Quad & Quartile Analysis for Customers and Products

What is Quad & Quartile analysis?

Quad & Quartile analysis is a structured way to examine customer concentration, product concentration, and the relationship between them. Customers are ranked by revenue. Products are ranked by revenue. Each list is divided into statistical quartiles, and those classifications are crossed so the team can see which customer-product combinations sit in the concentrated core and which sit in the long tail.

This matters because a one-dimensional ranking can hide the operating reality. A core customer may purchase many tail products. A core product may be sold mostly to smaller accounts. The intersection shows where scale, mix, and complexity meet.

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.

• Q1 is the highest-revenue 25% of the ranked entities
• Q2 is the next 25%
• Q3 is the next 25%
• Q4 is the lowest-revenue 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.

Document the ranked entity, revenue definition, period, boundary and tie rules, handling of zero and negative values, and the effect of filters. Clear definitions make the result repeatable and keep a discussion about the business from becoming a debate about labels.

Build the customer and product classifications

Begin with clean transaction data. Aggregate revenue by customer, rank customers from highest to lowest, and calculate cumulative revenue contribution. Repeat the same process independently for products.

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.

Assign each customer and product to its statistical quartile by position in the ranked list. Preserve revenue, rank, quartile, revenue share, and cumulative revenue share so users can inspect the evidence rather than seeing only a category.

Understand the four customer-product intersections

A simplified two-class view creates four intersections. Treat Q1 as the core and Q2 through Q4 as the tail. Names vary across organizations, so describe the business meaning rather than relying on a color or letter code.

Core customer / core product

This is 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?

Core customer / tail product

Important customers are buying products outside the concentrated product core. 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?

Tail customer / core product

Smaller 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?
• What does Zero-Up reveal about the service model and cost-to-serve?

Tail customer / tail 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:

• What does Zero-Up reveal about the activities and cost required to serve the relationship?
• 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?

Use Zero-Up before recommending action

Quadrant position describes revenue concentration and mix. It does not claim to solve profitability or cost-to-serve. Keep the revenue ranks and statistical quartiles stable while the team uses Zero-Up to examine activities, service requirements, exceptions, resources, and costs.

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 & 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 classification rules
• the revenue rank and statistical quartile for every entity
• the boundary and tie rules
• the revenue and cumulative revenue values
• the value flowing through each intersection
• the count of customers, products, orders, and lines where relevant
• filters and exclusions
• a record of the period analyzed

This turns the quadrant from a static presentation graphic into a transparent analytical view.

Use the classification as a decision aid

The quadrant narrows 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 classification is stable.

Frequently asked questions

What is a customer-product quadrant analysis?

It ranks customers and products independently by revenue, assigns each list to statistical quartiles, then places each transaction or relationship into one of four simplified intersections: core customer/core product, core customer/tail product, tail customer/core product, or tail customer/tail product.

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.