5 Benefits of Predictive Analytics for E-Commerce
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This document discusses predictive analytics and its applications. It provides an overview of predictive analytics, its lifecycle, common tools used like R and Python, and real-life examples of how organizations use predictive analytics. Specifically, it discusses how predictive analytics can be used for e-commerce, including predicting who is likely to buy, collecting and preparing data from Google Analytics to build predictive models, and provides a code demo.
5 Benefits of Predictive Analytics for E-Commerce
- 2. Slide 2 www.edureka.co/advanced-predictive-modelling-in-r What are we going to learn today ? At the end of this session, you will be able to know: What is Predictive Analytics Applications for Predictive Analytics How organizations are using Predictive Analytics Tools used for Predictive Analytics Use of Predictive Analytics in eCommerce Hands on - Designing a Predictive Model
- 3. Slide 3Slide 3Slide 3 www.edureka.co/advanced-predictive-modelling-in-r Predictive analytics is the analysis of data by using statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data Predictive Analytics
- 4. Slide 4Slide 4Slide 4 www.edureka.co/advanced-predictive-modelling-in-r Predictive Analytics Lifecycle Source: blogs.sas.com
- 5. Slide 5Slide 5Slide 5 www.edureka.co/advanced-predictive-modelling-in-r Applications for Predictive Analytics Source: www.academia.edu
- 6. Slide 6Slide 6Slide 6 www.edureka.co/advanced-predictive-modelling-in-r Real-Life Examples Source: www.academia.edu Commonwealth Bank can reliably predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction being initiated Lenovo achieved 50% reduction in issue detection time Salt River Project forecasting model helps them to know the best time to sell excess electricity for the best price Staples analyzes online and offline consumer behavior to provide a complete picture of their customers and realized a 137% ROI
- 7. Slide 7Slide 7Slide 7 www.edureka.co/advanced-predictive-modelling-in-r Thoughtful Predictive Analytics ? Thoughtful Predictive Analytics
- 8. Slide 8Slide 8Slide 8 www.edureka.co/advanced-predictive-modelling-in-r Which Path to Follow
- 9. Slide 9 www.edureka.co/advanced-predictive-modelling-in-r Predictive Analytics Tools & Softwares
- 10. Slide 10 www.edureka.co/advanced-predictive-modelling-in-r Why R ? Tool usage comparison http://www.oreilly.com/data/free/files/stratasurvey.pdf
- 11. Slide 11 www.edureka.co/advanced-predictive-modelling-in-r Why R ? “R has really become the second language for people coming out of grad school now, and there’s an amazing amount of code being written for it,” said Max Kuhn, Associate Director of Statistics, Pfizer Comparing R and SAS http://r4stats.com/articles/popularity/
- 12. Slide 12 www.edureka.co/advanced-predictive-modelling-in-r Most heated debate- R or Python Python is a generic programming language and it is great at that. But it is not specifically for data analysis Following points make it clear why to choose R for Data analysis than Python : R was specifically designed for Data Analysis The user base for R as a statistics language is gigantic compared to any other language R has more advanced statistical functionality than Python R has better visualization capabilities than Python R has a better cross platform compatibility than Python
- 13. Slide 13 www.edureka.co/advanced-predictive-modelling-in-r Predictive Analytics for e-Commerce
- 14. Slide 14 www.edureka.co/advanced-predictive-modelling-in-r Predictive Analytics for e-Commerce eCommerce and online retailers are perfect fit for use of predictive analytics. Predictive analytics can serve following purposes for eCommerce • Predictive Search • Recommendations • Pricing Management • Supply Chain Management • Business Intelligence
- 15. Slide 15 www.edureka.co/advanced-predictive-modelling-in-r Predictive Analytics for e-Commerce All eCommerce giants are in race for increasing their customer base. Increasing the customer base is a two step process : Attracting more users (visitors) Converting visitors into customers This leads to two questions Who will visit/revisit the website in next couple of days ? Who are likely to buy ?
- 16. Slide 16 www.edureka.co/advanced-predictive-modelling-in-r Predictive Analytics – Who is likely to buy Some users are more likely to buy when compared to others Following parameters can help, to find out users who are more likely to buy Visit Count Average time spent on website Days since last visit Page Views
- 17. Slide 17 www.edureka.co/advanced-predictive-modelling-in-r Collecting and Preparing the data To build a predictive model we will require the data, which can be achieved from many different places one of which is Google Analytics Google Analytics can be used to provide following data Visitor Id Visitor Type Visit Count Landing Page Exit Page Average time spent on website Page views Unique page views Days since last visit Medium (organic search or direct)
- 18. Slide 18 www.edureka.co/advanced-predictive-modelling-in-r Designing the Predictive Model Code Demo
- 19. Slide 19 Course Url Thank You … Questions/Queries/Feedback Recording and presentation will be made available to you within 24 hours