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Using CRM data : Modeling and measuring the effect of sales force knowledge on customer decision making Othman BOUJENA– Wesley JOHNSTON & Dwight MERUNKA
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Research context Increasing investments in CRM (Forester research report 2011 on CRM trends) Objective : value creation and customer equity valorization through a 360° view of the customer Each customer interaction/touch point : an opportunity for improving satisfaction and maximizing relationship profitability The crucial role of sales force in conveying market information One main challenge : an effective usage of CRM generated knowledge
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Previous main studies Engle and Barnes 2000; Ahearne et al. 2008; Widmier et al. 2002; Keillor, Bashaw and Pettijohn 1997; Speier and Venkatesh 2002; Zablah, Bellenger and Johnston 2004… CRM adoption by organizations Adoption of CRM/SFA by salespeople The impact of SFA/CRM usage on individual performance
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Research issue If and how CRM usage by sales force improves their knowledge and then customer decision making ?
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Research development process 1 1 3 3 2 2 Literature review Exploratory qualitative study Conceptual model
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Literature review: CRM and sales force performance Sales force : one of the main users of CRM CRM is first ‘’a company philosophy and strategy that aims at aligning all processes to improve customer satisfaction and loyalty’’ (Zablah, Bellenger and Johnston 2004) CRM definition: ‘’ CRM unites the potential of relationship marketing strategies and IT to create profitable, long-term relationships with customers and other key stakeholders. CRM provides enhanced opportunities to use data and information to both understand customers and cocreate value with them’’ - Payne and Frow (2005)
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Literature review: CRM and sales force performance… CRM/SFA Usage Leveraging opportunity and leads intelligence (Grant and Schlesinger 1995; Jayachandran et al. 2005) Formulating alternatives, effective decisions & relationships (Hill and Swenson 1994) Customer communication (Hunter and Perreault 2007; Rice & Blair 1984; Sproull and Kiesler 1986; Ahearne et al. 2008, Khandpur and Wevers 1998) Prospecting, development & customer profiling (Pullig et al. 2002)
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Exploratory qualitative study o Semi-structured in depth interviews o Matched sample : CRM editors, sales managers, salespeople, customers o Double coding process with meta-categorization (Miles & Huberman 1994) – manual and with NVIVO o Agreement based coding : Cohen’s Kappa = 80 % Thematic content analysis Lexical analysis Cognitive mapping
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Main qualitative findings
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Conceptual model : The impact of CRM usage on customer decision making
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Salesperson competence ‘’Customer perception that a salesperson is knowledgeable in important areas such as specific needs, product knowledge, industry trends, and competitive products’’ (Behrman & Perreault 1982; Narver & Slater 1990; Day 1994; Sinkula 1994) Knowledge contributes in sales effectiveness (Behrman et Perreault 1982 ; Leigh et McGraw 1989 ; Sujan et al. 1986 ; Weitz 1978 ; Weitz et al. 1986) Technology and CRM capacities for information gathering, storage aznd diffusion (Glazer 1991 ; Fletcher 1990 ; Huber 1991; Marshall, Moncrief and Lassk 1999) The impact of IT/SFA on market and technical knowledge (Ahearne et al. 2008)
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Customer decision making ‘’Process based on information requirements, decision making time, people involving in buying decisions, and buying criteria’’ (Sharma and Pillai 1996) The role of sales force in optimizing the decision process : providing critical information and showing the capacity of meeting expectations and integrating customer’s constraints (Atkinson and Koprowski 2006) Two critical components : decision making assistance and empathy (Marks 1988)
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Empirical study Online survey questionnaire Customers’ sample : 249 valid respondents 68% know their referent salesperson for at least two years Measures : established/adapted scales (Likert) – exploratory and confirmatory tests (SEM)
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Hypotheses test : Main results A positive effect of CRM applications usage on salesperson competence CRM usage is significantly related to market knowledge (H1; =0.408, R²=0.163, p<0.05). CRM usage influences positively product knowledge (H2; =0.322, R²=0.1, p<0.05) CRM usage influences positively customer knowledge (H3; =0.388, R²=0.147, p<0.05). The effect of salesperson’s competence on customer’s decision making Only product knowledge (H5; =0.460, p<0.05) and customer knowledge (H6; =0.137, p<0.05) have significant effect on customer decision assistance The hypothesis related to market knowledge was not supported (H4; =-0.162, p<0.05) Only product knowledge and customer knowledge have significant effect on empathy
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Results analysis Knowledge capacities show through interactions, sales calls and critical moments for specific needs or information The role of sales presentation Differences in explained variance of knowledge levels : information sourcing balance Behavioral conditioning due to established performance criteria The importance of the quality, updating and exhaustiveness of the database as perceived by sales force
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Results analysis… No (significative) impact of market knowledge on customer decision making : Customers tend to focus more on the ability of the salesperson to master the offer and capacity to adapt to specific needs and constraints Market knowledge is generally a domain that the customer knows and that can also be mentioned during other different sales presentations This result can be related to the maturity level of database content or usage that sometimes doesn’t integrate yet market aspects Empathy drivers
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Research contributions Contributing to better evaluate CRM effectiveness and customer centricity Adoption of the customer’s perspective (customer based metrics !) Methodological triangulation and sampling Revealing value creation ‘’intangible’’ mechanisms Providing insights on : data usage issue (balancing data collection, analytics and relationship monitoring) the capacity of capturing effective information perspectives about channeling CRM data Facilitating CRM adoption by showing the importance of the impact on customer
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Future research directions Testing an integrated model linking the improvement of customer decision making with key relationship variables : satisfaction, loyalty, commitment or predisposition to recommend the supplier Adopting a dyadic approach that combines different perspectives Consider moderating variables like : customer attitude toward IT or salesperson familiarity
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