Monitor daily traffic spikes with Databox, OpenAI and Slack

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Built by n8n Lab n8n Lab
Created on June 10, 2026

Description

Autonomous Traffic Spike Reporting: Databox, AI & Slack

This workflow gives PPC agencies and performance teams a daily traffic and performance check across multiple clients using Databox data.

It pulls yesterday’s and day-before-yesterday’s data for key metrics, compares changes, analyzes the results with AI, and sends both client-level and cross-client reports to Slack.

Who’s it for

PPC agencies managing multiple client accounts
Performance marketing teams tracking daily traffic changes
Account managers who need fast visibility into client performance
Operations leads who want a daily cross-client summary

How it works

A daily trigger starts the workflow.
The first Code node stores the client list and Databox metric setup.
The loop processes each client one by one.
Databox pulls yesterday and day-before-yesterday data for:
Sessions
New users
Ad cost
Clicks
The workflow merges and formats the metric data.
AI checks for traffic spikes, drops, and unusual performance changes.
A client-level Slack report is sent for each client.
After all clients are processed, a second AI agent creates a leadership summary across all clients.
The final cross-client report is sent to Slack.

How to set up

Connect the Databox MCP Client Tool with your endpoint and authentication headers.
Add your clients in the first Code node, including:
Client name
Databox data source IDs
Metric keys for sessions, new users, ad cost, and clicks
Connect your OpenAI credentials.
Connect the Slack nodes to the right channels.
Adjust the daily schedule if needed.

You can ask Databox Genie to provide the correct data source IDs and metric keys for each client, then format them in the same structure as the example Code node.

Requirements

n8n 1.0+
Databox MCP access
Metrics available in Databox for each client
OpenAI credentials
Slack workspace

Output

The workflow sends two reports:

Client-level report:** highlights daily traffic spikes, drops, and metric changes for each client.
Leadership report:** summarizes performance changes and priority risks across all processed clients.

Nodes Used (5)

AI Agent
@n8n/n8n-nodes-langchain.agent
Code
n8n-nodes-base.code
MCP Client
@n8n/n8n-nodes-langchain.mcpClient
OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
Slack
n8n-nodes-base.slack