Evaluating channel performance in multi-channel environments

Evaluating channel performance is crucial for actively managing multiple sales channels, and requires understanding the customers’ channel preferences. Two key components of channel performance are (i) the existing customers’ intrinsic loyalty to a particular channel and (ii) the channel's ability to attract switching customers. We apply the Colombo and Morrison (Colombo, R., Morrison, D., 1989. A brand switching model with implications for marketing strategies. Marketing Science 8, 89–99) model to assess channel performance along these dimensions. Using data from a large home-shopping company, we analyze the evolution in the performance of its main channels over time, and test for differences in channel performance among different product categories offered by the company, as well as between different customer segments. Based on the results, we derive implications for managers to operate a company's multiple sales channels more effectively.     

Investigating the dynamics of service attributes in multi-channel environments

Previous research has shown that customer evaluations of attributes in existing product and service categories are dynamic. In this study, we use a response surface model based on a conjoint analysis of service profiles in the context of mobile phone service to examine the dynamics of multi-channel service attributes and their influence on customers’ intention to renew a service agreement. Our results provide insights for the design of a service that may not be what customers have experienced in the past and demonstrate the qualities of a set of multi-channel service attributes that have not been included in previous studies.     

Identifying multi-channel value co-creator groups in the banking industry

The purpose of this paper is providing a value co-creation management framework in the banking industry using the data analysis. Moreover, a multi-channel segmentation approach will be developed in order to identify customer segments based on the use of each channel. Managing value co-creation can be defined as determining the channel that may be used by the customer and the kinds of encounters that lie in these channels to have different impacts on the customer for various types of encounters. The model is built on the basis of the related literature and the collaboration of a Customer Relationship Manager in a large bank in Iran. Then, a multi-channel segmentation model is developed based on the RFM variables of the five banking channels for each customer. Next, about 11,000 customers of the bank is segmented by comparing the k-means and DBSCAN algorithms. Finally, by adapting customer segments on the value co-creation framework, three general groups are identified based on the type of encounter that lies in each channel. Moreover, twenty-seven small groups are recognized based on the desirability of the customer use of the channels.     

Information Search and Product Returns Across Mobile and Traditional Online Channels

Product returns will soon cost firms a trillion dollars annually; e-commerce appears more at risk for these costs relative to offline channels. Retailers thus need strategic insights into which factors increase customers’ return propensity and when the short-term costs of returns might be offset by future customer purchases. This article reports two studies that use transaction data from two large apparel e-tailers. Study 1 demonstrates that due to the differences between mobile and traditional online channels, consumers form a larger consideration set when they shop through mobile channels, and this distinctive search practice spills over onto return rates. That is, return rates are lower in mobile channels, and the use of the mobile channel also alters the effect of discount promotions. The findings in Study 2 suggest that future purchases by consumers, after a return, are contingent on category characteristics. In categories in which customers can easily learn from their previous return experience, product returns positively increase future spending, but the opposite is true in categories in which it is difficult to learn.     

Does Online Information Drive Offline Revenues?: Only for Specific Products and Consumer Segments!

While many offline retailers have developed informational websites that offer information on products and prices, the key question for such informational websites is whether they can increase revenues via web-to-store shopping. The current paper draws on the information search literature to specify and test hypotheses regarding the offline revenue impact of adding an informational website. Explicitly considering marketing efforts, a latent class model distinguishes consumer segments with different short-term revenue effects, while a Vector Autoregressive model on these segments reveals different long-term marketing response.We find that the offline revenue impact of the informational website critically depends on the product category and customer segment. The lower online search costs are especially beneficial for sensory products and for customers distant from the store. Moreover, offline revenues increase most for customers with high web visit frequency. We find that customers in some segments buy more and more expensive products, suggesting that online search and offline purchases are complements. In contrast, customers in a particular segment reduce their shopping trips, suggesting their online activities partially substitute for experiential shopping in the physical store. Hence, offline retailers should use specific online activities to target specific product categories and customer segments.     

The influence of multi-channel pricing strategy on price fairness and customer confusion

ABSTRACT

Multi-channel retailers face the challenge of coordinating marketing variables across their channels. In this respect, one of the main issues arising is whether to differentiate or integrate prices. Our study examines the impact of three multi-channel price differentiation instruments on perceived price fairness, customer confusion, and their consequences. In a scenario-based online experiment, we use a 2 x 2 × 2 between-subjects design and manipulate product price differentiation, online promotion and online shipping fees. The results indicate that price differentiation has an impact on fairness evaluations and customer confusion. Product price differentiation and online promotion are perceived as more unfair and lead to more confusion than price parity. Price fairness perceptions of shipping fees depend on product price differentiation. Customers perceive shipping fees as fairer than no shipping fees when prices are cheaper online but perceive shipping fees as less fair when prices are integrated. These results suggest that customers expect a consistent consideration of channel cost advantages and disadvantages and that shipping fees might serve as a cue for customers to consider the retailer’s channel costs. We further show that price fairness and customer confusion mediate effects of pricing instruments (in particular online promotion) on attitudinal and behavioral consequences.     

Designing an emotional strategy: Strengthening digital channel engagements

The emergence of new technologies has revolutionized the way companies interact and build relationships with customers. The channel–customer relationship has traditionally been managed via a push approach in communication (“What can we sell customers?”) with the hope of cultivating customer loyalty. However, emotional understandings of customers and how they feel about a product, service, or business can drastically alter consumers’ engagement, behavior, and purchasing preferences. This rapidly evolving landscape has left managers at a loss, and what they are experiencing is likely the beginning of a tectonic shift in the way digital channels are designed, monitored, and managed. In this article, digital channel relationships are examined, and useful concepts for clarifying and refining the emotional meaning behind company strategy and their relationship to corresponding digital channels are detailed. Using three case study examples, we discuss the process and impact of such emotionally aware digital channel designs. Recommendations are made regarding how companies can select, design, and maintain digital engagements based on their strategy and industry needs.     

The promise and peril of dynamic targeted pricing

Dynamic pricing is widely adopted in many industries, such as travel and insurance. These industries are also gaining extensive capabilities in identifying and segmenting customers, partly fueled by the increasing availability of data. It is natural to ask whether firms should take advantage of such developments by charging different prices to different customer segments. If so, under what conditions? We seek answers to these highly managerially relevant questions.We consider a market with two customer segments served by a monopolist. The monopolist can choose among a set of pricing strategies to exploit consumers’ inter-temporal preferences and/or inter-segment variations. At one end of the spectrum, the firm can charge a constant price to all customers, which is called static pricing. At the other end of the spectrum, the firm can charge different prices to different customer segments and vary these prices over time, which is referred to as dynamic targeted pricing. We systematically compare these alternative pricing strategies. We show that dynamic pricing without targeting can be more effective than static targeted pricing when customers are not very forward looking, which corroborates the findings in the empirical literature. Interestingly, we find that the monopolist can be worse off when she adopts targeting in addition to dynamic pricing. We conduct laboratory experiments to test several key model predictions. The studies show that individuals behave in a manner consistent with the predictions of our model.     

Loyalty Formation for Different Customer Journey Segments

The proliferation of new touchpoints empowers today’s customers to design their own journey from search to purchase. To address this new complexity, we segment customers by their use of specific touchpoints in the customer journey, investigate the association of several covariates with segment membership, consider the rise of mobile devices as potential “game changers” of existing segments, and explore how the relationships among product satisfaction, journey satisfaction, customer inspiration, and customer loyalty differ across segments. Based on anticipated utility theory and using latent class analyses on large-scale data from two samples of 2,443 and 2,649 journeys, we identify five time-consistent segments―store-focused shoppers, pragmatic online shoppers, extensive online shoppers, multiple touchpoint shoppers, and online-to-offline shoppers―that differ considerably in their touchpoint and mobile device usage, their segment-specific covariates, and their search and purchase patterns. The five segments remain unchanged in the two data sets even though the usage of mobile devices has increased substantially. Furthermore, we find that the relationships between various loyalty antecedents and customer loyalty differ between the segments. The insights from this paper help retailers develop segment-specific customer journey strategies.     

A multi-category customer base analysis

Customer base analysis is an essential tool to measure and develop relationships with customers. While various models have been proposed in a noncontractual setting, they focus primarily on analyzing transactional patterns associated with a single product category or a firm-level activity, such as the times at which purchases are made at a particular retailer. This research proposes a modeling framework for customer base analysis in a multi-category context. Specifically, we model the time between a customer's purchases at the firm and the product categories that comprise her shopping basket arising from multi-category choice decisions. The proposed model uses a latent space approach that parsimoniously captures the dynamics of multi-category shopping behavior due to the interplay between purchase timing and shopping basket composition. We also account for interdependence among multiple categories, temporal dependence across category choices, and latent customer attrition. Using category-level transaction data, we show that the proposed model offers excellent fit and performance in predicting customer purchase patterns across multiple categories. The forecasts and inferences afforded by our model can assist managers in tailoring marketing efforts across categories.     

How does customer recognition affect service provision?

We examine how channel members’ ability to recognize repeat and new customers affects service provision, profits, and welfare. In decentralized channels, when only retailers can recognize customers, customer recognition increases service levels. However, in centralized channels or decentralized channels when both manufacturers and retailers can recognize customers, customer recognition reduces (increases) service levels if service investment persists (diminishes) sufficiently over time. Moreover, in centralized channels, customer recognition reduces firm profits and consumer surplus, whereas in decentralized channels, when manufacturers and retailers can recognize customers, customer recognition increases channel members’ profits but decreases consumer surplus.     

Cross-channel integration – is it valued by customers?

It has been common for retail companies to use multiple channels simultaneously. However, simultaneous use is only the first step in creating a customer-centric multichannel system that demands channel synergies rather than parallel retail formats. Therefore, the perceived integration of customer-related functions and processes between the channels of multichannel systems is analyzed with respect to its significance for customer loyalty and usage of a multichannel system. Drawing on a sample (n) of 981 customers, the results indicate that linkages between retail channels positively affect customer loyalty and verify the importance of establishing a well-integrated – ‘seamless’ as perceived by the customer – multichannel system.     

Unlocking the transformative potential of customer data in retailing

Retailers collect, process, and use large amounts of data about customers: what they buy, how they buy, when they buy – and when customer loyalty cards are used – who buys the product or service. The use of customer data within the customer relationship management (CRM) framework has largely remained one sided. The potential of customer data for the benefit of the customer’s well-being still needs to be addressed. Reverse use of customer data, i.e. the process of firms converting customer data into information that is meaningful for the customers, extends attention toward using customer data for the benefit of the customer. In addition, selling goods to customers retailers can revise customer data into relevant and meaningful information that can support their well-being. Consequently, the purpose of this study is to identify and illustrate the transformative potential of the reverse use of customer data in retailing. This is achieved by reviewing literature on CRM, S-D logic, and transformative consumer research, and conducting a case study of a food retailer using point-of-sale data to provide customers with information about the healthfulness of their food purchases. Anonymous customer feedback data is collected and analyzed, and as a result, the transformative potential of customer data is reflected through five themes: self-monitoring, enhanced diet, food literacy, peace of mind, and beyond food healthfulness. These themes illustrate customers’ well-being outcomes resulting from reverse use of customer data.     

Cross-selling across stores or within a store? Impacts of cross-buying behavior in online shopping malls

Abstract

This study investigates the impacts of cross-buying behavior in an online shopping mall where customers can purchase from additional product categories from various independent stores. We focus on the extent to which store loyalty and relationship duration moderate the cross-buying effects on three behavioral traits: customers’ purchase rate, lifetime duration, and spending. The results reveal that customers who engage in cross-buying more intensely purchase more frequently, have longer expected lifetime duration, and spend higher amounts in each transaction. The impacts on purchase frequency and customer retention are even greater for customers who exhibit higher behavioral loyalty toward some stores. However, store loyalty is found to weaken the association between cross-buying and average spending. Further, relationship duration appears to weaken the effects of cross-buying on purchase rate and spending. These results provide new insights into the impacts of cross-buying on customer value as well as managerial implications for shopping mall owners.     

The impact of risk perceptions on the attitude toward multi-channel technologies

ABSTRACT

Several studies show that retailers experience favorable outcomes if consumers use multiple channels. Thus, retailers aim to encourage consumers to do so in a convenient way by offering multi-channel technologies (MCTs). However, not much is known about what affects a positive attitude toward such technologies. Our study investigates how different risk perceptions influence the attitude toward specific MCTs in comparison (check and reserve, click and collect, availability check). Moreover, we examine whether the customer’s general purchase channel preference moderates these effects. Results show that three risk dimensions have significant impacts on the attitude toward MCTs. These effects are generally worst for click and collect, as the negative effects (product quality and data risk) are highest, while the positive effect (product availability risk) is lowest, compared to the other two MCTs. Additionally, we generally find that the more a customer prefers buying via the online channel, the weaker the effects of the risk dimensions on the attitude are. Our findings provide implications on how to improve consumers’ attitudes toward the selected MCTs by influencing their perceptions of different risk dimensions.     

The Role of Customer Factors in Multiple Store Patronage: A Cost-Benefit Approach

This paper is concerned with the use of multiple stores by supermarket customers. It relates the number of stores patronized to a set of customer factors under a unifying theoretical framework emphasizing cost-benefit analysis. Respective hypotheses are tested in a large random sample. This study is a first attempt to empirically address the structure of multiple store patronage. It is demonstrated that multiple store patronage is affected by variables such as customer income, satisfaction, and expenditure that are suggestive of heterogeneous cost-benefit tradeoffs and opportunity costs of time. It is shown that customers are intrinsically different in the predisposition to being loyal. In this respect, store patronage is a continuum between single store loyalty and use of several different stores, on which customers vary depending on individual preferences. The empirical analysis also suggests that exclusive patronage of the favorite store arises from two observationally equivalent latent segments that differ in their inclination to remain loyal. The results yield valuable insights into the structure of store patronage and lead to important implications for retail managers. Several extensions are considered. A broad set of research questions surrounding store patronage can be considered from a cost-benefit viewpoint in the sense that consumer decisions in this area involve trading off economic resources against assortment, spatial and temporal benefits.     

Modelling and Investigating the relationship of after sales service quality with customer satisfaction,retention and loyalty – A case study of home appliances business

After sales services (ASS) are activities that take place after the purchase of the product by customers and are devoted to supporting customers in the use and disposal of goods. ASS can create sustainable relationships with customers and contribute significantly to customer satisfaction. The purpose of this study is to evaluate the ASS quality by measuring the level of customer satisfaction (CS), customer retention (CR) and customer loyalty (CL) through the SERVQUAL dimensions comprising of ASS attributes and also to check which such ASS-based dimensions of SERVQUAL needs to be focused more on improving the quality of ASS with the consideration of firms involved in manufacturing the home appliances. For this purpose research models were proposed to examine the influence of ASS attributes on CS, CR and CL for three different products such as Gas Stove, Water Purifier and Mixer Grinder from the home appliances sector and tested by multiple regression analyses on data collected through the structured survey questionnaire, with a five-point Likert scale. The study has demonstrated the application of multiple regression analysis in studying the influence of ASS attributes on CS, CR and CL, and the results of the study have helped in analysing the performance of the case companies so as to devise suitable strategies in improving CS, CR and CL.     

Managing your most loyal customer relationships

Conventional wisdom regarding customer relationships suggests that a company should strive to deepen the loyalty of its customer base. While multiple approaches have been suggested, each approach advocates moving a subset of the customer base from one level of affinity (e.g., neither satisfied nor dissatisfied) to a higher one (e.g., satisfied). While seemingly appropriate, this approach assumes that moving customers up to higher categories is important and should be the focus of a firm’s efforts. Instead, we recommend an approach that involves focusing a firm’s resources disproportionately on its most satisfied customers. This approach provides two major benefits relative to conventional approaches. First, it focuses a firm’s resources on a narrow segment of customers. Hence, it requires significantly less financial outlay and associated financial risk than any approach that is aimed at all or even a majority of customers. Second, as we demonstrate, the financial benefit from leveraging high satisfaction levels among a subset of the current customer base significantly exceeds the financial benefit of other strategies (e.g., moving customers up from neutral to satisfied). We present the results from two case studies that illustrate our main points and provide useful examples of how to leverage a firm’s highly satisfied customers.     

Linking multi-channel customer behavior with shopping motives: An empirical investigation of a German retailer

Operating multiple channels, in store as well as non-store, allows a retailer to cater to the different shopping motives of its customers. This research into the buying behavior of 525 customers of a multi-channel retailer that operates five channels in Germany shows that most customers use only one channel within a buying process, selecting the channel that best satisfies their shopping motives in each situation. Based on exploratory qualitative and quantitative research, this study identifies five shopping motives: “recreational orientation”, “convenience orientation”, “independence orientation”, “delivery-related risk aversion” and “product- and payment-related risk aversion”. In the majority of the store channels examined in this study (chain stores and bakeries), single-channel users primarily look to satisfy emotional and social needs. Single-channel users of non-store channels (catalogue and online-shop) look for convenience and strive for independence. Multi-channel users, who obtain their information from the online-shop and then make their purchase in the chain store, are combining the independence of online-shop information with the reduction of risks associated with buying products in the chain store. These findings can be used to develop recommendations for managing the individual channels.     

Toward a contingency theory of CRM adoption

Acknowledging the paucity of research on customers who are the targets of customer relationship management (CRM) efforts, this paper presents a contingency theory framework and set of research propositions suggesting that successful CRM adoption is contingent on a variety of contextual factors that influence value exchange in B2B channel relationships. Grounded in theoretical perspectives on the customer selection process, channel governance, transaction costs, power distribution in marketing channels, and dynamic capabilities, the authors identify five broad categories of variables potentially important in studying CRM adoption and directly affecting CRM success/failure.