Brand love is a widely researched marketing concept, yet its prevalence across different high involvement product categories is relatively unexplored. The absence of clear empirical guidelines concerning the prevalence of brand love undermines the ability, for both researchers and marketers, to turn evaluations of brand love into meaningful conclusions for brand-building efforts. To address this issue, this study replicates and extends prior research by investigating how common brand love is across high- and low-involvement product categories, and its relationship with purchase intentions and behavioural loyalty, measured via Share of Category Requirements (SCR). The analysis of survey data from 1,554 US consumers for four product categories (cars, running shoes, retail banking, and beer) suggests that four in ten category users loved at least one brand within each category. Yet, brand-level love is more uncommon, ranging between 3.5% to 15%, on average, across the product categories tested. Brand love was higher in transformational high-involvement categories (cars, running shoes) than in informational ones, but high-involvement categories alone did not exhibit more love compared to low-involvement ones. Additionally, although brand love correlates with higher purchase intentions, the link with behavioural loyalty appeared rather weak across all categories. These results challenge assumptions about brand love's scalability and strategic value of brand love, offering empirically grounded benchmarks for marketing researchers and practitioners.

Distinctive Assets, non-brand name elements such as logos, colors, sounds, or slogans, are increasingly measured by marketers, yet industry benchmarks to guide expectations remain absent. This study addresses the gap by analyzing 1162 Distinctive Assets across 21 categories, four countries, and nine years, covering auditory, word, and visual asset types in retail, services, durables, and consumer goods. Asset strength is assessed by Fame (how often an asset is linked to a brand) and Uniqueness (how exclusively it is linked to the brand). Shape-based assets such as logos and packaging consistently perform best (40% Fame, 71% Uniqueness), whereas color assets perform weakest (12% Fame, 39% Uniqueness). Despite the overall strength of shape-based assets, other types show industry-specific potential. For instance, narrative-driven assets perform relatively better in service industries. These findings offer practical benchmarks and strategic guidance for marketers aiming to select and manage Distinctive Assets more effectively in branding and advertising.

Tiny brands - those with <1 % market share - have been overlooked in marketing research. Consequently, we know little about their brand performance metrics or growth patterns. This study examines whether tiny brands exhibit niche-like excess loyalty, and compares their market performance over five years, focusing on changes in market share and buyer behaviour. Findings show that even stable tiny brands have generally low levels of loyalty, often lower than expected (deficit loyalty). When tiny brands grow, their sales gains come primarily from increased penetration rather than purchase frequency. This challenges the idea that tiny brands depend on niche loyalty strategies. Instead, it highlights the importance of broad customer acquisition, even for the tiniest of brands.

This article uses the Negative Binomial Distribution (NBD) to model alcohol purchase frequency and predict the transitions among non-buyers, light (the bottom 50% of buyers), medium (the middle 30%) and heavy (the top 20%) from 1 year to another. Using purchasing data over 2 years in the United States (49,915 households) and the United Kingdom (9,316 households) including beer, wine and spirits, we find that the NBD accurately predicts these transitions. The model as such provides a useful baseline for evaluating the effects of interventions to reduce heavy alcohol buying. For both ethical and commercial reasons, instead of targeting heavy buyers, companies selling alcohol still can more responsibly strive to increase their sales by acquiring new alcohol buyers as most of them will become light buyers.

PurposeThe purpose is to review literature on sports season ticket subscriptions to distil current knowledge and guide future research and practice.Design/methodology/approachA systematic literature review is conducted of research on sports season tickets, a long-established and innovative subscription category.FindingsIn-depth examination of 28 papers showed a focus on drivers of satisfaction, churn and renewal causes, and product utilisation rates. Subscription markets typically involve many "solely loyal" consumers, most purchasing one or two subscriptions in a category. From reduced barriers to entry and exit to "curated" subscriptions, subscription marketing is changing very quickly. Sports marketers build relationships with subscribers using behavioural data, tier benefits to distinguish between casual and subscribing customers, and create recall and scarcity around key aspects of subscription to combat churn and increase utilisation.Research limitations/implicationsScarce research on subscription marketing practices remains the primary limitation. Existing research suggests that strong connections between subscriber and organisation, heavy product utilisation and/or strong barriers to switching drive customer satisfaction and retention.Practical implicationsRapid expansion of subscription products should reduce "excess loyalty", meaning that subscription models' main benefit will be limited to reoccurring revenue. Exceptions occur when consumers are heavily connected to the product or have little provider choice, so allocate their category buying exclusively. New subscription products face myriad challenges. Guidance on effective subscription marketing from sports marketing research and practice is outlined.Originality/valueBy combining research on market structure, marketing empirical generalisations and subscription marketing, this paper guides future research and practice.

Kotler popularised the Segmentation, Targeting, Positioning (STP) theory of brand competition. This theory still dominates marketing textbooks. In this article we show how the discovery of scientific laws concerning how brands compete, grow, and decline clash with the STP theory. The contradiction between these empirical regularities and STP theory has led to the recent emergence of a new market-based asset view of brand competition. We show how this theory fits the now well-established empirical laws, and we discuss some promising areas for future research.

Despite the concept of a suggestive brand name existing for over one hundred years (Viehoever, 1920), the prevalence of suggestive versus non-suggestive brand names has not been documented. Previously, to do so extensively would have taken considerable time and money. We now show that artificial intelligence can replace manual coding with increased accuracy. We found the coding performances of Chat GPT-4 are 34% more accurate than GPT-3.5 and 44% more accurate than human coders. Systematically expanding our research to over 4,600 brands from consumer goods, services, and durables in major English-speaking markets (United Kingdom, United States, and Australia), we find that overall, slightly more than a quarter of all brand names are suggestive - ranging from 10% of durables to 56% of service brands. Further, we expand the suggestiveness research to non-brand name elements of almost 600 Distinctive Assets (e.g., colours, logos) across consumer goods, services, durables, and retailers (in the same three countries), finding that two in five are suggestive. The brand name and Distinctive Asset prevalence distributions are positively skewed, with most categories falling beneath the respective averages. Furthermore, regarding performance, on average, suggestive Distinctive Assets display lower levels of Fame and Uniqueness than non-suggestive Distinctive Assets.

Practitioners and academics have long discussed strategies for brand sales growth. A recent example is an industry debate in which different brand growth strategies were argued: https://www.mmaglobal.com/thegreatdebate (MMA Global & Neustarr, 2021). A central question in this arena is whether a brand should focus on its heavy, light, or non-buyers in its efforts to grow its sales. This study contributes to our knowledge about how sales growth can occur by investigating the potential contribution these three buyer groups can make to any sales gain. Using both, a simulation study and an empirical study of purchases of approximately 12,400 households in the UK, across different brands and categories, we show that almost any brand's headroom growth potential lies mostly in light or non-buyers of that brand. Even for large brands with high penetration the growth potential of light brand buyers eclipses heavy brand buyers.

Although marketers are increasingly asked to manage brands for the long term, it is difficult to do so when no clear picture exists of long-term brand buying. This study reports cumulative behavioural loyalty outcomes for 200 UK consumer-goods brands when observed in a five-year household panel of continuous reporters. We examine these brands in intervals from one to five years against NBD (Negative Binomial Distribution) model projections. Stationary brands attract over twice as many buyers in five years as they do in one. Of these buyers, 80% purchase the brand at a rate of once a year or less, yet contribute 40% to total sales, a Pareto ratio of just 60:20. For managers, this light buying is broadly predictable from NBD fittings to annual data, and implies a renewed emphasis on nudging the brand buying propensities of the whole market.

Market share growth requires building mental and physical availability among all category buyers. However, if younger category buyers are more likely to purchase new-to-market products, then perhaps younger buyers are, relatively speaking, more important for growth. This research investigates the relationship between category buyer age, brand buyer age, and brand failure. When sub-brand buyer age is younger than category buyer age, the sub-brand is likely to be (a) new-to-market or (b) growing in market share. Older-than-category sub-brand-buyer age is likely for sub-brands that are (a) declining or (b) dead. Results from 17 years (1998–2014) of U.K. household panel data, including 5,913 sub-brands from 101 categories, show that age skews were uncommon (only 18% of sub-brands), and second, that growing, stable and declining sub-brands appealed equally to all ages. Finally, we identified that new launches and dead brands tend to skew to younger consumers, suggesting that new launches need to appeal to all ages to avoid failure.

Marketers are interested in the loyalty of their customer base. Increasingly this includes examining behavioural loyalty inferred from the frequency or weight of purchase. A typical approach is to divide the customer base into arbitrary segments based on weight of purchase and then attempt to move customers from lighter to heavier segments, rather than have them reduce purchasing or cease buying altogether. Effects can be monitored by examining how purchasing by groups of individuals evolves over successive periods. However, much of the flow between segments represents random fluctuations in period-to-period purchasing rather than true change to underlying loyalty. Accurate analysis requires true change to be separated from these stochastic changes, for example through benchmarks derived from conditional trend analysis (CTA). While CTA considers the two-period case, it provides no guidance for changes seen across three-periods. The three-period case is nonetheless regularly reported by panel companies and relied on by managers. We therefore develop the three-period CTA, using a tri-variate NBD, to allow the analysis of buyer flow across three successive periods. We provide an empirical illustration and demonstrate fresh insights into the evolution of consumer loyalty. The findings allay oft-raised concerns about supposedly 'lost' buyers, as perceived customer loss is often simply regression to the mean of the buying rates. Accordingly, the three-period CTA shows predictable proportions of buyers who move between different buying-weight segments, including first-year buyers who were apparently 'lost' in the second year but return to buy the brand in the third year.

Fifty years ago, Gerald Goodhardt's analysis of audience duplication across television programs led to the discovery of the Duplication of Viewing law. This law was then extended to describe and predict customer sharing within product categories: the Duplication of Purchase Law. Many replications and extensions documented the law-like status of this generalisation, providing important insight into how brands compete and the composition of consumers' repertoires. In this article we build on that seminal research, using Duplication of Purchase as an analytical method to measure loyalty across categories, or, in other words, the purchasing of brand extensions. Brand extensions are commonly cited as a way to capitalise on brand equity, and when asked, respondents often report high intentions to purchase brand extensions. However, the actual cross category buying of brand extensions has not been systematically examined. In this research we analyse panel data to understand whether purchasing a brand in one category does in fact increase the likelihood of a brand being bought in a second category. The study finds that a consumer who purchases from two categories is on average 2.4 times more likely to purchase a brand extension in the second category if they had purchased the same brand in the other category. This effect is larger for brands spanning similar, or complementary categories. Therefore, for many brand extensions the cross-category loyalty effect is much more modest.

Because of various financial reasons, or a change in strategic focus, sometimes brands stop broad-reach media advertising for a year or longer. These long dark periods have not been subject to much study, so little is known about the likely consequences. This exploratory study addresses this omission by documenting the sales performance of 41 beer, cider, and spirit brands that advertised intermittently over almost two decades. Changes in aggregate brand sales are reported for the years when brands stopped advertising relative to the last advertised year. On average, brand sales declined immediately in the first year and every subsequent year of advertising cessation. Decline generally was faster for smaller brands and for brands that already were declining in sales before advertising cessation.

This study shows that the impact of advertising on consumer memory can be observed using mental availability (MA) metrics. Four MA metrics are used to measure the effect of advertising on a brand's mental availability, with the results showing that in the majority of cases, MA metrics are greater among both brand users and non-users who are aware of the brand's advertising, with a greater effect among non-users. From a practical market research perspective, adding MA metrics to existing brand health tracking will have no data collection costs where brand perceptions are already being measured.

Many studies discuss the proliferation of brand extensions (eg. Romeo, 1991; Han, 1998; Milberg, Whan Park and McCarthy, 1997), but there is a lack of academic research to show how common they really are. The present study documents the incidence of brand extensions in three geographic consumer goods markets: US and UK and Australia. The study conducts a detailed analysis of the products and brands offered in major supermarket chains in each of these three countries. Safeway’s online store is utilized for the US Market, Sainsbury’s for the UK and Coles & Woolworths’ online stores for the Australian market. In each case the retailer’s website was examined and all available brands in all available categories were catalogued to determine the extent to which common brands appear in multiple categories. The research finds approximately 3/4 of all brands available in the online stores, appear in just a single product category. Of the remaining 25% of brands classified as brand extensions, around half are available in only two product categories. Surprisingly, extending a brand name across categories, in packaged goods markets at least, appears to be the exception rather than the rule. Given the intense interest in brand extensions from academia and industry publications, a brand manager could be forgiven for thinking they must seek avenues to extent their brand, given its apparent popularity as a strategy. However, the present study could reassure brand managers that brand extension is not as popular as might be thought. The study also suggests that cross-category transferability of a potential brand name is not necessarily a key factor in choosing a name. This is because most brand names are not transferred across categories, and when they are, the extension category tends to be closely related to the original category.

For almost nine decades, advertisers have relied on the Sainsbury Normal Method (SNM) to estimate net-reach where single-source data are either too expensive or unavailable. To the best of the authors' knowledge, no SNM validation studies have included catalogues, smartphone applications, websites, social media, or cinema. While few studies have applied the SNM across media, no study has addressed the limitation of the SNM, that is, the implied assumption of audience homogeneity. Given that audiences do differ by age within any medium, and across media, there is a need to incorporate audience heterogeneity into the method. The authors introduce the Sainsbury Weighted Method (SWM) which provides more accurate within medium net-reach estimations in 82% of the 9,680 cases analysed, with an average accuracy improvement for within medium net-reach estimations of 0.6 percentage points (or 13%). For across media net-reach, the SWM estimations are more accurate in 77% of the 968 cases analysed, improving the average accuracy by 0.5 percentage points (or 49%).

Purpose This study aims to independently test the predictive validity of the Persuasion Principles Index (PPI) for video advertisements for low-involvement products with a measure of in-market sales effectiveness. This study follows the inaugural test conducted by Armstrong et al. (2016) for print advertisements for high-involvement utilitarian products with a measure of advertising recall. Design/methodology/approach The method was in line with that developed by Armstrong et al. (2016) for rating advertisements and assessing the reliability of ratings. Consensus PPI scores were calculated for a data set of 242 matched pairs of television advertisements. For each pair, the authors determined whether the advertisement that better adhered to the persuasion principles performed better in-market. Findings Consensus PPI scores predicted the more sales effective television advertisement for 55% (confidence interval (CI) = 49%, 61%) of the 242 pairs. This result is no better than chance and much weaker than the result from the initial validation study, which found that the consensus PPI scores predicted the more recalled print advertisement for 74.5% (CI = 66%, 83%) of 96 pairs. Research limitations/implications This study replicated the application of the PPI as per Armstrong’s guidelines and extended validity testing to a different set of advertising conditions. Findings indicate that better adherence to the persuasion principles produces only a weak, positive effect for predicting the performance of television advertisements for low-involvement products. A research agenda that flows from the results is discussed. Practical implications The authors suggest that the PPI in its present form is best used to predict advertising performance under conditions as per the inaugural validation test (Armstrong et al., 2016). Originality/value Advertisers will require compelling evidence of the PPI’s predictive accuracy to adopt the tool for pre-testing advertising. This study is the first independent test of the predictive validity of the PPI and its generalisability across advertising conditions. Another contribution of this study is the assessment of Armstrong’s advice to remove unreliable ratings. The authors show that this procedure, surprisingly, does not improve the predictive accuracy of the PPI.

It is a common belief that users of a brand in one category are more likely to purchase brand extensions than non-users of the brand. This study examines whether extensions actually do facilitate purchase of the brand in a second category. We analyze one year of household purchasing data for approximately 60,000 US households to understand the level of cross-category purchasing brand extensions achieve. We investigate 98 extensions across 30 CPG category pairings. This study finds:• Purchasing a brand in one category makes a consumer, on average, 2.4 times as likely to purchase the same brand in a second category compared to a consumer who purchased a different brand in the original category.• Extensions in complementary categories achieve the highest levels of cross-category sharing.• Extensions in categories which are substitutable, or have high, moderate or low levels of similarity to the original product category all achieve cross-category purchasing higher than what is expected for two unrelated brands.The overall finding provides empirical support for the theory of brand equity, specifically that usage & familiarity with a brand name in one category facilitates purchase of that brand in other categories.

Background Despite the ongoing promotion of physical activity, the rates of physical inactivity remain high. Drawing on established methods of analysing consumer behaviour, this study seeks to understand how physical activity competes for finite time in a day – how Exercise and Sport compete with other everyday behaviours, and how engagement in physical activity is shared across Exercise and Sport activities. As targeted efforts are common in physical activity intervention and promotion, the existence of segmentation is also explored. Methods Time-use recall data ( n = 2307 adults) is analysed using the Duplication of Behaviour Law, and tested against expected values, to document what proportion of the population that engage in one activity, also engage in another competing activity. Additionally, a Mean Absolute Deviation approach is used to test for segmentation. Results The Duplication of Behaviour Law is evident for everyday activities, and Exercise and Sport activities – all activities ‘compete’ with each other, and the prevalence of the competing activity determines the extent of competition. However, some activities compete more or less than expected, suggesting the combinations of activities that should be used or avoided in promotion efforts. Competition between everyday activities is predictable, and there are no specific activities that are sacrificed to engage in Exercise and Sport. How people share their physical activity across different Exercise and Sport activities is less predictable – Males and younger people (under 20 years) are more likely to engage in Exercise and Sport, and those who engage in Exercise and Sport are slightly more likely to Work and Study. High competition between Team Sports and Non-Team Sports suggests strong preferences for sports of different varieties. Finally, gender and age-based segmentation does not exist for Exercise and Sport relative to other everyday activities; however, segmentation does exist for Team Sports, Games, Active Play and Dance. Conclusions The Duplication of Behaviour Law demonstrates that population-level patterns of behaviour can yield insight into the competition between different activities, and how engagement in physical activity is shared across different Exercise and Sport activities. Such insights can be used to describe and predict physical activity behaviour and may be used to inform and evaluate promotion and intervention.