8 results for 'workshop'
2 min read
Defining portfolio value that stands out from the competition
A global franchise team faced the challenge of increased competition all with similar portfolios of brands. They wanted to define how they could stand for something that was differentiated, but more importantly, resonated with their customers.
Pharma markets are changing at an unprecedented pace, with increased R&D spend and application of technological advances and an evolving patient mindset towards healthcare.
3 min read
Identifying challenges and opportunities for a leading cardiovascular brand, in a highly competitive market.
A global team needed to identify and align team members behind the key issues and growth opportunities of a brand whose leadership position was threatened, and to inspire team members to develop a strategy and plan.
Using design thinking to create a simpler experience
At our Fit for the Future Now forum in New York, we heard from Sven Stocker about how Design Thinking transformed Covestro’s presence at their most important trade show. Covestro is a global leader in high-tech polymers, employing over 15,000 people.
2 min read
Pharma: your customer won’t wait for you to listen to them
OxfordSM are pleased to be at eyeforpharma’s Marketing and Customer Innovation Europe summit – where we’ll be joining with customer-focused pharma leaders to harness the power of customer experience in healthcare.
4 min read
Pharma Leaders: best practice Brand Development
Launch excellence - how to get the most out of your Brand Development Capability through both inspirational, innovative brand development and agile launch deployment.
4 min read
Drive a perfect launch with inspirational brand development and agile deployment
Smart organisations are continuously looking for ways to reduce or eliminate friction and create smooth hand overs - here are the challenges and opportunities we see today.
6 min read
The emerging use of AI in Neurology to drive accurate diagnosis
It’s clear that AI is an ever-evolving technology that has wonderful applications. However, AI inherently relies on large, high quality, complete collections of data, and in Pharma those can be harder to find than perhaps you might expect.