How Much Advertising Moves Sales

ConversionAssmus, G.; Farley, J. U.; Lehmann, D. R. (1984) · 1984Journal of Marketing Research

Topicsadvertising elasticity·meta-analysis·carryover·replication analysis·econometrics·cpg

Your measurement team hands you a strong advertising number, and a media plan is riding on it. Do you fund the plan on that figure? Pause first. Across established packaged-goods brands, the reported response swings widely, and much of why one study beats another is not the market but how the model was built. The most-cited average is small, about a fifth of a percent, though adjusting for study design lifts the typical estimate to roughly seven-tenths. A high figure often reflects a model that skipped other sales drivers or mishandled carryover, the effect that keeps working after spend stops. Treat any lone number as a starting point.

With advertising's effect on sales, how the number is measured matters as much as the market it came from.

A reported advertising number reflects the analyst's choices as much as your market. Pooling many studies of established, frequently bought packaged goods, the most-cited average short-run response is small, about a fifth of a percent of sales for each 1% of spend, yet adjusting for how each study was built raises the typical estimate to roughly seven-tenths of a percent. Before you trust any single figure, ask how it was measured.

Data chart

What a model leaves out makes advertising look stronger than it is

A single advertising number can look big only because the model left something out.

Action guide

Before you trust an advertising number, ask whether the model counts carryover.If it does not, the figure is likely overstated in this evidence, so discount it.
Ask whether the model accounts for other sales drivers like price and seasonality.Leaving them out inflates advertising's apparent pull, so treat a bare-bones model's number as too high.
Use these benchmarks as your reality check.For established, frequently bought packaged goods, the most-cited average is about a fifth of a percent of sales per 1% of spend, rising to roughly seven-tenths once study design is accounted for. Treat a number well outside that band as a prompt to check the method.
When one team's number beats another's, look at how each model was built before concluding the markets differ.Differences in method, math, and how often data are measured explain much of the gap here.
Keep these benchmarks to established packaged goods.Do not carry them to new-product launches, durables, services, or digital channels the research never covered.

Evidence

The most-cited average is small: a 1% change in ad spend moves sales about a fifth of a percent, though adjusting for study design raises the typical estimate to about seven-tenths of a percent.
Carryover is real: roughly half of one period's advertising effect carries into the next, so spending keeps working after it stops.
Ignoring carryover makes advertising look far more powerful than it is.
Skipping other sales drivers, like price or seasonality, also inflates the advertising number.
How the model handles the math, and how often the data are measured, can swing the number as much as real market differences.
These figures come from established U.S. packaged goods before 1980, not new products, durables, or digital media.

Key takeaway

Advertising's short-run pull on sales is small, and how you measure it can matter as much as the market.

Source

Assmus, G., Farley, J. U., & Lehmann, D. R. (1984). How advertising affects sales: Meta-analysis of econometric results. Journal of Marketing Research, 21(1), 65–74. https://doi.org/10.1177/002224378402100107

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Evidence strength: Moderate. Based on 128 models from 22 published studies of predominantly established, frequently bought U.S. consumer products through 1980; generalizes most confidently to that setting, and less confidently to new products, durables, services, non-U.S. markets, digital media, or non-sales outcomes.