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Customer Services in Social Media Channels

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Presentation at AMA Conference August 2013 in Boston

Presentation at AMA Conference August 2013 in Boston

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Customer Services in Social Media Channels Presentation Transcript

  • 1. Customer Services in Social Media Channels: An Empirical Analysis AMA Summer Marketing Educator Conference | Boston | August 11th 2013 Alexander Rossmann | Professor for Marketing and Sales | Reutlingen University
  • 2. Page  2 Social Media and Marketing Web 2.0 / Social Media describe a general shift in the use of the internet. Key characteristics: Digital media, interaction, user generated content, users as prosumers, connected customer, social networks. Social Media is not only Facebook. Firms struggle in order to evaluate the value of social media for marketing. Theory of market orientation → interaction orientation → engagement. (Kumar et al. 2012). User engagement drives value, firms foster marketing strategies in order to engage customers and employees.
  • 3. Page  3 Engaging Customers in the Corporate Value Chain: Potential Pathways New Product Development. Open Innovation, Crowdsourcing. Test of Products and Services. Branding, Word-of-Mouth Communication. Customer Services, Complaint Management. Self Services, Peer-to-Peer Services, Contact Avoidance. etc.
  • 4. Page  4 Social Media versus Hotline: Differences in Customer Service Design Third-Party Visibility Service Design Level of Standardization Role of Service Agents Hotline Social Media closed shop open time lagged real time high anonymous public low
  • 5. Page  5 Social Media in the Service-Value-Chain Customer Complaint Service Reaction Perceived Service Quality Customer Satisfaction Word-of-Mouth Communication = Engagement = Reach = Service Knowledge
  • 6. Page  6 The Ultimate Goal … The ultimate goal of service strategies in social media channels is to turn complainers into fans.
  • 7. Page  7 Relevant Issues for Service Research Dimensions of service quality. Impact of service quality on customer satisfaction. Impact of customer satisfaction on firm performance. Contact avoidance, cost reduction, optimal service level. Word-of-Mouth communication, effect of positive service experiences. Service design and service interaction. Scalability of service capacity. Employee effects in different channels.
  • 8. Page  8 Research Questions How should customer service quality in social media channels be conceptualized on multiple levels? Which aspects of customer service quality are important in enhancing customer satisfaction? What outcomes are effected by customer service quality and customer satisfaction? How effective are customer services delivered through social media channels (as compared to customer services delivered through other channels)?
  • 9. Page  9 Theoretical Background Distributive Justice Procedural Justice Interactional Justice While early papers on post-complaint behavior center on fairness in general (Blodgett, Hill, and Tax 1997; Goodwin and Ross 1989), it is now quite agreed that customers perceive fairness in three dimensions. The distinctness of the three justice dimensions has recently been called into question (Gelbrich and Roschk 2012). Davidow (2003) and Liao (2007) report on high correlations between the three justice dimensions.
  • 10. Page  10 Conceptual Framework Customer Effort Customer Satisfaction Customer LoyaltyProcedural Quality Quality of Interaction Fairness Quality of Solutions Word of Mouth Cross-Sell Preferences Perceived Service Quality Customer Satisfaction Outcomes H3 H4 H5 H2 H6 H7 H8 H1 (-)
  • 11. Page  11 Context & Empirical Analysis
  • 12. Page  12 Methods Customer services at Telekom are delivered through traditional channels (hotline, email, letter) and through social media like Facebook and Twitter. Two different samples: Sample A = Traditional Channel (hotline) Sample B = Social Media (facebook, twitter) Complaining customers were interviewed immediately after a service experience in different channels. Questionnaire was developed on the same procedures as were recommended by Churchill (1979) and Gerbing & Anderson (1988). Pretest involving 186 customers was conducted. With a view to eliminating items with low loadings or high cross loadings, the measures for each construct were scanned for evidence of validity and reliability. Final sample: 220 customers from sample A 220 customers from sample B
  • 13. Page  13 Results of CFA, Structural Path Model Unidimensionality and convergent validity of the constructs were examined by confirmatory factor analysis (CFA) performed on both samples using LISREL. All items load on their respective constructs at the 0.01 level, demonstrating satisfactory convergent validity (Anderson and Gerbing 1988). To assess the discriminant validity of the constructs, a model constraining the correlation between a pair of constructs to 1 was compared with an unconstrained model (Bagozzi, Yi, and Phillips 1991). After the measurement models were deemed acceptable, we estimated a structural path model to test the depicted hypotheses. A chi-square difference test reveals that a model with direct effects (direct paths from the antecedent variables to the three target variables) does not have significantly better fit indexes than our full mediation model (Bagozzi and Yi 1988). Additionally, we applied an established multigroup method to analyze the differences between both samples according to our research model. Therefore, we used an extended LISREL model with mean structures (Jöreskog and Sörbom 1996).
  • 14. Page  14 Results of Multigroup Analysis Fit Indixes: χ2(618) = 992.40, CFI= .984; NFI= .964; RMSEA = .053 Hn H1 H2 H3 H4 H5 H6 H7 H8 Effect Customer Effort → CS Procedural Quality → CS Quality of Interaction → CS Fairness → CS Quality of Solutions → CS CS → Customer Loyalty CS → Word of Mouth CS → Cross-Sell Preferences Hotline -.19 .23 n.s. .21 .46 .55 .24 .68 SoMe -.28 .29 .26 .17 .19 .94 .72 .59
  • 15. Page  15 Implications Quality of interaction and the reduction of customer efforts are especially important in services delivered through social media. Key predictor of customer satisfaction in the hotline channel is the quality of service solutions. Word of mouth communication is particularly relevant for customer services delivered through social media, whereas the same effect is considerably weaker for traditional hotline services. Customer satisfaction created in social media impacts significantly stronger on customer loyalty.
  • 16. Page  16 Calculating the Return on Social Media: Integrating SEM and Analytical CRM Data Cost of Service Costs per service contact via social media are significantly higher (compared to hotline), but service agents in social media need fewer contacts per service case. Service Quality Customers report a significantly higher service quality in social media channels. Customer Effects Service quality improvements lead to a strong reduction of the firms churn rate and a significant increase in WOM communication.
  • 17. Page  17 Further Research Directions Scalability of service capacity in social media channels. Peer-to-Peer services, integration of third party resources in the service process. Employee effects in different channels. Moderating effects (e.g. role of different service cases). Theory of customer and employee engagement in the service process.
  • 18. Page  18 Thank you!
  • 19. Page  19 Contact Information Alexander Rossmann Reutlingen University Professor for Business Administration Alteburgstrasse 150 72762 Reutlingen | Germany mobile: +49 (172) 711 20 60 skype: alexander.rossmann alexander.rossmann@reutlingen-university.de
  • 20. Customer Services in Social Media Channels: An Empirical Analysis AMA Summer Marketing Educator Conference | Boston | August 11th 2013 Alexander Rossmann | Professor for Marketing and Sales | Reutlingen University