Currently researching in Support Vector Machines. Supervisors: Subhas Sarin Phone: 9873762277 Address: B 302 Garden Mansion Apartments Airport Road Bangalore 560008 Karnataka India
For a successful business, engaging in an effective campaign is a key task for marketers. Most pr... more For a successful business, engaging in an effective campaign is a key task for marketers. Most previous studies used various mathematical models to segment customers without considering the correlation between customer segmentation and a campaign. This work presents a conceptual model by studying the significant campaign-dependent variables of customer targeting in customer segmentation context. In this way, the processes of customer segmentation and targeting thus can be linked and solved together. The outcomes of customer segmentation of this study could be more meaningful and relevant for marketers. This investigation applies a customer life time value (LTV) model to assess the fitness between targeted customer groups and marketing strategies. To integrate customer segmentation and customer targeting, this work uses the genetic algorithm (GA) to determine the optimized marketing strategy. Later, we suggest using C&RT (Classification and Regression Tree) in SPSS PASW Modeler as th...
Brian Bowman is Technical Leader for Integrated Decisions and Systems in the Research and Develop... more Brian Bowman is Technical Leader for Integrated Decisions and Systems in the Research and Development Group. Brian is working on forecasting, optimisation and decision analysis problems in the area of pricing and revenue management. He has used his working expertise in ...
We discuss some of the problems in B2B (Business to Business) domain and discuss how graph based ... more We discuss some of the problems in B2B (Business to Business) domain and discuss how graph based association analysis can solve those with a focus on interactions among companies, though we also consider social network within an organization. Data Mining tools may no longer be sufficient or even relevant to handle explosion in data types. Graphs are a great visualization aid. Conversations today have become digitized, and so have the tacit components; but are still fuzzy. Here are B2B use cases that can be solved with Graphs: a) predict which clients could churn, b) detect relationships between businesses breaking away, c) predict which market will suffer next, such that the insights from it can help in generating better simulations, d) find key targets for acquisition, and e) content based link prediction for patent marketing. We explain algorithms on ranking using graphs and community detection in intersecting communities. We consider graph learning problems where the goal is to rank the objects relative to one another. We mention how B2B domain graph structure differs from that of Consumer segment. We mention support vector machines (SVM) as the chosen modeling technique. Our focus is to build use cases for association analysis in the B2B domain using graph theory, for which we can use some of the existing algorithms because solving the problem with large graphs can become an NP complete problem.
For a successful business, engaging in an effective campaign is a key task for marketers. Most pr... more For a successful business, engaging in an effective campaign is a key task for marketers. Most previous studies used various mathematical models to segment customers without considering the correlation between customer segmentation and a campaign. This work presents a conceptual model by studying the significant campaign-dependent variables of customer targeting in customer segmentation context. In this way, the processes of customer segmentation and targeting thus can be linked and solved together. The outcomes of customer segmentation of this study could be more meaningful and relevant for marketers. This investigation applies a customer life time value (LTV) model to assess the fitness between targeted customer groups and marketing strategies. To integrate customer segmentation and customer targeting, this work uses the genetic algorithm (GA) to determine the optimized marketing strategy. Later, we suggest using C&RT (Classification and Regression Tree) in SPSS PASW Modeler as th...
Brian Bowman is Technical Leader for Integrated Decisions and Systems in the Research and Develop... more Brian Bowman is Technical Leader for Integrated Decisions and Systems in the Research and Development Group. Brian is working on forecasting, optimisation and decision analysis problems in the area of pricing and revenue management. He has used his working expertise in ...
We discuss some of the problems in B2B (Business to Business) domain and discuss how graph based ... more We discuss some of the problems in B2B (Business to Business) domain and discuss how graph based association analysis can solve those with a focus on interactions among companies, though we also consider social network within an organization. Data Mining tools may no longer be sufficient or even relevant to handle explosion in data types. Graphs are a great visualization aid. Conversations today have become digitized, and so have the tacit components; but are still fuzzy. Here are B2B use cases that can be solved with Graphs: a) predict which clients could churn, b) detect relationships between businesses breaking away, c) predict which market will suffer next, such that the insights from it can help in generating better simulations, d) find key targets for acquisition, and e) content based link prediction for patent marketing. We explain algorithms on ranking using graphs and community detection in intersecting communities. We consider graph learning problems where the goal is to rank the objects relative to one another. We mention how B2B domain graph structure differs from that of Consumer segment. We mention support vector machines (SVM) as the chosen modeling technique. Our focus is to build use cases for association analysis in the B2B domain using graph theory, for which we can use some of the existing algorithms because solving the problem with large graphs can become an NP complete problem.
Uploads
Papers by Sanket Jain