From theory to practice: AI in retail with the CRISP-DM framework
In this micro-content offer, we dive into the world of artificial intelligence in retail with Daniela Wiehenbrauk and explore one of the most important tools in the field of data analysis: the CRISP-DM framework, short for Cross-Industry Standard Process for Data Mining.
CRISP-DM is a proven, cross-industry process that helps you carry out your data analysis projects in a structured and efficient way. Whether you are new to the world of data analysis or already have experience in it – this framework provides you with a clear and comprehensive guide.
The CRISP-DM framework also forms the starting point of our use cases. In our videos, experts from various companies show how they use artificial intelligence. They explain this step by step, based on the six phases of the CRISP-DM framework.
AI in retail: Size Recommendation with AI at Breuninger
In this use case, Alec Sproten, Head of Data Analytics at Breuninger, a leading luxury and premium fashion retailer, presents how Artificial Intelligence is being used to drastically reduce return rates. How is this supposed to work? With an AI-based size recommendation system.
What can you expect in this video?
In-depth insights into the world of data: Alec Sproten explains how various data sources are used to better understand the range and to suggest more suitable size recommendations for customers. From analysing the causes of returns to using fitting model data.
AI in action: Insights into the development of AI models to detect size discrepancies, and how continuous training and adjustments can constantly improve the model in order to increase effectiveness, boost turnover and reduce return rates.
AI in Retail: Age Verification Using AI at Diebold Nixdorf
Christoph Annemüller heads AI Product Management within the Retail Division at Diebold Nixdorf, a financial and retail technology company, and demonstrates how AI-based age verification can be used in retail.
What can you expect in this video?
Insight into the AI-based self-service area: Find out why a smooth process is so important for customers and how AI-based solutions help to avoid customer frustration.
Challenges of age verification: Christoph Annemüller explains why a seamless process in age verification is so crucial and how AI-based solutions can help to simplify the purchasing process for age-restricted items.
Data protection and anonymity: Both play a central role in such technology. It is explained how AI-based solutions can verify age with just a selfie from customers, without the need for further personal data.
Importance for retail: Gain an insight into how this technology can reduce the workload for staff.
AI in Retail: Contextual Product Recommendations at Würth
Bernd Mai (Head of Big Data & Sales Analytics), Mika Straka (Head of Big Data Research) and Silas Eyrich (Head of Big Data International) take you on an exciting journey behind the scenes at Würth, the global market leader in fastening materials. In the video, they show how Würth uses Big Data Analytics to offer every customer the right product at the right time.
What can you expect in this video?
The challenge of data analysis: Find out how transaction data, master data and movement data are used to generate precise product suggestions.
Hybrid recommendation system: Discover how different models and algorithms work together to generate the best recommendations for customers, whether online or in-store.
Integration into sales frontends: See how machine learning systems are integrated into the daily work of sales staff and how they support the sales process.
AI in retail: Preventing customer churn at Kaufland
Kay Sakkiettibutra, Head of Analytics (CRM) at Kaufland Stiftung & Co. KG, provides exclusive insights into how customer churn can be prevented and customer loyalty strengthened using data-driven solutions.
What can you expect in this video?
Identifying and preventing customer churn: Gain an insight into how Kaufland uses anonymised analysis of shopping data to measure customer loyalty and identify trends in customer churn.
Practical application of data: Discover how Kaufland leverages these analyses to develop individual and regional marketing strategies and foster customer loyalty.
Data protection: An important topic! Kay Sakkiettibutra explains how Kaufland handles customer data and ensures data protection.
Online course: AI in Retail
This micro-content is part of the online course "AI in Retail".
If you’ve become curious, you can enrol for the course here: