A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category

We develop an empirical model for the adoption process of a new durable product that accounts for consumer heterogeneity as well as consumers forward-looking behavior. Accounting for heterogeneity is important for two reasons. As the mix of consumers with different preferences and price sensitivities could change over time, firms need to update their marketing strategies. Further, it allows for a variety of shapes for the aggregate adoption process over time. As prices for durable and technology products fall over time with firms continually introducing enhanced products, consumers may anticipate these prices and improvements and delay their purchases in the product category. Forward-looking consumers optimize purchase timing by trading off their utilities from buying the product and their expectations on future prices, quality levels, and brand availability. Such forward-looking behavior will result in price dynamics in the marketplace as price changes today influence future purchases. And it results in different shapes of the new product sales pattern over time by influencing the time to take-off. We show how the parameters of our model can be estimated using aggregate data on the sales, prices, and attributes of brands in a product category. We apply our model to market data from the digital camera category. Our data are consistent with the presence of both heterogeneity and forward looking behavior among consumers. At the product category level, we are able to decompose the effects of the entry of Sony into primary demand expansion and switching from other brands. At the brand level, we find that there exist several segments in the market with different preferences for the brands and different price sensitivities leading to differences in adoption timing and brand choice across segments. For a given brand, we show how the changing customer mix over time has implications for that brands pricing strategies. We characterize how price effects vary across brands and over time and how price changes in a given time period influence sales in subsequent periods. Model comparison and validation results are also provided.     

Gibrat's law in marketing: The case of liquor brand sales

We test the applicability of Gibrat's Law in the liquor brand market. Basically, we model annual changes in the unit sales of the top fifty liquor brands as white noise. Our results reject this model, but we do find that changes in sales are independent of starting market sales. This leads to the interpretation that brands with above average market share do not tend to gain market share, i.e., initial market share does not affect the subsequent change in market share. Furthermore, brands with above average sales do not have more stable sales than do firms with below average sales. Changes in sales appear highly positively correlated between periods, i.e., brands that gain sales in one period tend to gain sales in the next. Finally, no major liquor type or manufacturer had consistently and significantly greater or lower success across our various annual time periods.     

A robust approach to measure latent, time-varying equity in hierarchical branding structures

The literature suggests that brand equity can be split into two parts - an attribute-based equity and a non-attribute based one that captures consumer preferences beyond the utility offered by individual attributes. In addition to measuring attribute-based equity, firms deploying portfolios of products within complex branding structures often seek to measure the presence, distribution and evolution of these potentially heterogeneous non-attribute based unique branding associations - which we label ‘intangible equity’ – at each distinct layer of a product’s brand hierarchy. We develop and operationalize a robust and flexible Bayesian semiparametric model to first separate the attribute-based equity from intangible equity, to jointly estimate this multi-level intangible equity and to allow it to exhibit state-dependence using a random-walk prior. The model is empirically illustrated on syndicated US national beer sales data. We find significant, heterogeneous and temporally stable intangible equity presence across the brand hierarchy and highlight some substantive implications arising therein.     

A cross-national investigation of incentive sales compensation

Why do managers choose one sales compensation form rather than another? Theoretical answers typically focus on the type of plans managers should design, not on the factors that managers actually consider. Managers from various national origins pursue and weigh objectives through experience in a way that theoretical models may not capture. Incorporating conceptualizations from a wide range of disciplines, we specify a model examining the influence of cultural factors on sales compensation decisions of managers (incentive vs. fixed pay and parity vs. equity allocation). The model, tested with data collected from bank managers across six European countries, illustrates the importance of considering national culture when designing sales force compensation policies applied across multiple countries. We also find evidence that most European bank managers accept incentive pay to motivate salespeople but, perhaps paradoxically, overwhelmingly reject equity allocations to achieve control and parity. We discuss the implications of our findings for research on international governance systems and the diffusion of sales force management practices.     

Investigating the roles of online buzz for new product diffusion and its cross-country dynamics

This research investigates the roles of online buzz activities in influencing the speed and scope of new product diffusion and also the interactive dynamics of online buzz activities within and across countries. This study applies a diffusion model with two latent adopters and a log linear model to monthly sales and online messages collected from major online communication sites in five different countries. The empirical study evaluates two different roles of online buzz: (1) accelerating the new product diffusion process by influencing imitation tendency; and (2) expanding potential market size. In addition, the empirical study shows the interactive dynamics of online buzz activities within and across countries, and provides some information on what encourages online buzz activities, and how they interact with each other within a country and across countries.     

Joint In-Season and Out-of-Season Promotion Demand Forecasting in a Retail Environment

Inaccurate forecasts of demand during promotions diminish the already meager profit margins of retailers. No forecasting method described in the literature can accurately account for the combination of seasonal sales variations and promotion-induced sales peaks over forecasting horizons of several weeks or months. We address this research gap by developing a forecasting method for seasonal, frequently promoted products that generates accurate predictions, can handle a large number of sales series, and requires minimal training data. In our method's first stage, we forecast the seasonal sales cycle by fitting a harmonic regression model to a decomposed training set, which excludes promotional and holiday sales, and then extrapolate that model to a testing set. In the second stage, we integrate the resulting seasonal forecast into a multiplicative demand function that accounts for consumer stockpiling and captures promotional and holiday sales uplifts. The final model is then fitted using ridge regression. We use sales data from a grocery retailing chain to compare the forecasting accuracy of our method with popular seasonal and promotion demand forecasting models at multiple aggregation levels for both short and long forecasting horizons. The significantly more accurate forecasts generated by our model attest to the merit of the approach developed here.     

No chills or burns from temperature surprises: An empirical analysis of the weather derivatives market

This article examines the efficiency of the weather futures market traded on the CME in both HDD and CDD futures contracts in 18 cities across the United States. Efficiency is examined in three ways. First, by comparing the market's implied forecasts for the weather against other forecasts. Second, by looking at whether market's overreact or under‐react to temperature surprises. Third, by looking at weather derivative patterns across cities. We find that generally the market seems very efficient despite its lack of liquidity. We also find risk premia that seem to vary across cities and over time. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:1–33, 2011     

The impact of daily weather on retail sales: An empirical study in brick-and-mortar stores

In this study, we examine the influence of weather on daily sales in brick-and-mortar retailing using empirical data for 673 stores. We develop a random coefficient model that considers non-linear effects and seasonal differences using different weather parameters. In the ex-post analysis using historic weather data, we quantify the explanatory power of weather information on daily sales, identify store-specific effects and analyze the influence of specific sales themes. We find that the weather has generally a complex effect on daily sales while the magnitude and the direction of the weather effect depend on the store location and the sales theme. The effect on daily sales can be as high as 23.1% based on the store location and as high as 40.7% based on the sales theme. We also find that the impact of extreme bad and good weather occurrences can be misestimated by traditional models that do not consider non-linear effects. In the ex-ante analysis, we analyze if weather forecasts can be used to improve the daily sales forecast. We show that including weather forecast information improves sales forecast accuracy up to seven days ahead. However, the improvement of the forecast accuracy diminishes with a higher forecast horizon.     

An Assessment of When,Where and Under What Conditions In-Store Sampling is Most Effective

In-store product sampling is a commonly used promotional technique designed to give prospective consumers an opportunity to experience a product prior to purchase. While prior research has documented a positive relationship between short-term sales and perceptual measures of the customer shopping experience, little is known about the long-term impact of sampling or factors that moderate its success. In this paper, we develop an empirical approach that allows us to study the short-term and long-term effects of in-store sampling on both own and competitive products. We apply our approach to six store-level scanner data sets across four different product categories and show that in-store sampling has both an immediate (short-term) and sustained (long-term) impact on sales. We also show that the impact of sampling on sales is moderated by the characteristics of the store conducting the event, and that repeated sampling for a single product leads to a multiplicative increase in its long-term sales performance. We find that, unlike many types of in-store promotion, sampling results in a category expansion effect as opposed to a pure substitution effect. We contrast the immediate and long-term sales patterns for in-store sampling to those of product displays and discuss managerially relevant differences. Finally, we demonstrate incremental profit implications and store selection scenarios for different incremental costs of conducting the in-store events using constrained optimizations.     

The role of country-specific fundamentals in sovereign CDS spreads: Eastern European experiences

We investigate the determinants of sovereign CDS spreads on a sample of Eastern European data. A dynamic hierarchical factor model is used to aggregate information in indicators of economic fundamentals. CDS spreads are regressed on forecasts of factors. We find that domestic fundamentals explain more of CDS spread variance than global factors, largely due to their ability to explain differences in sovereign risk across countries. The effects on CDS spreads are found to be time-varying. In terms of economic significance, the factor of institutional-political strength stands out. We apply the model to study CDS spreads of Poland, Russia and Turkey.     

Does shelf-labeling of organic foods increase sales? Results from a natural experiment

Can a simple point-of-purchase (POP) shelf-label increase sales of organic foods? We use a random-effects׳, random-coefficients׳ model, including a time adjustment variable, to test data from a natural experiment in a hypermarket in Gävle, Sweden. Our model incorporates both product specific heterogeneity in the effects of labeling and consumer adjustment to the labels over time. We find that the introduction of POP displays leads to an increase in sales of organic coffee and olive oil, but a reduction in sales of organic flour. All targeted products became less price-sensitive. The results reveal that product specific differences have to be accounted for, and in some cases consumers adjusted to labeling over time.     

Consumer Goods Packaging Innovation and its Role in the Product Adoption Process

Packaging has emerged as an important competitive tool. An innovative package can pre-empt a market position, create a new market and lead to greater sales and profits, just as an improved or reformulated product can. This report investigates the adoption process for packaging innovations in food and food-related items. It seeks to answer certain questions pertaining to the adoption process: Do early adopters differ from the general population? What factors serve to accelerate the rate of adoption of packing innovations by consumers? What kinds of packinging innovations become successes? The results of a small-scale empirical study comparing early adopters of packaging innovations with the general populations provide some answers to these questions. The report concludes with recommendations to management for successful innovative packaging.     

A Bayesian multivariate Poisson regression model of cross-category store brand purchasing behavior

The availability of cross-category transaction data in the retailing industry has enabled the investigation of interdependence in consumer purchase behavior across product categories. In this paper, we develop a multivariate count model to uncover and predict the pattern of cross-category store brand purchasing behavior. The proposed multivariate Poisson regression model, which we estimate using a Bayesian approach, provides flexibility in capturing cross-category correlations for sparse multivariate purchase data associated with infrequently purchased categories or purchasing in retail outlets such as warehouse clubs. We compare the goodness-of-fit of the proposed Poisson regression model with alternate benchmark models using customer purchase records across five product categories from a national warehouse club and find that the proposed model provides a superior fit. We also carry out a profitability analysis to illustrate the use of the model in planning cross-promotions.     

The effect of default options on choice—Evidence from online product configurators

Many firms use product configurators to enable customers to specify their desired products online. In such systems, defaults are pre-specified for levels of product features by the manufacturer or dealer. For example, when configuring a racing bike online, a default is predefined (e.g., the Shimano Ultegra model) for all required features (e.g., the gearshift levers). Such defaults, which may even adapt to previous choices, ensure that a functional and fully defined product emerges at the end of the configuration process. However, when designing sales systems, companies often fail to realize that these defaults also affect customer decision-making. We demonstrate the effect by a study that makes use of a fully simulated racing bike configurator. We find the following results: Moving the default of one feature (e.g. wheels) from the lowest to the highest level results in an increase in sales. In addition, the feature level defined as the default also acts as a reference point by increasing the sales of levels near to it. In order to maximize sales, the default should be set at the level of a feature that is between the medium and the highest price level. To conclude we discuss how manufacturers and dealers subtly yet powerfully influence the decision-making process with their sales systems.     

Economic determinants of the correlation structure across international equity markets

This study investigates whether, how, and why the matrix of correlations across international equity markets changes over time. A theoretical model is proposed to specify potential economic determinants of this correlation structure. The empirical validity of this economic model is investigated by employing daily returns for different national stock indexes, from 1972 through 1993, to construct a quarterly time series of the correlation matrix. This quarterly time series is used to investigate the stability of the correlation matrix over time, and to estimate the economic model. The model is then applied to generate out-of-sample forecasts of the correlation structure.Keywords: International market integrationJEL classification: F36, G15.     

Industry relatedness in trade sales and venture capital investment returns

We investigate relationships between the industry relatedness of venture capital-backed companies and their strategic acquirer in trade sales and the achieved investment returns of venture capitalists. Using a proprietary data set of 716 trade sales, we analyze return differences between lateral and synergetic trade sales, as well as between horizontal and vertical trade sales. We find that venture capitalists achieve higher returns with lateral rather than synergetic trade sales, and that the difference is greater for deals involving early stage companies characterized by strong information asymmetries. In addition, horizontal trade sales yield higher returns than vertical trade sales; however, in boom phases of the venture capital market, this effect reverses. Finally, we find that experienced venture capitalists are able to overcome disadvantageous situations in trade sales, resulting in comparable returns across all trade sale categories.     

Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants

We present a framework to measure empirically the size of indirect network effects in high-technology markets with competing incompatible technology standards. These indirect network effects arise due to inter-dependence in demand for hardware and compatible software. By modeling the joint determination of hardware sales and software availability in the market, we are able to describe the nature of demand inter-dependence and to measure the size of the indirect network effects. We apply the model to price and sales data from the industry for personal digital assistants (PDAs) along with the availability of software titles compatible with each PDA hardware standard. Our empirical results indicate significant indirect network effects. By July 2002, the network effect explains roughly 22% of the log-odds ratio of the sales of all Palm O/S compatible PDA-s to Microsoft O/S compatible PDA-s, where the remaining 78% reflects price and model features. We also use our model estimates to study the growth of the installed bases of Palm and Microsoft PDA hardware, with and without the availability of compatible third party software. We find that lack of third party software negatively impacts the evolution of the installed hardware bases of both formats. These results suggest PDA hardware firms would benefit from investing resources in increasing the provision of software for their products. We then compare the benefits of investments in software with investments in the quality of hardware technology. This exercise helps disentangle the potential for incremental hardware sales due to hardware quality improvement from that of positive feedback due to market software provision.     

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.     

The Impact of Mobile App Adoption on Cross-buying: The Moderating Roles of Product Category Characteristics and Adoption Timing

With more and more companies introducing mobile apps and consumers using them during the purchase journey, it is increasingly important to understand the consequences of app adoption on behavioral outcomes. This paper investigates the impact of app adoption on the number of additional products purchased by customers (i.e., cross-buying) and how this effect varies across different product categories and different customers. We focus on two key product category characteristics (utilitarian vs. hedonic nature and perceived risk) and on adopters who vary in their adoption timings (early vs. late adopters). Using data from an online retailer and a combination of propensity score weighting with difference-in-differences and Heckman correction estimation techniques, the results show that app adoption has a positive effect on cross-buying overall, but the effect varies greatly across products and consumers. App adoption promotes additional product purchases for hedonic products but leads to less cross-buying for utilitarian products. In addition, early adopters purchase a higher number of additional product categories than late adopters, with this difference decreasing over time and for new consumers compared to existing customers. These results offer novel insights into the behavioral consequences of app adoption and provide managers with useful recommendations for improving the effectiveness of their mobile app investments.