Multichannel customer purchase behavior and long tail effects in the fashion goods market

The fast-paced growth of e-commerce is impacting the type and variety of products consumers purchase across channels. A commonly held theory, known as long tail theory, posits that online sales are less concentrated at the top of the sales distribution than offline sales, and that more variety is bought online, making the tails of the overall sales distribution denser with the growth of e-commerce. Most of the literature testing the long tail theory has focused on examining entertainment goods markets that do not require much physical examination, and has predominantly found results consistent with the theory. However, the magnitude and antecedents of the observed long tail effects might be different for product categories containing products that require more physical examination before purchase, such as fashion goods. In this study, using detailed individual and transaction level panel data from two multichannel fashion goods retail brands, we show that while the shift to the online channel results in a decrease in the concentration of overall sales for both brands, this change mostly results from consumers buying different products online rather than consumers buying a greater variety online compared to offline. We show that the flattening of the overall sales distribution with the growth of e-commerce in our data is driven by consumers sorting their purchases into channels based on product characteristics. In contrast to the recommendations from the previous long tail literature, our results show that fashion apparel retailers do not need to offer broader assortments online compared to offline, but they may find it profitable to carry or emphasize a different product mix online compared to offline. Our results also provide guidance to fashion goods retailers in curating their online and offline assortments and setting inventory management strategies across the channels.     

How Online Product Reviews Affect Retail Sales: A Meta-analysis

A growing body of research has emerged on online product reviews and their ability to elicit performance outcomes desired by retailers; yet, a common understanding of the performance implications of online product reviews has eluded us. Scholars continue to navigate an array of studies assessing different design elements of online product reviews, and various research settings and data sources. We undertake a meta-analysis of 26 empirical studies yielding 443 sales elasticities to examine how these variables relate to retail sales. Building on well-established meta-analytical methods, we address the following questions: How does review valence influence the elasticity of retailer sales? What about review volume? For which product types and usage situations do online product reviews have a greater impact on retailer sales elasticity? Which types of online reviewers and websites exert the greatest influence on retailer sales elasticity? Our study answers these important questions and provides a much needed quantitative synthesis of this burgeoning stream of research.     

Market and Product Diversification: The Evidence From Retailing

This study explored the number of international regions in which retailers operate, the assortment of product categories they offer their customers, and the effects of these market and product category diversification strategies on retail sales volumes. The analysis used sales data about 246 global large-scale retail stores from diverse industries and countries of origin. We found that both strategies have positive effects on retail sales volumes. The two strategies interact such that the positive effect of market expansion on retail sales volumes is larger for retailers who also follow a strategy of greater product category diversity.     

Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category

Online reviews, which significantly influence product sales, have been a central research topic in the field of marketing. Meanwhile, some motivating factors related to online retailers have been linked to product sales. While several articles have examined the impact between online reviews and motivating factors on product sales, many of the conclusions drawn are contradictory. From 28 studies focusing on online reviews and sales, this study performs a meta-analysis to analyze the true impacts of six review-related factors (i.e., the number of reviews, star ratings, standard deviation of ratings, helpfulness, review length and sentiment), and two motivating factors (i.e., price discounts and special shipping) on product sales. Meanwhile, this paper also studies how one product-related factor (i.e., product age) and one reviewer-related factor (i.e., reviewer's reputation) influence the relationship between online reviews and product sales. In addition, to study the moderating effect of product category, we divide the selected literature into two subgroups which are search and experience products. The results indicate that only review length and special shipping have no significant impact on product sales, while product category has a valid and specific moderating effect on the relationship between these determinants and sales. The presented conclusions will have important implications for academic research and for future industry practice.     

Optimal retail sales strategies for old and new products in monopoly and horizontal competition scenarios

Advances in technology have made product updates more frequent and allowed consumers to choose different versions of the same product based on their preferences. It is crucial for retailers to understand how to formulate optimal sales strategies based on those different consumer preferences. To this end, we develop game models that consider the heterogeneity of consumer preferences under both monopoly and horizontal competition scenarios and perform the sensitivity analysis to examine the impact of consumer proportions and consumer preferences on retailers’ sales strategies. The results show that (i) regardless of competition or monopoly status, the original retailer can always maximize profit by setting prices based on the market share of traditional consumers, as long as the retailer sells both new and old versions of the product; (ii) the greater the competitive advantage of the competitor, the more advantageous the hybrid sales mode; (iii) if the price of the old product is below a certain threshold, there will be a positive profit for the original retailer when selling both the old and new products; and (iv) when consumer acceptance of competing retailers is lower, entering the retail market is not a good choice for competing retailers.     

Acquiring customers through online marketplaces? The effect of marketplace sales on sales in a retailer's own channels

Online retailers are increasingly using third-party online marketplaces (e.g., Amazon, Taobao) as an alternative sales channel to their website. While cross-channel sales elasticities have been established for many sales channel combinations (e.g., adding bricks to clicks), we lack an understanding of whether the use of third-party marketplaces grows or cannibalizes a retailer's sales. Practitioners argue that firms can build their e-commerce business through acquiring customers by selling on the marketplace. Indeed, a marketplace could complement a retailer's offering (e.g., acquiring new customer segments), although inventory effects might mitigate this complementarity. Alternatively, cannibalization might occur from losing customers from one's website to the online marketplace. The present research investigates which of the two opposing forces prevails using a time series of category sales data from one of the largest global marketplace sellers. The authors use vector autoregressive modeling to show that marketplace sales increase sales on a retailer's website (0.014% for every 1% in marketplace sales). This effect is strongest for categories with large choice and low product prices. Acquiring customers through the marketplace might be cheaper than through other sources (estimated at 24% of initial sales). However, online retailers should be aware that this strategy strengthens the marketplace and may have potential negative long-term consequences (e.g., through marketplace control of the customer relationship).     

See How Much We’ve Sold Already! Effects of Displaying Sales and Stock Level Information on Consumers’ Online Product Choices

Many online retailers make sales and stock level information live available to customers on their websites and offline retailers also increasingly can display this information. Yet it is unclear how consumers perceive such information and how it influences consumer choice. High sales and low stock may both signal popularity and quality, which will stimulate further sales. We however argue that sales level is more diagnostic than stock level as a cue and hence the effect of sales level on consumer choice will be stronger than that of stock level. Also, when both cues are available, the effect of sales level is expected to dominate. In addition, brand familiarity is expected to moderate these effects. These hypotheses are tested in two scenario-based online experiments in which participants choose between competing products for which information about either sales levels, stock levels, or both sales and stock levels is displayed. Results support the hypotheses and also show that sales level is indeed a more diagnostic cue than stock level. Analyses further reveal how perceptions of the popularity and quality of a product mediate the effects of sales and stock levels on consumer choice. The findings imply that retailers can successfully use sales and stock level information to induce quality perceptions and influence consumer choices.     

Why is Price Dispersion Higher Online than Offline? The Impact of Retailer Type and Shopping Risk on Price Dispersion

When physically similar products, of similar quality, are offered by retailers both online and offline, we often observe that the dispersion in prices of these products online is greater than the price dispersion offline. This observation runs counter to early theories that suggested price dispersion online would be smaller than that offline due to the ease of search and information availability online. This paper investigates and provides an explanation for this puzzling phenomenon by examining the impact of two important drivers of price dispersion: retailer type and consumers’ shopping risk. Retailer type refers to whether a retailer is a pure offline, pure online, or dual channel retailer. Shopping risk is defined as the product of consumers’ perceived risk of shopping and the transaction uncertainty related to shopping at different types of retailers.A game-theoretic approach is adopted to model consumers’ price search and product purchase, as well as price competition within and across retailer types in online and offline markets. Equilibrium pricing strategies are derived for different retailer types competing for different consumer segments with different levels of perceived shopping risk. The impact of retailer type and shopping risk on online versus offline price dispersion are quantified, and conditions when price dispersion is greater online than offline are identified.Results indicate that price dispersion is greater online when the number of pure online retailers is sufficiently large and is increasing in the number of pure online retailers. In addition, a reduction in online shopping risk may actually increase online price dispersion. Results further suggest that even without any online sales, dual channel retailers should maintain their online presence for the purpose of information dissemination, which justifies the importance for pure offline retailer to incorporate webrooming strategies, where consumers can search for prices online but purchase offline.     

The Sales Velocity Effect on Retailing

In an increasingly crowded marketplace, retailers need innovative ways of promoting products to their consumers. E-commerce retailers have utilized to great effect lists of top ranked products to promote product sales; the higher the sales rank, the more likely consumers buy that product. This influence to buy, based on observing what others bought is known as observational learning (OL). Prior OL research assumed that OL arises from observing a static outcome, such as the current sales rank of a product. However, prior research on intertemporal choice showed that people prefer outcomes with increasing trends over stable or decreasing trends. This suggests that observing an increasing sales rank, denoted as sales velocity, would have a positive effect on purchase likelihood. We conducted three studies to test the sales velocity effect. Results show that sales velocity has a significant effect on likelihood of purchases, reversing even participant preferences for a product with a higher sales rank. This effect is consistent across four broad products tested. For researchers, by joining the two previously disparate branches of research in OL and intertemporal choice, we addressed a gap in OL research which previously ignored the velocity dimension of OL. For retailers, the study demonstrated the impact of the sales velocity metric on making choices, and thus they could use sales velocity data as a cost-effective marketing tool for specific products.     

Linking online niche sales to offline brand conditions

With the dramatic growth in the online marketplace, online retailers are keen to understand and leverage the interplay between offline environment and online sales. This study examines the influence of offline brand conditions on online sales of niche brands. Specifically, we investigate the proximity to the leading brand's headquarters, city of origin, and the extent of its offline distribution, offline brand availability. We also examine the moderating effect of offline affinity for niche attributes, offline niche affinity. Using sales data of niche brands, we find that brand share is higher in regions closer to the city of origin and where brand availability is limited. The category sales benefit from proximity to the city of origin and increased offline brand availability. This positive impact of favorable offline brand conditions on category sales is more prominent in regions with lower niche affinity. Finally we offer managerial insights for marketing practice.     

Decomposing the sales promotion bump accounting for cross-category effects

Extant research on the decomposition of unit sales bumps due to price promotions considers these effects only within a single product category. This article introduces a framework that accommodates specific cross-category effects. Empirical results based on daily data measured at the item/SKU level show that the effects of promotions on sales in other categories are modest. Between-category complementary effects (20%) are, on average, substantially larger than between-category substitution effects (11%). Hence, a promotion of an item has an average net spin-off effect of (20 − 11 =) 9% of its own effect. The number of significant cross-category effects is low, which means that we expect that, most of the time, it is sufficient to look at within-category effects only. We also find within-category complementary effects, which implies that competitive items within the category may benefit from a promotion. We find small stockpiling effects (6%), modest cross-item effects (22%), and substantial category-expansion effects (72%). The cross-item effects are the result of cross-item substitution effects within the category (26%) and within-category complementary effects (4%). Approximately 15% (= 11% / 72%) of the category-expansion effect is due to between-category substitution effects of dependent categories.     

Demand forecast information sharing in the competitive online and traditional retailers

An important strategic issue for retailing business managers to study is the information strategy. In this paper, we develop a profit-maximization model to investigate the benefits of demand forecast information sharing for the competitive online and traditional retailers with the consideration of the compatibility of the product with online marketing. Both retailers use a Bertrand model to compete. We analyze and compare two scenarios: (1) when forecast information is not shared between the online and traditional retailers; (2) when forecast information is shared between the online and traditional retailers. Our results show that both the online and traditional retailers will be better off from information sharing. Especially when the channel forecast is less accurate, the product is more compatible with online marketing, and the market is more volatile, both retailers will profit more. Based on our results, optimal strategies are derived and probable paths of future research are identified.     

The Impact of Search and Recommendation Systems on Sales in Electronic Commerce

The Internet and related technologies have vastly expanded the variety of products that can be profitably promoted and sold by online retailers. Furthermore, search and recommendation tools reduce consumers’ search costs in the Internet and enable them to extend their search from a few easily found best-selling products (blockbusters) to a large number of less frequently selling items (niches). As a result, Long Tail sales distribution patterns emerge that illustrate an increasing demand in niches. We show in this article how different classes of search and recommendation tools affect the distribution of sales across products, total sales, and consumer surplus. We hereby use an agent-based simulation which is calibrated based on real purchase data of a video-on-demand retailer. We find that a decrease in search costs through improved search technology can either shift demand from blockbusters to niches (search filters and recommendation systems) or from niches to blockbusters (charts and top lists). We break down demand changes into substitution and additional consumption and show that search and recommendation technologies can lead to substantial profit increases for retailers. We also illustrate that decreasing search costs through search and recommendation technologies always lead to an increase in consumer surplus, suggesting that retailers can use these technologies as competitive advantage.     

Does online observational learning matter? Empirical evidence from panel data

The information presented on a product sales page plays an important role in consumers' purchase decisions. This study examines the persuasive effect, whether a customer's choice is heavily driven by information inferred from others' behavior, and how these impacts are moderated by market age and product type. Results show that online customer choice was significantly affected by historical cumulative sales and times saved. Positive cumulative sales and shop service quality have a significant positive impact on product sales. The times saved have no direct impact on product sales. For different types of products, the times saved of experiential products has a significant impact on product sales, while the shop service quality information of search products has a greater impact on product sales. The influence of online observation learning on product sales will be significantly moderated by a combination of product type and market age. These findings not only offer important theoretical contributions to e-commerce research but also provide practical implications for online sellers and managers of social commerce platforms.     

Performance Implications of Online Entry Timing by Store-Based Retailers: A Longitudinal Investigation

This paper utilizes Time Series Cross-Sectional (TSCS) Regression techniques to investigate long-term performance effects of the timing of online sales adoption by incumbent bricks-and-mortar retailers. Its findings support the resource-based theory of competitive advantage by showing that firm-specific resource endowments (bricks-and-mortar experience, catalog experience and firm size) determine the success of the order of online entry strategy. The study contributes to the development of strategic theory in the areas of multi-channel retailing and electronic commerce and assists managers in formulating more informed strategic objectives for achieving multi-channel competitive advantage.     

It's not only what they buy,it's also what they keep: Linking marketing instruments to product returns

Online retailers implement various marketing instruments to boost their sales. These marketing instruments can not only impact sales, but also product returns. However, when assessing the performance of marketing instruments, retailers often ignore potential return effects. Theoretically, marketing instruments could increase or decrease returns, depending on how they affect expected and experienced costs and benefits related to a product. In this paper, we empirically examine whether, and how a comprehensive set of marketing instruments (newsletters, catalogs, coupons, free shipping, paid search, affiliate advertising and image advertising) affects product returns. We use data from two major online retailers and show that return effects vary largely across marketing instruments. Surprisingly, none of the instruments reduces product returns. Newsletters, paid search, catalogs and free shipping increase returns substantially by up to 18%. For free shipping and catalogs, the return effects emerge prevalently for fashion categories, whereas online advertising and newsletters increase returns of both fashion and non-fashion products. These findings enhance our understanding of how firm-initiated marketing instruments affect returns and provide guidance for online retailers in multimedia environments.     

Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com

We investigate the cross channel effects of search engine advertising on Google.com on sales in brick and mortar retail stores. Obtaining causal and actionable estimates in this context is challenging: Brick and mortar store sales vary widely on a weekly basis; offline media dominate the marketing budget; search advertising and demand are contemporaneously correlated; and estimates have to be credible to overcome agency issues between the online and offline marketing groups. We report on a meta-analysis of a population of 15 independent field experiments, in which 13 well-known U.S. multi-channel retailers spent over $4 Million in incremental search advertising. In test markets category keywords were maintained in positions 1-3 for 76 product categories with no search advertising on these keywords in the control markets. Outcomes measured include sales in the advertised categories, total store sales and Return on Ad Spending. We estimate the average effect of each outcome for this population of experiments using a Hierarchical Bayesian (HB) model. The estimates from the HB model provide causal evidence that increasing search engine advertising on broad keywords on Google.com had a positive effect on sales in brick and mortar stores for the advertised categories for this population of retailers. There also was a positive effect on total store sales. Hence the increase in sales in the advertised categories was incremental to the retailer net of any sales borrowed from non-advertised categories. The total store sales increase was a meaningful improvement compared to the baseline sales growth rates. The average Return on Ad Spend (ROAS) is positive, but does not breakeven on average although several retailers achieved or exceeded break-even based only on brick and mortar sales. We examine the robustness of our findings to alternative assumptions about the data specific to this set of experiments. Our estimates suggest online and offline are linked markets, that media planners should account for the offline effects in the planning and execution of search advertising campaigns, and that these effects should be adjusted by category and retailer. Extensive replication and a unique research protocol ensure that our results are general and credible.     

Retail environment and manufacturer competitive intensity

We study how the intensity of competition and the degree to which manufacturers enjoy market power depends on the retail environment in a given market. Past research has discussed the growing importance of retailers and the power they enjoy over manufacturers. Yet, the empirical literature to date has not determined which retail characteristics have the largest impact on competitive behavior.Our starting point is the estimation of a structural demand-and-supply model, where both consumers’ decisions and the strategic interactions between manufacturers and retailers are explicitly modeled. We identify the type of competitive behavior of manufacturers by measuring the deviation from a Bertrand-Nash equilibrium. This measure of competitive conduct is expressed as a function of key retail characteristics such as size of the retailer, its assortment depth, and category expertise.We illustrate the proposed approach using data for the ground coffee category in Germany. Our findings indicate that retail characteristics have indeed a significant effect on competitive intensity among upstream firms and on their ability to exercise market power. Hence, a manufacturer considering entry into a new market should not only take into account its competitors but also the specifics of the retail environment in this market.     

Forecast information and traditional retailer performance in a dual-channel competitive market

Independent firms in a dual-channel competitive market are expected to have their own information about the nature of the market. In this research, we develop a game-theoretic model to examine the value of forecast information about consumers' willingness to pay. The model is based on a simultaneously played Bertrand game. Our results indicate that the profits of online as well as traditional retailers always increase with forecast accuracy, and that forecast accuracy has a greater effect on the performance of the traditional retailer than on that of the online retailer. Our results also show that the difference in profit between that of the traditional retailer and the online retailer increases with forecast accuracy. In addition we find that forecast accuracy is much more valuable to the traditional retailer when there is an increasing volatility in the market, an increasing level of consumer valuation of the product, and an increasing intensity in market competition. Based on our results, we derive optimal market strategies and identify directions of future research.     

Consumer Product Redistribution

Abstract

Formats for product redistribution are emerging and evolving, creating alternative channel options for consumers' disposition of unwanted possessions. These retail formats operate in both informal (e.g., garage sales) and formal (online auctions) economies. As consumers participate in redistribution channels, some retailers are confronted with new competition and the potential for declining sales. This article expands on both recycling and disposition literature by examining consumer disposition behavior and the flow of used products through various redistribution channels. Strategies are offered to address consumer disposition behaviors as well as to assist retailers in combating traditional and non-traditional competition.