Four AI trends making commercial life sciences smarter in 2019
Artificial intelligence (AI) has already proved to be useful when it comes to life sciences R&D, starting to fruitfully aid in plumbing the vast depths of discovery, clinical, and real-world data. However, the benefits of AI are now making themselves known on the business side of the life sciences industry, enabling commercial teams to better understand and address the needs of the healthcare providers and patients they serve. In 2019, the following trends will help to define how AI can become better integrated into the commercial side of life sciences businesses.
Novartis and Aktana Present the New Commercial Engagement Model at
Veeva Commercial and Medical Summit, Europe
The Veeva Commercial and Medical Summit, Europe welcomed over 1,000 attendees this year in Madrid, including speakers from some of the world’s largest life sciences companies. Concentrated on the era of intelligent customer engagement, attendees discussed the future of customer engagement across sessions, networking conversations, and on social media.
Four Essentials for Maximizing Engagement in AI Decision Support
Decision support has become a vital tool in connecting brand strategy with execution, resulting in increased CRM satisfaction and better healthcare professional (HCP) engagement. When you roll out your decision support program, how can you ensure that you’ll get the adoption and long-term user engagement with the technology to achieve these results? Below are four essentials for maximizing engagement in AI decision support.
Aktana Presents the Missing Link to Coordinated Sales and Marketing at Dreamforce 2018
This year’s Dreamforce event brought together over 170,000 people in downtown San Francisco for its 16th year. One of the major themes discussed was how to cut through the noise to create a better customer experience through the use of artificial intelligence (AI) and digital engagement.
The Future of Machine Learning in the Life Sciences Industry – Part 2
As discussed in part one of this series, machine learning and AI have already been applied in several ways in the commercial life sciences -- yet there’s potential to do even more. In part two, Veeva Systems SVP of Commercial Strategy Paul Shawah and Aktana CEO David Ehrlich discuss what’s in store for this emerging technology and how commercial and medical teams can make the most of it.
The Current State of Machine Learning in Commercial Life Sciences – Part 1
Q&A with Veeva’s Paul Shawah and Aktana’s David Ehr […]
V14 Introduces Seamless Integration with Salesforce Marketing Cloud and Increased Control and Visibility
Driven by customer success and innovation, Aktana introduces major product enhancements multiple times a year based on customer input, advancements in technology and data sciences, and industry changes. The latest enhancements to Aktana Decision Support center around improving control and visibility for customers, punctuated with a new integration with Salesforce Marketing Cloud.
Machine Learning in Decision Support: Q&A with Aktana Chief Science Officer Marc Cohen
Artificial intelligence (AI) and machine learning are all over the news today and a priority for most data companies. Aktana’s product marketing director, Lauren Schivley, talks with Chief Science Officer Marc Cohen about what makes machine learning so valuable and how it applies to the commercial life sciences.
Highlights from the 2018 Aktana Customer Roundtable
Our first annual customer roundtable took place last month in Manhattan, NY. Sales, marketing, IT, and innovation executives representing top bio-pharmaceutical companies convened to network and exchange ideas about decision support and artificial intelligence (AI) in commercial processes. The conversation was lively with many common experiences across companies, and a multitude of best practices were shared as well.
Aktana’s Machine Learning Method & Open Rate Results Published in PMSA Journal
Chief Science Officer Marc Cohen and the data science team at Aktana recently published their research in this area in The Journal of the Pharmaceutical Management Science Association, setting forth a successful machine learning approach for identifying message sequences that maximize open and click-through rates.