A Predictive Analytics Primer
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This document discusses predictive analytics and its importance for managers. It explains that predictive analysis uses customer data collected through digital marketing to predict future customer wants and needs. This allows companies to determine customer lifetime value, recommend the best products, and accurately forecast demand. The document outlines that while data scientists build predictive models, managers must communicate the meaning and implications of the analysis. It stresses that both making assumptions and checking their validity are essential parts of creating an effective predictive model.
A Predictive Analytics Primer
- 1. Predict the future! A Predictive Analytics Primer Thomas H. Davenport
- 2. Be a “Bhavisyavani” (The future predictor) What customers see or shop, all these data are collected and analysed to predict what they will want in future from you.
- 3. Learning 1: Customer lifetime value (CLTV) How much customer will buy from the company over a time Do you have the next best thing? Do your website recommends the best product offering? This questions are answered by using Predictive Analysis.
- 4. Learning 2: Digital Marketing is at heart Majority of the data are collected through online customer touchpoints. The manager needs to decide where will be the maximum customers attracted and data can be easily gathered. Digital marketing strategies are important in very useful for current as well as future market developments.
- 5. Learning 3: Forecasting Data gathered through digital marketing is also useful for production planning. Production department can forecast demand with more accuracy and provides opportunity to fulfil customer’s wish.
- 6. Manager Vs Data Scientist A manager’s work is to communicate data and make meaningful interpretation of analysis presented by data science team To form hypothesis, gather data and maintain properly, a data scientist is working around the area of statistics and predictive analysis modelling.
- 7. Statistics isn’t wizardry! The myth in managers that they don’t have to crunch number or data will speak for themselves. Managers have to communicate right meaning of right data at right time.
- 8. Approach to the future A Good Manager Statistics Assumptions Data
- 9. The Data What customers are buying? Most common barrier for good predictive analyse.
- 10. The Statistics Regression Modelling Analysis Check Validity Set hypothesis Know which models to use. Set a hypothesis. Create a predictive model.
- 11. The Assumptions Assumptions in model Check validity Modify Re-run modelling Applicability of model A model without stated assumptions is no model at all! The one clause that makes model dynamic with time
- 12. When any one of them fails, you fail! Everyone assumed housing price will go up No one assumed that it could come down
- 13. When all succeeds, you succeed!
- 14. The Ultimate Checklist Source of data Data size and characteristics OutliersAssumptions Conditions violation assumptions
- 15. Data analysis is a spiral. More you analyse, more you drawn deep to the core. Abhishek Rana Follow me on LinkedIn here..