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Leveraging Technology to Improve Customer Relationships

Leveraging Technology to Improve Customer Relationships

Three people working around computers in an engineering lab

By Martin Mende, Maura Scott, and Dhruv Grewal

Firms are shifting from a transaction- to relationship-focused approach to build long-term bonds with customers, making them assets the firms can nurture, expand, and allow to evolve (Mende, Bolton, and Bitner 2013; Kumar, Leszkiewicz, and Herbst 2018). Technology is transforming these relationships in many industries including retail, healthcare and hospitality, financial services, education, and others. But technological transformations might not always benefit the firms attempting to leverage them.

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Too often, firms focus on the efficiency benefits a technology can offer (e.g., an algorithm providing financial advice, a machine baking bread), rather than considering their broader customer-firm relationships. Businesses motivated primarily by creating “technology buzz” without considering customer implications can even destabilize or hinder their relationships.

Technology is a double-edged sword when it comes to customer relationships. Firms can use customer-technology encounters to gather useful information about how their relationships are likely to progress or regress. At the same time, firms must assess technology not only according to its efficiency or effectiveness, but also for its capacity to facilitate social elements of the customer-firm relationship. Managers must also consider technology’s unintended consequences for consumer identity.

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Mistargeted Technology                              

Several recent Journal of Marketing Research contributions underline technology’s unintended consequences. For example, consumers often display systematic algorithm aversion (Castelo, Bos, and Lehmann 2019), despite technology objectively performing certain tasks better than human employees. The averse consumers are less likely to use algorithms, which undermines the technology’s effectiveness, and they can lose trust in the administering firm.  

Identity-driven consumers avoid using technology (e.g., electric bikes, automatic fishing rods) despite superior performance because it can deprive them of consumption’s signaling utility (Leung, Paolacci, and Puntoni 2018). In other words, consumers identifying as dedicated bicyclists or fisherman resist automation, as it “prevents [them] from attributing consumption outcomes to their own skills” (Leung, Paolacci, and Puntoni 2018, p. 828). To address this, firms must establish new guidelines for consumer targeting (e.g., focus on beginners rather than enthusiasts when promoting automation), product innovation (e.g., understand consumer identity when seeking automation candidates), and communication (e.g., avoid blindly emphasizing convenience).

Technology and Consumer Identity

Social media can also have detrimental consequences at the intersection of technology and consumer identity. Although customers posting publicly about product-related experiences seems beneficial, it can backfire. Managers must understand that posting about products on social media fulfils consumers’ identity-signaling needs and can reduce their intention to re-purchase or buy related products (Grewal, Stephen, and Coleman 2019). Although, social media marketing might be effective in the short term, it can undermine future revenue streams.

Technology can affect consumer identities even more fundamentally. For example, sophisticated technologies like humanoid service robots (HSRs) can threaten consumers’ identity on a human level (Mende et al. 2019) and make them uncomfortable with the overall service experience. To mitigate the effects and reduce potential damage to the customer-firm relationship, companies might use HSRs in settings in which consumers sense strong belonging among other humans or increase their robots’ mechanization.

Multiple Journal of Marketing Research papers have illustrated how a technology’s task-level efficiency and effectiveness can be employed in the spirit of relationship marketing, including Gong, Zhang, and Zhao (2017) on tweets and Zhang, Kumar, and Cosguner (2017) on emails as marketing tools. Additionally, Herhausen et al. (2020) details how service firms can benefit from having their employees digitally present on their websites—employees’ digital presence shapes quality perceptions and positively influences consumer memories of previous encounters. Each of these effects can increase companies’ customer loyalty and improve financial performance.

Summary

Left untamed, advanced technology can undermine relationship marketing. To leverage technology for strong and profitable customer-firm relationships, managers must first assess each innovation on both its task-level efficiency/effectiveness and impact on broader relationship factors. Next, they can design interventions to bolster the technology’s benefits and prevent undesirable consequences. Both these strategies help firms with (a) establishing a promising foundation for combining technology and relationship marketing and (b) reaping the corresponding benefits, as Figure 1 illustrates.

Author Bios

Martin Mende is Professor of Marketing and Jim Moran Professor of Business Administration, Department of Marketing, Florida State University, Tallahassee, Florida.

Maura Scott is Persis E. Rockwood Professor of Marketing, Department of Marketing, Florida State University, Tallahassee, Florida.

Dhruv Grewal is Toyota Chair in Commerce and Electronic Business and Professor of Marketing, Babson College, Babson Park, Massachusetts.

Citation

Mende, Martin, Maura Scott, and Dhruv Grewal (2021), “Leveraging Technology to Improve Customer Relationships,” Impact at JMR, (May 17, 2022), Available at: https://www.ama.org/2022/05/17/leveraging-technology-to-improve-customer-relationships/

References

Castelo, Noah, Maarten W. Bos, and Donald R. Lehmann (2019), “Task-Dependent Algorithm Aversion,” Journal of Marketing Research, 56 (5), 809–825. https://doi.org/10.1177/0022243719851788

Gong, Shiyang, Juanjuan Zhang, Ping Zhao, and Xuping Jiang (2017), “Tweeting as a Marketing  

Tool: A Field Experiment in the TV Industry,” Journal of Marketing Research, 54 (6), 833–850. https://doi.org/10.1509/jmr.14.0348

Grewal, Lauren, Andrew T. Stephen, and Nicole V. Coleman (2019), “When Posting about Products on Social Media Backfires: The Negative Effects of Consumer Identity Signaling on Product Interest,” Journal of Marketing Research, 56 (2), 197–210. https://doi.org/10.1177/0022243718821960

Herhausen, Dennis, Oliver Emrich, Dhruv Grewal, Petra Kipfelsberger, and Marcus Schoegel (2020), “Face Forward: How Employees’ Digital Presence on Service Websites Affects Customer Perceptions of Website and Employee Service Quality,” Journal of Marketing Research, 57 (5), 917–936. https://doi.org/10.1177/0022243720934863

Kumar, V., Agata Leszkiewicz, and Angeliki Herbst (2018), “Are You Back for Good or Still Shopping Around? Investigating Customers’ Repeat Churn Behavior,” Journal of Marketing Research, 55 (2), 208–225. https://doi.org/10.1509/jmr.16.0623

Leung, Eugina, Gabriele Paolacci, and Stephano Puntoni (2018), “Man versus Machine: Resisting Automation in Identity-Based Consumer Behavior,” Journal of Marketing Research, 55 (6), 818–831. https://doi.org/10.1177/0022243718818423

Mende, Martin, Ruth N. Bolton, and Mary Jo Bitner (2013), “Decoding Customer-Firm Relationships: How Attachment Styles Help Explain Customers’ Preferences for Closeness, Repurchase Intentions, and Changes in Relationship Breadth,” Journal of Marketing Research, 50 (1), 125–142. https://journals.sagepub.com/doi/10.1509/jmr.10.0072

Mende, Martin, Maura L. Scott, Jenny van Doorn, Dhruv Grewal, and Ilana Shanks (2019), “Service Robots Rising: How Humanoid Robots Influence Service Experiences and Elicit Compensatory Consumer Responses,” Journal of Marketing Research, 56 (4), 535–556. https://doi.org/10.1177/0022243718822827

Zhang, Xi, V. Kumar, and Koray Cosguner (2017), “Dynamically Managing a Profitable Email Marketing Program,” Journal of Marketing Research, 54 (6), 851–866. https://doi.org/10.1509/jmr.16.0210