Social Media Data in Predictive Analytics
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Die Bedeutung und der Stellenwert von Social Media hat im Bereich der Marktforschung enorm zugenommen und wird in unserer Präsentation in den Mittelpunkt gestellt. Konsumenten informieren sich im Internet über Marken, Erfahrung werden ausgetauscht, Empfehlungen abgegeben bzw. Produkte über das Internet gekauft und hat sich als eine feste Komponente im Konsumentenverhalten etabliert. Social Media sammelt diese wertvollen Informationen und reflektiert realistisch Einstellung und Verhalten von Konsumenten, allerdings wird deren Potenzial nur unzureichend ausgeschöpft. Die Idee der Session ist sich auf Social Media zu konzentrieren und diese mit traditionellen Marktforschungsdaten zu vergleichen und zu verknüpfen. Anhand mehrerer Studien werden wir zeigen wie die Qualität von Vorhersagen durch eine Verknüpfung mit Social Media verbessert werden kann. In der Session werden dazu verschiedene Beispiele aufgegriffen und erläutert, wie verschiedene Daten integriert werden können und damit eine Verbesserung in Modellen erzielt werden kann. Gezeigt werden Anwendungen aus verschiedensten Bereichen wie Abverkäufe von DVDs in Frankreich bzw. Fahrzeugen in den US, auf Marken bzw. Store- Ebene, und die Verknüpfung verschiedener Datenquellen wie Brand Tracker bzw. Retailer-Daten. Die Beispiele zeigen die unterschiedlichen Facetten der Integration von Social Media und sollen Ideen für weitere Anwendungsbereich innerhalb der Predictive Analytics geben.
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Social Media Data in Predictive Analytics
- 1. 1© GfK 2015 | Predictive Analytics World Berlin | November 2015 Unleashing the potential of social media Predictive Analytics World Berlin - November 2015 Hwan Kim and Nina Meinel, GfK SE
- 2. 2© GfK 2015 | Predictive Analytics World Berlin | November 2015 Social Media is rich in information 1011010100101101010010110101001011010100101101010010110101001011010100101101010010110101001011 0101001011010100101101010001101010010110101001011010100101101010010110101001011010100101101010 0101101010010110101001011010100101101010010110101001011010100101101010010110101001011010100101 1010100101101010010110101001011010100101101010010110101001011010100101101010001101010010110101 0010110101001011010100101101010010110101001011010100101101010010110101001011010100101101010010 1101010010110101001011010100101101010010110101001011010100101101010010110101001011010100101101 0100101101010010110101001011010100011010100101101010010110101001011010100101101010010110101001 0110101001011010100101101010010110101001011010100101101010010110101001011010100101101010010110 1010010110101001011010100101101010010110101001011010100101101010010110101001011010100011010100
- 3. 3© GfK 2015 | Predictive Analytics World Berlin | November 2015 1011010100101101010010110101001011010100101101010010110101001011010100101101010010110101001011 0101001011010100101101010001101010010110101001011010100101101010010110101001011010100101101010 0101101010010110101001011010100101101010010110101001011010100101101010010110101001011010100101 1010100101101010010110101001011010100101101010010110101001011010100101101010001101010010110101 0010110101001011010100101101010010110101001011010100101101010010110101001011010100101101010010 1101010010110101001011010100101101010010110101001011010100101101010010110101001011010100101101 0100101101010010110101001011010100011010100101101010010110101001011010100101101010010110101001 0110101001011010100101101010010110101001011010100101101010010110101001011010100101101010010110 1010010110101001011010100101101010010110101001011010100101101010010110101001011010100011010100 Panels Brand Trackers Sales & Distribution But maximum value comes from connecting the dots
- 4. 4© GfK 2015 | Predictive Analytics World Berlin | November 2015 Social Media Intelligence Meets Brand Tracker
- 5. 5© GfK 2015 | Predictive Analytics World Berlin | November 2015 Or more provocative Can't we just crawl the web and replace traditional research?
- 6. 6© GfK 2015 | Predictive Analytics World Berlin | November 2015 Social Media Analysis 2.0: More than buzz & sentiment? An interdisciplinary team of experts from GfK Data & Marketing Sciences, GfK Brand & Customer Experience and Koen Pauwels from the University of Istanbul was investigating the following research questions Can Social Media KPIs … … possibly replace or enrich some or all brand tracking KPIs? … serve as an important augmentation to brand tracking KPIs in the prediction & mgmt. of brand performance? … serve as an early warning signal for brand tracking trends and sub- sequent shifts in market perfor- mance? … provide new and relevant diagnostic depth or breadth to brand tracking data?
- 7. 7© GfK 2015 | Predictive Analytics World Berlin | November 2015 Upper Funnel Key Measures Awareness, Familiarity Brand Measures Mentions, Net Sentiment, Passion Intensity Activity Read 3rd Party vehicle reviews etc Themes Around Activity Read 3rd party vehicle reviews etc Liked/ followed vehicle on facebook Twitter etc. Behavior Plan to shop at dealership Themes Around Behavior Plan to shop at dealership Intenders Over one year intenders Activity Google Trends, fb likes Make Share Reasons for First Choice Quality, Safety Media Share (Three Month) Outdoor/Billboard Ad Aware Themes Around Environmental Attributes Recall Economy Consumer confidence Dow Jones Index Unemployment rate Fuel price Ad Empathy Original, Likeable Make Principal Messages Styling, Durability Seasonality Seasonality Monthly Data (US) Full available starting from Jul 2009 onwardsSocial Media full available starting from Aug 2012 onwards Google & facebook: SMI definition, full available starting from Jan 2012 onwards Full available starting from Jul 2009 onwards Percentage of „top-box level“ or „yes“ answers GfK Owned Data Sources Help to Answer Research Objectives Brand Tracker Social Media Environmental Attributes ContentData
- 8. 8© GfK 2015 | Predictive Analytics World Berlin | November 2015 Causality Univariate Description Prediction Challenge The Twofold Process Contains a Filter Process as well as the Prediction Challenge • Variances too low/high Filtering 10% with lowest variance • Adjust season Deseasonalization for Sales • Sample size Social Media Mentions below 50 Correlation/Granger Conduct Granger analysis and Correlations Evaluation
- 9. 9© GfK 2015 | Predictive Analytics World Berlin | November 2015 Evaluation • Prediction performance • Model complexity • Interpretation Models DLM VAR SVM Hierarchical Bayes Lasso Dynamic Linear Models Fit and forecast univariate or multivariate time series that meet the assumption of a Gaussian distribution. Hierarchical Bayes Fits the time series using an iterative approach maximizing the posterior likelihood. Support vector machines Analyze data and recognize patterns for classification and regression analysis. Predicting Sales doing a Challenge With Following …
- 10. 10© GfK 2015 | Predictive Analytics World Berlin | November 2015 Index VAR SVM** Bayes** Lasso** DLM RMSE* (Root mean square error) 7.785 3.259 4.671 2.708 19.344 MAPE* (Mean absolute percentage error) 23.95% 12.97% 21.40% 10.42% 82.54% Complexity Stability Interpretability Importance Total Model Challenge Shows VAR and LagLasso as Best Suitable *: E.g., a RMSE (MAPE) of 2.708 (10.42%) is better than one of 4.671 (21.40%) because the smaller the error the better. **: Including monthly effects
- 11. 11© GfK 2015 | Predictive Analytics World Berlin | November 2015 Index Brand Tracker Social Media Brand Tracker & Social Media RMSE* (Root mean square error) 2.206 2.808 1.980 MAPE* (Mean absolute percentage error) 8.9% 13.0 % 8.6 % # Brand Tracker 8-11 - 5-9 # Social Media - 5-12 0-4 Total *: E.g., a RMSE (MAPE) of 1.980 (8.6%) is better than one of 2.808 (8.9%) because the smaller the error the better. #: Number of variables used Summary of Prediction Shows Brand Tracker in Combination with Social Media is Suitable to Get a Better Quality of Predictions
- 12. 12© GfK 2015 | Predictive Analytics World Berlin | November 2015 Social Media Intelligence meets Retail
- 13. 13© GfK 2015 | Predictive Analytics World Berlin | November 2015 Research question Can we enhance DVD forecast by adding Social Media?
- 14. 14© GfK 2015 | Predictive Analytics World Berlin | November 2015 Cinema entrees Movie sales Buzz Seasonality Multimedia BlueRay, TV, tablet, gaming console sales Sentiment Events Awards, christmas User-generated Genre Action, drama, fantasy, etc. Themes around Movies Visual effects, actor, sound, design Country of origin US, FR, etc. IMDB ratings Limited number of titles between 2010 to 2014 with focus on French market GfK Owned Data Sources Help to Answer Research Objectives Retail – Box Office Social Media External Attributes ContentData Full available starting from 2010 to 2014Three month period per movie starting from 2010 to 2014 Full available starting from 2010 to 2014 on monthly and weekly level
- 15. 15© GfK 2015 | Predictive Analytics World Berlin | November 2015 Causality Univariate Description Prediction Challenge The Twofold Process Contains a Filter Process as well as the Prediction Challenge • Variances too low/high Filtering 10% with lowest variance • Missing detection • Avoid multicollinearity Correlation/Granger Conduct Granger analysis for single KPIs and sales Comparison
- 16. 16© GfK 2015 | Predictive Analytics World Berlin | November 2015 Sales Box office External Social Media Intelligence (SMI) Season, Characteristics Sales Box office External Social Media Intelligence (SMI) Season, Characteristics 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection Including SMI data as predictors improves the prediction… Predictive Power measured as average prediction error* *RMSE – Root Mean Square Error Averagepredictionerror* Averagepredictionerror* Model without Social Media Model with Social Media
- 17. 17© GfK 2015 | Predictive Analytics World Berlin | November 2015 Sales Box office External Social Media Intelligence (SMI) Season, Characteristics Sales Box office External Social Media Intelligence (SMI) Season, Characteristics 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection What happens, if we adjust for the outlier in the model? Superiority decreases once we adjust for major outlier. Hence, SMI data seems predicting outliers well. Averagepredictionerror* Averagepredictionerror* weaker superiority when adjusting for the outlier Model without Social Media Model with Social Media *RMSE – Root Mean Square Error
- 18. 18© GfK 2015 | Predictive Analytics World Berlin | November 2015 Sales Box office External Social Media Intelligence (SMI) Season, Characteristics Sales Box office External Social Media Intelligence (SMI) Season, Characteristics 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection … but is using much more information for predicting sales *RMSE – Root Mean Square Error 6-11 predictors 31-34 predictors Averagepredictionerror* Averagepredictionerror* Model without Social Media Model with Social Media
- 19. 19© GfK 2015 | Predictive Analytics World Berlin | November 2015 Sales Box office External Social Media Intelligence (SMI) Season, Characteristics Sales Box office External Social Media Intelligence (SMI) Season, Characteristics 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection 0 2000 4000 6000 8000 10000 12000 14000 16000 Stepwise Regression Lasso & Stepwise Regression Subset Regression Manual Selection However, even a manual variable selection with sparse information including SMI data as predictors improves the prediction. *RMSE – Root Mean Square Error 6-11 predictors 31-34 predictors 9 predictors Averagepredictionerror* Averagepredictionerror* Model without Social Media Model with Social Media
- 20. 20© GfK 2015 | Predictive Analytics World Berlin | November 2015 It is not "either – or" • Big Data alone can (and will very often) be misleading! • Smart integration of Big Data and traditional approaches guarantees valid and deep insights • Our expertise in integrative modeling is our asset
- 21. 21© GfK 2015 | Predictive Analytics World Berlin | November 2015 Contact +33 1 7418 6271 Hwan Kim hwan.kim@gfk.com France - Paris +49 911 395 3961 Dr. Nina Meinel nina.meinel@gfk.com Germany - Nuremberg
- 22. 22© GfK 2015 | Predictive Analytics World Berlin | November 2015 THANK YOU