Get Real Feedback With Perfectly Timed Surveys

AI sends personalized satisfaction surveys at the optimal moment after each interaction. Higher response rates, unbiased feedback, zero manual work.


Why this matters

Manual survey distribution is inconsistent — some customers get surveyed, others don't, and timing is often wrong. Sending a survey three days after a support interaction misses the window of fresh memory. The result is low response rates and feedback biased toward customers who feel strongly enough to respond on their own.


How MultiMail solves this

MultiMail's AI agent detects when support interactions are resolved, waits an optimal delay period, and sends personalized CSAT or NPS surveys automatically. Autonomous oversight is safe here because surveys are templated and low-risk. The agent handles timing optimization and personalization while you focus on analyzing the results.

1

Detect Issue Resolution

Your AI agent monitors for resolution signals — a support ticket marked as closed, a 'thank you' reply from the customer, or a configurable time window since the last interaction.

2

Wait for Optimal Timing

The agent waits a configurable delay (typically 2-24 hours) after resolution before sending the survey. This lets the customer experience the fix before being asked for feedback.

3

Personalize the Survey

Using the customer's interaction history and the specific issue they had, the agent personalizes the survey email to reference their actual experience rather than sending a generic form.

4

Send and Track

The survey is sent via send_email under autonomous oversight. The agent tags the email for tracking and logs which customers have been surveyed to prevent duplicate sends.


Implementation

Detect Resolution and Queue Survey
python
import requests
from datetime import datetime, timedelta

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

def check_for_resolutions():
    "cm"># Check inbox for resolution signals
    inbox = requests.get(
        f"{API}/mailboxes/support-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()

        if is_resolution_signal(email):
            # Schedule survey for 4 hours later
            schedule_survey(
                customer_email=email["from"],
                thread_id=email["thread_id"],
                send_at=datetime.utcnow() + timedelta(hours=4)
            )

Monitor for resolved support interactions and schedule satisfaction surveys.

Send Personalized Survey
python
import requests

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

def send_survey(customer_email: str, thread_id: str):
    "cm"># Get thread context for personalization
    thread = requests.get(
        f"{API}/threads/{thread_id}", headers=HEADERS
    ).json()

    topic = classify_topic(thread)

    response = requests.post(
        f"{API}/send",
        headers=HEADERS,
        json={
            "from": "[email protected]",
            "to": customer_email,
            "subject": "How was your experience with our support team?",
            "text_body": f"Hi {thread[&"cm">#039;customer_name']}, your recent "
                         f"{topic} issue was resolved {thread[&"cm">#039;resolution_time']} ago. "
                         f"We&"cm">#039;d love your feedback: [Survey Link]",
            "html_body": build_survey_html(thread, topic)
        }
    )
    print(f"Survey sent: {response.json()[&"cm">#039;id']}")

Compose and send a CSAT survey personalized to the customer's recent interaction.

MCP Tool Integration
typescript
"cm">// Using MultiMail MCP tools for satisfaction surveys

"cm">// 1. Check for recently resolved threads
const inbox = await mcp.check_inbox({
  mailbox_id: "support-mailbox-id",
  status: "unread",
  limit: 50
});

for (const msg of inbox.emails) {
  const email = await mcp.read_email({ email_id: msg.id });

  if (isResolutionSignal(email.text_body)) {
    "cm">// 2. Get thread context
    const thread = await mcp.get_thread({
      thread_id: email.thread_id
    });

    "cm">// 3. Tag for survey tracking
    await mcp.tag_email({
      email_id: email.id,
      tags: ["resolved", "survey-pending"]
    });

    "cm">// 4. Send personalized survey
    await mcp.send_email({
      to: email.from,
      subject: "How was your experience with our support team?",
      text_body: buildSurveyBody(thread)
    });
  }
}

Use MCP tools to detect resolutions and send surveys from an AI agent.


What you get

Higher Response Rates

Surveys sent at the optimal moment after resolution get 2-3x higher response rates than batch surveys sent days later.

Unbiased Feedback Collection

Every resolved interaction triggers a survey, not just the ones an agent remembers to follow up on. This eliminates selection bias in your feedback data.

Zero Manual Effort

Autonomous mode handles the entire survey workflow without human intervention. Your team focuses on analyzing feedback, not distributing surveys.

Personalized Survey Experience

Each survey references the customer's specific interaction, making the request feel personal rather than automated. This increases both response rates and response quality.


Recommended oversight mode

Recommended
autonomous
Satisfaction surveys are templated, low-risk communications that benefit from instant, automatic delivery. There is no brand or legal risk in asking for feedback, and autonomous mode ensures every resolved interaction is surveyed without human bottleneck.

Common questions

How do I prevent survey fatigue?
Your AI agent can enforce rate limits — for example, no more than one survey per customer per 30 days. The agent tracks which customers have been recently surveyed and skips them if they fall within the cooldown window. You configure the rules; the agent enforces them consistently.
Can I customize the survey content?
Yes. The survey template, questions, and branding are fully customizable in your AI agent's logic. MultiMail delivers whatever content you generate. You can use different survey types (CSAT, NPS, CES) for different interaction categories.
How do I collect and analyze responses?
Survey responses come back as inbound emails that your agent can process with check_inbox and read_email. The agent can parse responses, calculate scores, and feed data into your analytics platform. You can also use survey links that point to an external form for structured data collection.
What if a customer responds with a complaint instead of a rating?
Your AI agent can analyze survey responses for negative sentiment and automatically escalate complaints to a human agent. This turns your survey program into an early warning system for unresolved issues.

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