Advertising and Word-of-Mouth Effects on Pre-launch Consumer Interest and Initial Sales of Experience Products

This study examines how consumers' interest in a new experience product develops as a result of advertising and word-of-mouth activities during the pre-launch period. The empirical settings are the U.S. motion picture and video game industries. The focal variables include weekly ad spend, blog volume, online search volume during pre-launch periods, opening-week sales, and product characteristics. We treat pre-launch search volume of keywords as a measure of pre-launch consumer interest in the related product. To identify probable persistent effects among the pre-launch time-series variables, we apply a vector autoregressive modeling approach. We find that blog postings have permanent, trend-setting effects on pre-launch consumer interest in a new product, while advertising has only temporary effects. In the U.S. motion picture industry, the four-week cumulative elasticity of pre-launch consumer interest is 0.187 to advertising and 0.635 to blog postings. In the U.S. video game industry, the elasticities are 0.093 and 1.306, respectively. We also find long-run co-evolution between blog and search volume, which suggests that consumers' interest in the upcoming product cannot grow without bounds for a given level of blog volume

Sustained spending and persistent response: a new look at long-term marketing profitability.

An intuitively appealing decision rule is to allocate a company's scarce marketing resources where they have the greatest long-term benefit. This principle, however, is easier to accept than it is to execute, because long-run effects of marketing spending are difficult to estimate. We address this problem by examining the over-time behavior of market response and marketing spending, and identify four commonly occurring strategic scenarios: business as usual, hysteresis in response, escalating expenditures and evolving-business practice. We explain and illustrate why each scenario can occur in practice, and describe its positive and negative consequences for long-term profitability.When good time-series data on revenue and marketing spending are available, it is possible to apply multivariate persistence measures to identify which of the four strategic scenarios is taking place. We apply these ideas to data from two major companies in the packaged-foods and pharmaceuticals industries. We observe several long-term marketing effect, some with profitable and some with unprofitable consequences, and offer recommendations for each case.We conclude that high-quality databases along with modern time-series methods can be instrumental in extracting vital long-term marketing-effectiveness information from readily available data. Therefore, managing marketing resources with long-run performance in mind need no longer be a pure act of faith on behalf of the executive. We hope that this and future work will contribute toward an improved allocation of scarce marketing resources in our companies.Marketing; Profitability;

Persistence Modeling for Assessing Marketing Strategy Performance

The question of long-run market response lies at the heart of any marketing strategy that tries to create a sustainable competitive advantage for the firm or brand. A key challenge, however, is that only short-run results of marketing actions are readily observable. Persistence modeling addresses the problem of long-run market-response quantification by combining into one measure of “net long-run impact†the chain reaction of consumer response, firm feedback and competitor response that emerges following the initial marketing action. In this paper, we (i) summarize recent marketing-strategic insights that have been accumulated through various persistence modeling applications, (ii) provide an introduction to some of the most frequently used persistence modeling techniques, and (iii) identify some other strategic research questions where persistence modeling may prove to be particularly valuable.long-run effectiveness;marketing strategy;time-series analysis

Persistence models and marketing strategy.

Marketing; Persistence; Models; Model; Strategy;

Marketing and Data Science: Together the Future is Ours

The synergistic use of computer science and marketing science techniques offers the best avenue for knowledge development and improved applications. A broad area of complementarity between the typical focus in statistics and computer science and that in marketing offers great potential. The former fields tend to focus on pattern recognition, control and prediction. Many marketing analyses embrace these directions, but also contribute by modeling structure and exploring causal relationships. Marketing has successfully combined foci from management science with foci from psychology and economics. These fields complement each other because they enable a broad spectrum of scientific approaches. Combined, they provide both understanding and practical solutions to important and relevant managerial marketing problems, and marketing science is already very successful at obtaining unique insights from big data

Metabolic fingerprinting to assess the impact of salinity on carotenoid content in developing tomato fruits

As the presence of health-promoting substances has become a significant aspect of tomato fruit appreciation, this study investigated nutrient solution salinity as a tool to enhance carotenoid accumulation in cherry tomato fruit (Solanum lycopersicum L. cv. Juanita). Hereby, a key objective was to uncover the underlying mechanisms of carotenoid metabolism, moving away from typical black box research strategies. To this end, a greenhouse experiment with five salinity treatments (ranging from 2.0 to 5.0 decisiemens (dS) m(-1)) was carried out and a metabolomic fingerprinting approach was applied to obtain valuable insights on the complicated interactions between salinity treatments, environmental conditions, and the plant's genetic background. Hereby, several hundreds of metabolites were attributed a role in the plant's salinity response (at the fruit level), whereby the overall impact turned out to be highly depending on the developmental stage. In addition, 46 of these metabolites embraced a dual significance as they were ascribed a prominent role in carotenoid metabolism as well. Based on the specific mediating actions of the retained metabolites, it could be determined that altered salinity had only marginal potential to enhance carotenoid accumulation in the concerned tomato fruit cultivar. This study invigorates the usefulness of metabolomics in modern agriculture, for instance in modeling tomato fruit quality. Moreover, the metabolome changes that were caused by the different salinity levels may enclose valuable information towards other salinity-related plant processes as well