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Retailers’ communication support for their price promotions is shifting from traditional flyers and circulars (so-called feature ads) to conventional media channels, especially digital ads. It is not clear, if and how supporting price promotion with advertising in digital media benefits sales of the promoted product above and beyond the price promotion itself. Further, retail managers require guidance on whether only the promoted product or also their overall business gains from ad support (e.g., from category or cross-period expansions) to negotiate trade promotion support with manufacturers of the promoted products. Using a field experiment with a grocery retailer, we decompose the effects of the advertising support of price discount promotions across digital and print marketing channels. We find that the effectiveness assessment of the advertising channels depends on the beneficiary: while digital channels most effectively support sales of the promoted product (35 % uplift vs. non-promotion period) – especially for popular consumer-pull products (+85 %), traditional print channels improve the performance for the retailer as a whole (+3 % uplift of the total category sales), with a combination of ads having the largest effect (+5 % uplift of the total category sales). This research offers guidance for retail and manufacturer managers tasked with designing price promotions and configuring the ad support across channels, and negotiating trade promotion budgets or manufacturer support for the advertisements.
Empirical dynamic modelling for exploring complex time series in management and marketing research
(2022)
Many research problems are characterized by complex relationships between time series variables, such as simultaneity (e.g., feedback loops between communication channels) and state-dependence (e.g., marketing interactions with observed and unobserved sales channel variables). The authors introduce empirical dynamic models (EDM) to management and marketing research. EDM is a nonlinear methodology that helps researchers to investigate simultaneous (i.e., bidirectional and same-period) and state-dependent (i.e., nonlinear and interacting) relationships with aggregate time series data. The authors demonstrate EDM capabilities and boundaries within the challenging omnichannel case. To study omnichannel systems, researchers often must rely on aggregate data: Despite more individual tracking, the data is often not available for offline channels or comprehensively integrated across channels. A simulation study, that derives aggregate time series from an individual data generation mechanism, explores conditions and boundaries under which EDM is suitable for identifying, predicting and attributing relationships between variables. We benchmark EDM against vector autoregression, regression, and machine learning models and provide application criteria for EDM. Next, the authors confirm the capabilities of EDM in an empirical investigation of interrelated brick-and-mortar, online, and mobile channels from a large European fashion retailer, finding evidence for mostly synergetic but strongly state-dependent relationships among the channels.
Today’s retailers have a strategic imperative to integrate their channels. Some have implemented electronic shelf labels (ESL) to replace paper tags to technologically enable the omnichannel transformation by aligning the presentation of price and product information between online and offline channels. However, consumer reactions to ESL are yet unexplored. They could be positive or negative: on one hand, the fear of frequent price changes, a known phenomenon in e-commerce, could spread to offline channels and reduce consumer purchase intent and overall revenue; on the other hand, ESL could prevent showrooming by signaling price consistency and offering consistent information (e.g., including reviews) between the on- and offline channels. We explore a retailer data set that allows isolating the “mere ESL effect”, as the retailer’s pricing strategy remained unchanged over the introduction of ESL (i.e., no dynamic pricing), but the presentation of the price and product information was integrated through ESL. A difference-in-difference analysis establishes that revenue in product categories in which ESL was introduced grows at the expense of those product categories in which it was not introduced. Visitor numbers are not affected by introducing ESL. This finding supports the adoption of e-commerce capabilities in a brick-and-mortar store as it could help prevent shopper behavior aimed at exploiting channel differences (i.e., showrooming for price or more information).
Information Systems research continues to rely on survey participants from crowdsourcing platforms (e.g., Amazon MTurk). Satisficing behavior of these survey participants may reduce attention and threaten validity. To address this, the current research paradigm mandates excluding participants through filtering heuristics (e.g., time, instructional manipulation checks). Yet, both the selection of the filter and the filtering threshold are not standardized. This flexibility may lead to suboptimal filtering and potentially “p-hacking”, as researchers can pick the most “successful” filter. This research is the first to tests a comprehensive set of established and new filters against key metrics (validity, reliability, effect size, power). Additionally, we introduce a multivariate machine learning approach to identify inattentive participants. We find that while filtering heuristics require high filter levels (33% or 66% of participants), machine learning filters are often superior, especially at lower filter levels. Their “black box” character may also help prevent strategic filtering.
Location is an impactful but irrevocable driver of retail store performance. Unless retailers rely on their gut feelings for finding high potential locations, they have to invest in extensive location research, calibrating performance models on expensive rich data (e.g., income or education of households in each prospective trading area). To prevent “the death of the high street”, also public administrators care for location potentials. This research proposes a parsimonious new model for location potentials, drawing from emerging urban scaling literature outside of marketing. We show that a measure of the local urban scale explains stores’ sales, local competitive intensity, and defining aspects of store lifecycles (managers’ location choice, sales ramp-up to a steady state after opening, store closure). We demonstrate these capabilities of the scaling approach using six datasets, including data from two retail chains (grocery and variety stores), public data, map data, and an experiment with retail managers. Our parsimonious model compares well to more complex multivariate benchmarks and remains more robust across modeling choices. We put forth a scale measure that can be cheaply obtained from map data, offering accessible applications for retail and public policy managers (e.g., “heat maps” across all potential locations in a city) and to marketing research in general (e.g., as input or control variable for geo or mobile marketing).
Jedes Weihnachtsfest geben wir zu viel Geld für Geschenke aus, die niemandem gefallen. Der neue Füller für den Schwiegersohn, die zu teure Küchenmaschine oder die leere Verheißung „Wir schenken uns nichts!“. Warum eigentlich? Der folgende Artikel wagt acht, teils etwas augenzwinkernde, konsumpsychologische Erklärungen.
This research iestigates the impact of e-commerce on energy consumption in all four sectors of the U.S. economy (commercial, industrial, residential, and transportation), using macroeconomic data from 1992 to 2015. These data capture all the development phases of e-commerce, as well as direct and rebound effects in and across sectors. Empirical dynamic models (EDMs), a novel methodology in industrial ecology, are applied to the e-commerce/energy relationship to accommodate for complex system behavior and state-dependent effects. The results of these data-driven methods suggest that e-commerce increases energy consumption mainly through increases in the residential and commercial sectors. These findings contrast with extant research that focuses on transportation effects, which appear less prominent in this iestigation. E-commerce effects also demonstrate state dependence, varying over the magnitude of e-commerce as a percentage of the total retail sector, particularly in commercial and transportation realms. Assuming these effects will continue in the future, the findings imply that policy makers should focus on mitigating the eironmentally deteriorating effects of e-commerce in the residential sector. However, this iestigation cannot provide root causes for the uncovered e-commerce effects. Robustness of the empirical findings, limitations of the novel EDM methodology, and respective avenues for future methodological and substantial research are discussed.
Fluent contextual image backgrounds enhance mental imagery and evaluations of experience products
(2018)
Online shoppers rely on product images to gain information about products. Helpful product images allow a detailed mental imagery of the product and its use. Product images with a fitting contextual background, as opposed to a plain white background, increase such mental imagery and in turn product liking and purchase intent. This effect, however, is preceded by imagery fluency—the ease with which mental images come to mind in the first place. As a result, effective product images need to facilitate fluent perceptions, while also evoking fitting mental imagery. Two experimental studies confirm this pathway which links research on mental imagery with research on imagery fluency. Moreover, the experiments show that this effect of contextual backgrounds works for fitting but not for non-fitting backgrounds, better for ambiguous than unambiguous products, and for experience products, but not for search products. Online retailers could leverage contextual backgrounds in product images to enhance consumers’ evaluations of their merchandise as long as the beneficial effects via mental imagery outweigh the added photography costs.
In e-commerce websites, products may be presented either deprived of context, in a product image on white background, or with context, in an image with a contextually fitting background. Extant fluency research would suggest preferring context-less to contextual images, because detailed image contexts increase the complexity of the image, possibly decreasing viewers’ fluency perceptions and, in turn, liking. The current research, however, establishes that despite their higher complexity, contextual images can also be perceived more fluently and liked more, because they facilitate the recognition of the product. Three experimental studies show this positive effect of contextual backgrounds in an e-commerce setting (e.g., actual product images from e-commerce). Furthermore, the present iestigation shows that the positive effect of contextual backgrounds is amplified for ambiguous products, as they profit more from a facilitation of recognition. Online retailers can thus profit from presenting products in contextual images, particularly if the products are ambiguous or difficult to recognize. Keywords: Product images, contextual background, fluency, ambiguity