Alexander Buoye, Yuliya Komarova Loureiro, Sertan Kabadayi, Mohammad G. Nejad, Timothy L. Keiningham, Lerzan Aksoy and Jason Allsopp
The satisfaction and loyalty research argues that customer satisfaction is an antecedent to share of wallet (SOW). The double jeopardy view, however, argues that satisfaction and…
Abstract
Purpose
The satisfaction and loyalty research argues that customer satisfaction is an antecedent to share of wallet (SOW). The double jeopardy view, however, argues that satisfaction and SOW levels are driven exclusively by penetration levels. Customer satisfaction and penetration, however, are not always positively related. The purpose of this paper is to explore the relevance and validity of these two divergent perspectives to creating growth in customer share of spending.
Design/methodology/approach
The authors examine a series of models evaluating the impact of both the relative penetration of a brand, and the satisfaction ratings of its customers on SOW using data covering 11 industry sectors, 188 brands, and 4,263 customers.
Findings
The authors find that part of the problem in reconciling these two views has been in how satisfaction is measured and analyzed. When using absolute satisfaction ratings of the firm/brand, the explanatory power of satisfaction on SOW is very weak at both the individual and firm level. When using satisfaction metrics relative to other competing brands, however, satisfaction is a strong predictor of customers’ share of category spending.
Research limitations/implications
As predicted by double jeopardy, penetration is a strong predictor of firm-level SOW, but has almost no explanatory power at the individual level.
Practical implications
Managers need to focus on both improving penetration/reach and becoming the preferred brand in a customer’s usage set.
Originality/value
The research examines if (and if yes, how) satisfaction and penetration contribute to customers’ SOW allocations both at the individual and brand level.
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Bryony Jardine, Jenni Romaniuk, John G. Dawes and Virginia Beal
This paper aims to investigate factors associated with higher or lower television audience retention from one programme aired sequentially after another, referred to as lead-in…
Abstract
Purpose
This paper aims to investigate factors associated with higher or lower television audience retention from one programme aired sequentially after another, referred to as lead-in audience retention. Lead-in is a primary determinant of television programme audience size.
Design/methodology/approach
The study models a series of factors linked to lead-in audience retention, such as rating of the second programme, genre match and competitor options. The hypothesised relationships are tested across over 1,000 pairs of programmes aired in the UK and Australia, using multivariate linear regression models.
Findings
The study finds the factors consistently related to significantly higher lead-in audience retention are the rating of the second programme in the pair and news genre match in programming. Factors consistently linked to lower audience retention include the rating of the initial programme and the number of competitor options starting at the same time as the second programme.
Practical implications
The findings help television networks understand drivers of lead-in audience retention. Knowledge that can be used to inform the design of tailored marketing plans for programmes based on schedule, timing and adjacent programming. Further, the findings help advertisers and media buyers with scheduling television advertising to achieve reach or frequency objectives.
Originality/value
No previous studies have comprehensively combined all four factors driving lead-in audience retention into a single model. The testing across multiple markets adds to the robustness of the findings. In particular, the discoveries about the impact of competitor network activities and genre build considerably on past research.
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A.S.C. Ehrenberg and G.J. Goodhardt
Discusses repeat‐buying in terms of a case history of a non‐durable product. Measures the repeat‐buying behaviour by means of a survey carried out for the Market Analysis and…
Abstract
Discusses repeat‐buying in terms of a case history of a non‐durable product. Measures the repeat‐buying behaviour by means of a survey carried out for the Market Analysis and Evaluation Grant, Unilever. Reveals how repeat‐buying of a new brand soon reaches a par with its competitors.
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Cam Rungie, Mark Uncles and Gilles Laurent
This paper aims to extend a widely used stochastic model of purchase loyalty to include covariates such as demographics, psychographics and geodemographics. Potentially, this…
Abstract
Purpose
This paper aims to extend a widely used stochastic model of purchase loyalty to include covariates such as demographics, psychographics and geodemographics. Potentially, this allows covariates to explain variations in brand performance measures (BPMs) such as penetration/reach, average purchase frequency, sole buying, share of category requirements, repeat purchase and so forth. The result is to integrate consumer-based segmentation into previously unsegmented stochastic models of brand performance.
Design/methodology/approach
This paper describes a model for predicting BPMs. Covariates are then introduced into the model, with discussion of model specification, model estimation, overall model assessment, and the derivation of generalised theoretical BPMs. The outcome is a practical procedure for behavioural loyalty segmentation.
Findings
The implications for strategy and management in applying covariates to the BPMs are considerable. Where there are concentrations of consumers with high repeated purchase/consumption, then many aspects of the marketing mix will be affected. An investigation of the role of covariates in understanding BPMs in the laundry detergent market is presented as an example, and ways for market analysts to display results are demonstrated.
Originality/value
Despite the fact that BPMs are the best operationalisation of behavioural loyalty, until now there has not been a model to evaluate the impact of consumer characteristics as covariates on these BPMs. This paper's original contribution includes a model that fits covariates to the BPMs. New statistical and graphical methods are described. Computer software for fitting the model and generating the output is available from the authors.
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Studies purchasing behaviour for the leading brands of a frequently bought household product and discusses this. Enquires in depth about the nature and habits of those buyers of a…
Abstract
Studies purchasing behaviour for the leading brands of a frequently bought household product and discusses this. Enquires in depth about the nature and habits of those buyers of a brand – who in a given time period purchase that brand to the exclusion of competitors. Focuses on the purchasing behaviour of sole buyers (who mostly buy only one particular brand). Examines the incidence of sole buyers; frequency of buying, and period‐to‐period repeat buying; how many in a given period; how often purchased in that period; and how many buy it again in the next period? Concludes that present findings give one answer – showing that the sole buyer as defined is more regular in his/her buying behaviour than is the average buyer of the brand.
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Zachary Anesbury, Yolanda Nguyen and Svetlana Bogomolova
Increasing and maintaining the population’s consumption of healthful food may hinder the global obesity pandemic. The purpose of this paper is to empirically test whether it is…
Abstract
Purpose
Increasing and maintaining the population’s consumption of healthful food may hinder the global obesity pandemic. The purpose of this paper is to empirically test whether it is possible for healthful sub-brands to achieve higher consumer behavioural loyalty than their less healthful counterparts.
Design/methodology/approach
The study analysed three years of consumer panel data detailing all purchases from five consumer goods categories for 15,000 UK households. The analysis uses best-practice techniques for measuring behavioural loyalty: double jeopardy, polarisation index, duplication of purchase and user profile comparisons. Each sub-brand’s healthfulness was objectively coded.
Findings
Despite the level of healthfulness, all sub-brands have predictable repeat purchase patterns, share customers as expected and have similar user profiles as each other. The size of the customer base, not nutrition content, is, by far, the biggest determinant of loyalty levels.
Research limitations/implications
Consumers do not show higher levels of loyalty to healthful sub-brands, or groups of healthful sub-brands. Nor do they buy less healthful sub-brands less often (as a “treat”). There are also no sub-groups of (health conscious) consumers who would only purchase healthful options.
Practical implications
Sub-brands do not have extraordinarily loyal or disloyal customers because of their healthfulness. Marketers need to focus on growing sub-brands by increasing their customer base, which will then naturally grow consumer loyalty towards them.
Originality/value
This research brings novel evidence-based knowledge to an emerging cross-disciplinary area of health marketing. This is the first study comparing behavioural loyalty and user profiles towards objectively defined healthful/less healthful sub-brands.
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The present data illustrate the effectiveness of utilizing theoretically guided models to develop consumer-based micro-segmentation strategies. The results provide marketers with…
Abstract
The present data illustrate the effectiveness of utilizing theoretically guided models to develop consumer-based micro-segmentation strategies. The results provide marketers with a powerful discriminant function calculated from six variables to profile consumers and make informed decisions regarding promotional content and channel delivery to stimulate processing of marketing communication. The function also enables marketers to carve out casual, moderate, and loyal market segments with 74.3 per cent accuracy utilizing only 18 survey questions.
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There has been long‐standing interest in the duplication of audience between media vehicles, starting with work by Agostini and later developed by Goodhardt, Ehrenberg and Collins…
Abstract
Purpose
There has been long‐standing interest in the duplication of audience between media vehicles, starting with work by Agostini and later developed by Goodhardt, Ehrenberg and Collins into the “duplication of viewing law”. The aim of this paper is to further extend duplication analysis to radio listening. As radio markets are believed to have many partitions, the paper considers whether an un‐partitioned duplication analysis provides an adequate description of market structure.
Design/methodology/approach
The paper reports the results of a weekly radio diary with 1,129 responses in a regional New Zealand radio market. This data has special characteristics suitable for this research: the market has experienced rapid expansion in station numbers with substantial attempts at format segmentation, providing a strong test of the un‐partitioned nature of the duplication analysis; use of a single regional market avoids the aggregation bias inherent in national data; use of primary research allows the inclusion of non‐commercial stations, which are not included in syndicated radio research in this market.
Findings
Duplication of listening does broadly follow the duplication of viewing law. Contrary to industry belief, most of the deviations from a mass market are not due to micro‐formats (e.g. classic rock) but rather are explained by a broad partitioning of the market between “talk” and “music” segments, although the paper also identifies a unique station that still deviates from its parent partition.
Research limitations/implications
The duplication of listening law does hold for this market, showing that radio stations compete largely on the basis of cumulative audience. However, it also provides a tool for identifying partitions and benchmarking station performance within this broad market structure. Future research could consider demographic or psychographic correlates of market partitions, alternative methods of purchase‐based segmentation such as nested logit, latent segmentation and Hendry analysis, and breaking duplication analysis down from weekly level to dayparts.
Practical implications
Station and network managers can apply this methodology to identify partitions and benchmark brand performance in their own markets. They should expect to usually compete on the basis of cumulative audience rather than station loyalty, as customer loyalty tends to be a feature of the partition rather than the station. Media planners should also be aware of the duplication of listening law when designing media schedules: greater frequency can be achieved by choosing a set of stations with high duplications (generally higher share stations); greater reach can be achieved by including some smaller stations with low duplications.
Originality/value
This is the first application of duplication analysis to radio audiences, and the confirmation of the law goes against practitioner expectations. It is also a rare example of how duplication analysis can be used to identify not just segments, but also individually unique stations. Therefore, while this research disconfirms prior expectations it also provides a new tool for practical segmentation of radio markets.
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John Dawes, Jenni Romaniuk and Annabel Mansfield
The purpose of this paper is to examine competition between tourism destination brands in terms of how they share travelers with each other.
Abstract
Purpose
The purpose of this paper is to examine competition between tourism destination brands in terms of how they share travelers with each other.
Design/methodology/approach
The study analyzes survey data from four international markets (USA, UK, Japan and Singapore). The study examines the cross‐purchasing of travel destinations. It applies an established empirical generalization, the duplication of purchase law (DPL) to frame hypotheses and contextualize results.
Findings
The overall results are consistent with the DPL. Destination brands share tourists with other destinations generally in‐line with the popularity of the competing destination. However, there are very noticeable market partitions, most of which take two forms: destinations that are either geographically close to each other, or close to the point of origin. Destination brands in these partitions share travelers far more than they would be expected to, given their respective size.
Practical implications
Tourism marketers need to appreciate the broad nature of competition. A specific destination brand competes with many other travel destinations, sharing customers more with other broadly popular destinations and less with less popular destinations.
Originality/value
The analytical approach presented in this study provides a straightforward benchmark for assessing the expected level of competition between particular tourist destinations, given their respective overall popularity.
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Andres Musalem, Luis Aburto and Maximo Bosch
This paper aims to present an approach to detect interrelations among product categories, which are then used to produce a partition of a retailer’s business into subsets of…
Abstract
Purpose
This paper aims to present an approach to detect interrelations among product categories, which are then used to produce a partition of a retailer’s business into subsets of categories. The methodology also yields a segmentation of shopping trips based on the composition of each shopping basket.
Design/methodology/approach
This work uses scanner data to uncover product category interdependencies. As the number of possible relationships among them can be very large, the authors introduce an approach that generates an intuitive graphical representation of these interrelationships by using data analysis techniques available in standard statistical packages, such as multidimensional scaling and clustering.
Findings
The methodology was validated using data from a supermarket store. The analysis for that particular store revealed four groups of products categories that are often jointly purchased. The study of each of these groups allowed us to conceive the retail store under study as a small set of sub-businesses. These conclusions reinforce the strategic need for proactive coordination of marketing activities across interrelated product categories.
Research limitations/implications
The approach is sufficiently general to be applied beyond the supermarket industry. However, the empirical findings are specific to the store under analysis. In addition, the proposed methodology identifies cross-category interrelations, but not their underlying sources (e.g. marketing or non-marketing interrelations).
Practical implications
The results suggest that retailers could potentially benefit if they transition from the traditional category management approach where retailers manage product categories in isolation into a customer management approach where retailers identify, acknowledge and leverage interrelations among product categories.
Originality/value
The authors present a fast and wide-range approach to study the shopping behavior of customers, detect cross-category interrelations and segment the retailer’s business and customers based on information about their shopping baskets. Compared to existing approaches, its simplicity should facilitate its implementation by practitioners.