An AIoT-powered diagnostic tool for retail stores that analyzes customer traffic and conversion funnels using a dual-funnel methodology.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install retail-traffic-analysis
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install retail-traffic-analysis using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Retail Traffic Analysis?
Retail Traffic Analysis is a sophisticated diagnostic tool designed for the modern retail environment. By leveraging AIoT data, it provides deep insights into store performance through two primary lenses: customerFunnel (individual customer segments) and behaviorFunnel (trial and interaction frequency). This synthesis of data allows managers to identify exactly where potential sales are lost in the physical environment.
This specific addition to the ecosystem of Openclaw Skills focuses on a comprehensive five-step analysis methodology. It doesn't just provide numbers; it offers logical annotations for every conversion metric, including calculation formulas and causal explanations. This ensures that every stakeholder understands the 'why' behind the retail performance data, moving beyond simple reporting to actionable business intelligence.
Retail Traffic Analysis Use Cases
- Monitoring daily or monthly store traffic fluctuations to identify significant drops in visitor count.
- Diagnosing conversion rate issues by pinpointing whether losses occur during the initial trial or at the final purchase stage.
- Evaluating the effectiveness of store staff in converting 'Intent' customers into 'Deal' customers.
- Comparing historical performance data to current periods to visualize growth or decline across specific customer segments.
How Retail Traffic Analysis Works
- Data Acquisition: The skill fetches raw customer and behavior funnel data from the AIoT API via the integrated client.
- Customer Funnel Parsing: It validates customer segments (Normal, Potential, Intent, Deal) using a mutually exclusive bucket model to ensure data integrity.
- Behavior Funnel Parsing: The system analyzes trial behaviors, distinguishing between standard interactions and deep engagement sessions.
- Conversion Calculation: It computes core KPIs including Potential-to-Intent and Intent-to-Deal rates, using specific formulas designed for retail logic.
- Comprehensive Diagnosis: The final step identifies core issues, such as traffic decline or trial depth insufficiency, and generates a structured report.
Retail Traffic Analysis Setup
To integrate this skill into your environment, follow these steps:
- Ensure you have the Openclaw environment configured.
- Clone or download the traffic-analysis skill into your local skill directory.
- Install the required dependencies including the API client.
# Standard setup for Openclaw Skills
mkdir -p ~/.openclaw/skills/traffic-analysis
# Copy analyze.py and related assets to the directory
# Usage in your script
import sys
sys.path.insert(0, '~/.openclaw/skills/traffic-analysis')
from analyze import analyze
result = analyze(
store_id="YOUR_STORE_ID",
from_date="2026-03-01",
to_date="2026-03-25",
store_name="Main Street Branch"
)
Retail Traffic Analysis Data Schema & Taxonomy
The skill organizes data into a structured hierarchy based on the Mutually Exclusive Bucket Model:
| Segment | Definition | Logic |
|---|---|---|
| Normal Customers | No trial behavior | Validates against Potential |
| Potential Customers | Standard trial, no depth | Interaction intensity: Low |
| Intent Customers | Deep trial, no deal | Interaction intensity: High |
| Deal Customers | Successful purchase | Final funnel stage |
Output Metrics include:
conversion_rates: Periodic comparison of funnel transitions.per_customer_metrics: Deep trial frequency per person and average items per transaction.funnel_summary: Percentage change across all customer groups.
Retail Traffic Analysis Advanced Features
- Mutually Exclusive Bucket Validation: Ensures every visitor is counted only in their deepest funnel stage to prevent data duplication.
- Dual-Funnel Integration: Combines 'People' counts with 'Behavior' counts for a 360-degree customer profile.
- Automated Diagnostic Thresholds: Built-in triggers identify critical issues (e.g., alert if Intent-to-Deal conversion drops > 5%).
- Structured JSON Output: Designed for seamless integration with downstream reporting dashboards and multi-agent workflows within the Openclaw Skills ecosystem.
SKILL.md
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