242. IJRM. Innovating Customer Base Analysis.
December 1, 2022
Listen to full episode :
Marketing is the business of understanding marketplace competitors as well as defining, positioning, and promoting products to target customers. Marketers are tasked with developing the strategy to boost sales and revenue, and one of the ways they can do this is through customer base analysis.
Today we’re speaking with Jan Valendin.
Our conversation explores how he and his colleagues have used recurrent neural networks to innovate the process of customer base analysis.
Topics include:
How their deep learning model leverages observed behavior and auto feature extraction to predict future behavior.
Why having a large starting data set and at least a thousand customers is necessary to take advantage of the promise of this approach.
How using this approach can help marketers ranging from charities to streaming entertainment providers proactively manage their customer base.
Click here for the Research Article
The Up Next podcast’s access to this content is courtesy of the International Journal of Research in Marketing, an international, double-blind peer-reviewed journal for marketing academics and practitioners. IJRM aims to contribute to the marketing discipline by providing high-quality, original research which advances marketing knowledge and techniques. As marketers increasingly draw on diverse and sophisticated methods, IJRM‘s target audience is comprised of marketing scholars, practitioners (e.g., marketing research and consulting professionals) and policymakers.
IJRM aims to be at the forefront of the marketing field with a particular emphasis on bringing timely ideas to market. The journal embraces innovative research with the potential to spur future research and influence practice. Hence, it welcomes contributions in various aspects of marketing. The editors, while accepting a wide array of scholarly contributions from different disciplinary approaches, especially encourage research that is novel, visionary or path breaking.
Jan Valendin was born in Czechoslovakia in 1982, after finishing his Bachelors and Masters in Computer Science, Jan has worked with financial technology first as a developer, consultant, and application support analyst, later moving towards machine learning and working with neural networks as a research engineer. Between 2018 and 2022, Jan has helped to develop a tool for generating synthetic data and completed his PhD in Marketing focused on predicting customer behavior using recurrent neural networks.
Research co-authors:
Prof. Thomas Reutterer
Dr. Michael Platzer
Dr. Klaudius Kalcher
Follow & Learn More
International Journal of Research in Marketing
Jan Valendin