Tracking time-varying brand equity using household panel data

In this paper we develop a general class of dynamic brand choice models, called Lightning Bolt (LB) models, which are consistent with the theory of random utility maximization of consumer choice behavior. The underlying random utility process is Markov, and the inter-temporal evolution of the (utility-maximizing) brand choice process is also Markov. The models permit parsimonious parameterizations of the random utility process in brand choices with the resulting switching probabilities being functionally related to explanatory variables. The model allows for structural state dependence (feedback), habit persistence (inertia), and unobserved heterogeneity. The theoretical development shows that several well known stochastic brand choice models can be deduced from random utility maximization theory. From a managerial perspective, the usefulness of the proposed model stems from its ability to separate out the effects of habits, state dependence and heterogeneity. Strong state dependence effects imply incentives for inducing trial of a brand (e.g., product sampling). In contrast with state dependence, a strong habit persistence effect may be indicative of buyer behavior where inducement of trial of a different brand may not be sufficient to maintain a (sustained) defection of the consumer from the habitually purchased brand to the trial brand. Failure to distinguish between these two effects has important implications. For example, a model that only accounts for state dependence effects would, in the presence of only habit persistence, incorrectly attribute it to state dependence. Based on this the manager could decide to embark on an expensive sampling program that might prove ineffective due to the absence of state dependence. It is also important to distinguish between the effects of unobserved heterogeneity and state dependence. In the absence of true state dependence, failing to account for unobservable variations across households (such as differences in price sensitivities), results in the temporally persistent unobservable elements showing up as state dependence in the model. Hence, a manager may incorrectly opt for sampling as the appropriate marketing action, whereas, a couponing or price promotion strategy should have been preferred. We estimate the model parameters using the AC Nielsen household scanner panel data set on catsup purchases. Further, we investigate empirical techniques for overcoming the “initial conditions” problem that affects many dynamic models of brand choice. Through simulation analysis using the estimated parameters, we show that the calculated profitability of a promotion must take into account the multi-period impact due to state dependence. Further, we demonstrate how ignoring heterogeneity can result in spurious state dependence, thereby making marketing tactics such as product sampling appear far more attractive than they actually are. Specifically, the model that accounts only for habit persistence and state dependence effects predicts a share increase of 2.4% points through sampling for one of the brands in the empirical analysis. Once the effects of unobserved heterogeneity are accounted for, however, this number drops precipitously to 0.3%. While even this low number might fulfill the objectives of the brand manager, it ensures that expectations are not eight times that number. It is important to note that this paper is a first attempt at analyzing the three fundamental dimensions of dynamic brand choice behavior. Our formulation represents a “reduced form” approach as opposed to a “structural” approach. Other limitations of the model include our focus on time-invariant choice sets. Extensions of the model to situations where choice sets vary over time are possible, but are difficult. Another limitation is the dependence on multivariate extreme value distributions. An alternative would be to use multivariate normal distributions, i.e., a probit-like structure which has certain useful properties. A comparison of the two approaches would be useful. Empirical extensions of the model to allow for higher-order processes and nested logit type model structures could also be fruitful. Explicitly incorporating variety-seeking behavior in the model would further enrich the theoretical framework proposed in this paper.

This is because it relates to the suitability of the target market that has been made. The large number of different brand choices on the market encourages Wardah to be superior in competition, namely through good brand equity in order to seize market share and gain profits, because good brand equity will create added value for consumers to make repeated product purchases in the future. come. Given the very high competition, encouraging the cosmetic industry to carry out various strategies to win the competition. Wardah carries out various strategies to attract customers to make purchases, one of which is a strategy based on emotional motives, namely by reviewing the experience that users get from the product. This study aims to analyze the effect of social media marketing on brand equity, brand loyalty and brand experience. The research method uses quantitative methods with purposive sampling technique. The distribution of the questionnaire was made online via the Google form which was distributed to 240 respondents within one week. Analysis can in this study use Partial Least Square (PLS). The results of this study are that there is a significant influence between social media marketing on brand equity, brand loyalty and brand experience. Brand experience has an influence on brand loyalty and brand equity.

Probabilistic versus random-utility models of state dependence: an empirical comparison

ABSTRACTSocial media marketing facilitated prospective students to communicate and collaborate to gather information relevant to higher education institutions and their respective brand equity. More complex and dynamic models focusing on customer-based brand equity often lack empirical support in higher education sectors, particularly from more than one country. Drawing from the elaboration likelihood model, this study empirically investigated how higher education institutions can develop brand equity using social media marketing. The quantitative findings from 936 undergraduates from Sri Lanka and Vietnam indicated social media marketing influences the brand equity of higher education institutions through brand credibility. Taking into the comparison between two emerging countries, Vietnamese students are more concerned about brand credibility through social media marketing activities to form brand equity compared to their Sri Lankan counterparts. The findings provide some practical implications for educational marketers to promote their higher education institutions.

As there is still only limited research on students brand choice of mobile phones, the focus of this study was to ascertain drivers of tertiary students’ mobile phone brand choice in Ghana. Using a structured questionnaire, data was collected from a random sample of 840 students from three tertiary institutions in Ghana. The study revealed that the most significant determinant of the students brand choice of mobile phones was perceived quality (p<0.05). Although price, prestige, and promotion also influenced their choice of mobile phone these were not significant at the 0.05 level. The study further revealed that availability and mobile phones as gift items also influenced the brand of phone used by the students (p>0.05). The study concludes that in spite of their economic handicaps, students brand choice was driven most by perceived quality and not price. Recommendations on how information technology manufacturers’ particularly mobile phone companies and marketers can exploit these drivers to sustain and improve their brand equity among students have been made.

We develop a structural model of brand management to estimate the value of a brand to a firm. In our framework, a brand’s value is the expected net present value of future cash flows accruing to a firm due to its brand. Our brand value measure recognizes that a firm can change its brand equity by investing in advertising. We estimate quarterly brand values in the stacked chips category for the period 2001–2006 and explore how those values change over time. Comparing our brand value measure to its static counterpart, we find that a static measure, which ignores advertising and its ability to affect brand equity dynamics, yields brand values that are artificially high and that fluctuate too much over time. We also explore how changing the ability to build and sustain brand equity affects brand value. At our estimated parameterization, if brand equity were to depreciate more slowly, or if advertising were more effective at building brand equity, then brand value would increase. However, counterintuitively, we find that when the effectiveness of advertising is sufficiently high, increasing the rate at which brand equity depreciates can increase the value of a firm’s brand, even as it reduces the value of the firm overall.Data and the online appendix are available at https://doi.org/10.1287/mksc.2016.1020 .

Social networks are actively penetrating into all spheres of our society. The influence of social media is strongly affected by the younger generation, who spends a lot of time in the virtual social space.&#x0D; The purpose of this study was to study the influence of social networks on the choice of a university brand by representatives of generation Y. The data was collected through an online survey using the Google forms service.&#x0D; The results obtained indicate that social media engagement has a positive effect on brand image. Firm-created and user-generated social media communication have a positive effect on brand equity in Higher Education Institutions. Brand equity positively influences Brand choice intention.&#x0D; The value of this study lies in the fact that it considered the influence of various factors on the formation of the intention of representatives of generation Y to choose brands of universities, taking into account the moderate effect of electronic word of mouth.&#x0D; The results obtained complement the scientific literature on the impact of social media on consumer behavior. The results of this study can help in understanding the choice of students of educational organizations, taking into account the influence of social networks and electronic word of mouth, which can help university marketing departments to promote brands more effectively in the social media space.

We examine levels of enforcement of conditions for two transfer programs and estimate how they affect teenagers’ time allocation, and in particular, time devoted to school attendance, labor supply and home production. We develop a structural discrete choice model in which young individuals and their parents decide how to allocate their time, including whether to attend school. They also choose how many hours to work in the market, how much time to devote to home production and leisure activity. To estimate the model, we use household panel data which combines administrative records and surveys covering the period of 2005–2012 in Uruguay, during which two consecutive CCT programs were introduced with different designs. Our model captures the share of individuals who are in school, who are working, who are both studying and working, as well as those who neither study nor work; we also capture the share and number of hours devoted to market work and home production, and individuals’ GPA distribution. The policy experiments performed indicate that school attendance can be increased by raising the level of enforcement and by sending the cash transfer to the teenagers rather than to their parents.

This study sought to create and test a customer‐based brand equity model for Spanish hotels with TripAdvisor data. Using a sample of 1514 hotels and structural equation modelling with an asymptotically distribution‐free method, a valid basic, unified model was developed. The results confirm the viability of online data for brand equity research. These data reduce the cost of data capture, allowing the collection of a large amount of data and the creation of more dynamic models. The results also show that aspects of brand equity are now under the control of users, a finding that requires further research. Copyright © 2016 John Wiley &amp; Sons, Ltd.

Consumers often benefit from increased competition in differentiated product settings. In this paper we consider consumer benefits from increased competition in a differentiated product setting: the spread of non-traditional retail outlets. In this paper we estimate consumer benefits from supercenter entry and expansion into markets for food. We estimate a discrete choice model for household shopping choice of supercenters and traditional outlets for food. We have panel data for households so we can follow their shopping patterns over time and allow for a fixed effect in their shopping behavior. We find the benefits to be substantial, both in terms of food expenditure and in terms of overall consumer expenditure. Low income households benefit the most.

PurposeThe purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.Design/methodology/approachThis study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.FindingsWith sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.Research limitations/implicationsFuture research could use the same method to study other types of sponsors, such as title sponsors or other product categories.Practical implicationsSponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.Originality/valuePrior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

PurposeThe aim of this paper is to provide evidence that private label brands (PLB) have the ability to build brand equity as they develop, and to determine whether the capitalization of PLB equity varies across consumer segments and product categories. The paper builds on previous research incorporating consumer‐level factors, showing their relevance as key determinants of PLB choice.Design/methodology/approachThe brand choice model used is a multinomial logit model (MNL) calibrated with a consumer panel database of two product lines of yoghurt from 8,000 Spanish households for a three‐year period.FindingsIt is shown that PLB have been able to build brand equity throughout their development, across product categories; however, brand equity is capitalized across only some consumer segments. The use of consumer‐level factors to segment the market prior to measuring brand equity is necessary to allow the identification of consumer groups where equity is created.Practical implicationsFindings provide key directions to PLB managers regarding how to determine, approach and leverage the equity of their PLB across different consumer segments and product categories.Originality/valuePrior research has attempted to measure PLB equity using product/brand factors and market factors, but without considering consumer‐level factors. In this research, consumer‐level factors, specifically consumer demographics, are incorporated into the analysis, and equity is measured across seven different consumer segments for two product lines.

Artificial neural networks can be seen as nonlinear generalizations of conventional Statistical or econometric models. This paper studies goodness-of-fit and forecasting Performance of neural networks with one hidden layer. The central dependent variables regarded are purchase incidence and brand choice within the context of consumer non-durables. The connectionist models are compared to the well-known NBD and conditional logit models. Models are estimated on the basis of household panel data. On the whole, the neural models achieve better results than their conventional counterparts. Influence of predictors is interpreted using a distribution of elasticities or change rates.

Gender Differences in the Consequences of Divorce: A Multiple-Outcome Comparison of Former Spouses

The determinants of gasoline demand in the United States: An investigation using household panel data: Kayser, Hilke Almut, Ph.D. The University of Wisconsin-Madison, 1994. 136 pp. Supervisor: James Andreoni