Market penetration pricing how to#

Price may not be the basis of your corporate strategy, but you must have a pricing strategy to implement your corporate strategy. In this edited excerpt, he explains several important pricing concepts.

Market penetration pricing how to#

Download the PDF to read the full article, in which we offer a glimpse of the shape that new world is likely to take.In Impact Pricing: Your Blueprint for Driving Profits from Entrepreneur Press, pricing expert Mark Stiving offers practical advice to business owners on how to price products and services. The way the industry works to treat disease is changing rapidly. Here we offer guidance on getting started and analyze the most promising use cases and the elements needed for gen AI to transform them. Leaders will have to grapple with tricky strategic decisions and operational challenges in an uncharted landscape marked by fast-changing technology and emerging risks. Harnessing this powerful new technology, of course, will not be easy. Finally, by automating tedious and time-consuming tasks like document creation and record keeping, gen AI stands to boost the productivity of researchers and medical liaisons, so they can better serve clinicians and patients. Gen AI tools could also make patient care more consistent by reducing deviations in the manufacture and delivery of therapeutics. The ability to generate insights and patterns from vast quantities of patient data will spark more personalized treatments-and improved patient outcomes. An accelerated drug discovery process, for example, will help cure more diseases more quickly, opening additional resources that could then be applied, say, to orphan diseases.

The impending gen AI–driven life-science revolution promises unquantifiable effects on human health and well-being. Readers will find full details in the appendix of “ The economic potential of generative AI: The next productivity frontier.” Our quantification of the economic impact reflected a bottom-up approach: expert opinion, the results of McKinsey’s internal experiments, and published research helped us estimate the potential quantitative range of impact, in both cost savings and revenue uplift, for specific use cases (for instance, the percentage of time saved when generative AI tools review the clinical-research literature).įinally, we scaled the impact to the life-science industry, basing our estimates on the percentage of domain operating costs spent on tasks related to a generative AI use case (for example, the percentage of life-science R&D costs allocated to reviewing clinical research) and the percentage of industry revenues spent on a domain (such as the percentage of life-science revenue allocated to R&D). Generative AI co-pilots for sales representatives, for example, were mapped to the commercial domain.įor generative AI use cases (such as synthesizing scientific literature) that affect multiple life-science domains, we based our allocation of the economic impact on the relative size of the relevant domains, as well as expert opinion. Each of them was then mapped to a specific life-science domain-an exercise based on the typical activities performed within it. We based the potential economic impact of gen AI in different domains of the life sciences on the McKinsey Global Institute’s analysis of its impact in 63 individual use cases. These leaders used their extensive work helping clients deploy gen AI over the past year to identify the use cases most likely to spark meaningful near-term productivity gains and economic value. Those numbers in hand, we tapped the experience and knowledge of McKinsey’s leaders in each domain. So we dug deeper into MGI’s data and modeling of 63 generative AI use cases in the life sciences and calculated the potential economic impact for five industry domains: research and early discovery, clinical development, operations, commercial, and medical affairs. With compelling new gen AI use cases emerging, we were eager to understand, in a more granular way, the most promising areas of potential value (see sidebar “A guide to our methodology”). AlphaFold2, ESMFold, and MoLeR, for example, all use deep learning to help predict the structures of nearly all known proteins, transforming our understanding of their underlying diseases. Even before last year’s explosion of interest, researchers were applying complex AI models to unlock the mechanisms of disease. Pharmaceutical companies, of course, have long been in the vanguard of artificial intelligence. This report is a collaborative effort by Chaitanya Adabala Viswa, Joachim Bleys, Eoin Leydon, Bhavik Shah, and Delphine Zurkiya, representing views from McKinsey’s Life Sciences Practice.