AI & Automation in Marketing
Master AI tools and automation to transform your marketing operations. Learn from practitioners who use these techniques daily. Complement this track with our Prompt Engineering course for deeper AI mastery.
AI Prompt Engineering
Module 1Master the art of crafting effective prompts for AI tools. Learn how to structure prompts for content creation, analysis, and automation tasks that deliver consistent, high-quality results.
📚 What You'll Learn:
- ⚡ Prompt structure and best practices (role, context, task, format)
- ⚡ Context management and token optimisation strategies
- ⚡ Output optimisation techniques for specific use cases
- ⚡ Iterative refinement and prompt versioning
- ⚡ Chain-of-thought prompting for complex reasoning
- ⚡ Few-shot learning and example-based prompting
- ⚡ Temperature and parameter tuning for consistency vs creativity
💻 Try It Yourself:
Create a reusable prompt framework for automated content briefs: 1) Define your content brief structure (target audience, key messages, tone). 2) Write a base prompt template with variables. 3) Test with 3 different topics and refine.
🔧 Code Example:
# Example: Automated Content Brief Generator
import openai
def generate_content_brief(topic, audience, tone="professional"):
prompt = f"""You are an expert content strategist. Create a detailed content brief.
Topic: {topic}
Target Audience: {audience}
Tone: {tone}
Provide:
1. Key messages (3-5 points)
2. Target keywords (primary + 3-5 secondary)
3. Content structure outline
4. Call-to-action recommendations
5. SEO considerations
Format as JSON with clear sections."""
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}],
temperature=0.7
)
return response.choices[0].message.content📈 Real-World Results:
Evaluating AI Results
Module 2Learn how to assess AI-generated content for quality, accuracy, and commercial viability. Build evaluation frameworks that ensure results meet your standards and deliver measurable business impact.
📚 What You'll Learn:
- ⚡ Quality assessment criteria and scoring rubrics
- ⚡ Accuracy validation techniques and fact-checking workflows
- ⚡ Commercial viability checks and ROI alignment
- ⚡ Human-in-the-loop workflows and approval processes
- ⚡ Automated quality scoring with custom metrics
- ⚡ Bias detection and brand safety protocols
- ⚡ Performance benchmarking and continuous improvement
💻 Try It Yourself:
Build a comprehensive AI output evaluation system: 1) Define 5-7 quality criteria (accuracy, relevance, tone, SEO, brand alignment). 2) Create a scoring rubric (1-5 scale per criterion). 3) Build a Python script that evaluates outputs against criteria.
🔧 Code Example:
# Example: AI Output Quality Evaluator
def evaluate_ai_output(content, criteria):
"""Evaluate AI-generated content against defined criteria."""
scores = {}
# Accuracy check (basic keyword matching - enhance with NLP)
scores['accuracy'] = check_factual_consistency(content)
# Relevance check
scores['relevance'] = check_topic_relevance(content, criteria['topic'])
# Tone check
scores['tone'] = check_tone_match(content, criteria['desired_tone'])
# SEO check
scores['seo'] = check_keyword_usage(content, criteria['target_keywords'])
# Brand safety
scores['brand_safety'] = check_brand_guidelines(content)
# Calculate overall score
overall = sum(scores.values()) / len(scores)
needs_review = overall < 3.5
return {
'scores': scores,
'overall': overall,
'needs_review': needs_review,
'recommendations': generate_improvements(scores)
}📈 Real-World Results:
Automating Content & Reporting
Module 3Automate repetitive marketing tasks using AI. From content generation to report creation, learn to build workflows that save time and improve consistency while maintaining quality standards.
📚 What You'll Learn:
- ⚡ Content automation pipelines and workflow design
- ⚡ Report generation workflows with data integration
- ⚡ Integration patterns with existing marketing tools
- ⚡ Error handling and fallback strategies
- ⚡ Scheduling and trigger-based automation
- ⚡ Multi-channel content adaptation
- ⚡ Version control and content management
💻 Try It Yourself:
Create an automated weekly marketing report system: 1) Connect to your analytics API (GA4, Google Ads, etc.). 2) Extract key metrics (traffic, conversions, revenue). 3) Use AI to generate insights and narrative. 4) Format as HTML report with charts.
🔧 Code Example:
# Example: Automated Weekly Marketing Report
import requests
from datetime import datetime, timedelta
import openai
def generate_weekly_report():
# Step 1: Fetch data from analytics
end_date = datetime.now()
start_date = end_date - timedelta(days=7)
traffic_data = fetch_ga4_data(start_date, end_date)
ad_data = fetch_google_ads_data(start_date, end_date)
# Step 2: Prepare data summary
summary = {
'period': f"{start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')}",
'sessions': traffic_data['sessions'],
'conversions': traffic_data['conversions'],
'revenue': traffic_data['revenue'],
'ad_spend': ad_data['cost'],
'roas': calculate_roas(traffic_data['revenue'], ad_data['cost'])
}
# Step 3: Generate AI insights
prompt = f"""Analyse this marketing performance data:
{summary}
Provide:
1. Key highlights (3-5 bullet points)
2. Performance trends
3. Recommendations for next week
4. Risk alerts if any metrics are concerning"""
insights = openai.ChatCompletion.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return format_html_report(summary, insights)📈 Real-World Results:
AI Workflow Integration
Module 4Integrate AI tools into your existing marketing workflows. Learn to connect AI capabilities with your current tech stack for seamless operations that scale with your business.
📚 What You'll Learn:
- ⚡ API integrations with OpenAI, Anthropic, and other providers
- ⚡ Workflow orchestration with Zapier, Make, and custom solutions
- ⚡ Tool selection criteria and cost-benefit analysis
- ⚡ Scaling strategies for high-volume operations
- ⚡ Error handling, retries, and rate limiting
- ⚡ Security and API key management best practices
- ⚡ Monitoring and performance optimisation
💻 Try It Yourself:
Design and implement an AI-enhanced marketing workflow: 1) Map your current content creation process. 2) Identify 3 automation opportunities. 3) Choose AI tools for each step. 4) Build API integrations (or use no-code tools). 5) Test end-to-end workflow.
🔧 Code Example:
# Example: AI Workflow Integration
import openai
from typing import Dict, List
class MarketingWorkflowAutomation:
def __init__(self, api_key: str):
self.openai_client = openai.OpenAI(api_key=api_key)
self.workflow_steps = []
def generate_content_brief(self, topic: str) -> Dict:
"""Step 1: Generate content brief"""
prompt = f"Create a content brief for: {topic}"
response = self.openai_client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return parse_brief(response.choices[0].message.content)
def generate_content(self, brief: Dict) -> str:
"""Step 2: Generate content from brief"""
prompt = f"Write content based on: {brief}"
response = self.openai_client.chat.completions.create(
model="gpt-4",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
def execute_full_workflow(self, topic: str, keywords: List[str]):
"""Execute complete workflow"""
try:
brief = self.generate_content_brief(topic)
content = self.generate_content(brief)
optimized = self.optimize_for_seo(content, keywords)
result = self.publish_to_cms(optimized, {'title': topic})
return {'status': 'success', 'post_id': result['id']}
except Exception as e:
return {'status': 'error', 'message': str(e)}📈 Real-World Results:
Related Learning & Resources
Expand Your Skills
- • Prompt Engineering - Master advanced AI prompting techniques
- • Data-Driven SEO - Combine AI with SEO automation
- • Performance Measurement - Track AI-driven marketing ROI
Services & Insights
- • Marketing Analytics Services - AI-powered analytics solutions
- • AI Training UK - Professional AI training courses
- • Recommended AI Tools - Curated tool stack for marketers
Ready to Master AI & Automation?
Work through each module progressively. Each guide includes practical exercises, code examples, and real-world marketing applications.
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