feat(notion): Finalize Competitive Radar v3.0 (Level 3 Relational Model)
- Extended import_relational_radar.py to include a 'Products' database. - Implemented full dual-way relations for Companies <-> Landmines, References, Products. - Updated documentation to reflect the 4-database architecture.
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@@ -72,11 +72,12 @@ Die App ist unter `/ca/` voll funktionsfähig und verfügt nun über eine "Groun
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### 📊 Relationaler Notion Import (Competitive Radar v2.0)
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Um die Analyse-Ergebnisse optimal nutzbar zu machen, wurde ein bidirektionaler Import-Prozess nach Notion implementiert (`import_relational_radar.py`).
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* **Architektur:** Statt Textblöcken werden drei vernetzte Datenbanken erstellt:
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1. **📦 Companies (Hub):** Stammdaten, USPs, Portfolio.
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* **Architektur:** Statt Textblöcken werden vier vernetzte Datenbanken erstellt:
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1. **📦 Companies (Hub):** Stammdaten, USPs, Portfolio-Summary.
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2. **💣 Landmines (Satellite):** Einzelfragen und Angriffsvektoren, verknüpft mit der Company.
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3. **🏆 References (Satellite):** Konkrete Kundenprojekte, verknüpft mit der Company.
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* **Dual-Way Relations:** Dank `dual_property` Konfiguration sind die Verknüpfungen in Notion sofort in beide Richtungen navigierbar (z.B. sieht man auf der Company-Seite sofort alle zugehörigen Landmines).
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4. **🤖 Products (Satellite):** Einzelne Produkte als Datensätze, ermöglicht marktweiten Vergleich (z.B. "Alle Reinigungsroboter").
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* **Dual-Way Relations:** Dank `dual_property` Konfiguration sind die Verknüpfungen in Notion sofort in beide Richtungen navigierbar.
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* **Daten-Qualität:** Durch die Map-Reduce Analyse und das gezielte Reference-Scraping werden nun echte Fakten statt KI-Halluzinationen importiert.
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---
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@@ -43,7 +43,7 @@ Die Schaltstelle für die hyper-personalisierte Ansprache.
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Automatisierte Überwachung der Marktbegleiter mit Fokus auf "Grounded Truth".
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* **Funktion:** Kontinuierliches Scraping von Wettbewerber-Webseiten, gezielte Suche nach Referenzkunden und Case Studies.
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* **Kill-Argumente & Landmines:** Erstellung von strukturierten Battlecards und spezifischen "Landmine Questions" für den Sales-Außendienst.
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* **Relationaler Ansatz:** Trennung in drei verknüpfte Datenbanken (Firmen, Landmines, Referenzen) für maximale Filterbarkeit und Übersicht.
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* **Relationaler Ansatz:** Trennung in vier verknüpfte Datenbanken (Firmen, Landmines, Referenzen, Produkte) für maximale Filterbarkeit und Übersicht.
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### 3.5 Enrichment Factory & RevOps
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Datenanreicherung der CRM-Accounts.
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@@ -93,6 +93,7 @@ Um die relationale Integrität zu wahren, sind folgende Datenbanken in Notion zw
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* **Messaging Matrix** $\leftrightarrow$ **Sector Master** (Welcher Schmerz gehört zu welcher Branche?)
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* **Competitive Radar (Companies)** $\leftrightarrow$ **Competitive Radar (Landmines)** (Welche Angriffsfragen gehören zu welchem Wettbewerber?)
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* **Competitive Radar (Companies)** $\leftrightarrow$ **Competitive Radar (References)** (Welche Kundenprojekte hat der Wettbewerber realisiert?)
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* **Competitive Radar (Companies)** $\leftrightarrow$ **Competitive Radar (Products)** (Welche Produkte hat der Wettbewerber im Portfolio?)
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* **The Brain** $\leftrightarrow$ **Product Master** (Welches Support-Wissen gehört zu welcher Hardware?)
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* **GTM Workspace** $\leftrightarrow$ **Product Master** (Welche Kampagne bewirbt welches Gerät?)
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* **Feature-to-Value Translator** $\leftrightarrow$ **Product Master** (Welcher Nutzen gehört zu welchem Feature?)
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@@ -6,12 +6,13 @@ import sys
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# Configuration
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JSON_FILE = 'analysis_robo-planet.de.json'
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TOKEN_FILE = 'notion_token.txt'
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PARENT_PAGE_ID = "2e088f42-8544-8024-8289-deb383da3818"
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PARENT_PAGE_ID = "2e088f42-8544-8024-8289-deb383da3818"
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# Database Titles
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DB_TITLE_HUB = "📦 Competitive Radar (Companies)"
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DB_TITLE_LANDMINES = "💣 Competitive Radar (Landmines & Intel)"
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DB_TITLE_REFS = "🏆 Competitive Radar (References)"
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DB_TITLE_PRODUCTS = "🤖 Competitive Radar (Products)"
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def load_json_data(filepath):
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try:
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@@ -81,7 +82,7 @@ def main():
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token = load_notion_token(TOKEN_FILE)
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data = load_json_data(JSON_FILE)
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print("🚀 Starting Relational Import...")
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print("🚀 Starting Relational Import (Level 3 - Full Radar)...")
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# --- STEP 1: Define & Create Competitors Hub DB ---
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props_hub = {
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@@ -130,6 +131,19 @@ def main():
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}
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refs_db_id = create_database(token, PARENT_PAGE_ID, DB_TITLE_REFS, props_refs)
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# Products DB
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props_products = {
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"Product Name": {"title": {}},
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"Purpose / Description": {"rich_text": {}},
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"Related Competitor": {
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"relation": {
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"database_id": hub_db_id,
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"dual_property": {"synced_property_name": "Related Products"}
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}
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}
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}
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products_db_id = create_database(token, PARENT_PAGE_ID, DB_TITLE_PRODUCTS, props_products)
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# --- STEP 3: Import Competitors (and store IDs) ---
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competitor_map = {} # Maps Name -> Notion Page ID
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@@ -156,10 +170,10 @@ def main():
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# Create Page
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props = {
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"Name": {"title": [{"text": {"content": c_name}}]},
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"Portfolio Summary": {"rich_text": [{"text": {"content": portfolio_text[:2000]}}]},
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"Portfolio Summary": {"rich_text": [{"text": {"content": portfolio_text[:2000]}}]}, # Limit length
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"USPs": {"rich_text": [{"text": {"content": usps[:2000]}}]},
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"Silver Bullet": {"rich_text": [{"text": {"content": silver_bullet[:2000]}}]},
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"Target Industries": {"multi_select": [{"name": i.replace(',', '')} for i in industries]},
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"Target Industries": {"multi_select": [{"name": i.replace(',', '')} for i in industries]}
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}
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if c_url: props["Website"] = {"url": c_url}
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@@ -185,10 +199,8 @@ def main():
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create_page(token, landmines_db_id, props)
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# 2. Weaknesses
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# The JSON has "strengths_vs_weaknesses" combined. We'll import them as general Intel points.
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for point in card.get('strengths_vs_weaknesses', []):
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# Try to guess type based on text, or just default to Weakness context from Battlecard
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p_type = "Competitor Weakness" # Assuming these are points for us to exploit
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p_type = "Competitor Weakness"
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props = {
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"Statement / Question": {"title": [{"text": {"content": point}}]},
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"Type": {"select": {"name": p_type}},
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@@ -224,7 +236,30 @@ def main():
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count_refs += 1
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print(f" - {count_refs} References imported.")
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print("\n✅ Relational Import Complete!")
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# --- STEP 6: Import Products (Portfolio) ---
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print("\nImporting Products...")
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count_prods = 0
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for analysis in data.get('analyses', []):
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c_name = analysis.get('competitor', {}).get('name')
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comp_page_id = competitor_map.get(c_name)
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if not comp_page_id: continue
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for prod in analysis.get('portfolio', []):
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p_name = prod.get('product', 'Unknown Product')
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p_purpose = prod.get('purpose', '')
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props = {
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"Product Name": {"title": [{"text": {"content": p_name}}]},
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"Purpose / Description": {"rich_text": [{"text": {"content": p_purpose[:2000]}}]},
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"Related Competitor": {"relation": [{"id": comp_page_id}]}
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}
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create_page(token, products_db_id, props)
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count_prods += 1
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print(f" - {count_prods} Products imported.")
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print("\n✅ Relational Import Complete (Level 3)!")
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if __name__ == "__main__":
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main()
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main()
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