Pharma companies realize the value of data-driven insights and decision making and employing best-in-class technology and infrastructure to enable them. So, in recent years, pharma companies are investing heavily in big data solutions. However, these systems do not yield the kind of value expected from the investment. Some of the reasons include:
As a follow-up to our first Pharmaceutical Management Science Association (PMSA) blog on Axtria’s participation in PMSA’s 2018 Annual Conference, we would now like to draw your attention to the thought-leading presentation we have planned for April 30th.
We all know that in sales, the outcomes you get are the outcomes you incent. This is especially important for large field forces such as pharma, where a very large number of influencers are being targeted and sales compensation is computed based on terabytes of external data! Good incentive compensation (IC) plans combined with well administered execution can motivate the sales force and drive the right behaviors to align with corporate goals. But how do we know if the measures we’ve selected contribute to an intelligent incentive plan design?
Dataquest - 30th June 2016 - Excerpts from the Interview
After spending a fair bit of time speaking to a lot of my friends and ex-colleagues who either run e-commerce companies or work in them and providing honorary advice to quite a few, I have come to the stark realization that a lot of the good, "old world" analytics is not being fully utilized in the "new age" e-commerce companies. A simple case in point is the "recommendation engine". While the world is agog with praise for the beauty of the "recommendation engines" which based on the product that you select in your cart, comes up with a "basket" of associated, recommended products, we fail to realize and acknowledge that the buying behavior of an individual is not an instantaneous phenomenon.