CRM has traditionally referred to a company managing relationships with customers. The rise of social media, which has connected and empowered customers, challenges this fundamental raison d'etre. ...This paper examines how CRM needs to adapt to the rise of social media. The convergence of social media and CRM creates pitfalls and opportunities, which are explored. We organize this discussion around the new “social CRM house,” and discuss how social media engagement affects the house's core areas (i.e., acquisition, retention, and termination) and supporting business areas (i.e., people, IT, performance evaluation, metrics and overall marketing strategy). Pitfalls discussed include the organization's lack of control over message diffusion, big and unstructured data sets, privacy, data security, the shortage of qualified manpower, measuring the ROI of social media marketing initiatives, strategies for managing employees, integrating customer touch points, and content marketing.
•We examined changes in order sizes and rates as customers become mobile shoppers.•After customers become mobile shoppers (M-shoppers), their order rates go up.•For low-spenders, both order sizes and ...rates increase after adopting M-shopping.•Customers tend to M-shop for habitual products that they have purchased before.•Retailers should invest in M-storefronts, especially to promote habitual products.
Mobile shopping (M-shopping) has become increasingly important in marketing and retailing. Using a unique dataset from an Internet-based grocery retailer, we evaluate changes in customers’ spending behavior upon adopting M-shopping, i.e., using smartphones or tablets to compose, modify, or place orders online. We find that order rate, i.e., number of orders placed per year, increases as customers adopt M-shopping. Especially for low-spending customers, both their order rate and order size, i.e., the amount of the order in dollars, increase as they become accustomed to M-shopping. In addition to the effect on customer's spending behavior, we also find that M-shoppers tend to use mobile devices to shop for habitual products that they already have a history of purchasing. We propose that customers utilize mobile devices because the technology provides convenient access, which leads them to incorporate M-shopping into their habitual routines. Managerially, we recommend that firms should fully leverage their mobile platforms, but they should also keep in mind that mobile devices may not be the most optimal channel for launching new products or promoting products that require more consideration during the buying process.
Customers read reviews to reduce the risk associated with a purchase decision. While prior studies have focused on the valence and volume of reviews, this study provides a more comprehensive ...understanding of how reviews influence customers by considering two additional factors—exposure to reviews and price relative to other products in the category. Data provided by two online retailers are used for the analysis. The results reveal a four-way interaction with the effect of valence on purchase probability strongest when (1) there are many reviews, (2) the customer reads reviews, and (3) the product is higher priced. The effects of valence are smaller, but still positive, in the other conditions. We develop theoretical explanations for the effects based on dual processing models and prospect theory, and provide a sensitivity analysis. We discuss implications for academics, manufacturers and online retailers.
•The effect of valence on purchase is stronger when the volume of reviews is large.•The valence effect is stronger for high-price products.•The valence effect is stronger for consumers reading reviews.•We use the elaboration likelihood model for a theoretical explanation.
The customer engagement ecosystem Maslowska, Ewa; Malthouse, Edward C.; Collinger, Tom
Journal of marketing management,
03/2016, Volume:
32, Issue:
5-6
Journal Article
Peer reviewed
Consumer engagement has been widely discussed in both the academic and practitioner literature, but there is no consensus about its meaning, what phenomena constitute engagement or what its ...antecedents and consequences are. Therefore, we propose that the term engagement should be eluded and that more specific terms should be used for the different phenomena. Building on the previous literature, we propose the customer engagement ecosystem, a conceptual model that encompasses brand actions, other actors, customer brand experience, shopping behaviours, brand consumption and brand-dialogue behaviours. The model posits that interactions between these elements are non-linear and reactive; meaning that each action causes a reaction of not only the intended recipient of the message, but the whole ecosystem. Hence, the model reflects the interconnected character of today's marketing environment. It also recognises the growing importance of empowered consumers by distinguishing different forms of brand dialogue behaviours, which describe customers' non-purchase focused behaviours.
Crowdsourcing ideas from consumers can enrich idea input in new product development. After a decade of initiatives (e.g., Starbucks’ MyStarbucksIdea, Dell's IdeaStorm), the implications of ...crowdsourcing for idea generation are well understood, but challenges remain in dealing with the large volume of rapidly generated ideas produced in crowdsourcing communities. This study proposes a model that can assist managers in efficiently processing crowdsourced ideas by identifying the aspects of ideas that are most predictive of future implementation and identifies three sources of information available for an idea: its content, the contributor proposing it, and the crowd's feedback on the idea (the “3Cs”). These information sources differ in their time of availability (content/contributor information is available immediately; crowd feedback accumulates over time) and in the extent to which they comprise structured or unstructured data. This study draws from prior research to operationalize variables corresponding to the 3Cs and develops a new measure to quantify an idea's distinctiveness. Applying automated information retrieval methods (latent semantic indexing) and testing several linear methods (linear discriminant analysis, regularized logistic regression) and nonlinear machine‐learning algorithms (stochastic adaptive boosting, random forests), this article identifies the variables that are most useful towards predicting idea implementation in a crowdsourcing community for an IT product (Mendeley). Our results indicate that consideration of content and contributor information improves ranking performance between 22.6 and 26.0% over random idea selection, and that adding crowd‐related information further improves performance by up to 48.1%. Crowd feedback is the best predictor of idea implementation, followed by idea content and distinctiveness, and the contributor's past idea‐generation experience. Firms are advised to implement two idea selection support systems: one to rank new ideas in real time based on content and contributor experience, and another that integrates the crowd's idea evaluation after it has had sufficient time to provide feedback.
Mobile applications (apps) have become an important platform for brands to interact with customers, but few studies have tested their effects on app adopters’ subsequent brand purchase behavior. This ...paper investigates whether adopters’ spending levels will change after they use a brand’s app. Using a unique dataset from a coalition loyalty program with implementations of propensity score matching and difference-in-difference-in-difference methods, we compare the spending levels of app adopters with those of non-adopters. Specifically, we examine whether the use of the app’s two main interactive features—information lookups and check-ins—influences adopters’ spending levels. We find that app adoption and continued use of the branded app increase future spending. Furthermore, customers who adopt both features show the highest increase. However, we also observe “the recency effect” – when customers discontinue using the app, their spending levels decrease. Our findings suggest that sticky apps which attract continuing uses can be a persuasive marketing tool because they provide portable, convenient, and interactive engagement opportunities, allowing customers to interact with the brand on a habitual basis. We recommend that firms should prioritize launching a mobile app to communicate with their customers, but they should also keep in mind that a poorly designed app, which customers abandon after only a few uses, may in fact hurt their brand experience and company revenues.
•We examine the effects of adopting and using a brand's mobile application on subsequent purchases.•We provide quantified evidence of the effects of mobile app using customers' mobile application log data and transaction histories that are more improved measures of these behaviors.•A brand's mobile app adoption has a positive effect on subsequent purchases.•The effect is elevated when customers use more interactive features available on the app.•Repeated use of the app increases future spending even more, while discontinuing the use of the app decreases it.
This editorial introduces the special section on Advances in Computational Advertising. We define computational advertising, as well as discuss the historical background and conceptualization of the ...computational advertising field. We describe the unique thought leadership forum (TLF) model that was developed to facilitate new research collaboration among scholars from varying academic disciplines, methodological and disciplinary perspectives, and different expertise to examine important and timely issues related to computational advertising and to set a forward-looking research agenda for the next decade and beyond. Five articles resulting from the TLF project and included in this special section are briefly discussed. Then, we identify future challenges and opportunities of computational advertising and propose future research directions.
We discuss consumer engagement with a website, provide a systematic approach to examining the types of engagement produced by specific experiences, and show that engagement with the media context ...increases advertising effectiveness. Based on experiments using measurement scales involving eight different online experiences, we advance two types of engagement with online media — Personal and Social-Interactive Engagement. Our results show that both types are positively associated with advertising effectiveness. Moreover, Social-Interactive Engagement, which is more uniquely characteristic of the web as a medium, is shown to affect advertising after controlling for Personal Engagement. Our results offer online companies and advertisers new metrics and advertising strategies.
The Impact of New Media on Customer Relationships Hennig-Thurau, Thorsten; Malthouse, Edward C.; Friege, Christian ...
Journal of service research : JSR,
08/2010, Volume:
13, Issue:
3
Journal Article
Peer reviewed
Recent years have witnessed the rise of new media channels such as Facebook, YouTube, Google, and Twitter, which enable customers to take a more active role as market players and reach (and be ...reached by) almost everyone anywhere and anytime. These new media threaten long established business models and corporate strategies, but also provide ample opportunities for growth through new adaptive strategies. This paper introduces a new ‘‘pinball’’ framework of new media’s impact on relationships with customers and identifies key new media phenomena which companies should take into account when managing their relationships with customers in the new media universe. For each phenomenon, we identify challenges for researchers and managers which relate to (a) the understanding of consumer behavior, (b) the use of new media to successfully manage customer interactions, and (c) the effective measurement of customers’ activities and outcomes.
This editorial introduces the special section on big data. We define big data by examining how it is, or will be, created in advertising environments. We propose a conceptual framework for ...understanding the different types of digital advertising touch points that create big data, and use the framework for identifying research opportunities. We discuss the types of research questions that big data can inform, including developing and testing theories, identifying insights, and optimizing the delivery of messages. New methods that advertisers will need to use big data are identified. Recommendations are provided for how to think about and approach big data. Using the framework, we identify specific opportunities for advertising researchers to use big data. We also discuss pitfalls in using big data.
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