The sales lead black hole: On sales reps' follow-up of marketing leads

The sales lead black hole-the 70% of leads generated by marketing departments that sales representatives do not pursue-may result from competing demands on sales reps' time. Using the motivation-opportunity-ability framework, the authors consider factors that influence sales reps' pursuit (or lack thereof) of marketing and selfgenerated leads. The proportion of time that sales reps devote to marketing leads depends on organizational lead prequalification and managerial tracking processes (extrinsic motivation), as well as marketing lead volume (opportunity), and sales rep experience and performance (ability). Consistent with a person-situation framework, individual sales rep factors should also moderate the influence of organizational processes on lead follow-up. Data from 461 sales reps employed by four firms confirm that as sales reps' experience increases, their responses to managerial tracking of lead follow-up and marketing lead volume decrease; responses to the quality of the lead prequalification process increase. As sales reps' performance improves, their response to the volume of marketing leads increases, but their response to managerial tracking decreases. The interplay of individual sales reps' abilities and organizational marketing and sales processes explains differences in sales reps' follow-up of marketing leads.' © 2013, American Marketing Association

The diffusion of a new service: Combining service consideration and brand choice

We propose an individual-level model of a two-stage service diffusion process. In the first stage, customers decide whether to "consider" joining the service. This (Consideration) stage is modeled by a hazard model. Customers who decide to consider the service move on to the Choice stage, wherein they choose among the service alternatives and an outside No Choice option. This stage is modeled by a conditional Multinomial Logit model. The service provider does not observe the transition in the first stage of potential customers who have yet to choose a brand. Such potential customers may have started to consider joining the service, yet chose the outside alternative in each period thereafter. One of the main contributions of the model is its ability to distinguish between these two non-adopter types. We estimated the model using data on the adoption process of newly introduced service plans offered by a commercial bank. We employed the hierarchical Bayes Monte Carlo Markov Chain procedure to estimate individual as well as population parameters. The empirical results indicate that the model outperforms competing models in breadth of analysis, model fit, and prediction accuracy

The B2B Knowledge Gap

© 2016 Elsevier B.V. Roughly equal in terms of the economic value of transactions, B2B (Business-to-Business) marketing sees a small fraction of the academic research attention that B2C (Business-to-Consumer) marketing sees. In this article, I cite some of the reasons for that imbalance. I then highlight three specific areas—B2B Innovation, B2B Buying and B2B Analytics—that have great potential for yielding academically significant research contributions that meet the needs of practitioners. In each area, I sketch the state of knowledge and then identify a few research questions. I then highlight a number of other areas of B2B that offer high potential. Next, I comment on the importance of crafting B2B academic research that is both rigorous and relevant and what we as a profession might do to encourage more such work. I then offer a few thoughts about how to increase the participation of practitioners in the B2B knowledge creation process

Bridging the academic-practitioner divide in marketing decision models

The marketing decision models field has produced many striking developments that have had a dramatic impact on practice. However, the field has produced orders of magnitude more developments that have received minimal use. In this article, the author notes the many successful marketing model developments but then considers the relatively low level of practical use (trial or adoption) and questions why that is the case. He then suggests how changes in the incentive and reward systems for marketing academics, marketing practitioners, and marketing intermediaries can bring about adoption and implementation improvements that would be mutually beneficial, bridging the academic-practitioner divide. The author also outlines a program of research in the area of the adoption and use of marketing decision models that will provide guidance on what to develop and how to get those developments adopted. © 2011, American Marketing Association