Marketing Mix Planning Research Articles

Marketing Mix in Educational Services Marketing in the Digital Age

In an era of technological dynamics and increasingly fierce competition, educational institutions are faced with the challenge of increasing the range of educational services. Marketing mix planning strategy is crucial in dealing with this change. The background to this issue reveals the need to understand the impact of marketing strategies on educational outreach amid an ever-changing environment. The purpose of this study is to find out what a marketing mix is, how the role of marketing mix in increasing the reach of educational services amid the dynamics of technological development, and to find out what are the main challenges in implementing a marketing mix planning strategy. This research uses a qualitative approach with a focus on literature review. Through a critical analysis of literature related to marketing mix planning in the marketing of educational services, this study aims to develop a solid theoretical foundation to understand the strategic role of marketing mix in the context of education. From the analysis of the literature review, the results of this study present an in-depth understanding of how marketing mix can affect the increase in the reach of educational services. The findings of this study provide insight into effective integration strategies, key challenges faced by educational institutions, and efforts to overcome these barriers. This research is expected to contribute new thinking in the context of marketing educational services, providing a foundation for the development of more effective and sustainable marketing strategies.

The importance of marketing mix planning and customer orientation for venture capital–financed startups: impacts on valuation, performance, and survival

PurposeExisting literature shows that marketing capabilities of new ventures are critical success factors affecting venture capital funding, startup performance and business failure. The purpose of this study is to investigate whether venture capitalists reward extensive marketing strategies in their startup valuation and whether the marketing mix planning and early strategies on customer orientation predict long-term development of startups.Design/methodology/approachTo address these gaps, this study investigate 107 business plans of new ventures which received venture capital based on these planning documents. The authors use computer-aided text analysis and regression analyses.FindingsThis study’s findings show that customer orientation has positive effects on new venture performance and intensive marketing mix planning increases the likelihood of survival. However, venture capitalists decrease their startup valuation when they read too much about customer orientation and operative marketing mix planning.Originality/valueThis study relies on unique internal documents and therefore provides valuable and new insights for research and practice. Further, this study investigate various short- and long-term effects from marketing and customer orientation for a startups’ development.

Research on Marketing Management Risk Decision Model Based on LINEST Function

The primary purpose of this research study is to describe the research on the marketing management risk decision model based on the LINEST function. This research is conducted in China and is used for measuring the risk decision model using different questions related to marketing management. The nature of this research is primary, and data were collected from 100 plus responders related to the marketing management fields. The research participants included males, females, employees of marketing, and other people from marketing management organizations. We used smart PLS software to measure the data and ran different informative statistical analyses. For measuring the marketing, research management used techniques that included a test of equality, PLS algorithm model, LINEST model, least-square model, histogram analysis, and different graphs related to the marketing management performance. The marketing management used different subvariables, including strategic management analysis, marketing mix planning, implementation, and control. The risk decision model is used as a dependent variable based on the LINEST functions. The overall result found that the marketing management risk decision model shows a significant relationship with each other based on LINEST function marketing management, which plays a vital role in organizations.

Retail tenant mix effect on shopping mall’s performance

PurposeAchieving a balanced tenant mix is a long-standing discourse in the retailing and consumer marketing literature. From the perspective of marketing mix planning, the diversity of tenants is beneficial to the performance of shopping malls. This paper aims to use a revealed preference approach to study empirically the effect of retail tenant mix planning on the rents of shopping malls.Design/methodology/approachThis study adopts a cross-disciplinary approach to develop the Island-Species-Area-Energy model to study the shopping mall marketing and management framework. The empirical data are obtained from the 129 major shopping malls in the UK.FindingsThe results confirm that the retail tenant mix is positively associated with mall size and shopping district purchasing power, implying a tenant mix equilibrium. Any deviations from the tenant mix equilibrium will impose a negative impact on total retail rents. Further, five factors, i.e. tenant mix equilibrium, building quality, locational convenience, leasing strategy and anchorage, are found to be contributing factors to retail rents.Originality/valueThe findings contribute to the current body of marketing knowledge from two perspectives: first, tenant mix effects on retail rents are empirically analysed based on the biogeography theory, which shows a tenant mix equilibrium for retail marketing planning. Second, a five-factor model on shopping mall marketing and management mix framework is developed and tested for the performance of shopping malls.

Developing a CBR System for Marketing Mix Planning and Weighting Method Selection Using Fuzzy AHP

Case-based reasoning (CBR) solves a problem through retrieving a similar past solution and reusing it in a new situation. In this study, a CBR system was developed for marketing mix planning (MMP) in a steel manufacturing company. In case retrieval, feature weighting is an important component and plays a key role in CBR efficiency. In recent years, several methods have been presented for feature weighting, which have used retrieval accuracy only for performance ranking of weighting methods and ignored other method dimensions. In order to consider other dimensions for weighting method ranking, this study applied a fuzzy AHP method to prioritize weighting methods. In this study, genetic algorithm (GA), rough set theory (RST) and fuzzy inference system (FIS) were used for feature weighting. Moreover, feature weighting based on FIS was introduced in this work for the first time.

On the Recoverability of Choice Behaviors with Random Coefficients Choice Models in the Context of Limited Data and Unobserved Effects

Random coefficients choice models are seeing widespread adoption in marketing research, partly because of their ability to generate household-level parameter estimates with limited data. However, the power of such models may tempt researchers to trust that they continue to produce reasonable estimates, when in fact either model misspecification or insufficient data limits the models' ability to recover household-level parameters successfully. If household-level choice behaviors are not recovered successfully, managerial decisions such as marketing-mix planning and targeting, direct marketing, segmentation, and forecasting may not produce the desired results. This study addresses the following questions. First, can random coefficients choice models correctly identify markets characterized by preference and response heterogeneity, state dependence, the use of alternative decision heuristics that result in reduced choice sets, and combinations of these effects? If so, how much data is required, and is this realistic given the size of data sets typically used in marketing analyses? Which model selection criteria should be used to identify these markets? When there is spurious market identification, which parameters contribute to the spurious result? An extensive simulation experiment is conducted wherein random coefficients logit models with varying specifications of parameter heterogeneity, state dependence effects, and choice set heterogeneity are applied to 128 experimental conditions. The results show which types of markets can be identified reliably and which cannot. Based on the results of the simulation, the authors develop a model selection heuristic that identifies the correct market in 81% of the experimental conditions. In contrast, strict application of the best model selection criterion alone results in correct market identification in at most 34% of experimental conditions. Interestingly, we find that the amount of data (number of households or number of purchases per household) does not affect our ability to identify the correct market type with this heuristic, so there is a good chance of identifying the correct market type even when little data is available.

A Service‐Orientated Approach to Marketing of Services

Looks at the author's research among several companies in Sweden and Finland with regard to market‐orientation of service as against physical goods. Found the main difference between them was the difficulty of developing a concrete, tangible service offering. Points out that many experts believe service marketing must differ from goods marketing, but, nevertheless, no radical effort to develop a marketing theory, or ever some marketing concepts, for service firms aiming at solving their problems, seems to have been made — service industry companies deserve a better deal. Discusses this related matter and suggests marketing mix planning to support a hypothetical framework. Investigates, in depth, service industries and their characteristics and weaknesses, accessibility, human resources, auxiliary services and intra‐corporate elements. Presents two case studies — one inclusive tours marketing and the other barber's shop marketing. Concludes that concepts and models for marketing mix planning do not seem applicable in service industries — but further research is required — such as a consumer study.