Webinars starting with A

Legacy self-service analytics require extensive data model training and deep technical knowledge to be used effectively. The alternative is developer-driven dashboards that have too broad a scope and are not unique to each user request, territory, or country. Every new data insight requires the creation of a unique dashboard, custom-built to address the specific needs of each request. These dashboards have long development cycles and can only answer the specific analytics questions for which they are built. With existing solutions it is difficult for business teams — especially non-technical users in sales and management positions — to use reporting and analytics to its full capacity.

These problems hurt an enterprise pharma’s competitive advantage in the industry.

Join us in this interactive presentation session to hear from industry stalwarts how life sciences-trained, AI-powered analytics enables accelerated decision-making to drive commercial success.

Key Discussion Points:

• The historical role of commercial insights and the exponential evolution of data and insights and what it means for commercial operations
• Key capabilities to look for when reimagining commercial insights and the role of AI, ML, and NLQ
• The business impact of democratizing access to insights to help business users make smarter, faster decisions at significantly lower cost

Personalized communication is something the HCPs have come to expect. To do that, pharma sales reps need to know their customers well. In order to know the customer well, they need data. Enterprise pharma companies, however, have many data resources and lots of data at their disposal. In fact, there is a data explosion. So what do you do? How do you quickly provide field reps with contextual insights (not data) about their customers to have meaningful personalized conversations? How can sales ops teams leverage incentive compensation data to drive sales motivation and help sales reps surpass their quotas? Using real-time insights, how can commercial life sciences leaders skip the long, boring reports?

Early treatment is a fundamental principle of MS disease management to help lower the risk of disease progression and prevent disability. However, patient awareness of the positive impact of early treatment and long-term continuous therapy remains an issue in MS disease management. Understanding key factors that delay new patient starts on therapy are critical to reaching and educating patients and getting them on the right therapy early on to prevent disease progression and keeping them adherent to prevent disability.

While pharma has been using real world data (RWD) to generate real world evidence (RWE) for clinical trials, post-marketing, and R&D for decades, the emergence and applicability of RWD to the sales, marketing, and commercial side of the house is now ramping up. Many organizations are exploring the possibilities related to targeting, segmentation, sales force effectiveness, and adherence, allocating growing budgets to acquire, analyze, and visualize data. How can pharma manufacturers leverage those resources to ensure they’re engaging HCPs and patients in a timely and precise manner to increase early start of MS treatment? That’s where advanced analytics in RWD translate into actionable insights that can drive positive outcomes.

In this webinar we’ll discuss how applications in advanced analytics and machine learning, can leverage clinical expertise in analyzing RWD to create predictive profiles of patients who meet the criteria for early MS treatment. In the same way, advanced analytics i can be applied to identify HCPs treating those patients along the journey and predict the best moments to share MS treatment information with those HCPs at the point of care – within their workflow. This ability to reach HCPs supports a proactive approach to treatment since they can be reached using an omni-channel approach, providing information beyond the EHR and even when they’re not with the patients.

Presenters:

• Eze K. Abosi, Head, Real World Evidence Solutions
• Adam Almozlino, Vice President, Data & Products, OptimizeRx
• Mark Bard, Co-Founder, The DHC Group
• Rebecca Love, RN, MSN, FIEL

Life sciences and Bio Pharma companies, small to large scale, in recent years have realized the importance of insights for effective drug launches in the market. It is no more the muscle power of the organization's sales and marketing team that guarantees the success of a new launch but is the ability of the organization to gather and leverage actionable Patient Insights, Physician Insights and Payer Insights effectively that makes the difference.

Companies procure data sets like CRM data, HCP/HCO Master data, Specialty Distribution data, among others for such use cases. Typically, these data sets are stored in data lakes or data warehouses. Building an effective, secure, and scalable data store is challenge which many enterprises are trying to solve.

Further drilling down, it becomes evident that there are 4 main aspects on which an effective data lake or data warehouse must deliver on:

• Data Integration & Preparation
• Data Democratization
• Data Lineage
• Data Ops & Governance

Based on analyst reports on an average it takes 6 to 9 months to build a data lake. Companies which are looking to launch will need the data infrastructure in a few weeks and require an alternative approach to achieve this.

In this webinar, we will talk about how a framework driven solution brings together all the aspects mentioned above in a single platform while specifically providing acceleration for life sciences and pharma companies.

Presenters:

• Karthik Mohan is working as a Product Architect at D Cube Analytics with 14 years of experience building Cloud/On Premise based Data Analytics Platforms and Products.
• Subidya Bharati is working as an Associate Product Architect at D Cube Analytics with 12 years of experience specializing in BI Solutions and Data Management within Cloud and On-Premises Platforms.

The coronavirus pandemic and responses in battling COVID-19 have created never-before-seen challenges for the pharma industry, such as public policy mandates (e.g., shelter-in-place orders, closing of non-essential businesses) and sweeping restrictions placed on industry representatives from entering offices and hospitals to see HCPs. These policy mandates and restrictions have generated an unprecedented adverse economic situation and caused companies to rethink their Go-To-Market strategy. The International Monetary Fund recently noted in their April 2020 World Economic Outlook, “The Great Lockdown. The world economy will experience the worst recession since the Great Depression.”

This webinar will conduct a strategic view on how the generation of economic effects from policy responses to COVID-19 are reflected through two critical processes: brand financial forecasting and marketing-mix analysis.

• Part 1: Discuss and measure how brand financial forecasts can be adversely affected by COVID-19. We will demonstrate the development of an urban-level econometrically estimated inferential new prescription brand model, specified with management control and economic variables. This model is then used to predict generated drag effects on brand financial forecasts from a COVID-19 recession. The prediction model will also show how changes in sales, marketing, and payer-related channels can be implemented and measured to mitigate brand financial drag effects.
• Part 2: Examine how a deep recession and continued industry representative access restrictions to see HCPs affect the optimal mix of commercialization activities. Pharma sales and marketing is evolving due to COVID-19, such as changes in sales rep access restrictions at physician offices and hospitals, greater use of digital channels to compensate for this decline in access to continue sales rep/HCP engagement, use of digital channels by HCPs as a means to acquire product information, potential changes in DTC spending to reflect patients decreasing their visits to their doctor, and a greater use of samples, copay cards, and other support programs to meet patient affordability and loss of insurance issues caused by the recession. Subject to caveats that pretty much everything is a bit uncertain (this is the first global pandemic for most of the living population), these changes will be analyzed by how they affect marketing mix optimization, scenario planning for budget allocation, and the measurement of budget trade-offs.

Presenters:

• George Chressanthis, PhD, Principal Scientist, Axtria
• Vipul Pandey, MBA, Director, Decision Science, Axtria
• David Wood, PhD, Senior Principal, Axtria

There is a lot of focus and investment in developing multiple analytical models each year by Insights and Analytics (I&A) teams. However, only a select few models are making the cut and being leveraged for translating strategy into actionable deployment. The objective of this webinar session is to provide an overview of various popular analytical models being leveraged across the Pharma Commercial Operations value chain. Additionally, the evolution/emerging industry trends will be addressed.

Presenters:

• Vineet Rathi, Principal, Axtria
• Erik Christianson, Director, Axtria