Predictive Analytics: Business Perspective & Use Cases
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This document discusses predictive analytics and its business applications. It references sources that describe how predictive analytics can help predict customer behavior and business trends, how its value increases as data becomes more complex, and how predictive models can help with customer segmentation, personalization, and anticipating package shipping needs for ecommerce companies. The document also briefly mentions the need for predictive analytics talent and issues around model adoption and integration.
Predictive Analytics: Business Perspective & Use Cases
- 2. Predicting The Future In Search of The Unknown Source: http://www.slideshare.net/idigdata/practical-predictive-analytics-with
- 3. Business Value Increases As It Gets Harder Source: http://diverseit.co.za/business-intelligence-bi-spectrum/
- 4. Hype Cycle for Emerging Technologies Source: http://www.gartner.com/newsroom/id/2819918
- 5. Trends, Customers, Business Source: The Data Warehousing Institute, TDWI Best Practices Report: Predictive Analytics for Business Advantage, 2014 First Quarter https://www.sap.com/bin/sapcom/en_us/downloadasset.2014-03-mar-17-21.predictive-analytics-for-business-advantage-pdf.html
- 6. Data-Rush Source: The Data Warehousing Institute, TDWI Best Practices Report: Predictive Analytics for Business Advantage, 2014 First Quarter https://www.sap.com/bin/sapcom/en_us/downloadasset.2014-03-mar-17-21.predictive-analytics-for-business-advantage-pdf.html
- 7. Talent, Adoption, Integration Source: The Data Warehousing Institute, TDWI Best Practices Report: Predictive Analytics for Business Advantage, 2014 First Quarter https://www.sap.com/bin/sapcom/en_us/downloadasset.2014-03-mar-17-21.predictive-analytics-for-business-advantage-pdf.html
- 8. Simple is Popular Source: The Data Warehousing Institute, TDWI Best Practices Report: Predictive Analytics for Business Advantage, 2014 First Quarter https://www.sap.com/bin/sapcom/en_us/downloadasset.2014-03-mar-17-21.predictive-analytics-for-business-advantage-pdf.html
- 9. They just need a start Source: The Data Warehousing Institute, TDWI Best Practices Report: Predictive Analytics for Business Advantage, 2014 First Quarter https://www.sap.com/bin/sapcom/en_us/downloadasset.2014-03-mar-17-21.predictive-analytics-for-business-advantage-pdf.html
- 10. Infrastructure
- 11. Funding = Expectations Source: http://conversionxl.com/predictive-analytics-changing-world-retail/?hvid=352IDw
- 12. Analytics Value Chain: Customer Purchase Decision for Ecommerce Source: http://www.slideshare.net/valiancesolutions/predictive-analytics-in-ecommerce
- 13. Segmentation & Personalization Segmentation is Product Centric “Customers who viewed/bought this also viewed/bought those” Better than a “lucky guess”, but not enough for Ecommerce True Personalization goes way beyond Segmentation Taking note of everything about the customer Every search query, every click, every add to cart, and every purchase, along with all the attributes associated with each Continuously learning about the customer and delivering an experience that responds directly to his or her intrinsic interests and immediate needs with each returned visit. Over time, this experience becomes smarter and more intuitive. The customer feels like their online engagement with a brand is natural, pain free, and even special. By combining individualized historical data with real time relevancy, we can better anticipate immediate needs Source: http://blog.reflektion.com/?p=17
- 14. If You are BIG, You can Predict HUGE Source: Amazon’s Patent on Anticipatory Package Shipping
Editor's Notes
- #9: Linear regression: Predict future price by using past data Decision tress: supervised learning to model specific target variables or outcomes of interest. Clustering is very useful in market segmentation, and marketing and sales are popular areas for predictive analytics among current users. Time series analysis is used for time-dependent data, popular for forecasting Logistic regression transforms info about the binary dependent variable into an unbounded continuous variable Neural networks are used when the exact nature of the relationship between inputs and output is not known. Nonlinear modelling. naive Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independenceassumptions between the features. Support Vector Machines (SVM) are used to detect and exploit complex patterns in data by clustering, classifying and ranking the data. They are learning machines that are used to perform binary classifications and regression estimations. They commonly use kernel based methods to apply linear classification techniques to non-linear classification problems. There are a number of types of SVM such as linear, polynomial, sigmoid etc. Survival analysis is another name for time to event analysis. These techniques were primarily developed in the medical and biological sciences, but they are also widely used in the social sciences like economics, as well as in engineering (reliability and failure time analysis). Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one.
- #10: Model Management Data Governance In-memory computing refers to data processing where data is stored in memory to reduce disk I/O. Models can run faster with in-memory computing, which can be good for iterative models. In-memory computing is also useful for interactive work such as visualization and data discovery. In-database analytics embeds analytics in the database. When the amount of data is large, it can be cheaper when computation is closer to the data