How to Build a Sales Content Recommendation Engine

This sales content recommendation engine suggests the right collateral based on deal stage and buyer persona. No more reps guessing which case study to send.

What You Need Before Starting

• Python 3.8+
• CRM API access
• pandas installed
• SMTP or Slack for notifications

Step 1: Build Your Knowledge Base

Collect and organize data for content recommendation.

import sqlite3
import json

def init_content_recommendation_db():
    conn = sqlite3.connect("content_recommendation.db")
    conn.execute("""CREATE TABLE IF NOT EXISTS content_recommendation_items (
        id INTEGER PRIMARY KEY AUTOINCREMENT,
        title TEXT, content TEXT, category TEXT,
        effectiveness_score REAL DEFAULT 0,
        created_at TEXT, updated_at TEXT
    )""")
    conn.commit()
    return conn

Step 2: Collect Source Data

Pull data from your CRM, call recordings, and deal notes.

def gather_source_data(crm_client, date_range="last_90_days"):
    deals = crm_client.get_deals(date_range=date_range, include=["notes", "activities"])
    sources = []
    for deal in deals:
        sources.append({
            "deal_name": deal["name"],
            "outcome": deal["status"],
            "notes": deal.get("notes", ""),
            "activities": deal.get("activities", []),
        })
    return sources

Step 3: Generate with AI

Use Claude to synthesize data into actionable content recommendation content.

import anthropic

def generate_content_recommendation(source_data):
    client = anthropic.Anthropic()
    message = client.messages.create(
        model="claude-sonnet-4-20250514", max_tokens=2000,
        messages=[{"role": "user",
            "content": f"Analyze this sales data and generate content recommendation content.\n\n{json.dumps(source_data[:20], indent=2)}"}])
    return message.content[0].text

Step 4: Distribute to Team

Push updates to your sales team via Slack or email.

import requests

def distribute_update(content, slack_webhook, team_emails):
    requests.post(slack_webhook, json={"text": f"New update:\n{content[:500]}"})
    for email in team_emails:
        send_email(email, "Sales Enablement Update", content)

Step 5: Track Effectiveness

Measure which content actually helps close deals.

def track_effectiveness(conn, item_id, deal_outcome):
    if deal_outcome == "won":
        conn.execute("UPDATE content_recommendation_items SET effectiveness_score = effectiveness_score + 1 WHERE id = ?", (item_id,))
    conn.commit()

def get_top_performers(conn, limit=10):
    return conn.execute("SELECT title, effectiveness_score FROM content_recommendation_items ORDER BY effectiveness_score DESC LIMIT ?", (limit,)).fetchall()

What to Build Next

Track engagement by prospect. Follow up on what they actually read.

Related Reading

• How to Set Up AI-Powered Content Recommendations - ai content recommendations guide
• Building Automated Sales Enablement Content - automated sales enablement content guide
• AI for Sales Pipeline Management - ai sales pipeline management