Analytics Dashboard
Real-time insights into your AI-powered ordering platform
📊 Order Statistics
Total Orders Today
127
Active Sessions
23
Conversion Rate
78.3%
Avg Order Value
€24.50
Irish WhatsApp Users
84%
WhatsApp Business Orders
67%
Daily Transaction Increase
+43%
Customer Retention Rate
89%
🌍 Global Channel Performance
Walk-in
53%
WhatsApp Orders
32%
Other Channels
15%
🤖 AI Performance
AI Resolution Rate
94.2%
Avg Response Time
1.2s
Customer Satisfaction
4.8/5
Handoff to Human
5.8%
💰 Revenue Insights
Today's Revenue
€3,115.50
Weekly Growth
+12.4%
Top Product
Americano
Peak Hour
2-3 PM
📈 Order Trends
📊 Interactive charts and graphs will be displayed here
Real-time data visualization showing order patterns, peak hours, and performance metrics
Real-time data visualization showing order patterns, peak hours, and performance metrics
🎯 Popular Items
1. Americano
32 orders
2. Cappuccino
28 orders
3. Latte
24 orders
4. Espresso
18 orders
5. Croissant
15 orders
🔬 Advanced Analytics with SageMaker
Leverage AWS SageMaker for advanced machine learning insights on customer behavior, demand forecasting, and personalized recommendations.
📊 Customer Behavior Analysis
import boto3
import pandas as pd
from sagemaker import get_execution_role
# Initialize SageMaker session
sagemaker_session = boto3.Session().client('sagemaker')
role = get_execution_role()
# Analyze customer ordering patterns
def analyze_customer_behavior(order_data):
# Customer segmentation using K-means
model = create_kmeans_model(
instance_type='ml.m5.large',
role=role,
train_data='s3://serv-analytics/customer-data/'
)
# Predict customer lifetime value
predictions = model.predict(order_data)
return predictions
# Real-time recommendation engine
recommendations = get_ml_recommendations(customer_id, session_data)
Use SageMaker's built-in algorithms to segment customers and predict ordering patterns for targeted marketing campaigns.
📈 Demand Forecasting
# SageMaker DeepAR for demand forecasting
from sagemaker.amazon.amazon_estimator import get_image_uri
# Configure DeepAR model for menu item demand prediction
deepar_image = get_image_uri(boto3.Session().region_name, 'forecasting-deepar')
estimator = sagemaker.estimator.Estimator(
image_name=deepar_image,
role=role,
train_instance_count=1,
train_instance_type='ml.c4.2xlarge',
sagemaker_session=sagemaker_session
)
# Train on historical order data
estimator.fit({'training': 's3://serv-analytics/order-history/'})
# Generate 7-day demand forecast
forecast = estimator.predict(current_trends)
Predict future demand for menu items to optimize inventory and reduce waste using time-series forecasting.
🚀 ML Insights Available:
- Customer lifetime value prediction
- Personalized menu recommendations
- Inventory optimization forecasting
- Churn prediction and retention strategies
- Peak hour demand analysis
Analytics update every 30 seconds • Last updated: