DAA Toronto 2018

12:15pm - 1:00pm

Registration and Networking

1:00pm - 1:15pm

Welcome Remarks

Emcee: Sharon Flynn
Digital Analytics Lead - Digital & Channels Marketing
BMO Financial Group

Toronto Chapter Leader
Digital Analytics Association

1:15pm - 1:25pm

Introduction to the Digital Analytics Association

Marilee Yorchak, CAE
Executive Director
Digital Analytics Association

1:30pm - 1:40pm

Host Sponsor Welcome
Loyalty One

1:45pm - 2:15pm

How Public Media Can Save You from Filter Bubbles

Christopher Berry
Director, Product Intelligence
Canadian Broadcasting Corporation

Almost every organization is currently thinking, if not actively working, into some sort of personalization and predictive recommendation initiative. Machine learning and AI algorithms that selectively guess what information a user would like to see and is more likely to respond to based on their profile, location, behavior, search history and other historical data is the future, isn’t it?

However, as companies strive to tailor their services, including news, product recommendations and even search results to tailor our predicted tastes and preferences, there is a dangerous unintended consequence: to get trapped on what some have started calling a filter bubble, a state of intellectual isolation, and don't get exposed to information that could challenge or broaden our worldview. Some would argue “this will ultimately prove to be bad for us and bad for democracy”.

Public media produces a public good in the form of social cohesion. Generally, countries with strong social cohesion enjoy better security, economies, and qualities of life. For reasons to be explained, filter bubbles erode social cohesion. It’s only very recently that they’ve grown stronger, more profitable, and more hackable.

Based on the work Christopher is doing with media at the CBC, leveraging diverse digital data sources and applying machine learning techniques, he has come to a set of reasons for this, and a proposal on how public media can save you from filter bubbles using some of the natural laws that got us here in the first place.

Participants can expect to learn:

• What is a filter bubble and why this concept should matter to you;
• An equation for predicting the spread of an idea or product through a population or market
• How deep learning algorithms and public media can help save you from the filter bubble

2:20pm - 2:25pm

Sponsor Remarks
IBM

2:30pm - 3:00pm

News Article Position Recommendation Based on The Analysis of Article’s Content; Time Matters

As more people prefer to read news on-line, the newspapers are focusing on personalized news presentation. In this study, we investigate the prediction of article's position based on the analysis of article's content using different text analytics methods including artificial intelligence.

Dr. Ayse Bener
Professor and Director, Data Science Laboratory
Director, Big Data, Office of Provost and Vice President Academic
Ryerson University

3:00pm - 3:30pm

Networking Break

3:30pm - 4:15pm

Interactive Roundtable Session
Join your colleagues for a facilitated, but informal interactive exchange of ideas of various issues and topics in analytics.

4:15pm - 4:45pm

Applications for Machine Learning in Shopper Measurement and Tracking

Gary Angel
CEO
Digital Mortar

In this session, we’ll introduce the technology and analytics involved in tracking the customer journeys in the physical world. We’ll briefly survey the data collection technologies used, the type of data that gets generated and look at some basic visualizations to understand how the data lays out. Then we’ll deep dive into three areas where machine learning can be fruitfully applied. First, we’ll look at problems in data quality and the application of machine learning to identify the behavioral patterns of Associates vs. Shoppers. This is critical for extracting associate data from the core shopper data stream in retail analytics. Second, we’ll look at issues in “zone-stitching” – following the customer across camera zones. Zone-stitching is essential for accurate journey measurement using video technologies. Finally, we’ll look at store path optimization – a classic optimization model designed to identify the “best” path through the store for a given type of shopper.

4:50pm - 5:20pm

What Aliens, Reese’s Pieces & Machine Learning Have in Common

James Glover
CEO
Coherent Path

In Steven Spielberg's E.T., Elliott lays a trail of Reese's Pieces to lead E.T. to his desired destination, similar to how we use machine learning to put the customer on a path toward brand loyalty, higher engagement and increased sales. The journey to loyal customers begins with knowing the strategic objective (e.g. reach a younger demographic, increase cross-sell, expose deeper in the catalog) and applying machine learning and hyperbolic geometry to create the optimal path forward for the individual customer.

In this session, James Glover will provide examples and explain how top companies are using advanced data science to understand the dynamics of the customer journey and to put them on the path that is best suited to their unique and evolving interests over time.

Attendees will learn:

• How AutoTrader is putting the right content in front of customers in their car buying journey;
• How Staples is using the email channel to redefine their business beyond paper, ink and toner;
• How L.L. Bean is able to sell deeper in their product catalog and expand to a younger demographic.

5:20pm - 5:30pm

Closing Remarks and Raffle

5:30pm - 6:30pm

Networking Reception