What’s having the most impact?
Editor’s note: Michael Latta is executive director of YTMBA, a Wilmington, Del., research and consulting firm, and assistant professor of marketing in the E. Craig Wall Sr. College of Business Administration at Coastal Carolina University, Conway, S.C.
Marketing strategists such as Kolossa (1997), Porter (1988), Smith (1991), and Thompson, Strickland and Gambel (2007) tell us that business success is related to the nature of our product, the competitive environment and the promotional leverage managers can generate. However, previous research on marketing-mix variables that effect pharmaceutical sales has used only a few predictors at a time. Every pharmaceutical product manager can benefit from knowing about drivers and barriers to sales in order to optimize the marketing mix. An integrated promotional strategy combined with effective allocation of marketing resources, given the competitive matrix, may help to overcome barriers to sales.
Research has tended to take a micro approach to increasing sales of pharmaceuticals. Groves, Sketris and Tett (2003) have examined samples, Neslin (2001) examined the ROI of detailing and more recently Lyles (2002), Parker and Pettijohn (2003), as well as Wittink (2002) and Wosinska, M. (2005) have examined the economics of advertising directly to consumers. All of these efforts are narrowly focused and do not include the rich variety of variables that may contribute to both the increase and the decrease of pharmaceutical sales.
The study chronicled in this article was undertaken to show the relative contribution to pharmaceutical sales from three types of multivariate marketing factors including product characteristics, competitive matrix and promotional mix. It is an attempt to use a rich variety of variables to see which factors contribute most to increase or decrease pharmaceutical sales.
Hypotheses
Product characteristics
Drug development and marketing strategy are directed at having products with characteristics that encourage adoption, trial and use and, hence, high sales. For example, lifestyle drugs may sell more than non-lifestyle drugs because the patient needs to take the drug regularly for an extended period. (An example of this effect would be oral contraceptives.) Likewise, chronic drugs, such as statins to lower cholesterol, may sell more than acute drugs because of loyal consumers and regular and lifelong administration. Similarly, drugs that treat specific symptoms such as allergy medications may sell more than non-symptomatic drugs because patients need the treatment with these drugs to relieve acute symptoms. Drugs with more than one indication - such as ACE inhibitors indicated for high blood pressure, diabetes and congestive heart failure - may also sell more because each indication allows the company to target additional markets. Similar to number of indications, FDA rating is favorable for sales because if a drug such as a cancer medication is given a high priority in the approval process by the FDA, it may appear to be innovative or more efficacious to doctors and patients, leading to early adoption. Finally, drugs that have been in the market longer, such as hormone replacement therapies, without becoming obsolete may sell more because of a more established position and more loyal customers.
H1: Product characteristics will have a positive impact on sales.
Competitive matrix
The competitive matrix is more complex than product characteristics in terms of effects on sales and includes such variables as order of entry. Later entry may be expected to have a negative impact on drug sales because drugs launched earlier into a category will likely have established a good position and have loyal customers. Doctors and patients tend to recognize the first brand as the gold standard. The number of drugs in a category may also have a negative impact on sales because if there are many drugs, the competition will be fierce and market share for each brand will decrease. Finally, the number of drugs a company has in its portfolio should provide a stronger market position and generate more sales.
H2: Overall, the competitive matrix will have a negative effect on sales.
Promotional mix
Promotional mix has been studied more in pharmaceutical sales than any of the three sets of variables. For example, the first promotional study done in pharmaceutical marketing was done in 1954 and it looked at the effects of medical journal ads, detailing by sales representatives, providing physicians with peer-reviewed journal articles, and sampling (Rogers, 1962). Here, we will look at the amount of money expended on hospital and physician office detailing. Office detailing has a positive impact on sales because it can not only promote the doctors’ understanding of the drugs but also enhance their friendship with the reps and their trust in the brands and the companies.
Likewise, hospital detailing can increase drug sales because hospital detailing not only increases the doctors’ understanding of the drugs but it also enhances exposure to soon-to-be-practicing physicians who are completing their internships. Developing relationships with physicians during their internship may lead to a lifetime of use of a specific brand.
In addition to detailing, samples can increase drug sales by giving physicians and patients no-cost experience with drugs, encouraging trial and adoption. More recently, direct-to-consumer (DTC) advertising has been found to increase drug sales. DTC many times raises public awareness of new drugs and prompts patients to ask for them by name in a physician office visit. In addition, DTC can foster a positive brand image among the public and can remind the doctors to write a prescription and help prevent substitution of a generic or other product at the pharmacy. Finally, advertising in professional journals may have a positive impact on sales because it reaches the physician regularly, helps them understand a new product and reminds them to write prescriptions.
H3: Promotional mix will have a positive effect on sales.
In addition, these single marketing factors may have synergistic or interaction effects. A product with good characteristics, in a competitive situation that is not too severe, when heavily promoted will have higher sales than a product not meeting these conditions.
H4: Product characteristics and promotional mix will interact to have a positive effect on sales.
Method
Both secondary and primary data were collected for 103 top products from 2001. (Please see below for the complete list.) Some of these products have gone generic (Claritin), some have been taken off the market (Vioxx) and some have been acquired by other companies (Sustiva) since the data set was created. However, the relationships explored in this research are not affected by these events.
Secondary data were provided by IMS (IMS Health, 2004a). Primary data were also collected from five clinical pharmacists who were asked to classify each drug according to three types: lifestyle, chronic, treating symptoms. The pharmacists’ classifications were collected in a Delphi approach and resulted in consensus assignments of each drug to these three product characteristics. Finally, a variable for size of company portfolio was created from the secondary data. The data were coded, entered into SPSS and checked for errors. The data file represented the three categories of independent variables presented below along with their range of values.
Product characteristics
The number of indications claimed (1-8)
Priority FDA rating (22 priority)
Lifestyle drug (19 were)
Chronic condition (56 were)
Symptom relief (69 were)
Competitive matrix
Order of entry (1-19)
Number of months since launch (4-695)
Number of competitive drugs in the class (1-244)
Size of company portfolio (1-18)
Promotional mix
Dollars of samples ($1,000-$328.5 million)
Dollars of hospital detailing
($30,000-$32.5 million)
Dollars of office detailing
($40,000-$131.6 million)
DTC advertising dollars
($12,000-$160.8 million)
Journal advertising dollars
($7,000-$14.9 million)
The general analytical framework can be represented as follows in equation form and diagram form. For analysis purposes, sales dollars (ranging from $3.3 million to $4.7 billion) can be predicted by the three classes of predictor variables and their three two-way interactions, and their single three-way interaction.
Sales Dollars = Product Characteristics + Competitive Matrix + Promotional Mix + (Product Characteristics x Competitive Matrix) + (Product Characteristics x Promotional Mix) + (Competitive Matrix x Promotional Mix) + (Product Characteristics x Competitive Matrix x Promotional Mix)
Model specification
When we have multiple correlated measures of a construct, such as five product characteristics, multicollinearity is likely to occur. Furthermore, with 14 correlated independent variables, multiple regression to analyze the predictors of sales dollars involves an over-specified model multicollinearity plus over-specification can present serious problems in multiple regression analysis since it tends to inflate the error term in statistical tests yielding too many significant effects. Factor analysis is a method of reducing a large number of correlated measures of constructs such as 14 measures in three marketing categories. It offers a way to find a single composite variable representing the unique contribution of each individual measure to the three marketing categories. The first factor in a principle components analysis is typically the most reliable representative of the latent or hidden variable underlying the marketing category and can be expressed as a single number for each product. Hence, three principle components analyses were done with the five product characteristics, four competitive matrix and five promotional mix variables used separately to produce a single composite factor representing the variable set.
Results
Factor analysis of variable sets
Each variable set was factor-analyzed using principle components and factor scores were generated for each of the 103 drugs for use in stepwise multiple regression analysis.
Product characteristics
The descriptive statistics correlations, and factor loadings for the analysis of the five variables defining product characteristics appear in Tables 1, 2 and 3.
The first principle component of the product characteristics variables accounted for 34.3 percent of the common variance.
Competitive matrix
The descriptive statistics, correlations and factor loadings for the analysis of the four variables defining competitive matrix appear in Tables 4, 5 and 6.
The first principle component of the competitive matrix variables accounted for 50.3 percent of the common variance.
Promotional mix
The descriptive statistics, correlations and factor loadings for the analysis of the five variables defining promotional mix appear in Tables 7, 8 and 9.
The first principle component of the promotional mix variables accounted for 66.9 percent of the common variance.
Relationships among the predictor variables were explored via correlation analysis presented in Table 10.
A stepwise multiple regression was performed with sales dollars as the dependent variable and seven independent variables comprised of the predictor variables above as specified in the general analytic framework.
Model summary
Because of the significant correlations among the factor score predictor variables and their interaction terms, stepwise multiple regression was the method of choice to determine the unique value of each predictor in predicting sales dollars. The stepwise regression model yielded an r-square of .668 that was significant overall (p<.0001). The significant predictors of sales dollars were product characteristics (standardized) ß = .155, p<.060), promotional mix (standardized) ß = .695, p<.0001), and the interaction of these two factors (standardized) ß = .279, p<.032).
The final model can be expressed as:
Sales Dollars = Constant + Product Characteristics + Promotional Mix + (Product Characteristics X Competitive Matrix X Promotional Mix) + Error
Sales Dollars = $513,000,000 + ($129,000,000 X Product Characteristics Factor Score) + ($582,000,000 X Promotional Mix Factor Score) + ($211,000,000 X Product Characteristics Factor Score X Promotional Mix Factor Score) + Error
Hypothesis 1 stated that product characteristics will have a positive impact on sales. This hypothesis was supported in that a unit increase in the product characteristics factor score resulted in an increase in sales of $1.29 million. Not surprisingly, better products produce more sales.
Hypothesis 2 stated that the competitive matrix will have a negative effect on sales. This hypothesis was not supported since the competitive matrix factor scores had no significant relationship to sales. This lack of effect may be due to this predictor variable having a significant correlation with all of the other predictor variables with the exception of the three-way interaction term.
Hypothesis 3 stated that promotional mix will have a positive effect on sales. Like product characteristics, this hypothesis was supported in that a unit increase in promotional mix factor scores produced an increase in sales of $5.82 million, a much stronger effect when compared to product characteristics.
Hypothesis 4 stated that product characteristics and promotional mix will interact to have a positive effect on sales. This hypothesis was supported in the analysis and yielded an increase in sales of $2.11 million, suggesting that when promotional resources are applied to products with good characteristics, sales increase over and above that due to the product itself, but not over and above the effects due to promotional mix itself.
Discussion
Research on the promotion of pharmaceuticals began in 1954 with the Columbia University drug diffusion study of tetracycline (Rogers, 1962). This field study, sponsored by Pfizer, was done among 125 general practitioners, internists and pediatricians in Bloomington, Galesburg, Peoria and Quincy, Ill. An additional 128 physicians who were colleagues of these physicians were included as members of the social system. The results indicated that medical journal ads, detailing by sales representatives, providing physicians with peer-reviewed journal articles, and sampling created awareness and knowledge of product attributes and benefits among members of the medical community but were insufficient to persuade the average physician to adopt tetracycline.
The results here suggest that additional promotional mix resources such as DTC advertising and hospital detailing may have had a positive effect in getting tetracycline adopted. Future analysis on recently-launched products may provide a better understanding of how much leverage there is in product characteristics and promotion mix factors. The current macro results suggest there is considerable leverage in having both factors in place simultaneously. Future research in this area could include such variables as distribution, pricing, brand image/equity, presence of generic competition, and packaging. Although these variables are deemed important, little research has been done to understand their effects on sales in context with the classes of variables studied here. | Q
References
Groves, K., Sketris, I., and Tett, S. (2003). “Prescription Drug Samples - Does this Marketing Strategy Counteract Policies for Quality Use of Medicines?” Journal of Clinical Pharmacy and Therapeutics, 28 (4), 259-271.
IMS Health (2004a). Leading 10 Therapeutic Categories by U.S. Sales Volume. http://imshealth.com.
Kolassa, M.E. (1997). Elements of Pharmaceutical Pricing. Binghamton: The Hawthorn Press.
Lyles, A. (2002). “Direct Marketing of Pharmaceutical to Consumers,” Annual Review of Public Health, 23, 73-91.
Neslin, S. (2001). ROI Analysis of Pharmaceutical Promotion (RAPP), www.rxpromoroi.org/rapp/.
Parker, R., and Pettijohn, C. (2003). “Ethical Considerations in the Use of Direct-to-Consumer Advertising and Pharmaceutical Promotions: the Impact on Pharmaceutical Sales and Physicians.” Journal of Business Ethics, 48 (3), 279-290.
Porter, M. (1998). On Competition. Boston: Harvard Business School Press.
Rogers, E. (2003). Diffusion of Innovation 5th Ed. New York: The Free Press.
Thompson, A.A., Jr., Strickland, A.J., III, and Gamble, J. (2007). Crafting and Executing Strategy: Text and Readings 15th Ed. Boston: Irwin McGraw-Hill.
Wittink, D. (2002). Analysis of ROI for Pharmaceutical Promotions, (September), video presentation, the Association of Medical Publications, http://www.vioworks.com/clients/amp.
Wosinska, M. (2005, August). “Direct-to-Consumer Advertising and Drug Therapy Compliance.” Journal of Marketing Research, XLII, 323-332.
