Customer purchasing mix memo

Compare what retail customers bought with purchases by hotels, restaurants, and cafés using historical public records.

Customer analysis · Segmentation interpretation · Source validation · Research recommendations

Prepared from the same 440 record historical UCI Wholesale Customers dataset used in the independent portfolio project. This companion compares recorded channel labels. It does not reproduce K means clusters.

Terms explained

Channel: The customer type recorded in this dataset: retail, or hotels, restaurants, and cafés.
Aggregate share: A percentage calculated from the total spending of the whole group, rather than an average of each customer’s percentage.
Monetary units: The spending units in the original data. The source does not identify a currency.

Historical public data / 440 customers

Two customer groups. Different purchasing mixes.

Milk, grocery, and detergents/paper as a share of each channel’s total spending

Hotels, restaurants, and cafés298 customer records30.6%

Retail customers142 customer records73.6%

A difference in what customers bought

The three categories made up a larger share of retail spending. The records do not explain why customers chose them.

Source: UCI Wholesale Customers, Cardoso (2013), CC BY 4.0. Shares use each channel’s combined spending, not the average customer’s percentage. The source link and exact totals appear below.

Scroll horizontally to compare all columns.

Recorded annual spending in monetary units (m.u.). Category share is a ratio of aggregate spending
Recorded channel	Customers	Total spending	Milk + grocery + detergents/paper
Horeca · Hotel / restaurant / café	298	7,999,569 m.u.	2,444,918 m.u. · 30.6%
Retail	142	6,619,931 m.u.	4,871,858 m.u. · 73.6%
All records	440	14,619,500 m.u.	7,316,776 m.u.

Recommendation

Use the mix difference to frame a follow up research question.

Milk, grocery, and detergents/paper account for 73.6% of recorded Retail spending and 30.6% of Horeca spending. A useful next question is whether category relevant examples improve message relevance within each channel.

Show the method

Group records by Channel, sum the six spending categories, and divide the three selected category totals by each channel’s total. The displayed shares describe aggregate spending. They are not the average customer’s percentage.

Separate observation from explanation

The records show a purchasing pattern. They do not establish customer motivations, profit margins, current market size, or the effect of a campaign. A few large customers can influence aggregate shares.

Recommend the next study

Review within channel variation and gather customer feedback before proposing a message test. For a current business, first validate the pattern using current, permissioned customer data.

Define the intended customer action.
Compare category relevant messaging with a standard message within each channel.
Use a planned sample and a consistent response window.
Review commercial value and customer relevance before scaling.

Learning note

Takeaway

Purchasing categories describe recorded behavior. They can suggest a research question, but they do not explain motivation or predict campaign impact.

Related experience

Customer Segmentation & BI case study ↗

Explore the related project for its context, approach, and outcome.

Files and calculations.

Download this brief ↓Channel summary CSV ↓Verification script ↓Original source CSV ↓

For the full set of sample briefs, CSVs, SQL, verification code, and 14 role briefs, download the collection ↓.

Sources & methods: Cardoso (2013), UCI Wholesale Customers · CC BY 4.0 ↗ · Creative Commons attribution license ↗