Analytics & Data Science

Welcome & Introductions (9:30- 9:45 AM ET)
Tina Jordan, Vice President, Association of National Advertisers

I. Applying Unified Attribution in Healthcare (9:45am - 10:25am ET)

Now that we have solved the most complex Omni-Channel Attribution challenges in Pharma, what have we learned about Data Strategy, Ai, ML and Econometrics and how might it apply to your industry?

With a plethora of platforms, sites, and networks available in digital advertising, salesforce effectiveness capabilities, awareness building activities, pricing and education programs, pharma companies are grappling with the optimal omni-channel strategy and deployment plans to achieve key business objectives across their portfolio of brands.

Achieving this lofty objective requires organizations to keep pace with the constantly changing data ecosystem, be able to quickly experiment and validate a range of AI, ML and Econometirc uses cases in a unified manner and lead the necessary change management initiatives to drive adoption, while also ensuring that the analytics are implemented in an agile manner to evolve with the business and competitive marketplace.

During this presentation, Arvind Balasundaram (Regeneron) and Doug Brooks (Ipsos MMA) will share how they have successfully implemented a Omni-Channel, Unified Attribution capability while navigating each of these challenges.

Key Takeaways:

• Integrating 1^st, 2^nd and 3^rd party to enable Unified, Omni-Channel Analytics
• Connecting the dots across patients, physicians and brands
• Validating “future simulations” with real-world data
• Gaining cross-functional buy-in and embedding this capability into business planning
• Using advanced AI and ML techniques to strengthen your data ecosystem
• Overcoming data compliance and privacy requirements
• What’s next?

Speakers: 
Douglas Brooks, EVP, Chief Client Officer, Ipsos MMA
Arvind Balasundaram, Executive Director, Commercial Insights & Analytics, Regeneron

II. Customer Centric Data Refinement in the Age of AI and Machine Learning (10:25am - 11:25am ET)

Advanced analytics is within every marketers’ reach thank to rapid advancements in modeling techniques based on machine learning and AI. Nevertheless, even machines prefer clean data. Especially when holistic personalization is one of the main marketing goals, properly managed customer-centric data will increase the effectiveness of model development, deployment efforts, and backend analyses.

Development of a customer-centric database requires a comprehensive data roadmap, as a Customer Data Platform is not just a pool of available data in one place. All data elements must be carefully converted into “descriptors of individual” for each target. Creating a Customer ID system through commercially available services is just the first step.

Further, all data elements must become “analytics-ready” for effective segmentation, modeling, and targeting, even when the bulk of such tasks are done by the machines. It has been quite common that data scientists often spend over 80% of their valuable time in pre-analytics processing such as data cleansing, categorization, and summarization. Such work should be done “before” any serious analytics efforts begin, and resultant “Analytics Sandbox” will expedite all analytics efforts from pre-testing to post-deployment studies and attribution.

Key Takeaways:

• Why marketers need “analytics-ready” customer-centric data depositories in the age of AI
• What modern CDPs must be
• Types of pre-analytics data work for 1:1 targeting and personalization
• Design concept of “Analytics-Sandbox” for advanced analytics
• Creating effective data menu for CDP development
• Data summary guidelines for transaction data and other behavioral data
• Data categorization rules for seemingly messy data fields such as product, offer, channel, source, etc.
• Check-list for 3^rd-party data procurement

Speaker:
Stephen H. Yu, President & Chief Consultant, Willow Data Strategy, LLC

III. SESSION 3

IV. SESSION 4

V. NETWORKING LUNCH