Day 2 - 29 November 2018

AI and the Consumer

Gartner predict that by 2020 – 85% of customer interactions will not be managed by humans. Customers now have a wealth of information at their fingertips and can easily compare price, quality and service so how can brands stand out from the crowd and really engage with their customers? From using chatbots to communicate to using AI to predict behaviours and buying patterns, to recommendations and personalisation, this session will look at how AI is changing the customer experience.


Keynote: How to Leverage Artificial Intelligence in Digital Transformation

  • Explore how AI is being used to accelerate customer-centric experience design and achieve digital transformation
  • Practical applications to leverage AI
  • How big data and contextual computing is influencing the consumer



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Panel: ChatBots – the next generation of messaging apps

  • Understanding the messaging platform of the future
  • Creating personalized interaction between the consumer and a brand
  • Measuring the success of your chatbot though engagement levels, sentiment analysis, response rates, bot mentions and click through rates

See Federico’s case study regarding the future of chatbots here.

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How AI is changing the shopping experience – Going beyond shopping assistants.

Case studies from Wayfair where we use innovative algorithms that helps consumers not just get quickly to the point of fulfillment but make that journey enjoyable and democratic.

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Networking Break


Case Study: Using an algorithm to figure out what luxury customers really want

  • Pursuing disruptive insights by creating and connecting a multi-medium customer listening strategy
  • How to use AI to turn your discerning customers into loyal fans by listening with extreme empathy
  • Identify touchpoints that matter. Design, craft and curate experiences around those key moments
  • Disrupt, prototype and reverse-engineer your organization to deliver ultra-luxury experiences
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Panel: The Revolutionising of Customer Experiences through AI

  • The impact of AI and understanding when AI is most effective as a tool for customer service
  • Why smart tech are key to creating contextual experiences
  • Using AI to predict consumers intentions
  • Effective personalisation of communications to create positive impacts
  • What’s the most effective way of engaging staff and help them work along-side AI?
  • What are the cost implications both short and long-term?


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Examining AI uses in Banking & Finance Services

  • Explore current AI applications within the financial & banking sectors
  • The advancement of Robo advisers into Robo trading
  • Automated financial advisors and planning developments, and data driven lending systems
  • The future value of machine learning within the finance & banking sectors enabling fraud reduction
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Networking Lunch


Digital transformation for banking as a business fuelled by AI

  • How Industry is perceiving growth acceleration via Digital Transformation using AI
    • Industry perspective
    • Technology perspective
  • The Synechron journey on Artificial Intelligence specifically on BFSI
    • AI driven RPA
    • Deep Learning Techniques
  • Sharing specific experiences and opportunities
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Panel: Predictive analytics for customer recommendations

  • How to deliver truly personal and unique recommendations
  • Deep learning tools that personalize a user’s experience
  • Enhancing suggestions using self-learning algorithms
  • Taking recommendations to the next level
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Case study: How Pinterest Uses Machine Learning in Homefeed and Obtain 200M Monthly Active Users

Being valued at 12B USD, Pinterest has always prioritized user experiences. Since 3.5 years ago we started to use machine learning in our most important product, i.e.,Pinterest Homefeed, to boost organic user engagement. The series of machine learned models we launched over these years, including linear models, Gradient Boosted Decision Trees (GBDT) models and deep neural network (DNN) models have dramatically increased both the number of active users on Pinterest and the level of user engagement, and we have more than 200M monthly active users now as a direct effect of the aforementioned ML model launches.

Yunsong is a staff software engineer and founding member of the Pinterest Homefeed ranking team, and is excited to share with the audience details of the evolution of Pinterest Homefeed machine learned recommendation models over the past 3.5 years. In particular, this talk includes 3 main parts: introduction of machine learning in various Pinterest products; how linear, GBDT and deep NN models are applied in Homefeed to improve user engagement, practical lessons on finding the most impactful features.

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