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PUBLIC-DATA CASE STUDYDESCRIPTIVE BUSINESS INTELLIGENCE

The database had the totals.The pattern changed the business question.

We used 541,909 public retail line items to move from a transaction dump to an operating picture: who really drives revenue, what the average hides, where demand concentrates and why a static forecast fails. This is a CADi business-intelligence exercise - not causal inference and not a claim about a client.

POSITIVE-SALE VALUE

£10.67m

20,002 observed orders

REPEAT PURCHASES

93.1%

of known-customer positive-sale value comes from repeat buyers

CUSTOMER CONCENTRATION

32%

of known-customer positive-sale value comes from the top 1%

DEMAND VARIANCE

16.6×

daily variance versus mean; homogeneous Poisson fails

THE EXECUTIVE READ

Six insights worth investigating

01 · REPEAT PURCHASES

65.6% repeat. 93.1% of known-customer value.

Repeat means more than one observed order in this window, not a retention rate. Repeat buyers dominate known-customer positive-sale value; this does not establish lifetime value or a campaign effect.

02 · CONCENTRATION

The top 10% of known customers carry 61.4% of known-customer value.

That creates key-account upside and concentration risk at the same time. A useful dashboard should expose both, not celebrate one blended average.

03 · ORDER TAIL

£533 average. £304 median.

The top 1% of orders contribute 19.3% of sales. One £168,470 bulk order can move a product leaderboard.

04 · PEAK SEASON

Orders rise 52.1%; basket value only 9.3%.

The September–November surge versus June–August is mainly a throughput problem. Capacity, fulfilment and stock availability matter more than a premium-basket story.

05 · IDENTITY BLIND SPOT

Anonymous orders average £1,219.

Identified-customer orders average £480. Anonymous orders are larger on average; the cause of missing identity is not observed.

06 · FORECASTING

The holdout runs 64.1% above training.

In the separate UK arrival cohort, a Poisson 90% interval covers only 7.1% of later days. Seasonality and overdispersion are candidates for further testing, not validated fixes.

WHEN GROWTH ARRIVES

September–November growth is mostly order volume

Positive-sale value rises 66.2% from the June–August baseline to September–November. Order count explains most of it: +52.1%, versus only +9.3% in average basket value. The operating question is therefore capacity under clustered demand.

Dec
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sept
Oct
Nov
Dec

Gross positive-sale value by source month. Both December 2010 and December 2011 are partial.

MARKET SHAPE

Small export markets, wholesale-sized baskets

The UK supplies 84.6% of gross value, but order economics differ sharply abroad. Netherlands and Australia orders are several times larger than UK orders - small markets with a different operating profile, not merely smaller versions of home.

United Kingdom£500
Eire£981
Australia£2,430
Netherlands£3,037

Average positive-sale order value

ASSORTMENT SIGNAL

Largest unit-volume item

PAPER CRAFT , LITTLE BIRDIE

UNITS

80,995

ORDERS

1

VALUE

£168.47k

One bulk order creates the top unit-volume product. A naïve “best seller” ranking would confuse a one-off wholesale event with broad customer demand. Frequency and concentration belong beside every product leaderboard.

THE DATA BLIND SPOT

Missing identity is not neutral

IDENTIFIED ORDER AVERAGE

£480

ANONYMOUS ORDER AVERAGE

£1,219

Only 7.2% of orders lack a usable customer ID, yet they carry £1.76m of gross value. Customer-level retention and concentration metrics are therefore informative, but not a complete view of the commercial base.

WHAT THIS SUPPORTS

Better questions, not causal claims

Repeat purchasing and key-account exposure deserve separate management views.
Test seasonal and overdispersed models before using them for peak planning.
Product rankings need outlier and order-frequency context.
Identity coverage should be monitored as a commercial bias, not just completeness.