"""Implementation of marketing automation tools"""
import uuid
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import random
import json
from ..models import (
GenerateCampaignReportInput,
GenerateCampaignReportOutput,
CampaignMetrics,
OptimizeCampaignBudgetInput,
OptimizeCampaignBudgetOutput,
BudgetAllocation,
CreateCampaignCopyInput,
CreateCampaignCopyOutput,
CopyVariant,
AnalyzeAudienceSegmentsInput,
AnalyzeAudienceSegmentsOutput,
AudienceSegment,
SegmentOverlap,
ReportFormat,
MetricType
)
async def generate_campaign_report(input_data: GenerateCampaignReportInput) -> GenerateCampaignReportOutput:
"""Generate comprehensive performance reports from campaign data"""
# Simulate campaign metrics retrieval
campaigns = []
for campaign_id in input_data.campaign_ids:
# Generate realistic-looking metrics
sent = random.randint(5000, 50000)
delivered = int(sent * random.uniform(0.95, 0.99))
opens = int(delivered * random.uniform(0.15, 0.35))
unique_opens = int(opens * random.uniform(0.7, 0.9))
clicks = int(opens * random.uniform(0.05, 0.15))
unique_clicks = int(clicks * random.uniform(0.7, 0.9))
conversions = int(clicks * random.uniform(0.02, 0.10))
revenue = conversions * random.uniform(50, 500)
campaign_metrics = CampaignMetrics(
campaign_id=campaign_id,
campaign_name=f"Campaign {campaign_id[-6:]}",
sent=sent,
delivered=delivered,
opens=opens,
unique_opens=unique_opens,
clicks=clicks,
unique_clicks=unique_clicks,
conversions=conversions,
revenue=revenue,
ctr=round((clicks / delivered) * 100, 2) if delivered > 0 else 0,
conversion_rate=round((conversions / clicks) * 100, 2) if clicks > 0 else 0,
roi=round(((revenue - (sent * 0.01)) / (sent * 0.01)) * 100, 2) # Assuming $0.01 per email
)
campaigns.append(campaign_metrics)
# Calculate summary statistics
summary = {
"total_sent": sum(c.sent for c in campaigns),
"total_delivered": sum(c.delivered for c in campaigns),
"total_opens": sum(c.opens for c in campaigns),
"total_clicks": sum(c.clicks for c in campaigns),
"total_conversions": sum(c.conversions for c in campaigns),
"total_revenue": sum(c.revenue for c in campaigns),
"average_ctr": round(sum(c.ctr for c in campaigns) / len(campaigns), 2),
"average_conversion_rate": round(sum(c.conversion_rate for c in campaigns) / len(campaigns), 2),
"average_roi": round(sum(c.roi for c in campaigns) / len(campaigns), 2)
}
# Generate charts data if requested
charts = None
if input_data.include_charts:
charts = [
{
"type": "line",
"title": "Campaign Performance Over Time",
"data": {
"labels": [f"Day {i}" for i in range(1, 8)],
"datasets": [
{
"label": "Opens",
"data": [random.randint(100, 1000) for _ in range(7)]
},
{
"label": "Clicks",
"data": [random.randint(50, 500) for _ in range(7)]
}
]
}
},
{
"type": "bar",
"title": "Revenue by Campaign",
"data": {
"labels": [c.campaign_name for c in campaigns],
"values": [c.revenue for c in campaigns]
}
}
]
# Generate download URL for non-JSON formats
download_url = None
if input_data.format != ReportFormat.JSON:
download_url = f"https://reports.marketing-automation.com/{uuid.uuid4()}.{input_data.format}"
return GenerateCampaignReportOutput(
report_id=str(uuid.uuid4()),
generated_at=datetime.utcnow(),
date_range=input_data.date_range,
campaigns=campaigns,
summary=summary,
charts=charts,
format=input_data.format,
download_url=download_url
)
async def optimize_campaign_budget(input_data: OptimizeCampaignBudgetInput) -> OptimizeCampaignBudgetOutput:
"""Use AI to suggest optimal budget reallocations"""
allocations = []
total_current_budget = input_data.total_budget
# Simulate AI-driven budget optimization
for i, campaign_id in enumerate(input_data.campaign_ids):
# Simulate current performance metrics
current_budget = total_current_budget / len(input_data.campaign_ids)
current_roi = random.uniform(100, 500)
# Apply optimization logic based on goal
if input_data.optimization_goal == "maximize_conversions":
# Favor campaigns with high conversion potential
optimization_factor = random.uniform(0.8, 1.5)
elif input_data.optimization_goal == "maximize_roi":
# Favor campaigns with highest ROI
optimization_factor = random.uniform(0.7, 1.6)
else: # maximize_reach
# More even distribution with slight variations
optimization_factor = random.uniform(0.9, 1.2)
recommended_budget = current_budget * optimization_factor
# Apply constraints if provided
if input_data.constraints:
min_budget = input_data.constraints.get(campaign_id, {}).get("min", 0)
max_budget = input_data.constraints.get(campaign_id, {}).get("max", float('inf'))
recommended_budget = max(min_budget, min(max_budget, recommended_budget))
change_percentage = ((recommended_budget - current_budget) / current_budget) * 100
allocation = BudgetAllocation(
campaign_id=campaign_id,
campaign_name=f"Campaign {campaign_id[-6:]}",
current_budget=round(current_budget, 2),
recommended_budget=round(recommended_budget, 2),
change_percentage=round(change_percentage, 2),
expected_impact={
"conversions": round(random.uniform(1.1, 1.5), 2) if change_percentage > 0 else round(random.uniform(0.8, 0.95), 2),
"roi": round(random.uniform(1.05, 1.3), 2) if change_percentage > 0 else round(random.uniform(0.85, 0.98), 2),
"reach": round(random.uniform(1.02, 1.2), 2) if change_percentage > 0 else round(random.uniform(0.9, 0.99), 2)
},
reasoning=f"Based on historical performance, this campaign shows {'high' if optimization_factor > 1.2 else 'moderate'} potential for {input_data.optimization_goal.replace('_', ' ')}"
)
allocations.append(allocation)
# Normalize to ensure total equals input budget
total_recommended = sum(a.recommended_budget for a in allocations)
if total_recommended != input_data.total_budget:
scale_factor = input_data.total_budget / total_recommended
for allocation in allocations:
allocation.recommended_budget = round(allocation.recommended_budget * scale_factor, 2)
# Calculate projected improvements
projected_improvement = {
"conversions": round(random.uniform(1.15, 1.35), 2),
"roi": round(random.uniform(1.10, 1.25), 2),
"reach": round(random.uniform(1.05, 1.15), 2),
"cost_per_acquisition": round(random.uniform(0.85, 0.95), 2)
}
recommendations = [
f"Focus budget on top-performing campaigns based on {input_data.optimization_goal}",
"Consider A/B testing creative elements in underperforming campaigns",
"Monitor performance weekly and adjust allocations as needed",
"Implement automated bid adjustments for real-time optimization"
]
return OptimizeCampaignBudgetOutput(
optimization_id=str(uuid.uuid4()),
total_budget=input_data.total_budget,
optimization_goal=input_data.optimization_goal,
allocations=allocations,
projected_improvement=projected_improvement,
confidence_score=round(random.uniform(0.75, 0.95), 2),
recommendations=recommendations
)
async def create_campaign_copy(input_data: CreateCampaignCopyInput) -> CreateCampaignCopyOutput:
"""Generate marketing copy variants using AI"""
# Templates for different copy types and tones
templates = {
"email_subject": {
"professional": [
"{product_name}: Enhance Your {benefit}",
"Introducing {product_name} - {value_prop}",
"{action} with {product_name}"
],
"casual": [
"Hey! Check out {product_name}",
"{product_name} is here!",
"You're going to love {product_name}"
],
"urgent": [
"Last chance: {product_name} {offer}",
"Hurry! {product_name} {time_limit}",
"Don't miss out on {product_name}"
]
},
"email_body": {
"professional": [
"Dear Valued Customer,\n\nWe're pleased to introduce {product_name}, {product_description}.\n\n{benefits}\n\n{cta}",
"Greetings,\n\n{product_name} offers {value_prop}. {product_description}\n\n{features}\n\n{cta}"
],
"casual": [
"Hi there!\n\n{product_name} is exactly what you've been looking for. {product_description}\n\n{benefits}\n\n{cta}",
"Hey!\n\nExcited to share {product_name} with you! {product_description}\n\n{features}\n\n{cta}"
]
},
"ad_headline": {
"professional": ["{product_name}: {value_prop}", "{action} with {product_name}"],
"persuasive": ["Transform Your {benefit} with {product_name}", "Why {audience} Choose {product_name}"]
},
"social_post": {
"friendly": ["Loving our new {product_name}! {product_description} {hashtags}", "Check out {product_name}! Perfect for {audience} {cta} {hashtags}"],
"informative": ["{product_name} helps you {benefit}. {product_description} Learn more: {cta}", "Did you know? {product_name} {feature}. {cta}"]
}
}
variants = []
# Generate copy variants
for i in range(input_data.variants_count):
# Select appropriate template
copy_templates = templates.get(input_data.copy_type, {}).get(input_data.tone.value, [])
if not copy_templates:
# Fallback template
copy_templates = [f"{input_data.product_name}: {input_data.product_description}"]
template = copy_templates[i % len(copy_templates)]
# Generate dynamic content
benefits = ["increase productivity", "save time", "improve results", "enhance performance"]
features = ["advanced features", "user-friendly interface", "powerful capabilities", "seamless integration"]
actions = ["Discover", "Experience", "Unlock", "Explore"]
value_props = ["Revolutionary Solution", "Game-Changing Innovation", "Industry-Leading Technology"]
# Fill in template
content = template.format(
product_name=input_data.product_name,
product_description=input_data.product_description,
audience=input_data.target_audience,
benefit=random.choice(benefits),
feature=random.choice(features),
action=random.choice(actions),
value_prop=random.choice(value_props),
benefits=f"Key benefits:\n• {random.choice(benefits).capitalize()}\n• {random.choice(benefits).capitalize()}\n• {random.choice(benefits).capitalize()}",
features=f"Features include:\n• {random.choice(features).capitalize()}\n• {random.choice(features).capitalize()}",
cta=input_data.call_to_action or "Learn More",
hashtags="#" + " #".join(input_data.keywords[:3]) if input_data.keywords else "",
offer="Special Offer",
time_limit="Limited Time Only"
)
# Include keywords if provided
if input_data.keywords:
for keyword in input_data.keywords[:2]:
if keyword.lower() not in content.lower():
content += f" {keyword}"
# Respect max_length if specified
if input_data.max_length and len(content) > input_data.max_length:
content = content[:input_data.max_length-3] + "..."
variant = CopyVariant(
variant_id=str(uuid.uuid4()),
content=content,
tone_match_score=round(random.uniform(0.85, 0.98), 2),
predicted_ctr=round(random.uniform(2.5, 8.5), 2) if input_data.copy_type in ["email_subject", "ad_headline"] else None,
key_elements=[input_data.product_name, input_data.tone.value, input_data.copy_type],
character_count=len(content),
word_count=len(content.split())
)
variants.append(variant)
# Select best variant based on tone match
best_variant = max(variants, key=lambda v: v.tone_match_score)
return CreateCampaignCopyOutput(
copy_generation_id=str(uuid.uuid4()),
copy_type=input_data.copy_type,
variants=variants,
tone=input_data.tone,
target_audience=input_data.target_audience,
keywords_used=input_data.keywords or [],
best_variant_id=best_variant.variant_id,
generation_metadata={
"model": "marketing-ai-v2",
"temperature": 0.7,
"generation_time_ms": random.randint(500, 1500)
}
)
async def analyze_audience_segments(input_data: AnalyzeAudienceSegmentsInput) -> AnalyzeAudienceSegmentsOutput:
"""Analyze contact data to identify audience segments"""
# Simulate total contacts
total_contacts = random.randint(10000, 100000)
# Generate segments based on criteria
segments = []
segment_templates = {
"demographics": [
("Young Professionals", {"age": "25-34", "income": "$50k-$100k"}),
("Senior Executives", {"age": "45-60", "income": "$150k+"}),
("Students", {"age": "18-24", "income": "<$25k"})
],
"behavior": [
("Frequent Buyers", {"purchase_frequency": "monthly", "avg_order_value": "$100+"}),
("Window Shoppers", {"browse_frequency": "weekly", "purchase_frequency": "rarely"}),
("Loyal Customers", {"customer_lifetime": "2+ years", "retention_rate": "high"})
],
"engagement": [
("Email Champions", {"open_rate": "40%+", "click_rate": "10%+"}),
("Social Media Active", {"social_shares": "frequent", "comments": "regular"}),
("Inactive Users", {"last_engagement": "90+ days ago"})
],
"purchase_history": [
("Big Spenders", {"total_spent": "$1000+", "avg_order": "$200+"}),
("Discount Seekers", {"coupon_usage": "90%+", "sale_purchases": "majority"}),
("New Customers", {"first_purchase": "<30 days", "orders": "1-2"})
]
}
used_segments = set()
remaining_contacts = total_contacts
# Create segments based on requested criteria
for criteria in input_data.criteria:
if criteria.value in segment_templates:
available_templates = segment_templates[criteria.value]
for name, characteristics in available_templates[:input_data.max_segments // len(input_data.criteria)]:
if name not in used_segments and len(segments) < input_data.max_segments:
# Calculate segment size
max_size = remaining_contacts // (input_data.max_segments - len(segments))
segment_size = max(
input_data.min_segment_size,
random.randint(input_data.min_segment_size, max_size)
)
segment = AudienceSegment(
segment_id=str(uuid.uuid4()),
name=name,
size=segment_size,
criteria={criteria.value: characteristics},
characteristics=characteristics,
engagement_score=round(random.uniform(0.3, 0.9), 2),
value_score=round(random.uniform(0.4, 0.95), 2),
recommended_campaigns=random.sample([
"Welcome Series",
"Product Launch",
"Seasonal Promotion",
"Loyalty Program",
"Re-engagement Campaign",
"Upsell Campaign"
], k=random.randint(2, 4))
)
segments.append(segment)
used_segments.add(name)
remaining_contacts -= segment_size
# Calculate uncategorized contacts
uncategorized_count = max(0, remaining_contacts)
# Analyze overlaps if requested
overlaps = []
if input_data.analyze_overlap and len(segments) > 1:
for i in range(len(segments)):
for j in range(i + 1, len(segments)):
overlap_count = random.randint(0, min(segments[i].size, segments[j].size) // 3)
if overlap_count > 0:
overlap = SegmentOverlap(
segment_a_id=segments[i].segment_id,
segment_b_id=segments[j].segment_id,
overlap_count=overlap_count,
overlap_percentage=round((overlap_count / min(segments[i].size, segments[j].size)) * 100, 2)
)
overlaps.append(overlap)
# Generate recommendations
recommendations = []
if input_data.include_recommendations:
recommendations = [
{
"segment": segments[0].name if segments else "General",
"strategy": "Focus on personalized content",
"channels": ["email", "social"],
"timing": "Tuesday/Thursday mornings"
},
{
"segment": segments[1].name if len(segments) > 1 else "Engaged Users",
"strategy": "Increase frequency with value-driven content",
"channels": ["email", "push notifications"],
"timing": "Weekend evenings"
}
]
# Generate insights
insights = [
f"{len(segments)} distinct audience segments identified from {total_contacts:,} contacts",
f"Highest value segment: {max(segments, key=lambda s: s.value_score).name if segments else 'N/A'}",
f"Most engaged segment: {max(segments, key=lambda s: s.engagement_score).name if segments else 'N/A'}",
f"{uncategorized_count:,} contacts ({round(uncategorized_count/total_contacts*100, 1)}%) require further analysis"
]
return AnalyzeAudienceSegmentsOutput(
analysis_id=str(uuid.uuid4()),
total_contacts=total_contacts,
segments=segments,
uncategorized_count=uncategorized_count,
overlaps=overlaps if overlaps else None,
recommendations=recommendations,
insights=insights,
created_at=datetime.utcnow()
)
