Sales Force Sizing and Deployment Using a Decision Calculus Model at Syntex Laboratories

Lodish, Leonard M.; Curtis, E.J.C.; Ness, M P; Simpson, Margaret

doi:10.1287/inte.18.1.5

Cited by 94 publications

(42 citation statements)

References 2 publications

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“…Sizing, resourceallocation, and territory-alignment models have had a big impact in this industry. One of the earliest documented examples was a project done for Syntex [Lodish et al 1988]. Syntex's sales force was grossly undersized and its main product was underfunded.…”

Section: Is Wisdom Better Than Models?mentioning

confidence: 99%

Sales-Force Decision Models: Insights from 25 Years of Implementation

Sinha¹,

Zoltners

2001

Interfaces

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Over 25 years, we have developed many sales-force and modeling insights through over 2,000 projects with several hundred selling organizations in over 50 countries. Content insights are useful in making sales-force decisions. Examples are that profitability is flat for a wide range of sales-force sizes; phased sales-force growth is rarely optimal; focused strategies dominate scattered strategies; most sales territories (55 percent) are too large or too small; and no compensation plan satisfies everyone. Implementation insights concern model building, use, and implementation, for example, a model's economic value can come from such sources as reduced uncertainty, accuracy, increased speed, objectivity, and stakeholder involvement; theory and practice have different and complementary perspectives; experience and wisdom are sometimes better than models; and models provide insights, while people make decisions.T wo global firms recently merged and asked us to help them design new sales organizations in each of 40 countries. The integration would affect over 15,000 salespeople. In just two months, over 100 people from our consulting firm used welltested normative sales-force decision models with strong implementation processes to design and integrate the sales forces. We could not have imagined such an undertaking when we began our careers as marketing modelers in the early '70s.

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Section: Is Wisdom Better Than Models?mentioning

confidence: 99%

Sales-Force Decision Models: Insights from 25 Years of Implementation

Sinha¹,

Zoltners

2001

Interfaces

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“…We restrict detailing elasticity measurements from past studies included in this metaanalysis to those that are (1) based on ratio -scaled objective (e.g., Rich et al 1999 ) measures of selling output (e.g., sales volume in units or dollars, number of prescriptions), and input effort , e.g., "size" measures such as the total number of salespeople or dollar expenditures on detailing, "frequency" measures such as the number of sales calls or details, and "time" measures such as number of selling hours; (2) derived from objective, econometric data and not subjective decision calculus data (e.g., Lodish et al 1988 ); (3) fi rm-level rather than industry-level response function parameter estimates; (4) current-period measures, either directly provided or derivable using author-reported lagged effects; and (5) unambiguously reported or derivable from the estimated coeffi cients and/or other relevant data reported in the study.…”

Section: Database Scopementioning

confidence: 99%

Pharmaceutical Detailing Elasticities: A Meta-Analysis

Sridhar

Mantrala

Albers

2013

International Series in Quantitative Marketing

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Facing maturing product portfolios and growing physician resistance to their promotion by medical sales representatives (i.e., detailing) in developed markets, the pharma industry is struggling to develop more effective selling strategies and appropriately size and deploy its sales forces. In this process, having benchmark answers to the following questions can be helpful: (1) How effective is personal selling or detailing to physicians? (2) What is a generalizable quantitative estimate of detailing effectiveness? (3) How does detailing effectiveness vary by product life cycle stage and geographic region? To provide these answers, the authors present a meta-analysis of 373 econometric estimates of pharma detailing elasticities appearing in 48 previous papers. They fi nd the mean estimate of current-period detailing elasticity is 0.21. Further, elasticity estimates are higher for products that are offered in early life cycle stages and Europe compared to the USA. Also, product life cycle stage and geographic location have a signifi cant interaction effect on detailing elasticity estimates. Specifi cally, the mean detailing elasticities of late stage products in Europe and the USA are 0.17 and 0.14 respectively; while the mean detailing elasticities of early stage products in Europe and the USA are 0.41 and 0.23 respectively. Also, year of data collection and stage in life cycle have a signifi cant interaction effect on elasticity estimates. Specifi cally, elasticities from more recent studies of early stage products are lower than those from earlier studies. This comports with

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“…SPRINTER allocates effort to marketing activities for the launch of a new product (Urban, 1970). Lodish, Curtis et al (1988), used a custom model to analyse the optimal sales force size and how an organisation should deploy it. For a brief review of these approaches, see Richardson (2004).…”

Section: Decision Modelling Approachesmentioning

confidence: 99%

Beyond matrices and black-box algorithms: setting marketing priorities with Marketing Strategy Conferences

Schilling¹,

Schulze-Cleven²

2009

Journal of Marketing Management

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SummaryWith this paper, we introduce the Marketing Strategy Conference approach to set strategic marketing priorities effectively and allocate marketing-related resources accordingly. The system is based on managerial preference modelling with a decision model (analytical side) and communication-enhancing strategy conferencing (interactive side). After a review of alternative resource allocation frameworks, as over-the-thumb approaches, matrix-based analyses, statistical analyses or management science models, we analyse existing analytical, behavioural and organisational impediments to effective marketing resource allocation.Addressing some of this impediments, this papers outlines two Marketing Strategy Conference cases, which we carried out for the pharmaceutical, Schering Argentina.-3-

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Sales Force Sizing and Deployment Using a Decision Calculus Model at Syntex Laboratories

Cited by 94 publications

References 2 publications

Sales-Force Decision Models: Insights from 25 Years of Implementation

Sales-Force Decision Models: Insights from 25 Years of Implementation

Pharmaceutical Detailing Elasticities: A Meta-Analysis

Beyond matrices and black-box algorithms: setting marketing priorities with Marketing Strategy Conferences

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