customerio-observability

jeremylongshore's avatarfrom jeremylongshore

Set up Customer.io monitoring and observability.Use when implementing metrics, logging, alerting,or dashboards for Customer.io integrations.Trigger with phrases like "customer.io monitoring", "customer.io metrics","customer.io dashboard", "customer.io alerts".

⭐939stars🔀114forks📁View on GitHub🕐Updated Jan 11, 2026

When & Why to Use This Skill

This Claude skill provides a comprehensive framework for implementing observability in Customer.io integrations. It enables developers to set up full-stack monitoring, including Prometheus metrics for API performance, structured logging with PII redaction, OpenTelemetry distributed tracing, and ready-to-use Grafana dashboards and Prometheus alerting rules to ensure reliable email delivery and system health.

Use Cases

  • API Performance Tracking: Monitor Customer.io API latency (p50, p95, p99) and request success rates to identify and resolve integration bottlenecks.
  • Email Delivery Health: Set up automated alerts for high bounce rates or delivery failures to proactively protect sender reputation and campaign effectiveness.
  • Secure Debugging: Implement structured logging that automatically redacts sensitive PII like emails and phone numbers, ensuring GDPR/SOC2 compliance while troubleshooting.
  • Visualizing the Marketing Funnel: Deploy pre-configured Grafana dashboards to track the end-to-end flow from API requests to email delivery and webhook processing.
namecustomerio-observability
description|
allowed-toolsRead, Write, Edit, Bash(kubectl:*), Bash(curl:*)
version1.0.0
licenseMIT
authorJeremy Longshore <jeremy@intentsolutions.io>

Customer.io Observability

Overview

Implement comprehensive observability for Customer.io integrations including metrics, logging, tracing, and alerting.

Prerequisites

  • Customer.io integration deployed
  • Monitoring infrastructure (Prometheus, Grafana, etc.)
  • Log aggregation system

Key Metrics

Metric Type Description
customerio_api_latency_ms Histogram API call latency
customerio_api_requests_total Counter Total API requests
customerio_api_errors_total Counter API error count
customerio_email_sent_total Counter Emails sent
customerio_email_delivered_total Counter Emails delivered
customerio_email_bounced_total Counter Email bounces
customerio_webhook_received_total Counter Webhooks received

Instructions

Step 1: Metrics Collection

// lib/metrics.ts
import { Counter, Histogram, Registry } from 'prom-client';

const register = new Registry();

// API metrics
export const apiLatency = new Histogram({
  name: 'customerio_api_latency_ms',
  help: 'Customer.io API call latency in milliseconds',
  labelNames: ['operation', 'status'],
  buckets: [10, 25, 50, 100, 250, 500, 1000, 2500, 5000],
  registers: [register]
});

export const apiRequests = new Counter({
  name: 'customerio_api_requests_total',
  help: 'Total Customer.io API requests',
  labelNames: ['operation', 'status'],
  registers: [register]
});

export const apiErrors = new Counter({
  name: 'customerio_api_errors_total',
  help: 'Total Customer.io API errors',
  labelNames: ['operation', 'error_type'],
  registers: [register]
});

// Email metrics
export const emailsSent = new Counter({
  name: 'customerio_email_sent_total',
  help: 'Total emails sent via Customer.io',
  labelNames: ['campaign_type'],
  registers: [register]
});

export const emailsDelivered = new Counter({
  name: 'customerio_email_delivered_total',
  help: 'Total emails delivered',
  labelNames: ['campaign_type'],
  registers: [register]
});

export const emailsBounced = new Counter({
  name: 'customerio_email_bounced_total',
  help: 'Total email bounces',
  labelNames: ['bounce_type'],
  registers: [register]
});

// Webhook metrics
export const webhooksReceived = new Counter({
  name: 'customerio_webhook_received_total',
  help: 'Total webhooks received from Customer.io',
  labelNames: ['event_type'],
  registers: [register]
});

export { register };

Step 2: Instrumented Client

// lib/customerio-instrumented.ts
import { TrackClient, RegionUS } from '@customerio/track';
import * as metrics from './metrics';

export class InstrumentedCustomerIO {
  private client: TrackClient;

  constructor(siteId: string, apiKey: string) {
    this.client = new TrackClient(siteId, apiKey, { region: RegionUS });
  }

  async identify(userId: string, attributes: Record<string, any>): Promise<void> {
    const timer = metrics.apiLatency.startTimer({ operation: 'identify' });

    try {
      await this.client.identify(userId, attributes);
      timer({ status: 'success' });
      metrics.apiRequests.inc({ operation: 'identify', status: 'success' });
    } catch (error: any) {
      timer({ status: 'error' });
      metrics.apiRequests.inc({ operation: 'identify', status: 'error' });
      metrics.apiErrors.inc({
        operation: 'identify',
        error_type: error.statusCode || 'unknown'
      });
      throw error;
    }
  }

  async track(userId: string, event: string, data?: Record<string, any>): Promise<void> {
    const timer = metrics.apiLatency.startTimer({ operation: 'track' });

    try {
      await this.client.track(userId, { name: event, data });
      timer({ status: 'success' });
      metrics.apiRequests.inc({ operation: 'track', status: 'success' });
    } catch (error: any) {
      timer({ status: 'error' });
      metrics.apiRequests.inc({ operation: 'track', status: 'error' });
      metrics.apiErrors.inc({
        operation: 'track',
        error_type: error.statusCode || 'unknown'
      });
      throw error;
    }
  }
}

Step 3: Structured Logging

// lib/logger.ts
import pino from 'pino';

export const logger = pino({
  name: 'customerio',
  level: process.env.LOG_LEVEL || 'info',
  formatters: {
    level: (label) => ({ level: label })
  },
  base: {
    service: 'customerio-integration',
    environment: process.env.NODE_ENV
  }
});

// Logging wrapper for Customer.io operations
export function logOperation(
  operation: string,
  userId: string,
  data: any,
  result: 'success' | 'error',
  error?: Error
) {
  const logData = {
    operation,
    userId,
    result,
    data: sanitizeForLogging(data),
    ...(error && {
      error: {
        message: error.message,
        stack: error.stack
      }
    })
  };

  if (result === 'error') {
    logger.error(logData, `Customer.io ${operation} failed`);
  } else {
    logger.info(logData, `Customer.io ${operation} succeeded`);
  }
}

// Remove PII from logs
function sanitizeForLogging(data: any): any {
  if (!data) return data;

  const sanitized = { ...data };
  const piiFields = ['email', 'phone', 'address', 'ssn'];

  for (const field of piiFields) {
    if (sanitized[field]) {
      sanitized[field] = '[REDACTED]';
    }
  }

  return sanitized;
}

Step 4: Distributed Tracing

// lib/tracing.ts
import { trace, SpanKind, SpanStatusCode } from '@opentelemetry/api';

const tracer = trace.getTracer('customerio-integration');

export async function withTracing<T>(
  operationName: string,
  attributes: Record<string, string>,
  operation: () => Promise<T>
): Promise<T> {
  return tracer.startActiveSpan(
    `customerio.${operationName}`,
    {
      kind: SpanKind.CLIENT,
      attributes: {
        'customerio.operation': operationName,
        ...attributes
      }
    },
    async (span) => {
      try {
        const result = await operation();
        span.setStatus({ code: SpanStatusCode.OK });
        return result;
      } catch (error: any) {
        span.setStatus({
          code: SpanStatusCode.ERROR,
          message: error.message
        });
        span.recordException(error);
        throw error;
      } finally {
        span.end();
      }
    }
  );
}

// Usage
await withTracing('identify', { userId }, () =>
  client.identify(userId, attributes)
);

Step 5: Grafana Dashboard

{
  "dashboard": {
    "title": "Customer.io Integration",
    "panels": [
      {
        "title": "API Latency (p50, p95, p99)",
        "type": "timeseries",
        "targets": [
          {
            "expr": "histogram_quantile(0.50, rate(customerio_api_latency_ms_bucket[5m]))",
            "legendFormat": "p50"
          },
          {
            "expr": "histogram_quantile(0.95, rate(customerio_api_latency_ms_bucket[5m]))",
            "legendFormat": "p95"
          },
          {
            "expr": "histogram_quantile(0.99, rate(customerio_api_latency_ms_bucket[5m]))",
            "legendFormat": "p99"
          }
        ]
      },
      {
        "title": "API Request Rate",
        "type": "timeseries",
        "targets": [
          {
            "expr": "rate(customerio_api_requests_total[5m])",
            "legendFormat": "{{operation}} - {{status}}"
          }
        ]
      },
      {
        "title": "Error Rate",
        "type": "stat",
        "targets": [
          {
            "expr": "sum(rate(customerio_api_errors_total[5m])) / sum(rate(customerio_api_requests_total[5m])) * 100",
            "legendFormat": "Error Rate %"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "thresholds": {
              "steps": [
                { "value": 0, "color": "green" },
                { "value": 1, "color": "yellow" },
                { "value": 5, "color": "red" }
              ]
            }
          }
        }
      },
      {
        "title": "Email Delivery Funnel",
        "type": "bargauge",
        "targets": [
          {
            "expr": "sum(customerio_email_sent_total)",
            "legendFormat": "Sent"
          },
          {
            "expr": "sum(customerio_email_delivered_total)",
            "legendFormat": "Delivered"
          },
          {
            "expr": "sum(customerio_email_bounced_total)",
            "legendFormat": "Bounced"
          }
        ]
      }
    ]
  }
}

Step 6: Alerting Rules

# prometheus/alerts/customerio.yml
groups:
  - name: customerio
    rules:
      - alert: CustomerIOHighErrorRate
        expr: |
          sum(rate(customerio_api_errors_total[5m]))
          / sum(rate(customerio_api_requests_total[5m])) > 0.05
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: Customer.io API error rate > 5%
          description: Error rate is {{ $value | printf "%.2f" }}%

      - alert: CustomerIOHighLatency
        expr: |
          histogram_quantile(0.99, rate(customerio_api_latency_ms_bucket[5m])) > 5000
        for: 10m
        labels:
          severity: warning
        annotations:
          summary: Customer.io p99 latency > 5s
          description: p99 latency is {{ $value | printf "%.0f" }}ms

      - alert: CustomerIOHighBounceRate
        expr: |
          sum(rate(customerio_email_bounced_total[1h]))
          / sum(rate(customerio_email_sent_total[1h])) > 0.05
        for: 30m
        labels:
          severity: warning
        annotations:
          summary: Email bounce rate > 5%
          description: Bounce rate is {{ $value | printf "%.2f" }}%

      - alert: CustomerIOWebhookProcessingFailed
        expr: |
          sum(rate(customerio_webhook_errors_total[5m])) > 0
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: Customer.io webhook processing failures
          description: {{ $value }} webhooks failed in last 5 minutes

Observability Checklist

  • API latency metrics collected
  • Error rate tracking enabled
  • Structured logging implemented
  • Distributed tracing configured
  • Grafana dashboard created
  • Alert rules defined
  • PII redacted from logs
  • Log retention policy set

Error Handling

Issue Solution
Missing metrics Check metric registration
High cardinality Reduce label values
Log volume too high Adjust log level

Resources

Next Steps

After observability setup, proceed to customerio-advanced-troubleshooting for debugging.