The current and future sales impact of a retail frequency reward program

This research presents an empirical study of the impact of a retail frequency reward program on store sales. We examine both the “points pressure,” or short-term impact, and the “rewarded behavior,” or long-term impact. The points-pressure impact is due to forward-looking customers increasing their purchase levels in order to earn the reward. The rewarded-behavior impact is evidenced as purchases above baseline levels after an individual has received a reward and could result from either behavioral learning reinforcement or positive affect resulting from the reward. We investigate a turkey reward program that awarded free turkeys to shoppers who accumulated the required sales levels during an 8-week period. We find both a points-pressure and rewarded-behavior impact. These effects are statistically significant and managerially relevant in that the program is apparently profitable. The points-pressure impact is especially strong among customers who do not place value on frequent shopper programs that in general deliver immediate price discounts. The key implications are that frequency reward programs of the form, “buy x, then receive xx” can be profitable, are segmentation strategies, and can complement a store's overall frequent shopper program.     

An Empirical Analysis of the Extreme Cherry Picking Behavior of Consumers in the Frequently Purchased Goods Market

Extreme cherry pickers are customers who seek price deals and excessively avail themselves of deep discount offers, which generates negative profits for retailers. This study uses market transaction and primary consumer survey data to provide insights into the determinants, prevalence, and profit impacts of such behavior in the frequently purchased goods market. We find that the extreme cherry picking segment is small (about 2% of all shoppers), but its relative value varies across stores, and consumers manifest this behavior only in secondary stores. An inverse U-shaped relationship marks consumers’ opportunity costs for cross-store price search and likelihood of extreme cherry picking behavior. Finally, we also find that a loss leader promotional strategy adds to retailers’ bottom lines, despite the pure loss generated by extreme cherry pickers.     

Modeling and Forecasting the Sales of Technology Products

Managers in technology product markets require sales response models that provide substantive insights into the effects of marketing activities as well as reliable sales forecasts. Such markets are characterized by frequent introductions and withdrawals of multiple models by different companies. Thus, the data available on the performance of any individual model is scarce. A second characteristic is that the effects of product attributes and marketing activities could change over time as different types of consumers participate in the market at different points in time. Given sparse data, it becomes critical to specify a model that allows pooling of information across brand-models while at the same time providing brand-model specific parameters. We accomplish this via a hierarchical Bayesian model specification. Further, to capture the effects of changing consumer preferences over time, we specify a time varying parameter model. Our modeling framework therefore, integrates a hierarchical Bayesian model within a time varying parameter framework to develop a dynamic hierarchical Bayesian model. We employ data on digital cameras in the U.S. market to estimate the parameters of our proposed model. We use thirty-three months of national level data on the digital camera market with the data series beginning very close to the inception of this product category. We find that while there is little variation in reliance of benefits by early adopters, the second wave of adopters focus on Ease of Use followed by later adopters who rely on Storage and Image Quality. Looking at the elasticities of demand with respect to the various benefits, we find that at around the halfway point of our data series, the industry as a whole would have been better off investing in increasing image quality rather than storage if costs associated with the two are equal. However, at the end of the time horizon both benefits appear to have about equal impact. Further, the relative benefits of improving these attributes vary across brands and points in time. We then generate single period and multiple period ahead sales forecasts. We make different assumptions about information availability and find that the average (across brand-models and time) MAPE ranges from 7.5 to 14.5% for the model. We provide extensive comparisons of our model with 4 potential alternatives and find that our model outperforms these alternatives on the nature of substantive insights obtained as well as in forecasting out-of-sample especially when there is a very short time window of data.     

Mobile Shopper Marketing: Key Issues,Current Insights,and Future Research Avenues

The intersection of mobile marketing and shopper marketing, known as mobile shopper marketing, is a rapidly evolving area. We formally define mobile shopper marketing as the planning and execution of all mobile-based marketing activities that influence a shopper along and beyond the path-to-purchase: from the initial shopping trigger, to the purchase, consumption, repurchase, and recommendation stages. However, not much is known about mobile shopper marketing. We plug this gap by first discussing mobile shopper marketing and its scope in depth and then presenting a process model that connects the mobile shopping journey with four key entities, i.e., shopper, employee, organization, and mobile technology. For each of these themes, we identify the challenges that offer future research opportunities.     

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.     

Do frequency reward programs create switching costs? A dynamic structural analysis of demand in a reward program

This paper examines a common assertion that customers in reward programs become “locked in” as they accumulate credits toward earning a reward. We define a measure of switching costs and use a dynamic structural model of demand in a reward program to illustrate that frequent customers’ purchase incentives are practically invariant to the number of credits. In our empirical example, these customers comprise over 80% of all rewards and over two-thirds of all purchases. Less frequent customers may face substantial switching costs when close to a reward, but rarely reach this state.

Substitution or Promotion? The Impact of Price Discounts on Cross-Channel Sales of Digital Movies

Technology is transforming the marketing function in many ways, and this transformation is particularly apparent for information goods such as movies where digital technologies provide marketers with new distribution channels, which in turn create new opportunities for cross-channel effects. However, these digital channels also provide researchers with new opportunities to measure micro-level customer behavior to understand the impact of cross-channel effects in real-world settings.In this paper, we study cross-channel effects between movies sold in digital purchase (commonly known as Electronic Sell Through or EST) and digital rental (commonly known as Video-On-Demand or VOD) markets. We do this using a unique sales dataset from a major digital movie retailer provided by a major movie studio. Our analysis takes advantage of a 14-week field experiment that allows us to measure the impact of price discounts on own- and cross-channel sales. We use this experiment to estimate own and cross price elasticities, whether price discounts cannibalize future sales, and most importantly whether price discounts in one channel affect sales for the same product in a presumably competing channel.Our analysis indicates that digital movie consumers are highly sensitive to price promotions. However, we also find that, contrary to expectations, price promotions in a digital sales channel for a movie do not seem to cannibalize digital rentals. Indeed, our results suggest that, if anything, price promotions for digital movie sales can increase digital rentals. We explore a variety of explanations for this counterintuitive result, including the possibility that the ease of information transmission online through third-party websites, blogs, and online discussion areas may create information spillovers such that price discounts in one channel may increase product awareness in other competing sales channels. From a managerial perspective, our results suggest that cross-channel cannibalization can be reduced or even reversed in the presence of information spillovers, and that there are many new opportunities for marketers to directly measure these cross-channel effects using experimental data from online platforms.     

Exploring supermarket loyalty card analysis to identify who buys fairtrade

The aim of this paper is to show how supermarket loyalty card data from a panel of over 1.7 million shoppers can be analysed to provide insights to profile the fairtrade shopper in order to enhance making targeted marketing decisions. The paper demonstrates the huge marketing potential that loyalty-card-based shopper segmentation can bring to objectively describe who buys fairtrade products, compared to profiling shoppers through a claimed/reported behaviour data-set. A paired-samples t-test is used to test the degree of appeal of fairtrade tea, coffee, chocolate, drinking chocolates, banana and sugar categories in Tesco to life-stage and lifestyle shopper segments in terms of their retail sales values over 104 weeks. The results show that analysing loyalty cards based on actual behaviour provides a more detailed picture of how specific fairtrade food product categories appeal to the various life-stage and lifestyle shopper segments.     

Similarity-Based Spatial Methods to Estimate Shelf Space Elasticities

A retailer may allocate shelf space to brands based on factors, unobservable to researchers, which also determine sales. As a consequence, both sales and shelf space are endogenous in historical data, and this leads to inconsistent estimates of shelf space elasticities based on OLS. To obtain valid estimates of shelf space elasticities for allocation decisions, we propose an approach that incorporates the spatial correlation between shelf space and the error term resulting from store-, consumer- and competitor characteristics. The empirical results suggest that our model based on a single cross section of stores corrects for endogeneity and provides valid shelf space elasticities. We also obtain superior predictions compared to several benchmark models. With the same cross section and two observations over time, the alternative methods we use provide comparable shelf space elasticity estimates. However, our proposed method is still superior in the sense that its estimates have somewhat smaller standard errors.     

Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis

Online reviews provide consumers with rich information that may reduce their uncertainty regarding purchases. As such, these reviews have a significant influence on product sales. In this paper, a novel method that combines the Bass/Norton model and sentiment analysis while using historical sales data and online review data is developed for product sales forecasting. A sentiment analysis method, the Naive Bayes algorithm, is used to extract the sentiment index from the content of each online review and integrate it into the imitation coefficient of the Bass/Norton model to improve the forecasting accuracy. We collected real-world automotive industry data and related online reviews. The computational results indicate that the combination of the Bass/Norton model and sentiment analysis has higher forecasting accuracy than the standard Bass/Norton model and some other sales forecasting models.     

Online service failure and propensity to suspend offline consumption

Many retailers have expanded their businesses by adding Internet sales channels. There are many advantages of such multi-channel business operation, however, these may be offset by an overlooked negative consequence of cross-channel shopper activity – poor service online may lead customers to suspend consumption in a company's offline channels. Support is found for this proposition, and an investigation into the influence of purchaser characteristics and purchase criticality on propensity to engage in such behavior is conducted. The study makes contributions to understanding cross-channel customer behavior and developing implications for future research as well as management practice.     

Frequent Shopper Programs

During the past three years, frequen~ shopper pro- grams have proliferated among retail grocery chains. The present paper defines these programs, describes variations of them, and dis- cusses their operation. The paper suggests directions these programs may take in the future and raises a number of issues associated with their design and implementation.     

An Empirical Analysis of the Impact of Promotional Discounts on Store Performance

The received wisdom, reflected in popular marketing textbooks, is that featuring deeply discounted items will generate additional store traffic for retailers that in turn will lead to increased sales and profits. However, there is surprisingly little systematic evidence about the impact of these deep discounts on aggregate store traffic, sales, and profits. In this paper, we study the effects of promotional discounts and their characteristics on various store performance metrics employing a store level dataset pooled over 55 weeks and 24 stores. Many findings of our study lend credence to the continued popularity of such promotions by retailers. We find that feature promotions build store traffic, especially when the categories being featured are high penetration, high frequency. Also, promotions of branded items are found to be more effective than promotions of unbranded items. Discounting on more items in a category leads to lower store margins suggesting that the cost of discounting a large proportion of items in a category may not be justified by the profits generated by the sale. Using the coefficients from our model estimates, various counterfactuals provide insights into strategic change in level of discounts across categories. We discuss several implications of our findings for retailers.     

Learning to export from neighbors

This paper studies how learning from neighboring firms affects new exporters' performance. We develop a statistical decision model in which a firm updates its prior belief about demand in a foreign market based on several factors, including the number of neighbors currently selling there, the level and heterogeneity of their export sales, and the firm's own prior knowledge about the market. A positive signal about demand inferred from neighbors' export performance raises the firm's probability of entry and initial sales in the market but, conditional on survival, lowers its post-entry growth. These learning effects are stronger when there are more neighbors to learn from or when the firm is less familiar with the market. We find supporting evidence for the main predictions of the model from transaction-level data for all Chinese exporters over the 2000-2006 period. Our findings are robust to controlling for firms' supply shocks, countries' demand shocks, and city-country fixed effects.     

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.     

Managing advertising investments in marketing channels

This paper investigates how should manufacturers optimally allocate resources to retailer-initiated (retailer) advertising through cooperative advertising programs and own (manufacturer) advertising in a bilateral monopoly. Retailer advertising stimulates immediate sales but may also harm long-term (post-advertising) demand, whereas manufacturer advertising aims at building brand equity and stimulates both immediate and long-term sales. A game-theoretic model in which a manufacturer and a retailer set pricing and advertising decisions over a two-period planning horizon is developed to account for the differences between manufacturer and retailer advertising. We characterize equilibrium solutions for four advertising scenarios for the manufacturer, ranging from no investment in any advertising activity to undertaking own advertising and supporting retailer advertising simultaneously. Comparing the two players’ equilibrium strategies and profits across these scenarios, we find that manufacturers should avoid offering exclusively cooperative advertising programs to retailers. When retailer advertising positively influences long-term sales, manufacturers should offer cooperative advertising supports to retailers in addition to undertaking their own advertising. When retailer advertising negatively affects long-term sales, manufacturers can still undertake own advertising and offer cooperative advertising under certain conditions. However, if these conditions are not met, focusing exclusively on own advertising is their best advertising strategy. Retailers also prefer scenarios in which manufacturers advertise, but may choose not to participate in manufacturers’ cooperative advertising programs. This leads to suboptimal outcomes if cooperative advertising programs are not enhanced by additional incentives (e.g., side payments or other services).     

The Impact of Advertising on Consumer Demand for Beef:

Thi study used two related data sets to obtain new estimates of food shopper responses to prices and advertising. Supermarket scan data comprised the source of information of sales and prices. Chain level newspaper and broadcast media advertising in the area measured the marketing program. Unique features of the study include the use of item movement and the accomodation of possible cross media effects. Consequently, the paper presents a way of monitoring promotions and relating them to sales. Three fresh beef aggregates (ground, roasts, and steaks) are used to estimate the impacts of broadcast media and newspaper adevertising by a supermarket chain. New estimates of direct and cross advertising impacts are also reported.     

shopper taxonomy for retail strategy development

Segmentation of shoppers has been explored by many academic researchers and business practitioners seeking to understand shopping behaviour or to develop marketing strategies for particular customer groups. Market segmentation holds the key to successful marketing strategy as it encourages understanding of the key variables that differentiate specific segments.

The shopper taxonomy determined through this study is based on a set of variables that is relevant and appropriate for shopper segmentation and reflects the key aspects important to shoppers in motivating shopping behaviour towards a specific retail location. This taxonomy extends the proposed motivational taxonomy of Westbrook and Black (1985), derived from Tauber's (1972) earlier research. Westbrook and Black defined this taxonomy through shopping motives, and identified categories of product-oriented, experiential and a combination of product and experiential shoppers. Their research findings, however, pointed to a six-cluster typology, defining department stone shopping around seven motivations aligned with evaluating options and acquiring the products, engaging in the sales process and gaining stimulation and affiliation through the retail environment.

The “apathetic”, “shopping-processed involved” and “choice optimisation” shopper segments described by Westbrook and Black align with the “have to” “experiential” and “practical” “segments found in this study in terms of their focus on the shopping activity itself and the affiliation and stimulation motives associated with the shopping activity. Other associations between shopper segments across the two studies are less clear, and may be explained through the differing purposes for which the studies were undertaken and resulting variation in the measures used to define the motivational constructs.