The field of causal Machine Learning (ML) has made significant strides in recent years. Notable breakthroughs include methods such as meta learners [4] and heterogeneous doubly robust estimators [3] introduced in the last five years. Despite these advancements, the field still faces challenges, particularly in managing tightly coupled systems where both the causal treatment variable and a confounding covariate must serve as key decision-making indicators. This scenario is common in applications of causal ML for marketing, such as marketing segmentation and incremental marketing uplift. In this work, we present our formally proven algorithm, iterative causal segmentation, to address this issue. The integration of machine learning into market segmentation has significantly transformed the development of marketing messages and strategies.

New Jersey, United States, Jan 15, 2023 /DigitalJournal/ AI can aid in structure-based drug discovery by predicting 3D protein structure as the design conforms to the chemical environment of the target protein site, thus helping to predict the effect of a compound on the target as well as safety considerations prior to their synthesis or manufacture. Growing demand for the discovery and development of new drug therapies and increasing manufacturing capabilities in the life science industry are driving the demand for artificial intelligence (AI) based solutions in drug discovery processes. Manufacturers in the life science industry are constantly focused on replenishing their product pipelines as the majority of big sellers drop their patents. The global Artificial Intelligence (AI) in Drug Discovery Market is expected to grow at a Massive CAGR of 29.5% during the forecasting period of 2022 to 2029. The Artificial Intelligence (AI) in Drug Discovery Market research report provides all the information related to the industry.

Ishaan and Elizabeth, both graduate students in business, are attending a marketing strategy lecture at a business school in the Northeast. While learning about the principles of market segmentation, Ishaan texts "outdated" followed by three thinking--face emojis to Elizabeth. He wonders how demographic-, geographic-, or psychographic-based segmentation--the topic of the lecture--can help his family's franchise restaurant deal with the hundreds of sometimes-not-so-positive online reviews and social media posts. Meanwhile, Elizabeth hopes that the fast-food restaurant where she ordered her lunch understands that she now belongs to the segment of'extremely displeased' customers. Earlier, she used the restaurant's new app to order a burrito without cheese and sour cream, only to discover that the meal included both offending ingredients. Her lunch went straight into the trash can and she angrily tweeted her disappointment to the restaurant. Elizabeth replies to Ishaan's text, "that is so passé," followed by a face_with_ rolling_eyes. This simple vignette illustrates an important point. Organizations of every size are challenged with capitalizing on enormous amounts of unstructured organizational data--for instance, from social media posts--particularly for applications such as market segmentation. The purpose of this article is to give the reader an idea of the challenges and opportunities faced by businesses using market segmentation, including the impacts of big data.

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While pharmaceutical marketers have long used attitudinal and behavioral segmentation approaches to identify potential customers and tailor marketing activities, traditional methods lack utility for healthcare-level commercial operations and tactics. Behavioral segmentation uses administrative data to segment physicians. So, for example, you might learn which physicians are early adopters, who prescribes what treatment options most, or whether a physician is based in a hospital or clinic setting – but segmentation factors are ultimately limited to data constructs available in administrative data. These data sources provide an accurate representation of certain specific behaviors, but cannot provide insights into motivations or triggers of their behavior, meaning the "why" and/or thought processes remain indeterminable. In addition, results are not always data-driven, because researchers often inject their own personal biases when defining healthcare provider characteristics and segments. Attitudinal segmentation uses surveys tailored to the exact business need -- measuring such healthcare provider characteristics as peer influence, industry friendliness, perception of safety signals, mechanics of decision making and therapy choice, or receptiveness to channels of communication, and treatment selection making, for example -- to understand what messages or information are most likely to resonate with a physician.