10/24/2015. Review the extant Marketing Literature to provide initial answers to the MSI research priorities. Review Big Marketing Data Analytics

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1 Review the extant Marketing Literature to provide initial answers to the MSI research priorities Review Big Marketing Data Analytics Identify open issues and an outlook for the future Our Framework Types of Data Analytics and Analytical Methods Analytics for Marketing Decisions Marketing Mix Personalization Privacy & Security Analytics Education & Training Implementing Big Data & Analytics In Firms The Future of Marketing Analytics 1

2 1910 First Market/Sales Assessment (Parlin) 1919 Internal/External Data (Duncan) 1924 Eye tracking for advertising (Nixon) 1935 AIDA and copy research (Starch) 1923 Nielsen: store audits, radio & TV audience 1935 GfK: (Telephone) interviewing 1972 POS scanning 1995 DARPA: www, clickstream data 1998 Google: Keyword search data 2004 Facebook: Social media data 2006 Twitter: online WOM data 2007 SmartPhones: location and path data Digital data Rich Data : Descriptive methods to characterize observable market conditions, : Use of theories on consumer behavior from psychology and economics to measuring variables and to provide insights, : Predictive Statistical and Econometric approaches for supporting marketing decision making, : Prescriptive OR based optimization methods to derive optimal marketing decisions These phases are likely to repeat themselves in the Rich Data era: As data Volume, Variety and Velocity increase, predictive and prescriptive analytics play catch up. 2

3 Small Stats on Big Data Developments in computation (practitioners) Grid and cloud computing, computing on graphic cards, parallel processing Simpler descriptive modeling approaches Probability models, CS and Machine Learning methods, closed form solutions Speed improvements in algorithms Variational inference, direct sampling, parallelization of EM and MCMC Application of aggregation and sampling Data fusion, bagging/boosting, variable selection for reducing dimensionality Big Stats on Small Data (academics) 3

4 Generation of Data Strategic time frame Short term Long term Within the Firm Web/Mobile/ Retail Analytics Marketing Mix Recommendations Personalization Security /Privacy Search Marketing Behavioral Targeting Pth Path to Purchase Online reviews Big Big Data Analytics CRM Analytics Mobile Web UGC External to Firm Social Analytics Trend Forecasting Competitive Intelligence Granular Customer Level Data Observational, Experimental, Social, In Store, Web, Mobile Consumer Sentiment, Sales, Marketing Actions Better identification of causal effects and issues of endogeneity Customer Path to Purchase, Cross Channel Behavior & Migration Future Research Questions Aligning insights from multiple data sources, a.o. aggregate data and aggregate data Dynamic, multi time period problems, accommodating forward looking behavior Different planning cycles and levels of granularity for different marketing instruments 4

5 Personalization Adapting product/service/marketing mix to individuals Passive Personalization Pull Personalization Push Personalization e.g., personalized coupons at e.g., customer specifically e.g., pushing to customers checkout demand it without explicit request Recommendation Systems Adaptive Personalization Website morphing 5

6 Two trends Wide availability of personal data has led to mosaic effect Government privacy and security laws not keeping pace with technology Two outcomes Stricter laws from governments e Self policing by firms Two implications for marketing analytics Data anonymization Data minimization Solutions Retaining sufficient stats, models for aggregate data, data augmentation Analytics is becoming pervasive across functional areas Need for a culture of evidence based decision making: In God we trust, all others must bring data Edward W. Deming Big Data center of excellence (CoE) & Chief Analytics Officer A hybrid governance structure for analytics Reducing the impact of silos Best in class: Amazon, LinkedIn 6

7 Industry practice has demonstrated that opportunities are enormous Important to understand the degree to which data/analytics fit the needs of specific defensive and offensive marketing problems Judicious mix (fusion) of observational data (transaction, web, social, mobile), quasi and field experiments, i and descriptive, i explanatory and predictive i analytics Challenge is reconciliation of academic big stats on small data with practitioner small stats on big data Academic marketing analytics involves disparate domains Challenge is further integration of these fields in the education of the next generations of marketing analysts to have sufficiently broad and deep knowledge in each of these Privacy and security concerns will limit collection/retention of data Focus on the development of analytics that can overcome such limitations Proactive development of methods for protection of customer privacy For marketing analytics to have its full impact for big data Firms need to have a culture that recognizes the importance of data, analytics and data driven decision making Have a critical mass of marketing analysts covering different domains Have a governance structure that prevents silos and facilitates integrating data and analytics into the firm s overall strategy 7

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