Are My New Product's Sales Incremental or Cannibalistic?
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A new product launch into a saturated category can grow share, or it can just move units the company already had. A toothpaste line adding a whitening variant, a beverage brand adding a flavor, a household-goods company adding a size: the sales figure at the end of the quarter looks the same whether those units came from a competitor's shelf or from the company's own existing product. The decision that matters happens before launch, when the spend, shelf space, and trade dollars are still committed but the answer is not yet known.
Why doesn't the sales number alone answer the question?
In an invented example, a new SKU sells 100,000 units. Without launch, existing products would have sold 1,000,000 units. If existing products still sell 1,000,000, portfolio volume rises by 100,000. If they instead sell 900,000, portfolio volume is unchanged. Identical new-SKU sales can therefore mask different net portfolio effects. The no-launch counterfactual is what distinguishes them.
Telling them apart requires a counterfactual: what would the existing products' sales have been if the new product had never launched? That is not a number that exists in the sales data. It has to be estimated, and how it is estimated changes the answer.
Why the obvious model breaks
A simple interrupted-time-series specification projects prelaunch sales into the postlaunch period. Under its assumptions, deviations can help estimate substitution. A competitor’s sales decline alone does not establish that the new SKU caused it.
The PyMC Labs case illustrates why a simple multivariate ITS can fail when multiple product moves or other shocks overlap. Broader ITS specifications can include observed covariates and interventions, but require assumptions about trends, timing, and unmeasured changes. This is a specification risk, not an established failure rate across CPG categories.
Two other constraints compound the problem in practice:
- The question usually needs answering at the product level, not just the portfolio level: whether a new whitening product displaces other whitening products more than children's or cavity-protection lines is a product-level pattern that a model capturing differential substitution across products can surface, and that a single net incrementality number for the whole category cannot.
- A single point estimate ("12% incrementality") understates how much uncertainty is in play; a workable estimate needs a range, not a single figure.
How do you test the decision before the data exists?
The retrospective approach above is only available after a product has shipped and months of sales data have accumulated, by which point the launch spend is already gone. The alternative is to test the launch decision itself before committing to it: define the launch as one action and no-launch (or an alternative attribute set) as a second action, run both as a scenario on a simulated buyer population, and compare the resulting share of choice, including an outside or no-purchase option, across the company's own portfolio and its competitors' products.
A study brief should define the competitive set and report which choice-share comparison is estimated. Confirm the actual deliverable with Subconscious rather than assuming an always-on cannibalization matrix.
What this method can and can't tell you
A pre-launch scenario test answers a different question than the retrospective decomposition described above, and the two shouldn't be confused:
- Retrospective decomposition uses historical sales and a specified counterfactual model. Overlapping moves require explicit treatment and adjustment assumptions.
- Pre-launch scenario testing compares a proposed action against a counterfactual action before either has shipped. It does not require historical sales history for the new product, because the product doesn't exist in the market yet. It also does not replace the retrospective view once real sales data exists: the two answer adjacent but different questions, at different points in the product's life.
Naming exactly what a method requires is what lets a buyer check it before relying on it. A cannibalization or substitution comparison is study-specific: it needs the competing products and attributes defined explicitly as part of the scenario design, not delivered as a standard, always-on output. Where the decision is high-stakes enough to justify it, a team can move from a simulated scenario test to validating a study with real human participants without changing the underlying causal question being asked.
Where to take this next
A portfolio or revenue-growth-management leader deciding whether a proposed launch is worth the shelf space can test the launch scenario against a no-launch counterfactual before committing budget. For the category context this kind of decision usually sits in, see how Subconscious approaches CPG portfolio decisions and how a study like this gets structured.
This article draws on Ben Vincent’s PyMC Labs sales-incrementality case. Its product-launch decomposition requires a credible no-launch counterfactual. Promotional cannibalization is related, but a temporary promotion is a different intervention and its estimates cannot be transferred directly to a new-product launch.