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●Industry 02

E-commerce & retail
from cart to shelf.

Operational chart scenarios for merchandising, growth, and store teams balancing demand, margin, inventory, and customer behavior.

Industry context How the work happens

Not a generic chart request.

Retail data changes by product, channel, location, and week. A useful scenario does more than celebrate revenue: it isolates which assortment, campaign, or store condition requires a buying, pricing, or replenishment decision.

3 Detailed workflows on this page

  1. 01
    Holiday category demand shifts by weekCategory merchandising director
  2. 02
    Landing-page conversion after a campaign launchE-commerce growth manager
  3. 03
    Store-level stockout risk before a promotionRetail replenishment planner

Scenario 01 Category merchandising director

Holiday category demand shifts by week

Trigger

The first two holiday promotion weeks changed the category mix and the buying team must place the final replenishment order.

Business question

Which categories are accelerating fast enough to deserve scarce inbound inventory?

Source data

Weekly net sales by category from POS and commerce orders, excluding returns and cancelled orders.

Decision

Increase replenishment for Beauty and Home while reducing the planned Electronics reorder.

Deliverable

A bar-race sequence for the Monday trade meeting with the final ranking attached to the purchase-order brief.

NET SALES · $000S · HOLIDAY WEEKS 1—4

Beauty overtook electronics in week three

Week 1
0420840
01Electronics840
03Beauty610
04Home540
02Apparel720
05Toys430
LIVELYCHARTPLAY THE NUMBERS
What the chart should make obvious

Beauty moved from third to first and continued accelerating; the movement matters more for replenishment than the final ranking alone.

  • Use net sales after returns
  • Highlight Beauty from the first frame
  • End with Week 4 visible for the order discussion
Build with Bar Chart Race
View the exact example data
Category,Week 1,Week 2,Week 3,Week 4
Electronics,840,910,970,1010
Beauty,610,790,1040,1280
Home,540,680,860,1090
Apparel,720,760,810,880
Toys,430,590,730,850

Synthetic example data for demonstrating the workflow. Replace it with approved source data before publishing.

Scenario 02 E-commerce growth manager

Landing-page conversion after a campaign launch

Trigger

A new campaign creative launched across four acquisition channels and blended conversion fell despite higher traffic.

Business question

Is the decline caused by the landing page or by one channel sending lower-intent visitors?

Source data

Session and purchase events by UTM channel, deduplicated to one attributed order per session.

Decision

Keep the landing page, pause the weakest paid placement, and update its message to match the offer.

Deliverable

A before/after comparison for the daily performance stand-up and the campaign postmortem.

SESSION-TO-ORDER RATE · BEFORE VS AFTER

Display traffic diluted conversion

Paid search55Email67Organic social44Display31Affiliates44
BeforeAfter
LIVELYCHARTPLAY THE NUMBERS
What the chart should make obvious

Every major channel held or improved except Display, so the blended decline should not be attributed to the shared landing page.

  • Keep the same attribution window
  • Label values as percentages
  • Use the accent only for Display
Build with Dumbbell Delta
View the exact example data
Channel,Before,After
Paid search,4.8,5.1
Email,6.3,6.6
Organic social,3.5,3.7
Display,3.1,1.4
Affiliates,4.2,4.0

Synthetic example data for demonstrating the workflow. Replace it with approved source data before publishing.

Scenario 03 Retail replenishment planner

Store-level stockout risk before a promotion

Trigger

A seven-day promotion begins Friday and the planner needs to find stores where cover will fall below two days.

Business question

Which store-day combinations are likely to stock out if demand follows the campaign forecast?

Source data

On-hand inventory, inbound purchase orders, forecast units, and store trading calendar.

Decision

Transfer stock between nearby stores and expedite only the locations with consecutive high-risk days.

Deliverable

A heatmap for the operations room and a PNG included in the daily allocation email.

STOCKOUT RISK SCORE · PROMOTION WEEK

Four stores enter the red zone by Saturday

MonTueWedThuFriSatSunCentralNorthWestAirportRiverside
LIVELYCHARTPLAY THE NUMBERS
What the chart should make obvious

Central and Airport remain high risk for three consecutive days, making them better transfer targets than stores with a one-day spike.

  • Define the risk score in the source note
  • Keep weekdays in trading order
  • Use a color-blind-safe sequential palette
Build with Calendar Heatmap
View the exact example data
Store,Mon,Tue,Wed,Thu,Fri,Sat,Sun
Central,18,22,31,47,72,91,88
North,12,16,24,39,68,84,79
West,9,14,20,33,55,76,81
Airport,25,28,36,52,79,94,90
Riverside,11,13,19,27,44,61,65

Synthetic example data for demonstrating the workflow. Replace it with approved source data before publishing.

Production From example to output

Turn the scenario into your chart

Use the example dataset to test the visual structure, then replace it with approved data from the named source system and validate the decision context before export.

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FAQ Industry questions

Before you publish

Should retail charts use gross or net sales?

Use the measure that matches the decision and label it explicitly. Merchandising and profitability decisions usually need net sales after returns, while live trading monitors may use gross demand.

How frequently should inventory animations update?

Match the operational cadence. Hourly or daily updates can support allocation teams; weekly animation is usually enough for executive and category reviews.

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