Big Data and Marketing
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2 Big Data and Marketing Professor Venky Shankar Coleman Chair in Marketing Director, Center for Retailing Studies Mays Business School Texas A&M University Copyright Venkatesh Shankar. All rights reserved.
3 Agenda What is Big Data? The Big Deal about Big Data Marketing Problems Models/Tools Applications My Research Future Outlook Copyright Venkatesh Shankar. All rights reserved. 2
4 What is Big Data? Copyright Venkatesh Shankar. All rights reserved.
5 What is Big Data? Large and complex data with challenges to collect, curate, store, search, transfer, analyze, and visualize Requires massively parallel software running on thousands of servers Characterized by 4Vs: Volume, Velocity, Variety, and Veracity Propelled by the rise of SMACIT (Social, Mobile, Analytics, Cloud, Internet of Things) Analysis requires models to accommodate 4Vs 4 Shankar, Berry, Dotzel. All rights reserved.
6 The Big Deal about Big Data Copyright Venkatesh Shankar. All rights reserved.
7 The Big Deal about Big Data In God we trust, all others bring data. - William Deming Copyright Venkatesh Shankar. All rights reserved. 7
8 The Big Deal about Big Data Internet data volume: 670 exabytes in 2014; 2.6 exabytes daily Business data double every 1.2 years 500,000 data centers By 2020, 1/3 data through cloud, 35 zettabytes of data Market for big data applications $17B 2015; $50B in 2017 (BMO Capital market report) $200B business (McKinsey & Co) Companies investing in Big Data will outperform others by 20% (Gartner) Volume, Velocity (Walmart: 1M transactions/hr-2.5k terabytes) IBM spearheading it Emergence of data science/data scientist: 4.4M needed by 2015 (Gartner) Copyright Venkatesh Shankar. All rights reserved. 8
9 Big Data Social Application Copyright Venkatesh Shankar. All rights reserved. 9
10 Marketing Problems for Big Data Copyright Venkatesh Shankar. All rights reserved.
11 Marketing Problems CLV analysis Segmentation and targeting Marketing mix allocation Promotional planning Sales force optimization Sales force productivity analysis Optimal media allocation Retail assortment planning Product recommendation system Cross-category management Business model analysis Mobile couponing Copyright Venkatesh Shankar. All rights reserved. 11
12 Big Data Application Copyright Venkatesh Shankar. All rights reserved. 12
13 Big Data Models/Tools Copyright Venkatesh Shankar. All rights reserved.
14 Collaborative Filtering Copyright Venkatesh Shankar. All rights reserved. 14
15 Models: Supervised Learning Big data ideal for predictive and prescriptive models Linear regression, Logistic regression, Decision trees [CHAID] Ensembles (Bagging, Boosting, Random forest) Neural networks Backpropagation algorithm, Gradient checking, Random initialization, Autonomous driving Support vector machine (SVM) Large scale machine learning systems Copyright Venkatesh Shankar. All rights reserved. 15
16 Models: Unsupervised Learning Clustering (BIRCH, Hierarchical, k-means, EM) Dimensionality reduction (Factor analysis, PCA) Anomaly detection (knn, Local outlier factor) Self organizing maps (SOM) Adaptive resonance theory (ARF) Latent Dirichlet Allocation (LDA) Structured prediction/graphical/visualization tools (CRF, HMM, VOS). Copyright Venkatesh Shankar. All rights reserved. 16
17 Other Models Propensity scoring/matching model Stochastic models, MVDP Spatial models Genetic algorithms Agent modeling Learning curves Recommendation systems Social network mapping Copyright Venkatesh Shankar. All rights reserved. 17
18 Big Data Applications Copyright Venkatesh Shankar. All rights reserved.
19 Applications: Gilt Within 1 minute at noon, 3,000 versions of message sent to customers based on analysis of 5-year history 65% sales in a 90-minute period = Amazon s daily sales 30% sales from mobile device Cloud based data capture Big data analysis helped add 1M customers each year Helped launch gourmet food, wine Copyright Venkatesh Shankar. All rights reserved. 19
20 Applications: Linkedin Successful data products People you may know Jobs you may be interested in Groups you may like Inmaps Annual network summary Marketing decisions Decision-maker targeting using Inmaps Linkedin Services using propensity to buy models Rapid testing of multiple website changes Copyright Venkatesh Shankar. All rights reserved. 20
21 Other Applications Chemical company: Sales force territory alignment Business equipment firm: Third party sales force productivity GE: Intelligent aircraft engines, turbines, BOPs, MRIs Wells Fargo: Path to customer attrition Vodafone: Customer churn analysis Nationwide: Demand generation: 15-20% for 4 years; 11% fewer headcount; $5M savings; National campaign over high spending in sales agent concentrated MSAs Tesco: Targeted promotions. Net profits grew 7X ( ) PayPal: Identified an overlooked acquisition channel Copyright Venkatesh Shankar. All rights reserved. 20
22 Amazon: Anticipatory Shipping Copyright Venkatesh Shankar. All rights reserved.
23 My Research in Big Data Aisle and display adjacency through spatial modeling (with Bezawada, Balachander, Kannan) Third party sales force productivity using MVDP models (with Voleti) Multichannel marketing allocation using Bayesian HMM and optimization (with Kushwaha) Multichannel targeting using HB conditional probability models Market evolution through semi-parametric market response model (with Lin) Business model evaluation using Bayesian clustering and graphical models (with Mallick) Market basket size forecasting through Bayesian variable selection model (with Johnson) Copyright Venkatesh Shankar. All rights reserved. 22
24 Future Outlook for Big Data Copyright Venkatesh Shankar. All rights reserved.
25 Future Outlook Will big data lead to better/newer models? Will big data lead to bigger insights or better judgment? Successes: Google translate, Google trends Failures: Google flu trends, Award predictions based on tweets Machine learning: Science of getting computers to learn without being explicitly programmed. Examples: Google s page ranking Apple s iphoto Spam recognition Reducing big data to small relevant data for decision-making Marketing expert systems, Marketing machines Test and learning built-in models Blend of marketing and engineering research cultures Copyright Venkatesh Shankar. All rights reserved. 25
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