DescriptionLeading international researchers and practitioners present new perspectives on AI and advanced analytics in retail.
The Centre for Retail Research at Lund University is proud to present a webinar at which leading international researchers and practitioners will present new perspectives on AI and advanced analytics in retail research.
In the near future, there will be a shift from person-centric decision-making to more data-centric and automated decision-making. Predictive and prescriptive analytics, including AI, play a key role in this shift. With AI / machine learning, we can today develop self-learning and self-optimizing software, which automates data analysis and gives us autonomous decision-making. To realize the potential and obtain a more sustainable retail, there is a powerful need to know how these technologies are going to impact the converging worlds of online and offline. In these two worlds, retail must begin to make better use of technology, large data sets, and analytics to respond to customers' future demands for products and services. Newer technologies, big data, and advanced analytics suggest that retail is on the verge of a quantum leap into an unknown shopping realm. This webinar gives you a glimpse into where the retailing field will be evolving in the near future.
This webinar will feature input from some of the Centre’s international guest researchers. Researchers with a broad interest in applying AI and advanced analytics in retail are invited to attend. We look forward to lively discussions so please invite a colleague and share this event in your networks.
Welcome by Daniel Hellström - Centre for Retail Research
Eleanora Pantano - School of management, University of Bristol
With the topic - Using AI to predict consumer demand - Eleanora will discuss how prescriptive AI systems can be used to systematically provide consumers purchase based on characteristics and historical purchases (i.e., subscriptions to food, beauty, etc.) and to replace consumers’ traditional shopping. Specifically, she will discuss how AI systems combined with subscription modalities allow predicting consumers’ preference of certain products, which would be delivered directly at home in boxes on weekly/monthly bases, while the benefits for both retailers and consumers will be analysed with examples from the Food (e.g., Hello Fresh) and Beauty industries (e.g., Birchbox).
Cameron Taylor - UX Research lead at Boost.ai.
Cameron is Ph.D. in Linguistics at Cambridge University and now specializes in human-computer interaction (HCI), product design, and user experience (UX) research. Cameron set the strategy for using evaluative methods from survey measurement to experimental design and log data analysis to understand better how people interact and engage through and with AI-powered virtual agents and how that research translates into product design decisions. Boost.Ai collaborates with universities and research institutions and is looking to expand these partnerships
Arno de Caigny - IÉSEG School of Management, Paris
In his presentation, Arno will discuss how companies can use data analytics to better manage the effects of customers’ life events on their future customer needs. Using a real-world data set of over 21,000 customers from a financial services retailer, this study empirically shows the impact of life events on product possession and demonstrates the added value of predicting life events for decision making. A deep convolutional neural network using structured customer data and unstructured textual data achieved best predictive performance.
Yulia Vakulenko – Lund University, Sweden
This presentation will focus on a piece of retail research applying text mining techniques to capture customers’ experience. The research take a closer look at online star ratings and associated reviews as a proxy for customer satisfaction and a reflection of consumer experience. In line with previous evidence, online user ratings go beyond the traditional notion of “product rating”, as online consumers keep utilising online review platforms to cover various elements on product and service related matters. The latest investigation adopts text mining techniques to reveal which experience touch points compose the online ratings.
|Period||2021 Apr 16|
|Degree of Recognition||International|
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