Ask for Feedback at the Perfect Moment

AI detects user milestones and sends personalized feedback requests automatically. Behavioral triggers replace random timing.


Why this matters

Product teams lack qualitative feedback because they ask at the wrong time — either too early when users haven't formed opinions, or too late when they've lost interest. Generic feedback emails yield single-digit response rates, and the responses they do get are biased toward the most vocal users.


How MultiMail solves this

MultiMail's AI agent monitors user behavior for key milestones — first successful API call, 30-day mark, feature adoption — and sends personalized feedback requests at the optimal moment. Autonomous mode handles delivery instantly when milestones trigger, since templated feedback requests are low-risk.

1

Define Milestone Triggers

Configure behavioral triggers that signal the right moment to ask for feedback: first API call, feature adoption, usage thresholds, or time-based milestones.

2

Detect and Personalize

When a user hits a milestone, the AI agent composes a feedback request referencing their specific experience. A user who just tried email threading gets asked about threading, not generic product feedback.

3

Send Automatically

With autonomous oversight, feedback requests are sent immediately when milestones trigger. No human bottleneck delays the ask past the optimal moment.

4

Process Responses

The agent uses check_inbox to monitor for replies, categorizes feedback themes, and surfaces actionable insights to your product team.


Implementation

Send a Milestone-Based Feedback Request
python
import requests

API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}

def send_feedback_request(user_email: str, user_name: str, feature: str):
    response = requests.post(
        f"{API}/send",
        headers=HEADERS,
        json={
            "from": "[email protected]",
            "to": [user_email],
            "subject": f"Quick question about your experience with {feature}",
            "text_body": (
                f"Hi {user_name},\n\n"
                f"You&"cm">#039;ve been using our {feature} feature for 2 weeks now. "
                f"We&"cm">#039;d love to hear: what's working well and what could "
                f"be better?\n\n"
                f"Just reply to this email with your thoughts — even a "
                f"one-sentence answer helps us improve.\n\n"
                f"Thanks,\nThe Product Team"
            ),
            "html_body": f"<p>Hi {user_name},</p><p>You&"cm">#039;ve been using our {feature}...</p>"
        }
    )
    return response.json()

# Triggered when user hits 2-week milestone
send_feedback_request("[email protected]", "Dev", "email threading")

Trigger a personalized feedback email when a user hits a usage milestone.

Monitor Feedback Replies
python
import requests

API = "https://api.multimail.dev/v1"
HEADERS = {"Authorization": "Bearer mm_live_xxx"}

"cm"># Check for feedback replies
inbox = requests.get(
    f"{API}/mailboxes/product-mailbox-id/inbox",
    headers=HEADERS,
    params={"status": "unread", "limit": 50}
).json()

for msg in inbox["emails"]:
    email = requests.get(
        f"{API}/emails/{msg[&"cm">#039;id']}",
        headers=HEADERS
    ).json()

    # Tag feedback by feature area
    requests.post(
        f"{API}/emails/{msg[&"cm">#039;id']}/tags",
        headers=HEADERS,
        json={"tags": ["feedback", "email-threading"]}
    )
    print(f"Feedback from {email[&"cm">#039;from']}: {email['text_body'][:100]}")

Check the inbox for feedback responses and categorize them.

MCP Tool: Feedback Collection Workflow
typescript
"cm">// Send feedback request at milestone
const result = await mcp.send_email({
  to: "[email protected]",
  subject: "Quick question about email threading",
  text_body: "Hi Dev, you've been using email threading for 2 weeks..."
});

// Tag for tracking
await mcp.tag_email({
  email_id: result.id,
  tags: ["feedback-request", "threading", "2-week-milestone"]
});

// Later: check for responses
const inbox = await mcp.check_inbox({
  mailbox_id: "product-mailbox-id",
  status: "unread"
});

"cm">// Read and categorize each response
for (const msg of inbox.emails) {
  const email = await mcp.read_email({ email_id: msg.id });
  console.log(`Feedback: ${email.text_body}`);
}

Use MCP tools to send feedback requests and process responses.


What you get

3x Higher Response Rates

Behavioral triggers ensure you ask when users have a fresh, relevant experience to share. Timing matters more than the ask itself.

Contextually Relevant Questions

Each feedback request references the specific feature the user just experienced, making the ask feel personal rather than generic.

Zero-Delay Delivery

Autonomous mode sends feedback requests the moment milestones trigger, capturing users at peak engagement before the experience fades.

Automated Response Processing

The AI agent categorizes incoming feedback by theme, sentiment, and feature area, surfacing actionable insights without manual review of every reply.


Recommended oversight mode

Recommended
autonomous
Feedback requests are templated, low-risk emails triggered by behavioral events. The personalization is limited to feature names and usage context, making autonomous delivery safe. Delays from human review would miss the optimal ask window.

Common questions

How do I prevent feedback fatigue?
Your AI agent tracks which users have been asked recently and enforces cooldown periods between requests. You can configure minimum intervals (e.g., no more than one feedback request per 30 days) and prioritize the most valuable milestones.
What milestones work best for triggering feedback?
The most effective triggers are feature-specific adoption milestones: first successful API call, first week of active usage, or first time using a new feature. Time-based milestones (30-day, 90-day) work well for general product feedback. Avoid asking during onboarding when users are still learning.
Can the AI analyze feedback responses automatically?
Yes. Your AI agent can read incoming replies using check_inbox and read_email, then categorize feedback by theme, sentiment, and urgency. This turns unstructured email replies into structured product insights without manual processing.
Should I use autonomous mode for all feedback requests?
Autonomous works well for standard milestone-triggered requests. If you want to ask about a sensitive topic (pricing, competitor comparison), switch to gated_send for that specific campaign so your team can review the messaging.

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