Retailers have the opportunity to improve product recommendations to consumers and increase sales conversions by using personalisation providing highly relevant content and product recommendations using machine learning. Watch this webinar where our subject matter experts, and customer TEG, will discuss how businesses can use machine learning to gain insights on consumer buying and browsing behaviour, increasing the likelihood that they will respond to a targeted offer and driving improved sales conversions.
TEG (better known as Ticketek in Australia & New Zealand), a global leader in live entertainment, ticketing, and technology sells more than 30 million tickets at some of the world’s most iconic venues, and connects hundreds of entertainment and brand partners to new audiences each year. TEG operates in 40 countries, having recently expanded beyond ANZ to Southeast Asia, the United Kingdom, and the United States.
About the case study:
TEG built a data platform and event recommendation engine using technology like Amazon Personalize, which uses the same ML technology used by Amazon.com for real-time personalised recommendations. TEG’s data science, engineering, and marketing teams worked alongside AWS to design, build, and launch the solution in less than four weeks. This collaborative approach delivered an immediate improvement in the click-to-convert rate by 228%.
- Romina Sharifpour, Senior AI/ML Specialist SA, AWS
- Romain Vivier, Solutions Architect Manager, AWS
- Victor Condogeorges, Head of Ecommerce & Marketing Technology, TEG
- Michelle Grujin, Vice President, Retail & Consumer Goods Industry, Capgemini Invent Australia and New Zealand