Does Selective Sales Force Training Work?

Atefi, Yashar; Ahearne, Michael; Maxham, James G.; Donavan, D. Todd; Carlson, Brad D.

doi:10.1509/jmr.15.0334

Cited by 11 publications

(18 citation statements)

References 23 publications

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“…Successful communications often hinge on the degree to which coaches can adapt feedback to agents’ learning capability and offer interpersonal support such as empathy, acknowledgment, and encouragement (Simon 1955; Tversky and Kahneman 1974). Such interpersonal skills of human coaches can reduce agents’ resistance to coaching feedback (as agents gain more trust in the coaches) and overcome their learning barriers (Atefi et al 2018; Román, Ruiz, and Munuera 2002). In summary, human coaches’ distinctive interpersonal skills constitute a relative advantage over AI coaches in communicating feedback to agents.…”

Section: Ai Versus Human Coachesmentioning

confidence: 99%

“…They also provide new insights into assessing the effectiveness of sales training by AI coaches vis-à-vis human managers. Earlier studies have focused on the average linear effect of sales training programs by human coaches (Atefi et al 2018; Román, Ruiz, and Munuera 2002). Examining the heterogeneous effects of sales training by AI helps pinpoint the employee-specific challenges and then craft effective solutions.…”

Section: Field Experiments 3: the Ai–human Coach Assemblagementioning

confidence: 99%

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Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Luo¹,

Qin²,

Fang³

et al. 2020

Journal of Marketing

139

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Firms are exploiting artificial intelligence (AI) coaches to provide training to sales agents and improve their job skills. The authors present several caveats associated with such practices based on a series of randomized field experiments. Experiment 1 shows that the incremental benefit of the AI coach over human managers is heterogeneous across agents in an inverted-U shape: whereas middle-ranked agents improve their performance by the largest amount, both bottom- and top-ranked agents show limited incremental gains. This pattern is driven by a learning-based mechanism in which bottom-ranked agents encounter the most severe information overload problem with the AI versus human coach, while top-ranked agents hold the strongest aversion to the AI relative to a human coach. To alleviate the challenge faced by bottom-ranked agents, Experiment 2 redesigns the AI coach by restricting the training feedback level and shows a significant improvement in agent performance. Experiment 3 reveals that the AI–human coach assemblage outperforms either the AI or human coach alone. This assemblage can harness the hard data skills of the AI coach and soft interpersonal skills of human managers, solving both problems faced by bottom- and top-ranked agents. These findings offer novel insights into AI coaches for researchers and managers alike.

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Section: Ai Versus Human Coachesmentioning

confidence: 99%

Section: Field Experiments 3: the Ai–human Coach Assemblagementioning

confidence: 99%

Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Luo¹,

Qin²,

Fang³

et al. 2020

Journal of Marketing

139

View full text Add to dashboard Cite

show abstract

“…Because our treatment variable has more than two levels, standard propensity score or selection models cannot be used for addressing selection, as these models are designed for binary treatment variables. Therefore, for both model types (Heckman selection and propensity score modeling), we employed recent advances in addressing selection issues related to multivalued treatment variables (Atefi et al 2018). First, for multivalued treatment variables, instead of propensity score matching methods, propensity score weighting methods should be used (Atefi et al 2018).…”

Section: Study 1: Observational Studymentioning

confidence: 99%

“…Therefore, for both model types (Heckman selection and propensity score modeling), we employed recent advances in addressing selection issues related to multivalued treatment variables (Atefi et al 2018). First, for multivalued treatment variables, instead of propensity score matching methods, propensity score weighting methods should be used (Atefi et al 2018). We applied a recently developed propensity score weighting method that uses generalized linear models for multivalued treatments (Li and Li 2019; Yoshida et al 2017).…”

Section: Study 1: Observational Studymentioning

confidence: 99%

Open Negotiation: The Back-End Benefits of Salespeople’s Transparency in the Front End

Atefi¹,

Ahearne²,

Hohenberg³

et al. 2020

Journal of Marketing Research

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Negotiations today are less likely to be characterized by information asymmetry—the notion that buyers are less informed than sellers—due to the amount of information available to buyers. A number of industries have reacted to this change by shifting their attention to earning profits in aftermarkets: products and services that augment the main purchase (e.g., add-ons, insurance, financing, service and maintenance). In these aftermarkets, firms often retain an information advantage, even if information asymmetries are eliminated from the main purchase. This has given rise to an interesting setting untapped by prior research: information “symmetry” in the front end (main purchase) and information “asymmetry” in the back end (aftermarket). The authors argue that symmetry in the front end provides an opportunity to build trust, as the knowledgeable customer can verify the information disclosed by the seller. In an observational study in the automotive industry, the authors find that customers to whom the salesperson revealed the cost of a car at the beginning of the negotiation spent significantly more in the back end than others. As corroborated in subsequent studies, this effect holds only when cost is disclosed at the beginning of the negotiation and when customers can verify the cost information.

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“…In response to this growing business concern, millions of dollars are invested by business-to-business (B2B) firms annually to establish and operate better salesforce systems and technologies to enhance productivity, communications and customer relationships (Rodriguez and Honeycutt Jr, 2011; Zhang and Glynn, 2015). Despite the increased importance of sales performance and huge investment by firms, approximately 50% of salespeople nevertheless fail to reach their annual performance goals (Atefi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Passion on Sales Performance: Is Negotiation a Missing Link?

Tran

Nguyen

2020

Australasian Marketing Journal

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This study introduces the passion construct to the sales management literature and proposes a model of passion that links two types of passion – harmonious passion and obsessive passion – to sales performance via four different negotiation strategies. Results from a survey of 322 salespeople from various industries in a metropolitan city indicate that harmonious passion positively influences the integrating and obliging negotiation strategies, which enhance sales performance. On the other hand, obsessive passion is found to positively affect avoiding and dominating negotiation strategies, which are also related to sales performance. The study also provides future research and practical implications for job passion and performance management in B2B companies in a transition market.

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Does Selective Sales Force Training Work?

Cited by 11 publications

References 23 publications

Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions

Open Negotiation: The Back-End Benefits of Salespeople’s Transparency in the Front End

The Impact of Passion on Sales Performance: Is Negotiation a Missing Link?

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