San Jose State University
`SJSU ScholarWorks
`
`Faculty Publications
`
`2011
`
`School of Management
`
`An Exploratory Study to Improve Sales Operations
`When Selling Multiple Prescription Drugs
`John C. Yi
`Saint Joseph's University
`
`Ming Zhou
`San Jose State University, ming.zhou@sjsu.edu
`
`Taeho Park
`San Jose State University, taeho.park@sjsu.edu
`
`Follow this and additional works at: http://scholarworks.sjsu.edu/org_mgmt_pub
`Part of the Business Administration, Management, and Operations Commons, Operations and
`Supply Chain Management Commons, and the Organizational Behavior and Theory Commons
`
`Recommended Citation
`John C. Yi, Ming Zhou, and Taeho Park. "An Exploratory Study to Improve Sales Operations When Selling Multiple Prescription
`Drugs" Journal of Supply Chain and Operations Management (2011): 43-57.
`
`This Article is brought to you for free and open access by the School of Management at SJSU ScholarWorks. It has been accepted for inclusion in
`Faculty Publications by an authorized administrator of SJSU ScholarWorks. For more information, please contact scholarworks@sjsu.edu.
`
`Exhibit 1096
`IPR2017-00807
`ARGENTUM
`
`000001
`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
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`
`
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` An Exploratory Study to Improve Sales Operations
`
`
`
`
`
`
`
`
`
` When Selling Multiple Prescription Drugs
`
`
` John C. Yi *
`
`
`Saint Joseph’s University, Philadelphia, PA, U.S.A.
`
`Ming Zhou
`
`
`Taeho Park
`
`
`San Jose State University, San Jose, CA, U.S.A.
`
`
`
`
`
`
`
`
`
`
`
`
`
` This paper explores the importance of integrating knowledge with quantitative modeling process to
`
` improve sales operations in multiple product selling situations in the pharmaceutical industry. A
`
`
`
`
`
`
`
`
`
` knowledge-based approach is proposed to minimize challenges in detailing multiple products to
`
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`
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`
`
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` physicians who are more and more difficult accessing in recent years. The performance of this new
`
`
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`
`
`
`
`
`
`
`
`
` approach is compared against the traditional approach via actual implementation by the firm that is
`
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`
`
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`
`
`
` sponsoring the research. Results based on three months of implementation indicate that the
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`
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` knowledge-based approach performs significantly better with increasing the number of responsive
`
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`
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`
`
`
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`physicians by 71% and profit by 9%.
`
`*Corresponding Author. E-mail address: jyi@sju.edu
`
`
`I. INTRODUCTION
`
`
`
`
`
`
` The pharmaceutical industry has faced a
`
`
`
` number of challenges in the recent years, with
`
`
`
` many branded drugs going off patent without
`
`
`
`
`
`
` enough blockbuster drugs in the pipeline to
`
`
`
`
` replace them (PricewaterhouseCoopers, 2008). In
`
`
`
` addition, the industry has received a lot of
`
`
`
`
`
` negative press from both the government and
`
`
`
`
`
`
` consumers for the aggressive investment in to
`
`
`
`
`
`
`
` their sales and marketing efforts (Gagnon and
`
`
`
` Lexchin, 2008; Washington Post, 2002).
`
` Obviously, the industry needs to find a way to
`
`
`
`
`
`
` better utilize their sales and marketing spending
`
`
`
`
`
` to fend off some of these challenges.
`
` Sales
`
`
`is
`
`
`
` the most expensive
` force
`investment
`that a pharmaceutical
`
` marketing
`
`
`
`
`company can make. The primary function of
`
`
`
`
`sales force is to provide detailing to their target
`
`
`
`
`
`
`physicians. The target physicians are those who
`
`
`
`
`
`
`already prescribe or have potential to prescribe
`
`
`
`
`
`
`
`the firm’s prescription drugs; detailing involves
`
`
`
`
`
`pharmaceutical sales representatives visiting each
`
`
`
`
`
`of their physicians to disseminate the latest
`
`
`
`
`
`
`information on the firm’s prescription drugs that
`
`
`
`is meaningful to the physician’s specialty and the
`
`
`
`patients he or she is treating. The detailing is
`
`
`
`
`
`
`
`
`done with the goal of encouraging the physician
`
`
`
`
`
`to correctly prescribe the firm’s drugs for those
`
`
`
`
`
`
`patients who fit the diagnostic criteria, and given
`
`
`
`
`
`similar
`treatment
`situation where
`a
`two
`
`
`
`
`prescription drugs are equal in providing help to
`
`
`
`patients, the firm assumes that the sales rep’s
`
`
`
`
`
`
`selling capability would sway the physician to
`
`
`
`
`
`prescribe their product. With a heavy price tag of
`
`
`
`
`
`
`$150 to $200 per detail, companies put a
`
`
`
`
`significant effort into determining the right
`
`
`
`
`
`physicians to target, the order of the details, also
`
`
`
`
`
`known as detailing sequence, when multiple
`
`
`
`products are involved, and the frequency of
`
`
`
`
`
`
`details to the targeted physicians over time
`
`
`
`
`
`
`(Gagnon and Lexchin, 2008).
`
`
` California Journal of Operations Management, Volume 9, Number 1, February 2011
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`43
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`000002
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`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
` This paper explores the importance of a
`
`
`
`
`
`
` knowledge-based approach in improving sales
`
` force operations in multiple product detailing
`
`
`
`
` situations by integrating domain knowledge with
`
`
`
` quantitative modeling process. The approach
`
`
`
`
` specifically targets to minimize major limitations
`
`
`
`
` of the traditional approach in planning for
`
`
`
`
`
`
` detailing multiple products. The result from this
`
`
`
`
`
`
` study is implemented and tested in a real-world
`
`
`
` environment to a sample of physicians in a
`
`
`
`
`
`
` territory to explore its performance against a
`
`
`
`
`
` control group of similar physician size and sales
`
`
`
`
`
`volume.
`The remainder of the paper is organized
`
`
`
`
`
`
`as follows: Section II gives an overview of the
`
`
`
`
`
`
`
`background of the pharmaceutical industry and
`
`
`
`
`
`sales operations related challenges taking place
`
`
`
`
`in the industry. Section III explains the data sets
`
`
`
`
`
`
`used for this study. Section IV describes a
`
`
`
`
`knowledge-based approach developed to derive a
`
`set of weights for planning detailing strategy, and
`
`
`
`
`
`
`Section V summarizes the plan’s performance
`
`
`
`
`based on actual implementation of the approach.
`
`
`
`Section VI discusses
`approach
`the
`and
`
`
`
`
`concluding remarks.
`
`
`II. BACKGROUND OF THE INDUSTRY
`
`Physician detailing is the primary means
`
`
`
`
`to market pharmaceutical drugs because in this
`
`
`
`
`
`market the physicians are the ones who decide
`
`
`
`
`
`
`
`the best treatment algorithm for their patients,
`
`
`
`
`
`
`who are
`the end users. This dynamic of
`
`
`
`
`
`promoting
`to physicians,
`is different from
`
`
`
`its
`traditional marketing, which
`targets
`
`promotional efforts directly to the end users;
`
`
`
`
`
`however, detailing is similar to other forms of
`
`
`
`
`
`
`promotion, used in traditional markets, in a sense
`
`
`
`is both a marketing
`tool and an
`it
`that
`
`
`
`
`
`
`informational source (Nelson, 1974).
`
`The detailing efforts have been losing its
`
`
`
`
`
`
`impact over the years due to significant changes
`
`
`
`
`
`
`
`in the selling. The primary change is from
`
`
`
`
`
`managed care organizations’ growing influence
`
`
`
`in regulating the use of drugs coupled with an
`
`
`
`
`
`increasing number of physicians seeking more
`
`
`
`
`
`
`benefits
`of
`evidence
`scientific
`objective
`
`
`
`
`
`(Robinson, 2001). Moreover, a more competitive
`
`
`
`
`
`detailing environment (LeadDiscovery, 2006);
`
`
`
`
`
`lack of new blockbuster drugs to gain physicians’
`
`
`attention (PricewaterhouseCoopers, 2008); and
`
`
`
`
`role of direct-to-consumer
`the
`increasing
`
`
`
`advertisements
`and
`electronic
`detailing
`
`
`and Sivadas, 2004)
`all have
`(Davidson
`
`
`
`
`contributed to the declining detailing impact. In
`fact, the average detailing duration dropped from
`
`
`
`
`five minutes in 1998 to less than one minute in
`
`
`
`
`
`2004 (Yi, 2008), signaling
`the physicians’
`
`
`
`declining interest in hearing from the reps.
`Many researchers have found evidence of
`
`
`
`
`
`high market share of detailing voice positively
`
`
`
`
`
`
`impacting the market share of detailing product
`
`
`
`
`
`(Jones, 1990; Shimp, 2000; Gonul, Carter,
`
`
`
`
`Petrova, and Srinivasan, 2001; Pesse, Erat, and
`
`
`Erat, 2006). As a result, pharmaceutical firms are
`
`
`
`committed to maximizing their share of voice
`
`
`
`within their resource constraint in an effort to
`
`
`
`
`increase sales; one way to increase the share of
`
`
`
`
`
`
`
`
`voice without adding more sales reps is to detail
`
`
`
`
`multiple products instead of single product.
`
`2.1. Share of Voice Computation
`
`To derive the share of detailing voice,
`
`
`
`
`physician detailing equivalent (PDE) weights for
`
`
`
`
`the product and the market are computed first;
`
`
`
`
`
`PDE is used by the industry to calculate total
`
`
`
`
`
`detailing efforts when detailing is done in
`
`
`
`
`
`
`multiple sequences, and the PDE weights reflect
`
`
`
`
`the relative detailing impact of each sequence.
`
`
`
`
`Equation (1) shows how PDEjkl, which denotes
`
`
`
`
`physician detailing equivalent for physician j, in
`
`
`
`
`
`time period k, for product l, is calculated:
`
`
`PDE (W D
`i
`
`
`
`
`
`
`
` Dijkl is defined as the total number of details
` made in sequence i to physician j in time period
`
`
`
`k, for product l, while Wi defines the PDE weight
`
`
`
`
`
`
`for detailing sequence i. In addition, the weights
`
`
`
`
`play an instrumental role in computing share of
`
`
`
`
`
`
`voice in time period k, for product l, denoted as
`
`
`
`
`SOVkl, as shown in Eq. (2):
`
`
`)
`
`ijkl
`
`
`
` fo r \ i, j,k,l
`
`
`
`(1)
`
`jkl
`
`i
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
`
`
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`44
`
`000003
`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`PDE jkl
`
`
`
`
`SOV kl
`
`j
`
`for k\
`
`(2)
`
`PDE jkl
`
`l
`
`j
`
` 2.2. Impact of Detailing Multiple Products to
`
`
`Share of Voice
`
`
`
`
`
`
`
`
` The traditional PDE weights, shown in
`
` Table 1, are provided by the firm sponsoring this
`
`
`
`
`
`
` research; the table shows that the full weight of
`
`
`
`
`
`
`
`
`
` one is assigned to the first detailing product
`
`
`
`
`
`
` independent of the number of products in the
`
`
`
`
`
`
`
` detailing portfolio. In other words, as long as the
`
`
`
`
` product is detailed in the first position, it will
`
`
`
`
`
`
` always carry the full detailing weight. Similarly,
`
`
`
`
`
`
` if the product is detailed in the second sequence,
`
`
`
`
`
`
`
`it will always have the PDE weight of 0.6.
`
`
`
`
`
`
`
`
` Finally, any product detailed
` the
`third
`
`
`
`in
` sequence or beyond will have the PDE weight of
`
`
`
`
`
`
`
`
`0.3. Clearly, the firms detailing multiple products
`
`
`
`
`
`will have higher share of voice with 1.9 PDE
`
`
`
`
`
`
`
`when a sales rep details three products to a
`
`
`
`
`
`
`physician in a single visit versus 1 PDE when a
`
`
`
`
`
`
`rep details a single product.
`According to the sponsoring firm, the
`
`
`
`
`
`origination of the PDE weights is based on
`
`
`
`
`
`
`primary market
`research
`to
`physicians.
`
`
`Interviews with sales operations professionals in
`
`
`
`
`other companies made possible by pre-existing
`
`
`
`
`
`
`professional contacts have validated that these
`
`
`
`
`
`values are similar across the industry.
`
`
`TABLE 1: SUMMARY OF TRADITIONALLY APPLIED PDE WEIGHTS FOR DETAILING
`
`
`SEQUENCE BASED ON THE NUMBER OF DETAILING PRODUCTS
`
`
`2nd position
`3rd + position
`1st position
`
`In single product detailing
`
`In two-product detailing
`
`In three+ product detailing
`
`1.00
`
`1.00
`
`1.00
`
`-
`
`0.60
`
`0.60
`
`-
`
`-
`
`0.30
`
`2.3. Limitations of Traditional Approach
`
`The traditional approach in utilizing PDE
`
`
`
`
`
` weights has two major limitations. The first
`
`
`
`
`
` limitation is that the approach always gives
`
`
`
`
`
`
` to detailing more products versus
`
`
`
`
`
` benefit
` detailing fewer products by a way of increasing
`
`
`
`
`
`
` SOV. This is a flawed assumption because it is
`
`
`
`
`
`
` hard enough to access physicians in recent years
`
`
`
`
`
` and when the access is granted, they are not
`
`
`
`
`
` allowing for more time if reps detail more
`
`
`
`
`
`
`
`
`
` products with average details lasting less than a
`
`
`
`
`
` minute (Yi, 2008). More likely, the detailing
`
`
`
`
`
` products will likely cannibalize the individual
`
`
`
`
`
`
`
` detailing
` impact due
`
` the spreading of
`
`
`
`
`to
` information in a fixed time. Thus, always giving
`
`
`
` SOV advantage
` to multi-product detailing
`
`
`
` strategy may mislead management in making
`
`
`
`sound sales operations decisions.
`
`
`
`
`
`
`the PDE weight for each
`Secondly,
`detailing sequence is constant for all physicians
`
`
`
`
`
`
`regardless of how well they respond to details.
`
`
`
`
`
`is another flawed assumption because
`This
`
`
`
`
`physicians and their patients’ needs are different;
`
`
`
`
`
`
`if the firms do not accommodate for these
`
`
`
`
`
`
`
`differences and neglect to provide individualized
`
`
`
`
`strategy,
`a
`significant negative
`detailing
`
`
`
`
`consequence
`such as
`suboptimal
`resource
`
`
`
`
`undesirable
`sales
`force
`and
`allocation
`
`
`
`likely consequences
`(Yi,
`performances are
`
`
`
`
`
`Anandalingam, and Sorrell, 2003).
`In spite of the significance of these
`
`
`
`
`
`
`weights have on sales operations decisions,
`
`
`
`
`surprisingly little is known about them via
`
`
`
`
`
`
`published research. In this paper, we propose a
`
`
`
`
`new approach to minimize the impact of the
`
`
`
`
`
`
`aforementioned limitations to sales performance,
`
`
`
`and investigate the feasibility and performance of
`
`
`
`
`
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
`
`
`
`
`
`
`
`
`
`45
`
`000004
`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
` the approach when implemented to a small
`
`sample of physicians.
`
`
`
`
`
`III. DATA
`
`
`
`
`
` A pharmaceutical company with annual
`
`
` US sales over $2 billion sponsored this research
`
`
`
` on the condition that the company would receive
`
`
`
`
`
` the model and report of the findings while
`
`
`
`
`
`
`
` remaining anonymous and having a say in when
`
`
`
`
` to release the research publication. The firm
`
`
`
`
`
` provided (1) the detailing history and respective
`
`
`
`
` sales data for one of its territories in the
`
`
`
`
`
`
` Northeast region, comprising a total of 72
`
`
`
` physicians on its target list, and (2) team of
`
`
`
`
`
` domain experts and their time to help in this
`
`
`
` research on a $275 million prescription drug
`
` product. This drug was launched in late 1990s, is
`
`
`
`
`
`
` promoted by multiple sales forces in different
`
`
`
`
` detailing sequences, and competes against four
`
`
`
`
`
` branded products for market share. The product
`
`
`
`
` was selected for this research mainly due to the
`
`
`
`
`
`
` wealth of detailing data available.
`
`
`
`
` Pharmaceutical
` generally
` companies
` target physicians detail based on the volume of
`
`
`
`
`
`
`prescriptions they generated in both the drug
`
`
`
`class and the drug itself. The physicians were
`
`
`
`
`
`
`sorted in order of prescription volume in the
`
`
`
`
`disease class, and then they were grouped into 10
`
`
`
`
`equal segments, with the first decile representing
`
`
`
`
`the lowest prescribers and the 10th decile the
`
`
`
`
`
`
`highest; the higher-decile physicians received
`
`
`
`
`
`more detailing visits from the sales reps than did
`
`
`
`
`
`
`
`
`the lower-decile physicians.
`the
`initiate
`this study and find
`To
`
`
`
`
`direction of the research, we merged two sets of
`
`
`
`
`
`
`data, by physician identification number, to form
`
`
`
`the database. One data set contains the number of
`
`
`
`
`
`
`
`
`prescriptions
`that
`the physicians on
`the
`
`
`
`
`company’s target list wrote for the studied drug
`
`
`
`
`
`
`its competitors. The second contains
`and
`
`
`
`information about
`the sales reps’ detailing
`
`
`
`
`activity with the physicians. Two years’ worth of
`
`
`
`
`1st
`data, broken out into eight quarters from
`
`
`
`
`4th
`quarter 2003 through
`quarter 2004, were
`
`
`
`collected for the study; we used quarterly data
`
`
`
`
`
`
`
`because monthly data contained too much noise
`
`
`
`
`for the research.
`
`
`
`In addition, the company provided the
`
`
`
`
`
`
`
`competitive sales activity data at the territory
`
`
`
`
`
`
`
`
`
`level for the same period as that used for the data
`
`
`
`analysis.
`It
`captured
`information on
`all
`
`
`
`
`in
`competing products marketed
`the same
`
`
`
`
`
`
`
`therapeutic area of the company’s product: the
`
`
`
`
`
`
`competitors’ sales force structure; the number of
`
`
`
`
`
`sales reps detailing the drugs; and the detailing
`sequences of the products for each territory.
`
`
`
`
`
`
`There were concerns about data integrity of other
`
`
`
`promotional events, such as direct-to-consumer
`
`
`advertising,
`detailing,
`journal
`electronic
`
`
`advertising, and sponsored medical educational
`
`
`
`
`programs; these data points were excluded from
`this study.
`
`
`IV. NEW APPROACH: KNOWLEDGE­
`BASED APPROACH
`
`
`
`A knowledge-based approach is defined
`
`
`
`
`
`
`as one designed to extract and integrate the tacit
`
`
`
`and explicit knowledge within the organization
`
`
`
`
`
`and then to apply it as a vital component in the
`
`
`
`
`
`quantitative modeling process to improve the
`
`
`
`
`
`organization’s performance as well as gaining
`
`
`
`
`
`insights that can provide competitive advantage
`
`
`
`(Blattberg and Hoch, 1990). This paper proposes
`
`
`
`
`a knowledge-based approach at the physician
`
`
`
`
`
`
`
`
`level to explore whether or not limitations of the
`
`
`
`
`traditional approach can be alleviated while
`
`
`
`
`
`improving sales operations involving multiple
`
`
`
`
`products. The theoretical framework for this
`
`
`
`
`founded on knowledge and
`is
`approach
`micromarketing.
`
`
`
`
`
`4.1. Theoretical Framework for Knowledge
`and Micromarketing
`
`
`
`
`
`
`
`Knowledge is defined as the set of
`
`
`
`
`
`
`justified beliefs that enhance a firm’s capability
`
`
`take effective action
`(Nonaka, 1994).
`to
`
`
`
`
`Knowledge can largely be divided into two areas:
`
`
`
`
`tacit and explicit. Tacit knowledge refers to
`
`
`
`
`
`insights, intuitions, and hunches that are not
`
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`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
` easily verbalized or communicated. This tacit
`
`
` knowledge is critical in decision making process
`
`
`
`
` because it is the primary source of problem
`
`
`
`
`
`
`
` definition and alternatives
` (Davenport and
`
`
` Prusak, 1998). On the other hand, explicit
`
`
`
`
` knowledge refers to that which can be formally
`
`
`
`
`
` expressed and collected as data, words, and
`
`
`
`
` software, therefore, be easily diffused throughout
`
`
` an organization (Davenport and Prusak, 1998).
`
` Researchers have found that converting tacit
`
`
`
`
`into
` knowledge
`
` and
`
`
`
` knowledge
` explicit
`
` integrating the two significantly enhances a
`
`
`
` company’s competitive position by improving
`
`
` organizational
`capability,
`competence,
`
`and
`
`
`
` (Brown
` and Duguid, 1998).
` performance
` Moreover, knowledge integration across different
`
`
`
`
` functions within a
` firm has demonstrated
`
`
`
`
` improvement in decision making quality and
`
`
`
` organizational performance (Blattberg and Hoch,
`
`
`
`1990; Cai, 2006; Liebowitz, 2008).
`shown
` studies
`
`
`
` Recent
` that
`have
` knowledge capture and management can be
`
`
`
`
`improved by integrating visualization into the
`
`
`
`modeling process, with visually agreed-upon
`
`
`
`knowledge being very successful in capturing
`
`
`
`
`and segmenting complex knowledge (Coffey,
`
`
`
`
`Hoffman, and Cañas, 2006; Strohmaier and
`
`
`
`Lindstaedt, 2007). Also,
`integrating domain
`
`
`experts’ knowledge with secondary data that can
`
`
`
`
`
`
`
`be used
`to derive visually agreed-upon
`
`
`
`
`promotional response patterns has proven to be
`
`
`
`
`an effective way
`to
`identifying responsive
`
`
`
`
`physicians,
`leading
`to derivation of more
`
`
`
`
`accurate response functions and, consequently,
`
`
`
`
`improvement in the quality of the detailing plan
`
`
`
`
`
`(Yi et al., 2003). Moreover,
`it has been
`
`
`
`
`demonstrated
`the promotional response
`that
`
`
`
`
`function parameters for individual physicians can
`
`
`
`its accuracy by calibrating
`improve
`the
`
`
`
`
`
`
`parameters to reveal responsiveness as defined
`
`
`
`
`
`by the experts (Yi, 2008).
`
`
`
`
`
`Based on these previous studies, this
`
`
`
`
`
`paper hypothesizes
`that optimally utilizing
`
`
`
`
`
`
`knowledge is critical to improvement of detailing
`
`
`
`
`planning. In addition, accurate PDE weights are
`
`
`
`
`
`reveal
`physicians’
`those
`that
`visually
`
`
`
`
`
`responsiveness by matching its pattern to the
`
`
`
`predetermined responsive patterns developed by
`
`resulting
`in
`improved
`experts,
`domain
`
`
`
`functions and detailing plans.
`promotional
`
`
`
`
`
`
`Moreover, since PDE weights are inputs to SOV
`
`
`
`
`
`computation as well as to detailing planning,
`
`improvement in the weights will also improve the
`
`
`
`
`
`
`
`qualities of SOV calculation as well as detailing
`
`
`
`
`
`planning. These benefits are expected to result in
`
`minimization of non-value-added costs, making
`
`
`
`
`
`
`the sales reps more effective and therefore
`increasing revenue.
`
`
`
`
`Micromarketing is tailoring marketing
`consumer
`to better
`at
`plans
`the
`level
`
`
`
`
`
`
`accommodate individual differences in responses
`
`
`
`
`to promotions (Leeflang and Wittink, 2000;
`
`
`
`Zhang and Krishnamurthi, 2004). In addition,
`
`similar
`traditional consumers, physicians
`to
`
`
`
`respond better to marketing messages tailored to
`
`
`
`their individual needs (Yi, 2008). Therefore,
`
`
`
`
`incorporating micromarketing as part of a
`
`
`
`
`
`knowledge-based approach is expected to be
`
`
`more effective than the traditionally targeting
`
`
`
`
`physicians at a macro level, and further increase
`
`
`
`
`the effectiveness of sales operations.
`
`
`4.2. Process Flow of the Knowledge-Based
`
`
`
`
`
`Approach
`
`The process flow of this approach is
`
`
`
`
`
`
`shown in Figure 1. This flow is developed to
`
`
`
`
`
`provide transparency to the proposed process.
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
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`47
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`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
` 3. Eight qtrs
` of Rx and
`
`
`
` PDE data for
`
`
` physician i
`
`
`
`
`
`
`
`
`
` 1. Define & Agree
` on Patterns of
`
`
`
`Responsiveness
`
`
`
`
`
` 2. Develop, Train,
` & Test Neural
`
`
`
` Nets (NN) Model
`
`
`
` 4. NN Model
`
`
`Application
`
`
`
`
`
`
`
` 7 Search for
`
` PDE
`weights
`
` 5.
`
`Responsive?
`
`No
`
`Yes
`
`Yes
`
`
` 6.
` First Entry?
`
`
`No
`
`
` 8. Collect info of
`
`
` doctor i
`
`
`
`
`
`
` 9. Optimization &
`Reporting
`
`
`
`
`
`
`
` FIGURE 1: PROCESS FLOW OF THE KNOWLEDGE-BASED APPROACH
`
`
` of
`1,
`Step
` In
`definition
`
`
`
` the
`
`
` responsiveness, based on the visual relationship
` between PDE and prescription volume over time,
`
`
`
`
` is constructed by working with a cross-functional
`
`
`
`
` team that includes representatives from Sales
`
`
`
`
`
` Operations, Sales, Marketing, Market Research,
`
`
`Information Management. Each
`
` team
`and
`
` member carries a title of manager or higher and
`
`
`
`
`
`
`
`
` at least three years of work experience in this
`
`
`
`
`
` brand as well as familiarity with the territories
`
`
`
`
`
`
`
`
`
`
`
` selected for the research. This cross functional
` team defined responsiveness based on two sets of
`
`
`
`
`rules and those not meeting these rules are
`
`
`
`
`
`
`
`defaulted as non-responsive. The two rules of
`
`
`
`
`responsiveness are: 1) synchronize movement for
`
`
`
`
`
`
`all eight quarters, and 2) allowing for a single
`
`
`
`
`
`
`
`deviation
`quarter
`from
`synchronize
`the
`
`
`
`
`movement property. Figure 2 illustrates examples
`
`
`
`
`of responsiveness based on these two rules.
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
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`000007
`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
` Two rules
`
`
`
` Responsiveness examples
`
`
`
` 1. Synchonized
`
`movement
`
`
`
` # of Rx
`
`PDE
`
` Time in
`
`quarters
`
`
`
` 2. Single Quarter
`
`
`
`Deviation
`
`
`
`
`FIGURE 2: EXAMPLES OF PREDETERMINED PATTERNS OF PHYSICIAN
`RESPONSIVENESS TO DETAIL
`
`
`
`
`
`
`
`examples, between PDE and prescription volume over time
`non-responsiveness
`The
`
`
`
`
`
`
`
`
`
`defaulted from not meeting the aforementioned
`and are shown in Figure 3. Clearly, detailing
`
`
`
`
`
`
`
`
`
`
`rules, demonstrate cases where there exists no or
`alone cannot explain these physicians prescribing
`
`
`
`insufficient visible pattern of
`relationship behavior.
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
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`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
` Single classification
`
`
`
`
`
` Nonresponsiveness examples
`
`
`
` # of Rx
`
`PDE
`
` Time in
`
`quarters
`
` FIGURE 3: EXAMPLES OF PREDETERMINED PATTERNS OF PHYSICIAN
`
`
`NONRESPONSIVENESS TO DETAIL
`
`
`
`
`
`
`
`
`
`
`
` In Step 2, a neural network (NN) model is
`
` developed to identify, from the target physician
`
`
`
` pool, individual physicians who are responsive to
`
`
`
` details. The main reason for using NN model in
`
`
`
`
`
`
` this study is because it automates otherwise
`
`
`
`
`
` manually
` intensive
` activity of
`
`
`
`
`classfying
` hundreds of physicians into two categories of
`
`
` responsiveness based on visual patterns between
`
`
`
`
`
` PDE and respective prescription volume for eight
`
`
`
`
`
` quarters developed
` in Step 1. In addition,
`
`
` strengths of NN models are the properties of
`
`
`
`
`
`
` adaptability, nonlinearity, fault tolerance, and
`
`
`
` input-output mapping (Jain and Vemuri, 1999;
`
`
`
` Kim, Lee, and Aguihotri, 1995). On the other
`
`
` hand, NN’s limitations are that its functionality is
`
`
`
`
`
`
` often perceived as black box,
` the model-
`
`
`
`
` development process is more art than science,
`
`
`
`
`
`time consuming data-preparation step.
`and
`
`(Livingstone, Manallack, and Tetko, 1997).
`Similar to the work done by Yi et al.
`
`
`
`
`
`
`(2003), this research uses a back-propagation
`
`
`
`network with 16 input nodes (8 quarters of PDE
`
`
`
`
`
`and 8 quarters of the respective prescription
`
`
`
`
`volume (TRx), 1 hidden layer containing 7
`
`
`neurons, and 1 binary output node (1 for
`
`
`
`
`responsive and 0 for nonresponsive physicians).
`
`
`
`The model was developed with 450 training
`
`
`
`
`samples with known results. With a predicted
`
`accuracy of 84%, the NN model compared
`
`
`
`
`
`
`favorably with the logistic regression model that
`
`
`
`
`produced a predicted accuracy of 53%, using Eq.
`
`
`
`
`(3).
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
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`50
`
`000009
`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`-
`
`1
` Predicted accuracy % =
`
`
` �
`
`
`where
`
`n
`
`
`
`
`i 1
`
`
`
`Abs ( Act i -
` Pred i )
`
`
`
`
` �
`
`
`n
`
`
`
`100%
`
`
`(3)
`
`
`actual output of physician i
`Acti
`Predi predicted value for physician i
`absolute value function
`Abs
`number of testing samples
`n
`
`
`
`
`
`
`
`In Step 3, the eight quarters of TRx and
`
` respective PDE data for physicians are prepared
`
`
`
`
`
`
` for Step 4, the NN model application. In Step 5,
`
`
`
`
`
`
` responsiveness of physicians is determined, with
`
`
`
`
`
` nonresponsive physicians’ data directed to Step 6
`
`
`
`
`and responsive physicians’ data to Step 8.
`All nonresponsive physicians entering Step 6 for
`
`
`
`the first time go through to Step 7, where a
`
`
`
`
`nonlinear mathematical model interface with the
`
`
`
`NN model searches for a set of PDE weights that
`
`
`
`
`
`
`
`
`
`reveal physicians’ responsiveness to detailing
`
`
`efforts; the nonlinear program interfacing with
`
`
`
`
`the NN model is shown here:
`
`Maximize NN (Rx jk , PDE jk ) for k 1,...,8
`.t.
`
`
`
`
`
`The objective function, given by (4),
`
`
`
`
`
`maximizes the number of responsive physicians
`
`
`
`
`
`
`in the first summation while maximizing the
`
`
`
`
`
`summation of
`the weights
`the second
`in
`
`
`
`summation. The first summation interfaces with
`
`
`
`
`the trained NN model by providing, to the model,
`
`
`
`
`the physician-level prescription data and the PDE
`
`
`
`
`
`data for all eight quarters, given by Rxjk and
`
`
`
`PDEjk,
`respectively,
`determine
`the
`to
`responsiveness of the targeted physicians.
`
`
`
`The first constraint, given by (5), defines
`
`
`
`
`PDE for each physician in each quarter. The set
`of PDE weights, for the ith position to physician j,
`
`
`
`
`Wij, is initialized to 1, 0.6, and 0.3 for detailing
`
`
`positions 1, 2, and 3+, respectively. Constraint
`
`
`
`
`
`
`
`
`
`(6) sets the upper limit for the weight to be one.
`
`
`
`
`
`
`Constraint (7) forces the weights of preceding
`
`
`
`
`than
`detailing positions
`to be bigger
`the
`
`
`
`
`
`
`subsequent ones
`reflect
`the
`inverse
`to
`
`
`
`
`relationship between the detailing time and the
`
`
`
`
`
`
`
`order in which a product is detailed as well as to
`
`
`
`
`
`
`limit the searching space for the nonlinear
`
`
`
`
`
`program. The last constraint, given by (8),
`
`
`
`
`
`
`the non-negativity condition for all
`
`defines
`
`
`
` Where NN(Rxjk, PDEjk) are trained neural
`
`
`
`
`
`variables. Figure 4 illustrates how this step works
`
`
` network function, returning 1 if physician j is by having a physician visually fitting to the
`
`
`
`
`
`
`
`
`
`
` responsive and 0 if physician j is nonresponsive nonresponsive definition with the traditional set
`
`
`
`
`
`
`
`
`
` based on relationship between Rx written and
` of PDE weights, but the optimization algorithm
`
`
`
`
`
`
`
`
`
`
`
` PDE over eight quarters; PDEjk is the physician
`
`
`
`
`
`
`
`
`
`
`
`
`found a new set of PDE weights to make the
`
` detail equivalent for physician j in quarter k; Rxjk
`
`
`
`
`
`physician fit the definition of responsiveness, and
`
`
`
`
`
` is the total number of prescriptions written by
`
`
`
`
`
`
`this new set of weights replaces the traditional
`
`
`
`
`
`
`
`
` physician j in quarter k; Wij is the detailing
`
`
`
`
`
`
`
`weights for this physician, with the physician
`weight for the ith sequence for physician j; Dijk is
`
`
`
`
`
`classified as responsive.
`the total number of details made from the ith
`
`
`
`
`
`
`
`
`sequence to physician j in quarter k.
`
`
`(4)
`
`(5)
`
`(6)
`
`
`(7)
`
`(8)
`
`
`s P
`
` 3
`
`
`
`DE jk
`
`(Wij D ijk
` ) for k 1,...,8
`i 1
`
`ij �
` 1 for
` \i
`W
`for i 1,2
`ij �
` Wi+1, j
`ll variables �
` 0
`
`W a
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
`
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`51
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`000010
`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
` FIGURE 4: AN EXAMPLE OF DERIVING A SET OF NEW PDE WEIGHTS FOR AN
`
` ACTUAL PHYSICIAN WHO APPEARED VISUALLY NONRESPONSIVE
`
`
`
`
`
` WITH THE TRADITIONAL SET OF PDE WEIGHTS.
`
`
`California Journal of Operations Management, Volume 9, Number 1, February 2011
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`
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`52
`
`000011
`
`

`

`
`
` John C. Yi, Ming Zhou, and Taeho Park
`
`
`
` An exploratory study to improve sales operations when selling multiple prescription drugs
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`
`In Step 9, physician responsiveness and PDE
`
`
` each
`information on
` In Step 8,
`
`
`
`
` responsiveness and
` physician’s
`
` the set of weights are merged with physician data and t