AI creates personalized seasonal promotions based on purchase history and preferences. Marketing reviews creative and offers before sending.
Seasonal campaigns require extensive planning and personalization to perform well. Generic seasonal emails with the same offer for everyone have declining open rates. Personalizing campaigns based on purchase history, browsing behavior, and customer preferences takes too long to produce manually at scale.
MultiMail's AI agent segments audiences and personalizes seasonal campaign emails based on each customer's purchase history, preferences, and engagement patterns. With gated_send oversight, marketing reviews creative, promotional accuracy, and brand alignment before campaigns reach customers.
Define seasonal events — holidays, sales periods, industry-specific seasons. The AI agent prepares campaign templates and audience segments for each event.
The AI segments your audience by purchase history, browsing behavior, and preferences. Each segment receives personalized product recommendations and offers relevant to their interests.
With gated_send, marketing reviews the creative, verifies promotional accuracy (prices, dates, discount codes), and ensures brand alignment before the campaign sends.
Approved campaigns are sent to all segments. The AI tracks open rates, click rates, and conversions by segment to optimize future seasonal campaigns.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def send_seasonal_email(customer: dict, campaign: dict):
"cm"># Get personalized product recommendations
recommendations = get_product_recs(customer["id"], campaign["season"])
products = "\n".join(
[f"- {p[&"cm">#039;name']}: ${p['sale_price']} (was ${p['original_price']})"
for p in recommendations[:3]]
)
body = (
f"Hi {customer[&"cm">#039;name']},\n\n"
f"{campaign[&"cm">#039;headline']}\n\n"
f"Based on your previous purchases, we think you&"cm">#039;ll love:\n"
f"{products}\n\n"
f"Use code {campaign[&"cm">#039;promo_code']} for {campaign['discount']}% off.\n"
f"Offer expires {campaign[&"cm">#039;end_date']}.\n\n"
f"Shop now: {campaign[&"cm">#039;shop_url']}"
)
return requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": customer["email"],
"subject": campaign["subject"],
"text_body": body
}
).json()Create personalized seasonal emails based on customer purchase history.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def segment_for_campaign(campaign_id: str):
"cm"># Get all customers
customers = requests.get(
f"{API}/contacts/search",
headers=HEADERS,
params={"tags": "customer,opted-in"}
).json()
for customer in customers["contacts"]:
segment = determine_segment(customer)
requests.post(
f"{API}/contacts/{customer[&"cm">#039;id']}/tags",
headers=HEADERS,
json={"tags": [f"campaign-{campaign_id}", f"segment-{segment}"]}
)
print(f"Segmented {len(customers[&"cm">#039;contacts'])} customers")Tag customers by purchase behavior for targeted seasonal campaigns.
"cm">// Seasonal campaign via MCP
"cm">// 1. Find opted-in customers
const customers = await mcp.search_contacts({
tags: ["customer", "marketing-opted-in"]
});
"cm">// 2. Send personalized campaign
for (const customer of customers.contacts) {
const recs = await getRecommendations(customer.email);
await mcp.send_email({
from: "[email protected]",
to: customer.email,
subject: "Spring sale: 30% off your favorite items",
text_body: `Hi ${customer.name}, based on your recent purchases, we think you'll love these spring deals...`
});
}Use MultiMail MCP tools for seasonal campaign management.
AI tailors product recommendations and offers based on each customer's purchase history instead of sending one-size-fits-all seasonal promotions.
AI generates personalized variations for each segment in minutes instead of the days or weeks manual campaign creation takes.
Gated send ensures marketing verifies promotional accuracy, brand alignment, and creative quality before campaigns reach customers.
Track conversion rates by segment and personalization strategy to continuously improve seasonal campaign performance year over year.
Email infrastructure built for AI agents. Verifiable identity, graduated oversight, and a 38-tool MCP server. Formally verified in Lean 4.