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deepak

HCP Centric Approach
Is spending on consumer channels an effective marketing method or should pharma companies continue with an HCP centric approach?
 Is spending on consumer channels an effective marketing method or should pharma companies continue with an HCP centric approach? 1024 683 deepak
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Traditional Marketing Engagement in Pharma: A Background Excellent customer experiences are a key driver of business value in life sciences companies. However, pharma companies are yet to take advantage of this trend. Most struggle to offer a consistent level of engagement across digital and non-digital channels and face an uphill challenge for relevance. Marketing methods…

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5 Emerging
5 Emerging Marketing Trends in Pharma Data Analytics Driving Sales and Actionable Insights
 5 Emerging Marketing Trends in Pharma Data Analytics Driving Sales and Actionable Insights 1024 683 deepak
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Pharma Marketing Analytics: A Brief Background Data drives the marketing and sales processes in the pharmaceutical industry. It powers drug discovery and clinical trials while boosting operational performance in commercial aspects. But sizeable chunks of data remain unseen and untapped to commercial operations, allowing insights and opportunities to leak away. This is where data analytics…

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Omnichannel Marketing
Does Omnichannel Marketing have a Future in the Pharmaceutical Industry?
 Does Omnichannel Marketing have a Future in the Pharmaceutical Industry? 1024 683 deepak
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Traditional Multichannel Marketing in Pharma: A Brief Look With growing digital sophistication, healthcare practitioners and life sciences companies engage in tailored and engaging experiences that help in marketing their drugs and services. Over the past 20 years, multichannel marketing has been evolving as a solution to marketing challenges plaguing the pharmaceutical industry. At first, this…

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Sentiment_analysis
Importance of Sentiment Mapping through Social Media Analysis: The Right Approach to CI Conferences
 Importance of Sentiment Mapping through Social Media Analysis: The Right Approach to CI Conferences 1024 555 deepak
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Introduction: Understanding Pharma CI Conferences Medical and scientific conferences are intersection points for industry dialogue and pharma company announcements. The interaction with KOLs makes such events important sources of primary and secondary competitive intelligence. Pharma companies participate in conference coverage by attending symposiums, oral, poster and booth sessions that gauge competitor positioning and messaging. They…

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Alteryx
Alteryx: Helping Pharma Companies run Efficient Business Solutions by Maintaining Comprehensive Data Simplification, Compliance and Lineage
 Alteryx: Helping Pharma Companies run Efficient Business Solutions by Maintaining Comprehensive Data Simplification, Compliance and Lineage 1024 683 deepak
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Big Data Management in Pharma: Background Post-Pandemic, the pharmaceutical industry saw significant changes. These include a flood of remote data and a combination of other factors, that accelerated the pace of change in the way this data is mined and managed. Pharma companies implemented several initiatives to increase access to care, value-based payment models and…

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Mining AI
Mining AI and ML: How the Pharma Industry has leveraged Automation to Give Rise to Value-based Healthcare?
 Mining AI and ML: How the Pharma Industry has leveraged Automation to Give Rise to Value-based Healthcare? 1024 550 deepak
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Artificial Intelligence (AI) and Machine Learning (ML) in Pharma: Background In recent years, technological, regulatory, and environmental changes along with supply chain imbalances have put a tremendous pressure on stakeholders to initiate automation in the life sciences industry globally. The application of AI and machine learning methods has produced demonstrable results in financial and manufacturing…

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Adoption Of Snowflake
Adoption of Snowflake in the Pharmaceutical Industry: A Brief Introduction
 Adoption of Snowflake in the Pharmaceutical Industry: A Brief Introduction 1024 426 deepak
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Cloud-based Data Warehousing in Life Sciences: A Background Traditionally, cloud-based data warehousing technologies presented significant value in healthcare by giving medical service providers a platform to wirelessly collect data for storage, computation, accessibility and sharing. Medical device manufacturers and healthcare providers also offered big data management services to clients without relying on traditional computing databases.…

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SI_Blog
Monitoring Development Milestones of Competitors in Pharma: A Brief Study
 Monitoring Development Milestones of Competitors in Pharma: A Brief Study 1024 406 deepak
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Competitor Monitoring in Pharma: Background The pharmaceutical organizations are facing a double whammy since the need for rapid innovation has been fuelled by evolving treatment landscapes and patient needs, and patent expiry for approved drugs. The need to keep the pipeline rich and strong has driven fierce competition in the industry. This in turn requires…

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Predictive_analytics
Advanced Predictive Analytics: Driving Operational Efficiency across Pharma Processes
 Advanced Predictive Analytics: Driving Operational Efficiency across Pharma Processes 1024 576 deepak
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Post-pandemic Pharma Landscape: Toward Agile Data-driven Efficiency The pharmaceutical industry is facing growing competition with increasing pricing pressures and strict regulations. Evolving drug regulatory landscape and supply disruptions caused by the pandemic cast a shadow on operations. Such challenges have been instrumental in keeping businesses on their toes. To reduce their costs and increase profitability,…

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Adoption Of Big Data Repositories In Pharma
Cloud-based Enterprise Data Warehousing: A Comparison of Snowflake vs Amazon Redshift vs Azure Synapse
 Cloud-based Enterprise Data Warehousing: A Comparison of Snowflake vs Amazon Redshift vs Azure Synapse 1024 683 deepak
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Adoption of Big Data Repositories in Pharma: Background The use of big data in drug research stemmed from more efficient data access and more secure data repositories for biopharma companies. Historically, data management systems and warehouses were based on heavily structured paradigms that assumed the data was being captured for specific questions. But with a…

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