The 2019 Programme

Retail 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 have on 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 casesthat 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
• How AI can help you to bridge the physical-digital divide - using AI to connect your online and offline channels and processes

Retailer Panel Debate: 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
• Building data protection requirements in to the AI / ML strategy to ensure compliance with privacy legislation

Ed Child, Head of Customer Data & Data Science, ASDA
TarunSamtani, Group Data Protection Officer, CISO, Boden
Orlando Machado, Chief Data Scientist, Aviva

Expert Opinion: Mind-opening innovations from other markets

• How AI 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

Retail Case Study: De-risking data science

• Enabling informed decisions about the investment of time and resources in AI
• Identifying and demonstrating the value of AI products at an early stage
• How data science teams can pitch projects to the business and gain executive buy-in
• Understanding when to prioritise data science and AI projects over competing work

Nick Masca, Data Scientist, Skyscanner

Retailer Panel Debate: 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, MD, IntuDigital
Natalia Konstantinova, Lead Software Engineer R&D, First Utility
Orlando Machado, Chief Data Scientist, Aviva

Retail Case Study: Embedding AI into your company culture

• Establishing the agile practices and team mindsetsneeded todeliver an AI strategy
• Employing peoplewith 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

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

 Retail 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
• Learnings and tips to apply to your business to achieve a personalisation programme

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

 Retail Panel Debate: Experiences and plans of early-stage AI adopters

• This panel will highlight the projects of retailers, as they take their first steps into the world of AI and machine learning. Hear their successes, their struggles, and their future plans.

Rob Wood, Head of Online & Digital, The Entertainer

 Retail 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

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

• Using chatbots and self-service to optimise how you communicate with customers and 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

Retail 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

 Retail Case Study: Creating engaging customer experiences with AI and 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 feed back into product development