Quad & Quartile Analysis for Customers and Products

What are Quad and Quartile analyses?

Quad and Quartile are complementary but separate views. Quad analysis crosses customer and product A/B classifications to show where core and tail relationships intersect. Quartile analysis independently divides ranked customers and ranked products into four equal-count groups. Quartiles do not create the Quads.

The two views answer different questions. Quad analysis shows how core and tail customers interact with core and tail products. Quartile analysis adds resolution within the ranked customer and product populations.

• Quad 1: A customer / A product
• Quad 2: A customer / B product
• Quad 3: B customer / A product
• Quad 4: B customer / B product

This matters because a one-dimensional ranking can hide the operating reality. An A customer may purchase many B products. An A product may be sold mostly to B customers. Quad analysis shows where scale, mix, and complexity meet, while Quartile analysis provides additional ranked detail.

Quartiles are statistical quartiles

A Quartile is one of four groups containing equal numbers of ranked observations. Rank customers by revenue from highest to lowest, then divide that customer list into four equal-count groups. Repeat the same process independently for products.

• Quartile 1 is the highest-ranked 25% of the entities
• Quartile 2 is the next 25%
• Quartile 3 is the next 25%
• Quartile 4 is the lowest-ranked 25%

When the population is not divisible by four, use a consistent boundary rule so group sizes differ by no more than one. Preserve a deterministic rule for ties at a boundary.

Cumulative revenue share remains useful evidence. It shows how concentrated the revenue is within and across the Quartiles. It does not determine the Quartile boundaries. A contribution band such as “customers producing the first 50% of revenue” is a contribution band, not a Quartile.

Quartile position is separate from A/B classification and separate from Quad assignment. Document the ranked entity, revenue definition, period, boundary and tie rules, handling of zero and negative values, and the effect of filters.

Build the customer and product classifications

Begin with clean transaction data. Revenue is the starting measure because it is usually the most complete, reconcilable, and broadly trusted metric in the business. After the team has completed a full 80/20 cycle, it may use another ranking method only if that method is well reasoned, repeatable, explainable, and trusted across the team.

Treat the classification layers separately:

1. Rank customers independently
2. Rank products independently
3. Determine each customer’s A/B classification using the selected 80/20 classification method
4. Determine each product’s A/B classification using the selected 80/20 classification method
5. Cross customer A/B with product A/B to create Quad 1–4
6. Independently assign customer and product statistical Quartiles for additional ranked detail

Preserve revenue, rank, cumulative revenue share, A/B classification, Quad assignment, and statistical Quartile so users can inspect each layer rather than seeing only a category.

Understand the four customer-product intersections

Quad analysis crosses the customer A/B classification with the product A/B classification. These four intersections are:

Quad 1 — A customer / A product

This is the core customer / core product relationship at the concentrated center of the current business. It often deserves strong service, reliable availability, and deliberate protection. It can also reveal dependency risk if a very small number of relationships account for a large share of value.

Questions to ask:

• Are service levels aligned with the importance of these relationships?
• Is capacity protected where it matters most?
• Are there focused growth opportunities with these customers and products?
• Is concentration creating unacceptable risk?

Quad 2 — A customer / B product

Core customers are buying tail products. This can reflect necessary breadth, strategic customization, bundling, legacy commitments, or unmanaged complexity.

Questions to ask:

• Does the assortment strengthen the core relationship?
• Are low-volume products priced for their complexity?
• Can specifications, configurations, or ordering patterns be simplified?
• Would a change create customer risk greater than the expected benefit?

Quad 3 — B customer / A product

Tail customers are buying products that matter to the portfolio. The product fit may be good even if each account is individually small. The opportunity may be to standardize service, improve channel economics, or identify which accounts can grow.

Questions to ask:

• Is the route to market appropriate for smaller accounts?
• Can quoting, ordering, fulfillment, or support be standardized?
• Which customers show credible potential to become more important?
• Does the service model make sense relative to its cost-to-serve and complexity?

Quad 4 — B customer / B product

Both sides of the relationship sit outside the concentrated core. This is often where operational complexity accumulates, but it is not an automatic elimination list.

Questions to ask:

• Does the relationship make sense relative to its cost-to-serve and complexity?
• Is it strategically necessary, new, contractual, or connected to a core relationship?
• Can price, minimum order quantities, lead time, or service terms improve the economics?
• What is the cost and risk of changing or exiting the relationship?

Test cost-to-serve and complexity before recommending action

Quad position describes revenue concentration and mix. It does not solve profitability or cost-to-serve. Test whether each relationship makes sense relative to the activities, service requirements, exceptions, resources, complexity, and resulting cost required to support it.

Zero-Up can help build the deeper cost-to-serve and operating view when the relationship warrants further investigation or redesign.

Reliable gross margin, order frequency, line count, freight, returns, and engineering time can help size and explain an opportunity. They are diagnostic overlays, not ingredients to force into the initial ranking. Do not rerank customers or products around a disputed cost model. The customer and product profitability guide explains how to add economic context without hiding the revenue backbone.

Make the analysis reviewable

A useful Quad and Quartile output should allow a reviewer to move from the summary into the underlying entities and transactions. At minimum, preserve:

• the revenue definition and customer and product A/B classification methods
• the A/B classification and Quad assignment for each customer-product relationship
• the statistical Quartile for every customer and product
• the revenue rank and cumulative revenue for every customer and product
• the boundary and tie rules used for statistical Quartiles
• the value flowing through each customer-product intersection
• the count of customers, products, orders, and lines where relevant
• filters, exclusions, and the period analyzed

This turns the Quad and Quartile views from static presentation graphics into transparent analytical views.

Use the classifications as decision aids

The classifications narrow where to look. The next step is to convert a pattern into a testable proposition: what should change, why, how much value is at stake, who owns the decision, and what measure should move?

That discipline prevents two common failures: treating every tail item as bad, and producing an attractive matrix that never changes a decision. Continue with from analysis to action when the classifications are stable.

Frequently asked questions

What is a customer-product quadrant analysis?

Quad analysis crosses the customer A/B classification with the product A/B classification. Quad 1 is A customer / A product, Quad 2 is A customer / B product, Quad 3 is B customer / A product, and Quad 4 is B customer / B product. Quartile analysis is a separate ranked view and does not create the Quads.

Are 80/20 quartiles the same as statistical quartiles?

Yes. Quartiles are statistical quartiles: four groups containing equal numbers of ranked observations. Cumulative revenue contribution bands can be useful, but they are not quartiles and should not be labeled as such.

Should a company eliminate everything in the tail-tail quadrant?

No. The classification identifies where investigation is warranted. Strategic fit, lifecycle, contractual commitments, customer needs, profitability, and the cost of change still matter.