EVENTS - AI in Retail Conference 2019

Keynote interview: What AI and ML can do (and what they can’t do) for your business

• Practical applications of AI and ML - what they are, and what they can-and can’t do
• What impact AI will haveon retailers and on customers over the coming 12 months
• How much to invest in AI and ML, and how much value you can get out of them
• Choosing the use cases that make most sense for your organisation
• What is achievable within certain timeframes
• What intermediate goals retailers currently have for AI and ML
• How digitally native brands and large offline retailers are each using AI

Keynote talk: Mind-opening innovationsfrom other markets

• HowAI innovations are already benefitting companies outside the retail sector
• What customers feel about AI - concerns, likes, trends and changing behaviour
• How marketing, digital, data and IT are working together to do more in the space

Dr. Geraint Evans, former Group Head of Marketing & Strategic Programmes / Head of Digital, Odeon Cinemas Group

Panel: Building a data platform to underpin your AI and ML strategy

• Understanding the importance - and role of building a robust infrastructure
• Deciding where to store your data
• Guaranteeing data quality – updating or removing inaccurate, incomplete, improperly formatted or duplicated data
• Ensuring your networking infrastructure, storage capacity, data analytics and security platforms are aligned with your AI aspirations

Ed Child, Head of Customer Data & Data Science, ASDA

Tarun Samtani, Group Data Protection Officer, CISO, Boden

Case study:Embedding AI into your company culture

• Establishing the agile practices and team mindsetsneeded todeliver an AI strategy
• Employing people with the skills to support your use of AI
• Knowing what a successful AI team looks like
• Attracting and retaining good data scientists and data engineers
• Positioning your AI researchers (centrally vs. within each department)
• Ensuring your AI team is in touch with your company’s problems, capabilities, value proposition and customers

Panel: Building out your technical AI capability

• Understanding the supplier base and the services offered by potential partners
• Accurately estimating costs, and optimising cost efficiencies on an on-going basis
• Understanding how customisable AI technologies can be
• Building vs. buying vs. renting your AI capability
• Knowing which AI and ML technologies may not work for a retail business
• Integrating AI and ML into your existing systems

Karen Harris, Managing Director, IntuDigital

Natalia Konstantinova, Lead Software Engineer R&D, First Utility

Case study: Personalisation for higher conversion rates and happier customers

• Personalised messaging - what’s cool and what’s creepy
• Choosing the right data sources for personalisation
• Collecting and stitching together data to understand each customers’ needs and behaviour, and send the right messages at the right times
• Modelling how changes to your website impact your customers’ actions
• Using machine learning to tweak your recommender system and get an uplift
• Churn prevention - identifying when you need to reactivate a customer

Kerry Dawes, Head of Digital Experience & Intel, Rank Group

Case study: Turning your customer contact centre into an ‘opportunity centre’

• Using chatbots and self-service to optimise how you communicate with customersand focus your employees on value-add work
• Dealing with the consumers who ask questions your chatbot doesn’t understand
• Seamlessly integrating man and machine - managing the live handoff between chatbot and real person
• Long-term benefits of chatbots and self-service - reducing headcount and costs
• Proactive customer service to reduce call volumes and increase customer retention

Natalia Konstantinova, Lead Software Engineer R&D, First Utility

Panel: Creating engaging customer experiences with IoT

• Using IoT and AI to gain visibility on your customers’ needs and interests, and know which products they are most / least receptive to
• Using augmented reality (AR) and virtual reality (VR) in-store to reduce friction in the buying process
• Allowing customers to interact with products after purchase, to deliver insights that feedback into product development

Case study: AI in fulfilment, supply chain and logistics to save delivery time and cost

• AI for inventory- and stock optimisation - ensuring the right products are in right place at the right time
• How IoT sensors can improve traceability, ensure products are kept in the right conditions, and help avoid counterfeit products being sold
• How AI can improve the speed and flexibility of delivering products to customers

Case study: Protecting your brand reputation with AI

• Automated tools to identify potentially harmful incidents and generate useful automated responses
• Gaining visibility on whether the relevant department(s) highlighted issues to the right people at the right time

Case study: Using AI to connect your online and offline channels and processes

• How AI can help you to bridge the physical-digital divide
• Deploying an end-to-end platform powered by AI