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New product introductions, particularly line extensions (LEs), are common in consumer goods categories. Despite their commonality, the success of LEs are not guaranteed. The purpose of this study is to provide brands that introduce LEs a benchmark about what success to expect.

This study investigates the success of 36,994 LEs in each quarter for the first three years after introduction. Four indicators are calculated using consumer panel data to benchmark how long LEs survive (failure rate), how competitive they are in the category (market share) and how they are adopted by category buyers (penetration and repeat buyer rate).

Most LEs survive after the first year, but many cease to exist or perform well in the long term. Around 50% of LEs fail a year after launch, but this failure rate halves once seasonal LEs are removed. Failure rates start to approach 80% after three years. Most LEs do not perform better than existing products. Around three in four LEs have a market share or penetration near or below the category norm. Although this percentage decreases the longer after launch, most LEs are still below the category norm.

These new product success benchmarks provide guidelines to practitioners about what success the “typical” LE will achieve. This research can help guide new product investment decisions because it provides context on what is feasible to achieve.

Four market success measures are used, a departure from past benchmarking research which uses practitioner evaluation on metrics seldom used in practice. The authors provide guidelines about when and how to measure LE and new product success more broadly.

Despite the growing availability of scanner-panel data, surveys remain the most common and inexpensive method of gathering marketing metrics. The purpose of this paper is to explore the size, direction and correction of response errors in retrospective reports of category buying.

Self-reported purchase frequency data were validated using British household panel records and the negative binomial distribution (NBD) in six packaged goods categories. The log likelihood theory and the fit of the NBD model were used to test an approach to adjusting the errors post-data collection.

The authors found variations in systematic response errors according to buyer type. Specifically, lighter buyers tend to forward telescope their buying episodes. Heavier buyers tend either to over-use a rate-based estimation of once-a-month buying and over-report purchases at multiples of six or to use round numbers. These errors lead to overestimates of penetration and average purchase frequency. Adjusting the aggregate data for the NBD, however, improves the accuracy of these metrics.

In light of the importance of purchase data for decision making, the authors describe the inaccuracy problem in frequency reports and offer practical suggestions regarding the correction of survey data.

Two novel contributions are offered here: an investigation of errors in different buyer groups and use of the NBD in survey accuracy research.

Retailers are increasingly adding banks, gas stations, mobile services and even real estate agencies to their portfolio and branding these new ventures with the retailer name, such as Tesco Bank or Asda Money. The purpose of this paper is to test the ability of a retailer brand to stretch from traditional packaged goods categories to very different categories such as banking.

Using data from an online survey collected from 953 UK grocery buyers, this paper examines consumers’ behaviour towards UK retailer brands across four categories: soft drinks, chocolate, fuel and banking.

The results show that cross-category retailer brand purchasing is stronger between categories with similar buying behaviour (e.g. soft drinks and chocolate) than in categories with very different buying behaviour (e.g. soft drinks and banking). The behavioural spill over effects are stronger for retailer brands from the same chain and persist even for unrelated categories. However, apart from fuel, the strongest cross-purchasing occurs across competing retailer-branded offers within the same category.

The main implication of this study is that behavioural spill overs for retailer brands are possible even between unrelated categories. The finding about the effects being strongest within a given chain implies that umbrella branded strategy is a key to take advantage of the effects.

These findings extend past literature about the cross-category buying of umbrella branded store brands to very different categories. This paper highlights the challenges retailers face regarding their ability to extend the retailer brand across categories. The findings also provide insights for cross-selling retailer brands in unrelated categories to current store brand buyers.

This is the first study to examine the use of retailer brands across a wide spectrum of categories from Soft Drinks to Fuel.

The purpose of this paper is to examine what happens to key brand performance metrics as brands change in market share, in the context of packaged goods. The metrics are: penetration—the number of buyers a brand has; and loyalty—measured as purchase frequency (PF) and share of category requirements (SCR).

The study utilizes 24 data sets in 17 packaged goods categories in three emerging markets: China, Malaysia and Indonesia. The authors examine changes in penetration, loyalty and SCR in the context of volume and value market share change. In addition, the authors examine whether initial price point and price movements influence the results.

The primary finding is that market share change is accompanied by a greater change in penetration than in any other metric. This finding is very consistent across categories and countries. The relative importance of the two loyalty metrics varies by country. SCR was a stronger factor in Indonesia, while PF was stronger in Malaysia. Analysis indicated that pricing strategy (initial price and promotional depth) did not alter the main pattern of results, suggesting the results hold for brands with different price levels and tactics.

Irrespective of circumstance, to grow in value or volume market share, brands should aim to grow in penetration, while the importance of changes in specific loyalty measures depends on market conditions.

This research extends past research on brand growth to the very different economic, geographic and cultural conditions of three crucially important emerging markets. Its main value lies in recommendations on how much to invest in building the size of the customer base vs consumer retention.

This paper seeks to compare how brand users and non‐brand users currently position private labels and national brands in three packaged goods categories. It aims to provide guidelines for positioning strategies for both private labels and national brands through the outcomes.

Data were collected in a telephone survey of 600 randomly recruited primary shoppers. Binary logistic regression was used to examine the informational cues consumers use to categorize private labels and national brands. The memory structures of users and non‐users of private labels were then separately modelled.

Results suggest that the perceptual categorization into private label brands and national brands differs once private labels have been purchased. Users of private label brands did not see them as being any less trustworthy than national brands. However, non‐users of private labels did use trust to discriminate between the two types of brands, and tended to use negative attribute information to categorize the brands into groups. Regardless of experience, however, private labels form a subgroup in consumers' memory, with low price and low quality as the main drivers of this categorization.

This paper extends past studies by measuring the perceptions of private labels as individual brands within a market, which more closely represents actual consumer memory structures. It also uses both positive and negative product attributes, which has not featured in prior work on private labels perceptions. The findings have implications for retailers looking to launch and manage private labels and manufacturers who need to compete with them.