BigData 2013 - Workshop on Big Data Mining Techniques for Online Sales and Customer Service

IEEE workshop on "Big Data Mining Techniques for Online Sales and Customer Service". The workshop is being conducted in conjunction with the 2013 IEEE International Conference on Big Data by [24]7.
Please visit the website for the workshop here: http://www.247-inc.com/IEEE-Conference/Workshop_Ma...
Our rationale behind conducting the workshop is as follows:
The last few years have seen an explosive growth of e-commerce, fueled by cloud-based technologies and smart devices. As a consequence, businesses have experienced massive increases in volume of online customer engagements and have evolved their sales and service programs to leverage platforms like social media, online chat and mobile apps.
Large amounts of data are generated today from various channels like web, IVR and mobile. This increase in volume and diversity of data presents severe challenges in understanding and modelling of customer intents and behaviour, needs and expectations because of factors like data size, data noise, anonymity of customers, among others. However, sales and service organizations fully realize the value of the competitive advantage that effective mining of this data can offer them.
Effective mining of Big Data continues to be a challenge and has emerged as one of the hottest areas of research among academicians and data scientists. This has led to an aggressive search for suitable methods that can intelligently process such data at scale to understand customer behaviour in general, and to achieve specific goals like increasing conversion rate, reducing shopping cart abandonment rate, providing personalized multichannel and multimodal interactive support, etc.
This workshop aims to bring together researchers from both industry and academia to participate and present their work related to various aspects of Big Data mining. The focus of the workshop is on the methods, frameworks, tools, and platforms related to big-data mining in the area of online sales and customer service.
Topics:
We invite the submission of original unpublished research and innovative work that is related to, but not limited to, the topics listed below:
? Big data Infrastructure for mining customer interaction data (e.g. cloud-based computation, map-reduce)
? Large scale data storage and retrieval for ad-hoc querying or otherwise
? Automatic discovery of new trends in online customer interests and intents
? Machine learning algorithms to provide customers predictive multichannel and multimodal support
? Dynamic predictive models for providing multichannel/multimodal customer support
? Algorithms for developing user profiles
? IVR analytics
? Mobile analytics
? Case studies of big data mining applications for providing online customer support
Program Committee Chairs
? Prof. Jaideep Srivastava (University of Minnesota, USA)
? Dr. Ravi Vijayaraghavan ([24]7 Innovation Labs, Bangalore)
Program Committee Members
? Prof Ram Akella (University of California, USA)
? Prof. Galit Shmueli (Indian School of Business, India)
? Dr Rajesh Parekh (Director, GroupOn, USA)
? Prof. Joydeep Ghosh (University of Texas. Austin, USA)
? Dr. B. Ravindran (Indian Institute of Technology, Madras, India)
? Dr. Ashish Tendulkar (Researcher, Machine Learning, Reliance Industries Ltd., Mumbai, India)
? Dr S R Kulkarni ([24]7 Innovation Labs, Bangalore, India)
Important Dates
July 30, 2013: Due date for full workshop papers submission
August 20, 2013: Notification of paper acceptance to authors
September 10, 2013: Camera-ready of accepted papers
October 6-9, 2013: Workshops
Please visit the website for the workshop at http://www.247-inc.com/IEEE-Conference/Workshop_Ma... for further details.
[24]7 provides software and services that make it simple for consumers to connect with companies to get things done. Our products are driven by predictive analytics, real-time decisioning, and data sciences. You can find out more about us by visiting our website at http://www.247-inc.com/.
Regards,
Abhishek Ghose (abhishek.ghose-AT-247-inc.com)
Senior Data Scientist, Innovation Labs, [24]7