Assortment Rationalization: The 80/20 Rule, the 9-Box Matrix, and the Volume You Get Back

Walk into a planning room that has not done a serious portfolio review in three years and you will find the same pattern. Forty SKUs in the brand range. Eight of them do most of the work. Fifteen of them sit at the bottom of the velocity report month after month, each one contributing a fraction of a percent of revenue, none of them quite small enough to delist on their own. Together they consume a quarter of the supply-chain complexity, a third of the trade-marketing time, and a meaningful slice of the listing-fee budget every year.

That is the long tail. It is not a marketing problem. It is a portfolio-architecture problem, and assortment rationalization is the discipline of fixing it.

The brutal version of the 80/20 rule

In most FMCG brand portfolios the Pareto split is sharper than the textbook version suggests. The top 20 percent of SKUs typically generates 70 to 80 percent of revenue. The bottom 50 percent of SKUs delivers under 10 percent of revenue and under 5 percent of margin. Every one of those tail SKUs costs money to make, ship, list, plan, and forecast, regardless of how few units it sells.

What rationalization actually buys you

A clean rationalization sweep does three things at once. First, the surviving SKUs absorb volume. Most shoppers who used to buy a delisted variant do not leave the brand or the category. They shift to your remaining SKUs. The typical pickup runs 8 to 15 percent of the delisted volume, landing on whichever surviving SKU is the closest substitute. Second, the supply chain gets simpler: fewer changeovers, fewer SKUs to forecast, fewer distribution gaps to chase. Third, the freed shelf space and listing budget can be redirected to higher-impact innovation, instead of being trapped in inertia.

Where it goes wrong

Assortment rationalization fails for one reason more often than any other. A SKU that looks bad on the standard velocity-and-margin report is sometimes the only reason a particular shopper segment walks the aisle at all. The only gluten-free option in the range. The only children's flavour. The only premium-tier line for the gifting trip. Cull that SKU and you do not lose its 0.3 percent of revenue. You lose the entire basket the shopper would have built around it, including the high-velocity items they would have picked up on the same trip.

Two tools do most of the work in a portfolio review. The 9-box assortment matrix, which sorts every SKU on velocity by margin and tells you which roles each SKU plays. And the three-filter delist rule, which gives you a short list of automatic cut candidates that you then sense-check.

The 9-box matrix

Plot every SKU on a grid. The horizontal axis is volume velocity (units per store per week, or share of brand volume). The vertical axis is margin contribution (percent gross margin, or absolute gross profit per kilo). Three bands on each axis (Low, Mid, High) give you nine cells.

The cells tell you what each SKU is for.

• Top-right cell (High velocity, High margin): the heroes. These are the engine of the brand. Protect them at all cost.
• Top-middle (Mid velocity, High margin): the upgraders. Often premium-tier SKUs. Worth nurturing into the top-right cell.
• Middle-right (High velocity, Mid margin): the workhorses. Big volume, average margin. The Routine pack lives here.
• Bottom-right (High velocity, Low margin): the entry SKUs. Loss-leaders or price-fighters. Earn their place by trip-driving.
• Middle-middle: the unclear ones. Decide what role they play, or kill them.
• Top-left (Low velocity, High margin): the niches. Premium-tier rarities, gifting SKUs. Defensible if they are the only option in a sub-segment.
• Bottom-left (Low velocity, Low margin): the tail. This is the delist zone unless a strategic role explicitly holds the SKU there.

The three-filter delist rule

A SKU is an automatic delist candidate when ALL THREE of the following hold at the same time. Any one of them on its own is not enough.

Filter 1: Category share below 0.1 percent
The SKU sells less than one unit per thousand of category volume. It is invisible at shelf and at scale.

Filter 2: Distribution under 30 percent
The SKU is listed in fewer than three of every ten relevant stores. Even if velocity per point of distribution is acceptable, the absolute footprint is too small to warrant the supply-chain cost.

Filter 3: Year-on-year trend worse than minus 20 percent
The SKU is shrinking fast. The trajectory is not random noise, it is a consistent decline.

When all three fire at once, the SKU is on the cut list. You then run the sense-check (next section) to confirm.

The cross-SKU overlap sense-check

Before any SKU on the automatic cut list is actually delisted, run one more test. Pull household-panel or loyalty-card data and ask: of the shoppers who bought this SKU in the last 12 months, what percentage also bought any other SKU in the brand in the same trip or the same period?

• Overlap above 80 percent: safe to cut. The shoppers are loyal to the brand and will switch to a remaining SKU. Volume pickup will land on the survivors as expected.
• Overlap 50 to 80 percent: cut with care. Some shoppers will be lost, but the bulk will stay. Acceptable.
• Overlap under 50 percent: this is a trip-anchor or sub-segment-only SKU. Cutting it loses entire shopper relationships. Reconsider, or pair the cut with an explicit re-direction (signage, range messaging) to a substitute SKU.

Imagine a mid-sized biscuit brand with a 40-SKU range that has grown unchecked for six years. Every product manager who passed through left two or three new SKUs behind. Nobody ever cut anything. The range has the shape of a brand that has been adding without pruning.

The starting picture

A weekly velocity ledger pulled across the full range shows the classic long tail.

• The top 8 SKUs deliver 81 percent of revenue. Three core flavours in two pack sizes plus the family share-pack. These are the heroes and the workhorse.
• SKUs 9 through 12 deliver another 9 percent. Mid-tier variants, including the chocolate-coated upgrade and the seasonal limited edition that has somehow stayed on shelf for two years.
• SKUs 13 through 25 deliver 9 percent between them. Each one sits at 0.5 to 1.2 percent of brand revenue. Most are flavour variants of the core line.
• SKUs 26 through 40 deliver under 1 percent collectively. Each one runs at under 0.1 percent of brand revenue. Distribution sits under 25 percent for every one of them. Year-on-year trend is between minus 18 and minus 35 percent for thirteen of the fifteen.

Running the three-filter rule

Of the bottom 15, twelve trip all three filters: under 0.1 percent share, under 30 percent distribution, and worse than minus 20 percent trend. Three sit slightly above one threshold each (one has 32 percent distribution, one has minus 19 percent trend, one has 0.11 percent share). The 12 are automatic cut candidates. The 3 are reviewed individually.

The cross-SKU overlap test changes the picture

The shopper-overlap data, pulled from loyalty-card panels, shows a clean split.

• 9 of the 12 candidates have shopper overlap with the rest of the brand above 80 percent. The shoppers who buy these tail SKUs almost always also buy the heroes in the same trip. Cutting them is safe: the volume will redirect to the surviving SKUs.
• 2 of the 12 candidates show overlap between 50 and 80 percent. These will lose some shoppers but most will stay. Acceptable cuts, with mild trade communication.
• 1 of the 12 candidates shows overlap of 23 percent. This is the gluten-free variant. It is the only gluten-free product in the range. The 23 percent of shoppers who buy it almost never buy anything else from the brand because the rest of the range is not for them. Cutting this SKU does not save 0.08 percent of revenue. It loses the entire 0.6 percent of revenue that the gluten-free shoppers contribute across other categories the brand competes in.

The gluten-free SKU stays. The other 11 are scheduled for delisting in two phases.

What landed after phase one

Six SKUs were cut in the first cycle, freeing six listing slots and roughly 11 percent of supply-chain SKU count. Across the next two quarters:

• Eight of the surviving SKUs absorbed measurable volume gains. The top three flavours each picked up 4 to 6 percent. The chocolate-coated upgrade picked up 9 percent. The family share-pack picked up 11 percent.
• Total brand volume across the surviving 34 SKUs landed roughly 3 percent above where the unrationalized 40-SKU range was running, despite the smaller line-up.
• Supply-chain forecasting accuracy improved measurably, freeing planner hours that went into the new premium-tier launch in cycle three.
• One retailer pushed back hard on the cut list and demanded one of the delisted variants be reinstated. The brand declined, and the retailer accepted, because the volume pickup on the survivors was visible in the retailer's own scan data within eight weeks.

What this confirms

The 8 to 15 percent volume pickup hypothesis held: the average pickup across the eight beneficiary SKUs landed at roughly 9 percent of the delisted volume. The cross-SKU overlap rule earned its keep on the gluten-free case: a brand that had cut on velocity and margin alone would have severed a quietly profitable shopper relationship and never noticed.

The curve flattens almost completely after SKU 12. The shaded region from SKU 26 onwards is the tail: 15 SKUs, 4 percent of revenue, and 27 percent of the supply-chain complexity cost. That picture is what a portfolio looks like when nobody has been pruning.

Rationalization done well feels boring. Rationalization done badly shows up six months later as lost basket size, retailer complaints, and a panicked relisting cycle. The difference is in the sequence.

Step 1: Build the SKU ledger

Pull every SKU in the brand range with the same six numbers: rolling 12-month units, rolling 12-month value, gross margin percent, distribution coverage, category share, year-on-year trend. One row per SKU. No exceptions, even for the SKUs everyone "knows" are core. The ledger is the discipline. Once a senior leader's pet SKU shows up in the same Excel as everything else, the conversation changes.

Step 2: Plot the Pareto curve

Sort the ledger by velocity, descending. Compute cumulative revenue percent. The shape of the curve tells you how concentrated the brand is. A healthy brand might hit 80 percent of revenue by SKU 8 of 40, 90 percent by SKU 12, 99 percent by SKU 25. Everything past SKU 25 is the tail.

Step 3: Apply the three-filter rule

Run the automatic-delist filter on every SKU. Mark the candidates. This is typically 20 to 40 percent of the SKU count and well under 5 percent of revenue.

Step 4: Run the cross-SKU overlap test

For every candidate, pull the shopper-overlap number. Reclassify any SKU with under 50 percent overlap as "review individually, do not auto-cut".

Step 5: Phase the cuts

Do not delist 15 SKUs in one cycle. Most retailers will resist a wholesale range cut and most internal teams will struggle to manage the transition cleanly. A typical phasing: cut the 5 cleanest cases this cycle, the next 5 in the following cycle six months later, the remaining 5 only after the first 10 have proven the volume-pickup hypothesis.

Step 6: Use the freed listing budget for innovation

The point of rationalization is not just to be smaller. It is to redirect the freed shelf space, listing fees, trade dollars, and forecasting hours into higher-impact moves: a new variant in a growing tier, a launch in an adjacent occasion, a premium-tier extension. A brand that rationalizes and does not redeploy ends up smaller and no faster-growing, which is the worst of both worlds.