Survival in today's economic climate and competitive retail environment requires more than just low prices and innovative products. To compete effectively, businesses must focus on the customer's shopping experience. To manage a customer's experience, retailers should understand what "customer experience" actually means. Customer experience includes every point of contact at which the customer interacts with the business, product, or service. Customer experience management represents a business strategy designed to manage the customer experience. It represents a strategy that results in a win–win value exchange between the retailer and its customers. This paper focuses on the role of macro factors in the retail environment and how they can shape customer experiences and behaviors. Several ways (e.g., promotion, price, merchandise, supply chain and location) to deliver a superior customer experience are identified which should result in higher customer satisfaction, more frequent shopping visits, larger wallet shares, and higher profits.

There is an ever-present need for managers to justify marketing expenditures to the firm. This can only be done when we can establish a direct link between marketing metrics and future customer value and firm performance. In this article, we assess the marketing literature with regard to marketing metrics. Subsequently, we develop a framework that identifies key metrics that firms should focus on that can give a firm a better picture of how they got to where they are now and insights towards how they can continue to grow into the future.We then identify several organizational challenges that need to be addressed in order for firms to build the capabilities of collecting the right data, measuring the right metrics, and linking those metrics to customer value and firm performance. Finally, we offer guidelines for future research with regard to marketing metrics to help firms establish successful marketing strategies, measure marketing effectiveness, and justify marketing expenditures to top management.

The phenomenon of cross-buying by consumers enables retailers to cross-sell their products and increase revenue contribution from existing customers. The effectiveness of cross-selling can be greatly improved by identifying the drivers of cross-buy and using them to target the right customers. In this study we identify exchange characteristics such as average interpurchase time, ratio of product returns, and focused buying, and product characteristics such as category of first purchase, as important drivers of cross-buy. The impact of marketing efforts of the firm on cross-buy is also identified. The results of the study have important implications for academicians in understanding what drives cross-buying as well as practitioners to help design more effective cross-selling strategies.

This study examines how customer lifetime value (CLV) can be computed at individual customer level in a retail setting to maximize profitability. The study finds that maximum positive impact to CLV occurs when the customer cross-purchases, shows multi-channel shopping behavior, stays longer with the firm, buys specific product categories and purchases more frequently with the firm. Interestingly, the CLV follows an inverted U relationship with increase in return of prior purchases. Other interesting findings include a surprisingly low correlation between customer loyalty and future profitability and low correlation between stores' historic revenues and future profitability. Several implications are suggested for retailers to manage and maximize customer profitability as well as store profitability.

Coupons account for over two-thirds of all consumer promotional efforts initiated by the manufacturers of consumer goods. In this study, the impact of coupons on brand sales is investigated and how that impact decays over the life of the coupon is demonstrated. Specifically, we present an econometric model that can capture coupon effects in terms of equivalent price reduction, account for coupon effects over time, allow inference of coupon effects when retailers decide to double or triple the coupon value, and provide both self-coupon and cross-coupon elasticities at different levels of aggregation. A widely used sales response model is adapted, and an analytical model is proposed to estimate both the self-coupon and cross-coupon (face value) elasticities of sales at the store level. From the store-level elasticity estimates for a given week, the authors analytically derive the coupon elasticities for the chain level by aggregating across stores, and over the life of the coupon by aggregating over time. The proposed sales response model is estimated with the data obtained from three markets for various product categories, and the coupon elasticities are computed. The proposed framework allows one to demonstrate the hypothetical equivalence of a shelf-price reduction for a given coupon face value in each week. Also, the effect of doubling the face value of a coupon results in more than a proportionate increase in elasticity. The authors find that both self and cross-coupon elasticities are much smaller in magnitude than the average self and cross-price elasticity measures reported in the literature.

The concept of customer loyalty is conspicuous by its ubiquity. Therefore, there is no surprise that is it one of the most widely studies areas by researchers and one of the most widely implemented marketing initiatives by practitioners. This article draws upon past research to review important findings related to customer behavior and attitude in the context of customer loyalty. Further, research related to linking loyalty to profitability and forward looking metric such as the customer lifetime value is reviewed to propose a conceptual framework for building and sustaining loyalty and profitability simultaneously at individual customer level. A two-tiered rewards structure is presented as a means for marketers to operationalize the framework. The conceptual framework hopes to serve as a platform to understand the evolving dominant logic of loyalty programs for building and sustaining loyalty in the twenty first century as well as induce further research in that direction.

This article shows that in a model of exogenously given sales expansion target with a dominant manufacturer, the relative profitability of coupon and price-reduction schemes depends on the values of coupon redemption rates and the equivalence ratio of price reduction to coupon face value. It is also shown that retailer’s margin has an uncertain impact on the compensation constraint on the manufacturer’s choice between the two schemes. However, higher retailer’s margin increases the likelihood that the chosen sales expansion scheme (SES) is the one that generates higher consumer welfare. A sales response model is used to estimate the equivalence ratio and critical coupon redemption rate below which the manufacturer will prefer a coupon promotion. A sensitivity analysis of the manufacturer’s decision reveals that changes in the magnitude of the retailer’s margin have little impact on the manufacturer’s choice between alternative SESs.

How many times have you ignored a banner ad or closed a pop-up window with a vengeance? For most Web users, the answer is "too many." The genesis of this problem lies in a poor marketing strategy that marketers adopt when advertising online. This article describes a framework for developing an effective online advertising strategy, followed by information on how to track the performance of online advertisements.

Retail stores are segmented using socio-economic characteristics of the trade area and it is shown that the effects of store environment on store performance vary across segments. Store performance is measured by a market-based measure – sales and a productivity-based measure – sales per square feet. The internal store environment includes the number of checkout counters per square foot of selling area, the number of non-grocery products sold (extent of scrambled merchandising), whether the store at least doubles manufacturers’ coupons, whether there is a banking facility and whether the store is open for twenty-four hours. The external store environment includes the type of neighborhood it is located in. A methodology for predicting store performance (for existing and new stores) based on the type of environment and store location using aggregate secondary data is demonstrated. The proposed models are estimated and validated using Market Metrics geodemographic data for 646 grocery stores provided by A.C. Nielsen. It is shown how the findings of this retail environment study can be used to offer guidelines to retailers for attaining desired levels of sales and sales per square feet using readily available data.

The authors develop a framework that incorporates projected profitability of customer in the computation of lifetime duration. Furthermore, the authors identify factors under a manager's control that explain the variation in the profitable lifetime duration. They also compare other frameworks with the traditional methods such as the recency, frequency, and monetary value and illustrate the superiority of the proposed framework. Finally, the authors develop several key implications that can be of value to decision makers in managing customer relationships.

The impact of internal reference price discrepancy (between actual price and internal reference price), and external reference price discrepancy (between actual price and external reference price) on brand choice is studied in two different contexts: whether the consumer faces a stockout condition at the time of purchase or not, and whether the consumer is a deal-prone consumer or not. Internal reference price based on the past prices paid for the brand by the consumer, and external reference price is dependent upon the prices of all brands in the category at the point of purchase. Hypotheses are tested by estimating brand choice models using IRI scanner panel data for two product categories-saltine crackers and baking chips. Results indicate that, in general, the impact of the external reference price discrepancy on brand choice is greater than that of the internal price discrepancy. However, this varies depending upon the contextual conditions. For consumers facing a stockout condition at the same of purchase, and for deal-prone consumers, the impact of the external reference price discrepancy on brand choice is greater than that of the internal reference price discrepancy. However, for consumers not facing a stockout condition, and for non-deal-prone consumers, there is no difference in the impact of the internal and external reference price discrepancy on brand choice. Also, the impact of both the external and internal reference price discrepancy on brand choice is greater for consumers not facing a stockout condition at the time of purchase relative to facing a stockout condition, and for deal-prone and non-deal-prone consumers. Implication for pricing strategies are discusses for retailers, and limitations and extensions of this study are also provided.

The authors focus on their 1990 model (B&K; see Balasubramanian and Kumar 1990), which indicates that market share, market growth, and their interaction are important predictors of the ratio of advertising and promotional costs to sales (A&P/S). In sharp contrast, Ailawadi, Farris, and Parry's (1994; AFP) subsequent replication attempt asserts that market share and market growth are not good predictors of A&P/S. The authors' research documents serious problems in AFP's study, including erroneous model estimates caused by incorrect execution of the SAS TSCSREG procedure, pooling data for analysis in ways that cannot be justified statistically, data preparation errors, and inappropriate operationalizations of market share and market growth. With regard to analyses of Profit Impact of Market Strategy and brand-level data reported by AFP, the authors again find that AFP have analyzed data in inappropriate ways, given the conceptual framework of the B&K model. Because these problems provide compelling evidence to disregard AFP's results, the authors conclude that the criticisms advanced by AFP against the B&K model are not valid.

This research hypothesizes and tests the following propositions: (1) competitive performance of a brand varies across retails chains, driven by differences in retailers' promotion decisions for the brand, and (2) type, timing, frequency, and magnitude of retail promotions affects brands' competitive performance. Further, we illustrate the presence of asymmetry in brand competition and test the expectation that differences in market shares and price levels are major factors contributing to competitive asymmetry. A model of brand competition is formulated at the retail level. The results indicate that the competitive performances of brands are not uniform across pairs of brands and the retails chains in a market.

The variation in the impact of a brand's retail price promotion on its own sales (promotional price elasticity) and the sales of its competitors (promotional cross-price elasticities) during the period of the price promotion is studied. Brand characteristics and retailer policies significantly explain the variation in promotional price elsaticities and cross-price elasticities across brands. Results are used to offer guidelines to retail managers for planning promotions in terms of what brand to promote, and how and when to promote them under three different retailer objectives: (1) to liquidate inventories; (2) to maximize product category sales; and (3) to maximize product category profits. These guidelines can be used by retailers in taking actions that will result in desirable levels of responses to price promotions of a brand-promotional price elasticity and promotional cross-price elasticities.

This study focuses on the short-term sales response to price promotions in retail grocery stores and attempts to explain its variation using frequency of price promotions and the consecutive scheduling of price promotions. Retail managers' expectations and tenets from behavioral theories provide the basis for the hypotheses that the frequency of price promotions and consecutive scheduling of price promotions affect short-term response to price promotions. The hypotheses are tested on three frequently purchased product categories, using store-level data from retail chains in three major markets. The analysis is validated with additional data on the same product categories and markets. A variety of managerial implications are drawn from the results and suggestions for future research are offered.

The research provides an assessment of the relevant literature on market share models and identifies the need for further research. Additional insights are generated in this study by using point-of -sale scanners for evaluating the forecasting performance of market share models under various conditions. Specifically, weekly store-level scanner data for four frequently purchased product categories-saltine crackers, baking chips, diapers and toilet tissue, and simulated data are used in this study. Consistent with theoretical expectations, the attraction models estimated by GLS produce the best forecasts even (1) at the brand level, and (2) when competitors' actions are predicted. However, the superiority of the attraction models is diminished when systematic errors are introduced to the values of the competitors' predictor variables in the holdout sample. In fact, naïve models outperform all types of econometric models when large errors are present in the competitors' predictor values, and among the econometric models, linear models produce better forecasts than attraction models. The need for estimating the models with GLS (as opposed to OLS) with the use of cross-sectional time-series data is also illustrated. Finally, guidelines are developed for practitioners and researchers on the usefulness of market share models for forecasting.

The authors decompose repeat buying for frequently purchased nondurables. There results are very similar for two categories each over a different city and time period. A factor analysis of 18 measures of repeat buying obtains four principal factors that explain 79-85% of the variance: Preference, Inertia, Coupon Proneness and Impulse Buying. A cluster analysis of factors on these dimensions yields four segments, with distinct behavioral characteristics.

Merger and Acquisition (M&A) activity in retailing is widespread. Nevertheless, little scholarly research has addressed this subject from the perspective of antecedent conditions present in retail M&A activity. The present study examines a variety of finance-, marketing-, and corporate-related variables that have been proposed as likely antecedent conditions for M&A activity. The results indicate that preconditions for M&A activity can be identified and the probability of becoming a bidder and target retailer can be determined on the bias of these variables. Implications of becoming a target firm are highlighted, as are characteristics of bidder and nonmerging firms.

Prior research assessing the predictive validity of alternate market share models produced conflicting results and often found that econometric models performed worse than naïve extrapolations. However, contributors to IJF's recent issue on market share models suggested that such models are often misspecified, in part because they exclude promotional variables and are estimated on aggregate data. Thus, we used weekly scanner data to assess full, reduced, and naïve form for linear, multiplicative, and attraction specifications across different levels of parameterization. Consistent with specification-based arguments (1) econometric models were superior to naïve models, (2) GLS estimates of attraction models were superior when models were fully specified, (3) OLS estimates of linear models were superior when models omitted important variables, and (4) attraction models predicted best overall. Moreover, in general, unconstrained models yielded superior forecasts relative to constrained models because brand-specific parameters were heterogeneous for the product category tested.

The authors extend prior research on explaining variations in the intensity of marketing communication (i.e., advertising and promotional expenditures/sales) with a simple model estimated on cross-sectional time-series data. Analysis on firm-level data pooled at the industry level, performed for each of several industries belonging to different markets, attest to the model’s explanatory power. Model validation analyses show an impressive similarity of model parameters and goodness of fit across split-half data samples for each industry analyzed. Further, for all industries belonging to a given market, the results indicate remarkable consistency in the signs of the associations between marketing communication intensity and (1) market share, (2) market growth, and (3) their interaction. However, this consistency does not hold across all markets. Plausible explanations for these results are advanced.

Advertising and promotional expenditures in the United States grew from $47.1 billion in 1975 to $ 159.3 billion in 1985 (Bowman, 1986), a 338 percent increase over a decade. Of this increase, promotional expenditures have grown rapidly, exceeding advertising expenditures by over $30 billion in 1987. As increasing dollars are spent on advertising and promotion, advertisers are interested in knowing whether consumers' purchase decisions are influenced by these variables, and more importantly, to what extent these variables differentially affect the consumers. In other words, do different people in the market react differently to advertising and/or promotion? If so, are there groups of people who exhibit similar behavior patterns? This is an example of a market segmentation problem which is important to advertisers. This article proposes a visual approach to searching for market segments.

Using store-level scanner data, the authors investigate the effect of retail store price promotion, featuring, and displays on sales of brands of disposable diapers within a city. A hierarchical, cross-sectional, and time-series modeling procedure is used to identify the competitive structure among retail stores within a test market city. Models are developed for pooled store pairs to investigate the effect of promotion on store substitution. The specification of these models is based on findings from within-store brand substitution, followed by featuring and displays. Similarly, these activities produced store substitution in certain instances. However which specific promotional variables are significant and the magnitude of the effect are a function of the geographic proximity of the stores.