PurposeThe purpose of this research is to determine the relationship between frequency and recall in radio advertising by studying the main features of reach and frequency.Design/methodology/approachThe authors consider the outcome of a frequency model specifically designed for radio campaigns that gives the probability distribution of recall as a function of weekly exposures and GRPs over a dataset of 1,117 radio campaigns broadcast in Spain.FindingsAn increase in factors such as advertising format and creativity are more significant to achieve effective recall than increasing the number of advertising exposures.Practical implicationsThis study has important managerial implications regarding radio campaigns' planning: (1) Effective frequency is a range between 4 and 17 impressions (being 7 the optimal average). (2) The way to optimize the campaign is by using the following factors: live read format (∆ 4.4%), good creativity (∆ 2.8%), endorsement format (∆ 2%), sponsorship format (∆ 1.8%), increase the length of the spot (∆ 1.5%), place the ad in first (∆ 0.8%) or last (∆ 0.7%) positions in the pod. From the results we conclude that the format is at least as important as the creativity itself.Originality/valueThis study contributes to the effective repetition literature in two ways: giving specific clues to the effective frequency in the radio medium and setting advertising factors that predict the effective frequency in radio.
The relationship between customer churn and their complaints is sufficiently contrasted in the telecom sector. Therefore, a key part of a company’s strategy is the measurement of this dissatisfaction. It is important to conduct periodic surveys on complaints in a standard form like the SERVPERF scale because it enables the organization to benchmark. Many of these complaints are stored in the company’s CRM. Our first aim is to define a model to transform CRM customer complaints, expressed in natural language, into SERVPERF scales. In the proposed model, we use the 2-tuple model, which allows computing this linguistic data without losing information. Our second purpose is to implement a prototype to apply the model in a 4G Company. As a practical conclusion, most complaints in this emerging technology (which still has some deficiencies) are related to technical aspects of the services rather than to staff.
It is essential for a company to be engagement-oriented and analyze how marketing variables affect customer value and how it improves efficiency in both customer attraction and retention. But a comprehensive, integrated assessment of all marketing variables and their interdependencies is an arduous and complex task and thereby, an unsettled issue. Using relationship marketing literature as the theoretical basis of this research, this study overviews marketing variables empirical research, from a customer value perspective. First, we describe the most relevant relationships between each variable and customer value. Then we present a structured framework of the relationships observed between the variables. Lastly, we give some guidelights to manage marketing variables in a unitary manner, considering that the strategies and budgets for attraction and retention should be carried out jointly. The resulting framework shows that customer value is necessarily achieved over customer lifetime, and mainly through four clear predictors: perceived value, purchase intention, satisfaction and switching costs. Such framework can be used by entrepreneurs and marketing managers as a roadmap to customer value that facilitates understanding the significance of marketing variables predicting customer value and their underlying relations.
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