E-commerce Analytics Dashboards with Vizly

E-commerce dashboards answer the same questions every week: what sold, how many people bought, and which customers matter most. Vizly covers the standard storefront visuals: revenue trends, product mix, cart distribution, customer segments, and checkout funnel.

Revenue over time

Track daily or weekly revenue with a combo of bar and line for a revenue plus order count view.

import vizly as vz

vz.set_theme("corporate")

sales = [
    {"date": "2026-06-01", "revenue": 12400, "orders": 420},
    {"date": "2026-06-02", "revenue": 13100, "orders": 445},
    {"date": "2026-06-03", "revenue": 11900, "orders": 398},
    {"date": "2026-06-04", "revenue": 14200, "orders": 471},
    {"date": "2026-06-05", "revenue": 15800, "orders": 512},
]

chart = vz.combo(
    sales,
    x="date",
    bar="revenue",
    line="orders",
    title="Revenue and orders",
)
chart.render("sales.html")

Product category mix

Show which categories drive revenue with a treemap, or a donut for a quick share view.

categories = [
    {"category": "Electronics", "value": 38400},
    {"category": "Apparel", "value": 22600},
    {"category": "Home", "value": 18200},
    {"category": "Beauty", "value": 12400},
    {"category": "Grocery", "value": 9800},
]

chart = vz.treemap(categories, names="category", values="value", title="Revenue by category")

Cart size distribution

Histograms of basket size reveal typical orders. A boxplot shows the spread including outliers.

import pandas as pd
import vizly as vz

orders = pd.DataFrame({
    "basket_size": [1, 1, 2, 2, 2, 3, 3, 4, 5, 6, 8, 12],
})

chart = vz.boxplot(orders, x="basket_size", y="basket_size", title="Items per order")

Checkout funnel

Find where shoppers drop off between view and purchase.

funnel = [
    {"stage": "Visits", "count": 40000},
    {"stage": "Product views", "count": 16800},
    {"stage": "Add to cart", "count": 5400},
    {"stage": "Checkout", "count": 2100},
    {"stage": "Purchased", "count": 1400},
]

chart = vz.funnel(funnel, names="stage", values="count", title="Checkout funnel")

Customer segments

Split customers by value tier with a donut, or compare segments across metrics with a radar.

segments = [
    {"name": "VIP", "value": 120},
    {"name": "Loyal", "value": 340},
    {"name": "Regular", "value": 810},
    {"name": "New", "value": 590},
]

chart = vz.donut(segments, names="name", values="value", title="Customer segments")

Geographic order distribution

Map order volume by country for a storefront with international shipping.

countries = [
    {"name": "United States", "value": 8200},
    {"name": "Canada", "value": 3100},
    {"name": "United Kingdom", "value": 2800},
    {"name": "Germany", "value": 2200},
    {"name": "Japan", "value": 1900},
]

chart = vz.map(countries, title="Orders by country")

Full e-commerce dashboard

Compose the storefront view into one dashboard.

import pandas as pd
import vizly as vz

vz.set_theme("corporate")

chart = vz.page(
    charts=[
        vz.combo(sales, x="date", bar="revenue", line="orders", title="Revenue and orders"),
        vz.treemap(categories, names="category", values="value", title="Revenue by category"),
        vz.funnel(funnel, names="stage", values="count", title="Checkout funnel"),
        vz.donut(segments, names="name", values="value", title="Customer segments"),
    ],
    title="E-commerce dashboard",
)
chart.render("ecommerce.html")

Tips

Tip	Detail
Combo for revenue plus volume	vz.combo shows money and counts together on shared axes.
Treemap for category hierarchy	Use vz.treemap when categories have subcategories.
Funnel order matters	Pass checkout stages top-to-bottom from most to least visitors.
Radar for multi-metric segments	Compare customer segments across several metrics with vz.radar.