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Evidence · Brand growth

New product diffusion and category growth: the evidence

The spread of new products over time is well described by the Bass model, in which some people adopt on their own and others follow earlier adopters; it predicted the size and timing of sales peaks for consumer durables. Meta-analysis shows that known success factors for new products have weaker effects than once reported, and that national culture changes them. For established packaged-goods categories, revenue growth comes mainly from more buyers and higher prices per unit, and decline mainly from losing buyers.

Definition

Diffusion is the process by which a new product spreads through a market, measured by the timing of first purchases. Diffusion models describe the adoption curve over time as the result of innovative buying, which does not depend on earlier adopters, and imitative buying, which grows with the number of people who have already adopted. Category growth is the related question of how the revenue of a whole product category rises or falls, broken down into how many households buy, how often, how much per trip and at what price.

What the research finds

Adoption follows an innovation and imitation curve

The Bass model assumes that the timing of a consumer's first purchase depends on how many people have already bought. Tested on eleven consumer durables, it predicted the sales peak and its timing well. Its many later applications have been pooled in a meta-analysis of diffusion models.[1][2]

Most new products fail, and success factors are weakening

New product success rates are generally below 25%. A meta-analysis of 233 empirical studies with 2,618 effect sizes found weaker effects for common success factors than an earlier synthesis, with effects declining over time. National culture moderates them: effects are weaker in individualistic countries and stronger in risk-averse ones.[3]

Categories grow through more buyers and higher prices

Across 13 years of U.S. household panel data covering 474 packaged-goods categories, revenue growth came mainly from penetration and price per unit of volume, with category size shaping their relative weight. Losing buyers was the leading cause of decline in categories of every size.[4]

Long-run growth is hard to observe

Retail scanner data show sales changes but not the buyer behaviour behind them. Household panels running for one, five and ten years make it possible to track how accumulating penetration shapes brand and category buying, but they raise theoretical and methodological problems that researchers are only starting to address.[5]

Hero products carry penetration

Across more than 90,000 products in 15 packaged-goods categories in the UK and US, a brand's top-selling variant contributed around 50% of its penetration and 40% of its sales. The authors conclude that top sellers should stay readily available rather than be sacrificed for new launches.[6]

New products shape the long-run effect of promotions

In seven years of data on 100 brands in seven categories, promotions produced lasting sales gains more often than expected, especially for small brands. Both permanent and cumulative effects were larger for brands with higher equity and more new product introductions, while low-equity brands gained more from the introductions themselves.[7]

How new customers are won affects their value

At one web-hosting firm, customers acquired through word of mouth added nearly twice as much long-term value as customers acquired through marketing, which added more value in the short term. Models for splitting budgets between acquisition and retention treat that balance as an empirical question.[8][9]

How strong is the evidence?

The Bass model is one of the most tested models in marketing, but its original test covered only eleven consumer durables, and the pooled evidence on its parameters is decades old. Success-factor meta-analyses pool correlational studies whose effects have weakened over time, so they indicate associations rather than causes. The category-growth findings come from U.S. packaged goods and from one research group; durables, services and digital products may behave differently. The evidence on word-of-mouth customers comes from a single firm. Popular ideas about a gap between early adopters and the mainstream market, set out in practitioner books, are not covered here because they lack comparable peer-reviewed tests.

Claims tested against the evidence

References

  1. Frank M. Bass (1969). A New Product Growth for Model Consumer Durables. Management Science.
  2. Fareena Sultan, John U. Farley, Donald R. Lehmann (1990). A Meta-Analysis of Applications of Diffusion Models. Journal of Marketing Research.
  3. Heiner Evanschitzky, Martin Eisend, Roger J. Calantone, Yuanyuan Jiang (2012). Success Factors of Product Innovation: An Updated Meta‐Analysis. Journal of Product Innovation Management.
  4. Steven Dunn, Magda Nenycz-Thiel, Charles Graham, John Dawes, Nick Danenberg (2025). How Categories Grow: The Behavioural Drivers of Revenue Growth. Journal of Business Research.
  5. Steven Dunn, Charles Graham, Magda Nenycz-Thiel, Arry Tanusondjaja (2021). Investigating Undercurrents of Stationarity and Growth With Long-Term Panel Data. International Journal of Market Research.
  6. Arry Tanusondjaja, Magda Nenycz-Thiel, John Dawes, Rachel Kennedy (2018). Portfolios: Patterns in brand penetration, market share, and hero product variants. Journal of Retailing and Consumer Services.
  7. Rebecca J. Slotegraaf, Koen Pauwels (2008). The Impact of Brand Equity and Innovation on the Long-Term Effectiveness of Promotions. Journal of Marketing Research.
  8. Julian Villanueva, Shijin Yoo, Dominique M Hanssens (2008). The Impact of Marketing-Induced Versus Word-of-Mouth Customer Acquisition on Customer Equity Growth. Journal of Marketing Research.
  9. Werner Reinartz, Jacquelyn S. Thomas, V. Kumar (2005). Balancing Acquisition and Retention Resources to Maximize Customer Profitability. Journal of Marketing.

Bibliographic details only. Follow the DOI to read each study at its publisher.

Related topics

A synthesis of 9 studies, written by ScholarSignal. Last reviewed 2026-09-26.

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