🚀 Merged technical article: trend‑to‑product engine, JSON logging, CRM piping, and dashboard UX

Purpose‑built for auditability, human‑in‑the‑loop outreach, and modular reuse in civic/health automation.

Overview ✅

This article merges four responses into a single, copy‑paste‑ready deliverable: evaluation of your matching engine, a production refactor with JSON logging, CRM integration workflow, and a sample dashboard layout for outreach teams.

💡 Evaluation of current algorithm: normalization, scoring, alerts.
🛠️ Refactor: configurable thresholds, strong‑match filtering, JSON audit log.
🔗 CRM piping: field mapping, automation rules, pseudocode.
📊 Dashboard UX: trends, products, alerts, audit viewer.

All sections are designed to be immediately reusable and reviewable by collaborators. The header stays visible as you scroll for rapid navigation.

Engine refactor with JSON logging 🧩

This version ensures only highest‑scoring matches are surfaced, provides configurable sensitivity, and writes an audit log for compliance reviews.

# production_ready_trend_engine.py
import re
import json
from typing import List, Dict, Any

# --- CONFIGURATION ---
MIN_SCORE = 2  # configurable threshold for relevance
TOP_N = 3      # configurable number of top matches to keep
LOG_FILE = "trend_matches.json"  # audit log file

# --- MOCK DATA (example) ---
PRODUCT_CATALOG: List[Dict[str, Any]] = [
    {"id": "P001", "name": "Advanced Sleep Aid Complex",
     "keywords": ["insomnia", "better sleep", "deep rest", "melatonin", "sleep issues"],
     "category": "Sleep & Recovery", "stock": 150},
    {"id": "P002", "name": "Keto-Friendly Protein Powder",
     "keywords": ["keto diet", "low carb", "meal replacement", "ketogenic", "fat burning"],
     "category": "Diet & Nutrition", "stock": 300},
    {"id": "P003", "name": "Natural Stress Relief Gummies",
     "keywords": ["anxiety", "stress management", "calm", "ashwagandha", "mental health"],
     "category": "Mental Wellness", "stock": 50},
    {"id": "P004", "name": "High-Dose Vitamin D3 Drops",
     "keywords": ["immune system", "winter blues", "bone health", "vitamin deficiency"],
     "category": "Vitamins", "stock": 500},
]

TRENDING_DATA: Dict[str, List[str]] = {
    "X_Trends": ["#sleepissues", "best natural sleep aid", "seasonal depression solutions"],
    "Facebook_Trends": ["quick keto meals", "managing winter stress", "low energy during winter"],
    "LinkedIn_Pulse": ["workplace mental health strategies", "boosting focus naturally"],
    "Searches": ["supplements for anxiety", "vitamin D shortage symptoms"]
}

# --- CORE FUNCTIONS ---
def normalize_text(text: str) -> str:
    text = text.lower()
    return re.sub(r'#|\W+', ' ', text).strip()

def score_match(product_keywords: List[str], trend_term: str) -> int:
    score = 0
    normalized_trend = normalize_text(trend_term)
    for p_keyword in product_keywords:
        normalized_p_keyword = normalize_text(p_keyword)
        if normalized_p_keyword in normalized_trend:
            score += 3
        overlap = len(set(normalized_trend.split()).intersection(set(normalized_p_keyword.split())))
        score += overlap
    return score

def find_matching_products(trends_by_platform: Dict[str, List[str]], catalog: List[Dict[str, Any]]) -> Dict[str, List[Dict[str, Any]]]:
    matches: Dict[str, List[Dict[str, Any]]] = {}
    for platform, trends in trends_by_platform.items():
        print(f"\n--- Analyzing Trends from {platform} ---")
        for trend_term in trends:
            scored_matches = []
            for product in catalog:
                score = score_match(product["keywords"], trend_term)
                if score >= MIN_SCORE:
                    scored_matches.append({
                        "product_id": product["id"],
                        "product_name": product["name"],
                        "category": product["category"],
                        "score": score,
                        "platform": platform,
                        "trend_term": trend_term
                    })
            if scored_matches:
                max_score = max(m["score"] for m in scored_matches)
                top_matches = [m for m in scored_matches if m["score"] == max_score][:TOP_N]
                matches[trend_term] = top_matches
                print(f"TREND: '{trend_term}' -> BEST PRODUCTS: {', '.join([m['product_name'] for m in top_matches])}")
    return matches

def log_matches(matches: Dict[str, List[Dict[str, Any]]], filename: str = LOG_FILE):
    with open(filename, "w", encoding="utf-8") as f:
        json.dump(matches, f, indent=4)
    print(f"\n📂 Audit log saved to {filename}")

# --- EXECUTION ---
def main():
    print("🚀 Starting Health Trend Matching Engine...")
    all_recommendations = find_matching_products(TRENDING_DATA, PRODUCT_CATALOG)
    print("\n=======================================================")
    print("✅ FINAL AUTOMATED MARKETING RECOMMENDATIONS (FOR REVIEW)")
    print("=======================================================")
    if not all_recommendations:
        print("No strong keyword matches found.")
        return
    for trend, products in all_recommendations.items():
        product_names = [p["product_name"] for p in products]
        print(f"\n[TREND TOPIC]: {trend}")
        print(f"   MATCHED PRODUCTS ({len(products)}): {', '.join(product_names)}")
        print(f"   CONTENT IDEA: Create content addressing '{trend}' and link to these products.")
        if any(word in trend.lower() for word in ["anxiety", "stress"]):
            print("   ⚠️ ACTION ALERT: High-intent health topic detected! Notify Sales Team.")
    log_matches(all_recommendations)

if __name__ == "__main__":
    main()

Note Replace mock data with your Firestore/DB payloads. Keep MIN_SCORE and TOP_N configurable for sensitivity tuning.

CRM integration workflow 🔗

Import the JSON log into your CRM, map fields, and automate campaign creation and team notifications for high‑intent trends.

Field mapping

JSON field	CRM field
trend_term	Campaign Topic / Keyword
product_name	Linked Product / Catalog Item
category	Product Category
platform	Source Platform
score	Match Strength (custom field)

Automation rules

High‑intent alerts: If trend contains “anxiety” or “stress”, notify outreach team.
Campaign generation: Create draft campaign tagged by trend term and platform.
Segmentation: Match opted‑in contacts by product category; queue WhatsApp outreach.

Pseudocode

# crm_workflow.py (pseudocode)
for trend, products in all_recommendations.items():
    for product in products:
        crm_record = {
            "CampaignTopic": trend,
            "ProductName": product["product_name"],
            "Category": product["category"],
            "Platform": product["platform"],
            "Score": product["score"]
        }
        crm_api.create_campaign(crm_record)

        if "anxiety" in trend.lower() or "stress" in trend.lower():
            crm_api.notify_team("High-intent health topic detected", crm_record)

Sample dashboard layout 📊

Design the CRM dashboard to make trend insights actionable: highlight matched products, stock visibility, alerts, and an audit viewer.

Trend insights panel

Trend	Platform	Product	Score	Action
#sleepissues	X	Advanced Sleep Aid Complex	5	Draft Campaign
managing winter stress	Facebook	Natural Stress Relief Gummies	6	Notify Sales
vitamin D shortage symptoms	Search	High‑Dose Vitamin D3 Drops	4	Draft Campaign
workplace mental health strategies	LinkedIn	Natural Stress Relief Gummies	3	Notify Sales

Widgets

Stock by category: Bar chart for availability before outreach.
High‑intent: Stress/Anxiety alert banner with one‑click notify.
Platform tone: Tailor messaging (LinkedIn vs X vs Facebook).
Audit log viewer: Searchable table of JSON entries; export for reviews.

Glossary & compliance notes 📚

Term	Definition
Normalization	Lowercasing and stripping special characters to compare keywords consistently.
Score	A relevance metric combining exact phrase matches (+3) and word overlap.
MIN_SCORE	Configurable threshold to filter out weak matches.
TOP_N	Configurable number of highest‑scoring products kept per trend.
JSON audit log	Machine‑readable record of matches for traceability and compliance.
High‑intent	Trends like “anxiety”/“stress” that require human review/notification.
CRM mapping	Aligning JSON fields to CRM campaign/product/score fields for automation.
Human‑in‑the‑loop	Workflow step where staff review sensitive matches before outreach.

Compliance tip: keep content informational, avoid medical claims, and always provide clear opt‑in/opt‑out paths in outreach.

canaangreen

Thursday, December 4, 2025

📊 Dashboard Insights That Drive Action “Visualizing Trends & Products: Building a CRM Dashboard That Keeps Outreach Teams Alert”