AI Agent for Delighted: Automate NPS Surveys, Customer Feedback Collection, and Trend Analysis

Here's the thing about Delighted: it's genuinely great at what it does. Beautiful surveys, solid response rates, clean data. The problem isn't collection. The problem is what happens after someone hits submit.

You get a notification. Maybe a Slack message. Someone on the CS team glances at it, thinks "hmm, that's not great," and goes back to whatever fire they were already fighting. The detractor comment sits there. The pattern across fifty similar comments never gets surfaced. The product team never hears about the billing UX issue that three enterprise customers mentioned this week. And the quarterly NPS report goes to leadership with a number and a shrug.

This is the gap. And it's exactly where a custom AI agent — built on OpenClaw and connected to Delighted's API — turns a feedback collection tool into an actual customer intelligence system.

Let me walk through what this looks like in practice.

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The Core Problem: Feedback Without Intelligence

Delighted's built-in automation is purely rule-based. Score below 7? Send a Slack notification to the CS channel. That's about the ceiling.

What it cannot do:

• Read the actual comment and understand what the customer is talking about
• Detect that five customers this week mentioned the same onboarding friction point
• Auto-generate a contextual reply for your account manager to send
• Create a Jira ticket with a summary, severity assessment, and linked customer data
• Distinguish between a detractor who's mildly annoyed and one who's about to churn
• Produce a weekly report that says "Pricing complaints among mid-market European accounts are up 34% — here are the representative quotes"

Delighted gives you data. But data without interpretation is just noise with good typography.

Why OpenClaw + Delighted

OpenClaw is purpose-built for exactly this kind of integration: connecting to SaaS APIs, processing data with AI intelligence, and taking autonomous action across your tool stack. It's not a chatbot. It's not a prompt playground. It's an agent platform that lets you build the connective tissue between your tools and add a layer of intelligence that none of them provide on their own.

For Delighted specifically, the architecture is straightforward:

1. Delighted webhook fires when a new survey response comes in
2. OpenClaw agent receives it, enriches it with context from your CRM/product data
3. AI processes the response — sentiment analysis, theme detection, urgency scoring, pattern matching
4. Agent takes action — routes to the right team, creates tickets, drafts replies, updates dashboards, flags emerging trends

The beauty of this setup is that Delighted keeps doing what it's best at (collecting clean, well-branded feedback at scale) while OpenClaw handles everything that happens next.

Let's get into the specific workflows.

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Workflow 1: Intelligent Comment Analysis and Routing

This is the highest-leverage thing you can build, and it solves the number one complaint about Delighted: feedback is collected but never actioned.

How it works:

Every time a survey response comes in via Delighted's webhook, your OpenClaw agent receives the payload. A typical response looks like this:

{
  "person": {
    "email": "sarah@acmecorp.com",
    "name": "Sarah Chen",
    "properties": {
      "plan": "enterprise",
      "mrr": 4500,
      "region": "EMEA",
      "account_manager": "jake@yourcompany.com"
    }
  },
  "score": 4,
  "comment": "The product is fine but your billing system is a nightmare. We've been trying to switch to annual billing for three months and every time we contact support we get a different answer. Considering alternatives at this point.",
  "survey_type": "nps"
}

Delighted's built-in automation sees: score is 4, that's a detractor, send a Slack notification. Done.

Your OpenClaw agent sees much more:

• Theme detection: Billing, support inconsistency, considering churn
• Urgency: High — "considering alternatives" is an explicit churn signal
• Sentiment: Frustrated but not hostile — there's a window to recover
• Customer context: Enterprise account, $4,500 MRR, EMEA region
• Pattern match: Third billing-related complaint from enterprise accounts this week

Based on this analysis, the agent takes multiple actions simultaneously:

1. Sends a priority Slack DM to the account manager (Jake) with a summary: "🔴 Sarah Chen at Acme Corp (Enterprise, $4.5K MRR) scored 4 on NPS. Primary issue: billing migration to annual has been unresolved for 3 months with inconsistent support responses. Churn risk: HIGH. Recommended action: Personal outreach within 4 hours."

2. Creates a Zendesk ticket tagged as escalation:billing, churn-risk:high, assigned to the billing team lead, with full context and the customer's exact words.

3. Drafts a response email for Jake to personalize and send — not a template, but a contextual draft that acknowledges the specific billing issue and commits to a resolution timeline.

4. Logs the interaction in Salesforce on the account record so the next QBR includes this context.

5. Updates an internal trend tracker — incrementing the "billing complaints" counter for enterprise EMEA accounts.

All of this happens within seconds of the survey response being submitted. No human had to read the comment, decide what to do, figure out who to notify, or draft anything.

The OpenClaw configuration for this involves setting up a webhook listener, defining your analysis prompt (which includes your company's specific themes, products, and routing rules), and connecting your action integrations. The agent logic lives in OpenClaw, not in a fragile Zapier chain or a custom-coded microservice you'll forget how to maintain.

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Workflow 2: Automated Trend Detection and Insight Reporting

Individual responses matter. But patterns across responses are where the real strategic value lives.

Here's what most companies do with Delighted data: someone exports a CSV once a quarter, drops it in a spreadsheet, eyeballs the comments, and writes a summary for leadership. It takes hours, it's subjective, and it misses things.

Your OpenClaw agent can do this continuously.

Setup:

The agent maintains a running analysis of all incoming responses, categorized by theme, segment, and time period. You configure it to produce automated insight reports on whatever cadence you want — weekly for the CS team, monthly for product, quarterly for the board.

A weekly report might look like this:

Weekly NPS Insight Report — June 9–15

Overall NPS: 42 (down from 47 last week)

Key Drivers of Decline:

• Billing/pricing complaints increased 34% week-over-week, concentrated in mid-market accounts ($1K–$5K MRR). Representative quote: "The price increase came with zero notice and no clear explanation of added value."
• Mobile app performance was mentioned negatively by 12 respondents (up from 3 last week). Appears correlated with the v3.2 release on June 10.

Positive Signals:

• Onboarding experience scores improved significantly among accounts that went through the new guided setup flow. Average score: 9.1 (up from 7.4).

Recommended Actions:

1. Product team should investigate mobile performance regression in v3.2
2. Finance/CS should prepare talking points for mid-market accounts about pricing changes
3. Consider expanding guided onboarding to all new accounts based on early results

5 Responses Requiring Immediate Attention: [linked list with summaries]

This report gets posted to Slack, emailed to stakeholders, or pushed into Notion — whatever your team actually uses. The agent writes it in natural language, not as a data dump. Leadership gets insight, not spreadsheets.

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Workflow 3: Smart Closed-Loop Recovery

The "detractor recovery" workflow is the most common Delighted automation, and it's also the most underbuilt. The typical implementation is: detractor responds → Slack notification → someone should follow up → maybe they do, maybe they don't.

With OpenClaw, you build an actual recovery system:

1. Detractor response comes in. Agent analyzes the comment and scores urgency based on content + customer value.

2. High-urgency responses (churn language, high MRR, strategic accounts) get immediate routing to the specific account owner with a drafted response and recommended action.

3. Medium-urgency responses get queued in a daily digest for the CS team with summaries and suggested next steps.

4. Low-urgency responses (score of 6 with a comment like "everything's fine, just not a 10 person") get an automated thank-you and are logged but don't trigger human intervention.

5. The agent tracks follow-up. If a high-urgency detractor hasn't been contacted within your SLA (say, 24 hours), the agent escalates — pings the manager, reassigns, or flags it in standup notes.

6. Post-recovery, the agent can trigger a follow-up survey via Delighted's API to measure whether the recovery effort actually worked.

This is a full closed-loop system, not just a notification. The difference between "we told someone about it" and "we tracked it to resolution" is massive for retention.

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Workflow 4: Proactive Product Intelligence

This one's for product teams who are drowning in feedback but still feel like they're building in the dark.

Your OpenClaw agent can continuously categorize every piece of qualitative feedback from Delighted into product-relevant buckets:

• Feature requests (with frequency counts and customer segment breakdowns)
• Bug reports (correlated with release dates and affected segments)
• UX friction points (mapped to specific product areas)
• Competitive mentions (which competitors are named, in what context)

When the agent detects a spike — say, seven customers mention the same export bug in a week — it automatically creates a Jira ticket with:

• A summary of the issue based on all related comments
• The number of affected customers and their combined MRR
• Representative quotes
• Severity recommendation
• Links back to individual Delighted responses

Product managers stop manually triaging feedback and start receiving pre-analyzed, prioritized intelligence. This alone can save hours per week and surface issues that would otherwise take weeks to bubble up through support channels.

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Workflow 5: Dynamic Survey Optimization

Delighted's surveys are static. Same questions, same order, same follow-up for everyone. This works fine for basic NPS, but response quality degrades over time as customers get survey fatigue.

With OpenClaw orchestrating your survey strategy, you can:

• Adjust send timing based on customer engagement data (don't survey someone who hasn't logged in for 30 days — they'll either ignore it or give you a useless score)
• Vary the survey type based on the customer's journey stage (CES after onboarding, CSAT after support, NPS at renewal)
• Suppress surveys for customers who've been surveyed recently or who are in an active support escalation
• A/B test follow-up questions by programmatically creating different survey configurations via the API and tracking which versions produce more actionable comments

The agent uses Delighted's People API to manage customer properties and the Survey API to trigger the right survey at the right time. Delighted handles the delivery and collection; OpenClaw handles the strategy.

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Technical Integration Details

For those who want to know how this actually connects:

Delighted's API is well-documented REST with API key authentication. The key endpoints you'll use:

• POST /people — Create or update customer records with properties
• POST /survey_requests — Trigger surveys programmatically
• GET /survey_responses — Pull responses (with pagination)
• GET /metrics — Retrieve NPS/CSAT scores and trends
• Webhooks — Real-time POST to your endpoint when responses come in

OpenClaw handles the agent layer:

• Webhook listener receives Delighted events in real-time
• Agent logic processes the payload — enriching with data from your CRM, product database, or data warehouse
• AI analysis runs on the comment text (sentiment, themes, urgency, entity extraction)
• Action engine executes the appropriate workflow (Slack, Jira, Salesforce, email, internal dashboards)
• Memory and state tracking enable trend analysis, SLA monitoring, and follow-up workflows

The entire system runs autonomously once configured. You're not maintaining a codebase or babysitting a chain of Zapier steps. OpenClaw manages the agent lifecycle, handles retries and errors, and gives you visibility into what the agent is doing and why.

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What This Actually Looks Like in Practice

Let me paint the picture of a company running this stack.

Before: The CS team checks Delighted once a day. They see a dashboard with a number. Sometimes someone reads through recent comments. Detractors get a Slack notification that disappears in the scroll. Quarterly, someone spends a full day building an NPS report. Product hears about feedback in all-hands meetings, anecdotally.

After: Every piece of feedback is analyzed in real time. Detractors are triaged by severity and routed to the right person with context and a drafted response. Trends are surfaced weekly in plain English. Product gets auto-generated Jira tickets when multiple customers mention the same issue. Leadership gets insight reports that actually tell them why the number moved, not just that it moved. Follow-up is tracked, and nothing falls through the cracks.

The feedback loop that was broken — collect → analyze → act → improve → measure — is now closed and running continuously.

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Getting Started

If you're already using Delighted and want to build this kind of intelligence layer, there are two paths:

Path 1: Build it yourself on OpenClaw. If you have technical resources and want full control over the agent logic, workflows, and integrations, OpenClaw gives you the platform to build, deploy, and manage your agents. Start with the highest-value workflow (usually intelligent comment analysis + routing) and expand from there.

Path 2: Let us build it for you. If you'd rather have experts handle the implementation, our Clawsourcing service is designed for exactly this. We'll scope your Delighted integration, build the agent workflows, connect your tools, and hand you a running system. You focus on acting on the intelligence; we handle the infrastructure.

Either way, the starting point is the same: your feedback data is already there in Delighted. The question is whether you're going to keep glancing at it or actually put it to work.