08.02.2018
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The document discusses the importance of predictive analysis for organizations. It recommends that companies measure customer lifetime value to understand how much customers will spend over time. Analytical models can be used to predict what customers are likely to purchase next through digital marketing. Quantitative analysis involves making assumptions about data, statistics, and other factors. Key factors that influence sales include customer purchase history, product attributes, and customer demographics. Regression analysis is useful for determining which correlated variables influence sales of particular products. Managers must ensure predictive models remain valid by updating assumptions over time to avoid outdated insights.
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08.02.2018
- 1. A Predictive Analysis Primer By : Thomas H. Davenport
- 2. Interesting Insights from the Article
- 3. Customer Lifetime Value (CLTV) should be measured by each and every organization to have an idea how much a customer will buy over time from the company.
- 4. Analytical Prediction of what the customer is likely to buy next should be done using digital marketing models.
- 5. Quantitative Analysis is done with a lot of important assumptions viz. Data, Statistics etc.
- 6. Who the customers are buying, what have they bought in the past, attributes of the products purchased and some demographic attributes of the customer are pivotal.
- 7. Regression analysis i.e. finding the right combination of correlated variables that influence the sales of a particular product is a primary tool for any organization.
- 8. The assumptions should be valid at every point of time so that the model cannot become outdated thus leading to crisis situation.
- 9. Relevancy of the Insights to a Manager in India
- 10. Manager should be highly aware of the validity of the assumptions used in the predictive model.
- 11. Moreover,