Meeting the expectations of healthcare providers (HCPs) should be obvious, but often this sentiment is easier said than done, especially in the life sciences industry. The complex landscape of this market in Japan makes this task even more challenging, filled with complicated regulations, contracts, and various stakeholders. How can we ensure our reps are providing […]
Machine learning is easily one of the most heavily discussed buzzwords within life sciences in the past few years. Defined as a process where large amounts of data and algorithms give a machine the ability to learn how to perform tasks and identify patterns, it often gets a bad rap. Although there are clear benefits […]
Across industries, innovators are finding ways to use data and analytics to be disruptive. This combination fuels the need for organizations to have advanced data capabilities in order to stay competitive. Many life sciences companies aim to meet this challenge by tapping the resources and expertise of their in-house analytics teams. However, these teams can […]
Life sciences organizations are continuing to adopt digital technologies as the industry experiences a digital revolution. With the incredible amounts of data set to be captured in 2019, artificial intelligence (AI) and machine learning are changing the industry landscape. Novartis CEO Vas Narasimhan discusses in a recent article how strategic technology partnerships are enabling acceleration […]
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.
It’s no secret that customer centricity and personalization tools are rapidly becoming the gold standard for healthcare professional (HCP) interactions. The growing consensus across the life sciences industry is that automation will help organizations target down to the individual level. Using tools enabled by artificial intelligence (AI) can help brand teams design customer journeys that […]
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.
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.
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.
Companies today are drowning in data. The average company exceeds 50 percent per year in volume of data growth and has an average of 33 unique data sources — these are overwhelming amounts that make extracting analytical insights an arduous task. For those swamped with data that’s not being put to use, data lakes can provide immense value. Data lakes are storage systems that hold large volumes of raw, highly diverse data from many sources. Aside from providing internal benefits such as architecture flexibility and scalability, they make processing data quicker and more accurate, uncovering analytical insights that companies didn’t even realize before.