AI sends targeted tips based on feature usage patterns. Proactive education reduces support tickets and increases adoption.
Customers don't discover features that could solve their problems, leading to underutilization and preventable support tickets. Product education is reactive — customers only learn about features when they encounter issues. Proactive, personalized education based on actual usage patterns dramatically improves adoption but doesn't scale manually.
MultiMail's monitored mode lets AI send targeted educational content triggered by usage patterns. When a customer manually polls for emails instead of using webhooks, the AI sends a tip about webhook setup. When usage drops, it sends re-engagement content. Monitored mode handles this autonomously while teams review for quality.
The AI monitors customer usage patterns to identify underutilized features, common mistakes, and opportunities for education that would improve their experience.
Based on usage gaps, the AI selects the most relevant educational content — tips, best practices, documentation links, or video tutorials that address the specific opportunity.
The AI composes a tip email that references the customer's specific behavior and shows how the feature would help their use case. Monitored mode handles delivery.
Monitor whether educational emails lead to feature adoption. Track the reduction in support tickets for topics covered by proactive education.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def send_education_tip(customer: dict, tip: dict):
body = (
f"Hi {customer[&"cm">#039;name']},\n\n"
f"{tip[&"cm">#039;observation']}\n\n"
f"Pro tip: {tip[&"cm">#039;suggestion']}\n\n"
f"{tip[&"cm">#039;details']}\n\n"
f"Learn more: {tip[&"cm">#039;doc_url']}\n\n"
f"Questions? Reply to this email and we&"cm">#039;ll help."
)
return requests.post(
f"{API}/send",
headers=HEADERS,
json={
"from": "[email protected]",
"to": customer["email"],
"subject": tip["subject"],
"text_body": body
}
).json()
# Example: customer polling instead of using webhooks
send_education_tip(
{"name": "Dev Team", "email": "[email protected]"},
{"subject": "Pro tip: Automate your email threading with webhooks",
"observation": "I noticed you&"cm">#039;re polling the inbox endpoint every 30 seconds to check for new emails.",
"suggestion": "Set up webhooks to get instant notifications when new emails arrive.",
"details": "Webhooks eliminate polling, reduce API calls by 95%, and give you real-time email delivery.",
"doc_url": "https://docs.example.com/webhooks"}
)Detect feature usage gaps and send targeted educational content.
import requests
API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}
def find_education_opportunities():
customers = requests.get(
f"{API}/contacts/search",
headers=HEADERS,
params={"tags": "customer,active"}
).json()
opportunities = []
for customer in customers["contacts"]:
usage = get_usage_patterns(customer["email"])
if usage["polling_frequency"] > 10 and not usage["uses_webhooks"]:
opportunities.append((customer, "webhook-tip"))
elif usage["uses_tagging"] is False and usage["email_volume"] > 100:
opportunities.append((customer, "tagging-tip"))
elif usage["uses_contacts"] is False:
opportunities.append((customer, "contacts-tip"))
return opportunitiesScan usage data to find customers who would benefit from specific feature education.
"cm">// Customer education via MCP
"cm">// 1. Find active customers
const active = await mcp.search_contacts({
tags: ["customer", "active"]
});
"cm">// 2. Send targeted tip
await mcp.send_email({
from: "[email protected]",
to: customer.email,
subject: "Pro tip: Automate your email threading with webhooks",
text_body: `Hi ${customer.name}, I noticed you're manually checking for replies. Did you know you can set up webhooks to get instant notifications?`
});
"cm">// 3. Tag as educated on topic
await mcp.add_contact({
email: customer.email,
tags: ["tip-sent-webhooks"]
});Use MultiMail MCP tools for customer education workflows.
Proactive education about common mistakes and underutilized features prevents the support tickets that arise from customer confusion.
Targeted tips based on actual usage gaps drive feature discovery and adoption more effectively than generic onboarding sequences.
Tips reference the customer's specific behavior, making them immediately relevant rather than generic product marketing.
Monitored mode handles education emails autonomously while product and support teams review aggregate effectiveness.
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