Quantitative Analytics: Past, present and future

Transcription

1 BADM Seminar, Horsens, Sep 2013 Quantitative Analytics: Past, present and future Joachim Scholderer Hans Jørn Juhl

2 OVERVIEW Who we are Our history QUANTS today What we do Our education Our research agenda Where we are going

3 WHO WE ARE From Institut for Statistik og Datalogi to Quantitative Analytics

4 IFI- MAR Exit ECON Econometrics Statistics Management Performance Management M er Quantitative Analytics (QUANTS) Finance Statistics Marketing g er Exit ICOA Logistics

5 QUANTS TODAY Professors Associate Professors Assistant Professors and PostDocs PhD Students and Research Assistants

6 WHAT WE DO Our education and research

7 WHAT WE TEACH YOUR STUDENTS BSc Statistics I Statistics II Philosophy of Science II Quantitative Methods Marketing Management Marketing and Consumer Behaviour HD Data Analysis I Data Analysis II Applied Marketing Research MBA Analytics and Decision Tools Marketing Management

8 WHAT WE TEACH YOUR STUDENTS MSc Business Analytics Management Research Methods Marketing Research Advanced Marketing Research SAS and SQL for Business Analytics Bayesian Networks Data Warehousing Data Mining Online Marketing Economic Psychology Neuroeconomics Managing Intellectual Property PhD Linear Statistical Models

9 OUR RESEARCH AGENDA The QUANTS group seeks to understand the behaviour of markets through the modelling of individual preferences and choices, based on attitudinal, behavioural and cognitive data. Our approach combines traditional psychometric and econometric techniques (multivariate analysis, models for limited dependent variables, analysis of panel data, stochastic market models) with data mining, simulation, and computational modelling

10 CURRENT WORK Implicit Measures of Social Cognition Nonlinear Structural Equation Models Psychometrics Multivariate Analysis Canonical Analysis Multiplex Network Analysis Finite Mixtures Process Tracing Methods Text Mining Machine Learning Market Basket Analysis Panel Models Marketing Research Efficient Designs for Choice Experiments Judgment and Decision Making Computational Models of Cognition Causal Search Algorithms NBD Dirichlet Models Point Processes Bayesian Networks Relational Data Structures Channel Attribution Customer Portfolio Models Invariance Violations and Prominence Effects

11 RELATIONS TO OTHER RESEARCH GROUPS COBE IS MAPP QUANTS IM ML

12 WHERE WE ARE GOING QUANTS and the future

13 FUTURE RESEARCH Marketing research in the age of big data Survey research becomes less and less important, objective data (customer databases, scanner data, web analytics, experimental data) begin to dominate Modelling and mining relational data structures One of our ambitions is to build a bridge between statistics, machine learning and network analysis AU Cognition and Behaviour Lab Closer collaboration with behavioural economists (at ECON) and cognitive neuroscientists (at CFIN/IMC)

14 FUTURE EDUCATION ISSUES Curriculum revisions Better integration of methods and stats courses into the overall programme curricula Capacity issues Teaching load has increased so far beyond nominal capacity that time for research is a rare luxury Focusing will be required We may have to phase out some duplicate and nonmethod teaching by QUANTS members. Alternatively, consolidate the BADM programme portfolio

15 QUESTIONS???