A SKU rationalization effort becomes commercially credible only when the business can answer one practical question: what will customers do if an item goes away? Volume and margin analysis show where the portfolio is weak. But substitution and transfer analysis determines whether the company can simplify the assortment without surrendering disproportionate revenue.
This chapter explains how to determine where demand can be migrated, how to estimate transfer rates, how to assess the risk of lost sales and customer attrition, and how to use substitution logic to shape pruning choices. Done well, it shifts the conversation from “What are we cutting?” to “Where will the demand go, and how do we guide it there?”
10.1 Determining Where Demand Can Be Migrated
Most SKUs do not exist in isolation. They sit inside a portfolio of near neighbors and overlapping choices. A customer buying one size, flavor, configuration, dosage, or accessory is often not expressing an absolute need for that exact item. They are choosing among the options the company has made visible and available. That means many rationalization decisions are really migration decisions.
Demand migration: the movement of customer purchases from one SKU to another after a product is discontinued, consolidated, or redesigned. Migration can be direct, when the replacement is obvious and highly comparable. It can be partial, when some customers accept the substitute and others do not. It can also be structured, when the company deliberately redesigns the range so customers are steered into a simpler set of choices.
The first task is to identify the substitute set for each candidate SKU. In some categories, this is straightforward. A discontinued pack size may map naturally to a neighboring size. In others, the answer is harder. An industrial component may have technical equivalence but different installation preferences. A medical product may require clinician acceptance. A good substitute is not merely similar in engineering terms. It must be close enough in customer terms that the buyer sees it as an acceptable next choice.
That is why substitute mapping should start with the few attributes that truly drive customer choice. In consumer categories, those may be brand, size, flavor, price point, and usage occasion. In technical categories, they may be compatibility, performance specification, compliance, and serviceability. The aim is to separate meaningful differences from cosmetic ones. Many portfolios carry variants that are visibly different to the company but barely differentiated to the customer. Those are usually the easiest migration opportunities.
Historical behavior can help confirm where demand can move. Temporary stockouts, past assortment resets, discontinued legacy items, and promotional switching often reveal what customers do when a first choice is unavailable. If customers repeatedly move between two neighboring SKUs with limited disruption, that is strong evidence of practical substitutability. If demand disappears or leaves the portfolio, the substitute relationship is weaker than the team may have assumed.
Commercial input matters as well, but it has to be structured. Sales, category management, and customer service often know which items customers see as interchangeable and which ones trigger objections. By the end of the exercise, every serious rationalization candidate should have a clear answer to the question, “If this SKU leaves, where should the demand go?”
10.2 Estimating Transfer Rates Between SKUs
Once likely substitutes have been identified, the next step is to estimate how much demand will actually transfer. Knowing that a substitute exists is useful, but not enough. Management needs a view of how much volume will stay within the portfolio, how much will shift to a preferred replacement, and how much may be lost.
Transfer rate: the percentage of a discontinued or simplified SKU’s demand that is expected to move to another SKU in the company’s portfolio. The balance represents lost demand, reduced purchase, or movement outside the portfolio. In practice, the transfer rate is almost never 0 percent or 100 percent. Most items fall between those extremes.
The strongest evidence usually comes from observed behavior. Past delistings, temporary out-of-stocks, assortment changes, channel tests, or geographic pilots can show where volume moved and how much stayed inside the business. These events are rarely perfect experiments, but they provide a useful anchor. If a similar item was removed last year and most of its demand moved to the next-best alternative, that history is far more informative than opinion alone.
When historical events are limited, proxy analysis can help. The team can examine customer overlap between neighboring SKUs, switching during promotions, basket patterns, and the degree of attribute similarity. If the same customers buy both items, if the products sit adjacent in the price or feature architecture, and if promotional activity causes demand to move between them, the likely transfer rate is usually higher. If customer sets barely overlap, the use occasions differ, or the replacement sits in a distinct price tier or technical category, the likely rate is lower.
Judgment is still required, but it should be disciplined judgment. A practical method is to work with transfer bands rather than false precision. Instead of claiming that 68.4 percent of volume will move, the team may classify the case as high transfer, medium transfer, or low transfer, supported by specific evidence. High-transfer cases typically involve obvious substitutes, strong historical switching, and limited customer dependence on the exact variant. Low-transfer cases usually involve unique functionality or no credible nearby alternative.
It is also important to estimate not only the total transfer rate, but the destination split. Demand may not move entirely into one replacement. It may divide across several surviving items. That matters because the economics depend on where volume lands. Management may prefer one destination because it has better margin, better availability, or lower complexity.
10.3 Identifying Risks of Lost Sales and Customer Attrition
Even when substitutes exist and transfer rates appear favorable, rationalization still carries risk. Customers do not react only to product functionality. They also react to change. If a discontinuation is handled poorly, the company can lose more than the demand tied to one SKU. It can damage trust, create service dissatisfaction, and weaken broader relationships.
Lost sales risk: the probability that some or all of a SKU’s demand will not transfer to the company’s remaining portfolio after rationalization. Customer attrition risk: the probability that a customer will reduce the broader relationship, shift future purchases to competitors, or become harder to retain because of the change. The two are related, but not identical. A customer may accept a substitute yet become less loyal. Another may reject the substitute for one item but continue buying everything else.
The first source of risk is functional mismatch. If the replacement does not truly meet the customer’s needs, the transfer estimate is too optimistic. This happens when internal teams overfocus on technical similarity while underestimating the practical details of use. A pack may be close in size but awkward for shelf conditions. A replacement part may fit but require a different installation step. A medical format may be clinically acceptable but less preferred in practice. In these cases, the company is not only migrating demand; it is asking the customer to adapt.
The second source of risk is commercial sensitivity. Some customers interpret assortment reductions as a loss of service or a sign that their needs are not important. Key accounts may react more strongly if the discontinued SKU was created at their request or if they believe the change reduces their differentiation. End consumers may react if a favorite item disappears without a clear replacement message.
The third source of risk is execution failure. Even a sound substitute plan can fail if the replacement is not in stock, if sales teams send mixed messages, or if customer service cannot explain the transition. In rationalization, operational execution and commercial execution are inseparable.
A useful risk screen asks three questions for each candidate. How essential is the exact SKU to the customer’s task or preference? How sensitive is the customer relationship to the change? How confident is the business that the replacement path will be executed well? When the answers are favorable, risk is usually manageable. When they are not, the company may still rationalize, but it should do so with mitigation such as phased transitions or account-specific communication.
10.4 Using Substitution Analysis to Shape Pruning Choices
The final purpose of substitution analysis is not simply to predict outcomes. It is to improve the quality of the rationalization choices themselves. Many companies treat substitution as a downstream check after they have already decided which SKUs to remove. That sequence is backward. Substitution logic should shape the pruning plan from the beginning because it determines which exits are easy, which should be bundled, and which should be redesigned rather than simply discontinued.
Pruning choice: the specific action taken on a SKU or SKU group, such as exit, merge, replace, resize, redesign, or keep. Substitution analysis makes these choices more precise. A high-complexity SKU with a strong substitute is often a clean exit candidate. A weak SKU with several partial substitutes may be better handled through guided migration, where the company actively steers customers into one preferred destination. A set of overlapping variants may be better simplified as a family, with a clearer size ladder or feature architecture, rather than pruned one by one.
This is especially important when the portfolio contains clusters of near-duplicates. If three or four items serve essentially the same demand, the business should not evaluate them independently. It should decide which one or two survivors provide the best commercial and operational outcome, then direct transfer toward those survivors. In this way, substitution analysis helps management choose not only what to remove, but what to strengthen.
The analysis can also identify cases where pruning should wait. A SKU may look weak economically yet still have low transfer confidence because the portfolio lacks a credible replacement. In those cases, the smarter move may be to redesign a surviving SKU, create a transitional pack or configuration, or prepare customers through selling guidance before making the cut. Rationalization works best when it reduces complexity and improves the remaining offer at the same time.
At a broader level, substitution analysis changes the philosophy of SKU rationalization. It reminds leaders that portfolios are systems of choices, not collections of isolated items. The objective is not to eliminate products mechanically. It is to concentrate demand into a simpler set of healthier, clearer, more scalable offers. When substitution is analyzed carefully, pruning decisions become less defensive, more evidence based, and more commercially intelligent.
