High Performance Pharma Brand Management Pdf Download _TOP

Current pharma brand managers and above who want to improve and progress in their career and command money. Learn advanced foundation skills/strategies on: professionalism, branding, communications, creativity, field, promotional inputs, differentiation, moving up, metrics, brand plan, NPD, customer, money skills, strategy, expenses, tactics, etc. The MBA student who wants to become a brand manager and wants to learn the ways to prepare for it. Those B.Pharm students who are not aware of this career option, and also those who want to become a Pharma Brand Manager, but have not planned for it yet. The B.Pharm /Science graduate who has joined as Professional Service Representative and wants to become a brand manager. It is the most exhaustive book you will ever need as a brand manager. It contains only true case studies that will help you build your career. This book is a must have as a desktop reference. It will guide you throughout your journey in Pharma Brand Management.

Since 2007, STEM (part of Inizio Advisory) has been helping life science companies optimize their performance by measuring the alignment between their brand strategy and execution at every stage of the product lifecycle. We are proud to have helped deliver actionable insights and recommendations to our clients, helping them drive greater customer engagement, return-on-investment, and ensure world-class medicines reach the right patients.

High Performance Pharma Brand Management Pdf Download

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It is very complex for pharmaceutical companies to test and validate label materials for their vaccines. One of the most crucial features in label solutions is to ensure their print performance. The right label material and print will remain securely intact and legible after harsh storage and handling conditions, without absorbing moisture or being sensitive for scuffing. Due to the pre-testing collaboration between HP Indigo and UPM Raflatac, it is now easier for pharmaceutical brand owners to select the right vaccine labels and printing inks for their own final testing, saving their time and resources. The new solution concept ensures high performance and stability of the product combination in harsh conditions.

The aim of the pre-testing is to help pharma brands to select the right End-to-end application solution for their vaccines from the beginning. Pre-testing saves their time and resources by ensuring that the performance of the product combination meets the requirements of their intended use before starting their own testing activities.

Peak sales are an important metric in the pharmaceutical industry. Specifically, managers are focused on the height-of-peak-sales and the time required achieving peak sales. We analyze how order of entry and quality affect the level of peak sales and the time-to-peak-sales of pharmaceutical brands. We develop a growth model that includes these two variables as well as control variables for own and competitive marketing activities. We find that early entrants achieve peak sales later, and they have higher peak-sales levels. High-quality brands achieve peak sales earlier, and their peak-sales levels are higher. In addition, quality has a moderating effect on the order of entry effect on time-to-peak-sales. Our results indicate that late entrants have longer expected time-to-peak-sales when they introduce a brand with high quality.

Peak sales can be characterized along two dimensions: the height-of-peak-sales and the time-to-peak-sales. Both metrics are closely related to new product performance such as cumulative sales. Intuitively, a brand with a higher level of peak sales is likely to have higher average sales over its life cycle. As a result, cumulative sales are also higher. Similarly, a brand with longer time-to-peak-sales enjoys a longer period of growth that contributes to accumulate sales and achieve a higher level of peak sales. Consequently, cumulative sales are again higher.

There are, however, exceptions to these rules. For example, a high level of peak sales may be achieved very fast. Although, we cannot rule out such a case theoretically, we do not believe it occurs often in reality because of restrictions to growth. Note that the growth rate needs to double if the same level of peak sales is to be achieved in half of the time. Firms can handle faster growth only up to a certain level due to supply and resource restrictions. In Appendix A, we also demonstrate that faster growth implies a higher variance of sales and therefore higher cash-flow volatility which is not desirable (Srivastava et al. 1998). Hence, even if demand might allow for a shorter time-to-peak sales, there are limits to growth from the supply side. The broad sample of new drugs that forms the basis of our empirical study supports our view. Time-to-peak-sales enhances height-of-peak sales and both peak-sales metrics increase cumulative brand sales. Together the two metrics explain more than 96% of observed variance in cumulative brand sales.

Time-to-peak-sales and height-of-peak-sales provide two important yardsticks that are easy to evaluate and predict even before launch. Assume management wants to assess the sales potential of a new product two years prior to launch. Cumulative sales may be obtained from the life cycle curve. Predicting the lifetime and sales for all periods, however, requires much more information than predicting only two peak-sales metrics. It is much easier to reach a consensus estimate for time-to-peak-sales and height-of-peak-sales. For pharmaceuticals, as an example, management can triangle information on the population size, the incidence of a disease and the reachable market share for the new drug to obtain an estimate for the height-of-peak-sales. Management would certainly use information on competitive entries, order of entry, marketing investment, etc. to predict the peak-sales metrics. Our empirical analysis provides important insights into the relevance of these variables for peak sales. Importantly, the analysis also suggests that those variables do not provide explanatory power for cumulative sales beyond the two peak-sales metrics. Hence, peak-sales metrics cannot simply be substituted by other predictors.

The main objective of this study is to determine the drivers of height-of peak-sales and time-to-peak-sales in the pharmaceutical industry. Through this study we contribute to the literature on new products and brand life cycles (Hauser et al. 2006), as this is the first study to investigate drivers of time-to- and height-of-peak-sales. We show that some drivers differentially impact height-of-peak-sales and time-to-peak-sales. For example, marketing expenditures increase the level of peak sales, while they decrease the time-to-peak-sales. We aim to contribute to the literature on drivers of new product performance with a further investigation on the relative roles of order of entry, quality, and marketing efforts (see Tellis and Johnson 2007). Importantly, our results suggest that quality has by far the strongest positive effect on height-of-peak-sales, while it reduces the time-to-peak-sales. Finally, by executing this study in the pharmaceutical industry we also contribute to existing knowledge on pharmaceutical marketing (e.g. Kremer et al. 2008; Stremersch and van Dyck 2009).

The article is organized as follows: In the next section, we review the literature on drug life cycles and provide explanations why peak sales is a quite common phenomenon in the evolution of drug sales. Subsequently, we discuss potential drivers of time-to- and height-of-peak-sales and how they might affect the two metrics. We develop a model to measure the effects, then describe data from the pharmaceutical industry and discuss estimation issues. We follow up with a discussion of the empirical results. We continue with a cross-sectional analysis of new product performance to substantiate the relevance of the suggested metrics. In the final section, we conclude with research implications, limitations, and suggestions for further research.

Studying the length and the shape of brand life cycles has a long history, including studies by Bauer and Fischer (2000), Brockhoff (1967), and Polli and Cook (1969). Research on specific metrics at the brand level in the development of the brand life cycle is scarce. Height-of-peak-sales and time-to-peak-sales have not been studied so far. Remarkably, studying the diffusion of article citations in Econometrica and the Journal of Econometrics, Fok and Franses (2007) model the time-to-peak citations and peak-citations of an article. Hence, though studied in a different context, there is academic attention for peak metrics in the econometric diffusion literature. We believe it is important to study such metrics in a new product context as well, as we will show that both these metrics are highly relevant for practice and they both are the most important determinants of cumulative brand sales.

Although some authors have questioned the transfer of the product life cycle concept to brands (Dhalla and Yuspeh 1976), brand life cycles have been reported quite frequently. The broadest evidence for brand life cycles is available for pharmaceuticals. Bauer and Fischer (2000), Corstjens et al. (2005), Cox (1967), Grabowski and Vernon (1990), Hahn et al. (1994), Lilien et al. (1981), Rao and Masataka (1988), and Simon (1979) all find strong evidence for the existence of a drug life cycle. In total, these researchers document the life cycles for more than 500 newly introduced drugs.

There are potentially several reasons why especially pharmaceuticals exhibit a peak in their sales trajectory. First, diffusion dynamics seem to be dominant for the evolution of (prescription) drug sales. Although refills have a large share in total drug sales, sales dynamics are predominantly driven by first-time prescriptions. This is simply due to the fact that physicians are reluctant to change a drug (i.e. the first-time prescription) once it has been found to work for a patient, even in response to heavy marketing initiatives by competitor brands. As a result, researchers have adopted diffusion approaches to model drug sales with repeats where repeat rates are assumed constant (e.g., Hahn et al. 1994; Shankar et al. 1998). Second, pharmaceutical companies concentrate the bulk of their marketing efforts (i.e., detailing) on the first two years after launch, which causes an immediate strong increase in prescriptions but results in slower sales growth or even decline in later years when marketing support is only limited (Osinga et al. 2010). Third, by definition there is a limit in users of a drug as it is only relevant for patients with a specific treatment for a disease. Fourth, the entry of new competitors may inhibit sales growth to continue. These competitors may be other innovative drugs within the same or new categories, or generics after the patent has expired. 17dc91bb1f