Using the ‘Goldilocks Principle’ to Develop Data-Driven Oncology Account Strategies

Posted by Anshul Agarwal on Jan 15, 2019 8:00:00 AM

Scott Sandford and Abhishek Singh co-wrote this blog post with Anshul Agarwal.

The “Goldilocks principle,” which borrows its concept from the famous children’s story to describe when something is “just right,” can be applied to many situations, and the oncology landscape is no exception. Given the evolution of pharmaceutical manufacturer strategies in approaching oncology accounts and the data needed to support these strategies, manufacturers need to know what’s “just right” when it comes determining the resources needed for each account—a lesson that they can learn from Ms. Goldilocks and her friends, the bears.


>
Read More

Is Your Oncology Analytics Organization Best in Class Yet?

Posted by Arup Das on Oct 25, 2018 8:00:00 AM

Arun Jain co-wrote this blog post with Arup Das.

Imagine a world where oncology analytics transform the way that a pharma manufacturer engages with its customer: Sales reps have real-time insight into where patients are being diagnosed. Accurate predictions help them anticipate when to follow up with a customer with a relevant message about their soon-to-relapse patient. Customers give them unencumbered access because they trust that the manufacturer will engage with them through their preferred channels at the right cadence. Does this sound too good to be true? Some oncology companies are already exploring these possibilities today, but according to ZS’s recent benchmarking study of oncology analytics organizations, many are not there yet in their analytics maturity and are missing out on critical opportunities to engage their customers when it matters most.


>
Read More

AI Is Driving Change in Oncology Practice and Commercialization. Are You Part of the Wave?

Posted by Shankar Viswanathan on Feb 15, 2018 9:15:07 AM

Arup Das co-wrote this blog post with Shankar Viswanathan.

Looking back at 2017, artificial intelligence and machine learning made impressive progress when it comes to improving cancer diagnosis and treatment. Using deep learning, computers are scouring images to detect signs of breast cancer in mammograms earlier than humans are currently capable of. Using AI “random forests,” a learning algorithm, investigators are more accurately predicting which drug combination will work better in BRAF mutant melanoma. The AI-facilitated discovery of Berg Health’s BPM 31510, a pancreatic cancer drug, has entered human development clinical trials. AI is not only powering oncology drug discovery, faster detection and personalized treatment but also helping to improve oncology commercialization effectiveness and agility through analytics. 


>
Read More