANCE CRITERIA
4:56
Given a search query “ADKAR digital transformation healthcare” 
- Actor returns ≥ 10 non-duplicate evidence records. 
- Each record contains: title, company, industry, frameworkUsed, successMetrics, 150-word summary, OKRs, KPIs, RACI. 
- EvidenceQuotes array has ≥ 3 verbatim snippets with URLs. 
- Arabic output uses RTL layout, Noto Arabic font, and correct numerals. 
- Lighthouse score ≥ 95 on generated HTML report (if HTML chosen).
[7] DELIVERABLE FORMAT 
- Apify Dataset (default) 
- Optional Google-Sheets tab (append) 
- Optional Notion database page (upsert) 
- Optional Slack webhook notification 
- Sample run JSON + README + CHANGELOG in repo.
[8] STYLE GUIDE 
- Code: Airbnb ESLint + Prettier. 
- Git: Conventional commits, semantic-release. 
- Docs: README + /docs/adr/*.md. 
- Arabic: RTL CSS, dir="rtl" where needed.
[9] TEST STRATEGY 
- Unit tests: Jest, 80 % coverage. 
- E2E test: sample run on Apify platform with mock search results. 
- CI: GitHub Actions (push, PR). 
- Validation script: npm run validate:sample spins up local Actor, asserts output schema.
[10] REVIEW & ITERATION 
- After v0 push, run 3 real queries, collect user feedback via Apify Issues. 
- Iterate weekly for 4 weeks, tag v1.0.0 after 0 open P1 issues.
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3. Updated Input & Output Schema (concise)
Input (TypeScript interface)
ts
interface Input {
  searchQuery: string;
  frameworks: Array<'ADKAR'|'EOS'|'BSC'|'7S'|'Kotter'|'BlueOcean'|'VUCA'>;
  sources: Array<'journals'|'blogs'|'reports'|'pdfs'>;
  dateRange: { from: string; to: string };
  industry?: string;
  companySize?: 'startup'|'SMB'|'enterprise';
  region?: string;
  languages: Array<'en'|'ar'>;
  maxPages: number;
  includePdf: boolean;
  outputFormats: Array<'json'|'csv'|'xlsx'|'md'|'gsheets'|'notion'>;
  googleSheetId?: string;
  notionDbId?: string;
}
Output (per dataset row)
json
{
  "title": "...",
  "company": "...",
  "industry": "...",
  "frameworks": ["ADKAR"],
  "challenge": "...",
  "timeline": "...",
  "successMetrics": { "adoption": 0.92, "roi": 0.35 },
  "okrs": [ { "o": "...", "krs": [...] } ],
  "kpis": [ { "name": "...", "formula": "...", "target": 70 } ],
  "raci": [ { "task": "...", "R": "...", "A": "...", "C": [], "I": [] } ],
  "evidenceQuotes": [ { "quote": "...", "url": "..." } ],
  "lang": "en",
  "confidence": 0.87,
  "scrapedAt": "2025-08-12T12:34:56Z"
}
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4. One-liner to run & validate
bash
apify run --input '{"searchQuery":"ADKAR digital transformation healthcare","maxPages":10,"outputFormats":["json","gsheets"]}' && npm run validate:sample
Paste the refined spec into the Apify Actor editor, hit **“Build & Test”**, and you’re store-ready.