1.Find out clusters of customers who bought the product. Find out the distance of the cluster from this customer to indicate the likelihood of buying.
2.Use regression analysis. Y = a1*X1 + a2*X2 .....
where Y represents revenue for a product and X1,X2,X3 could represent causal factors such as age,geography etc.a1,a2,a3 are the coefficients. If age is statistically signigicant, the coefficient will have a significant value. Once age is confirmed to be statistically significant, we could have multiple causal variables
for each age bracket and then find out which among those is the most significant.
Oracle offers predictive analytics at several layers. PL/SQL/JAVA comes with an API for predictive analytics called DBMS_PREDICTIVE_ANALYTICS . Oracle also has a product called RTD - Real time decision making that is bundled with OBIEE - Oracle business intelligence enterprise edition.
Other tools like crystall ball, excel add on for predictive analytics and Oracle data mining are some other tools in Oracle's arsenal . Oracle Demantra provide predictive analytics related to forecasting and demand management. With IBM having acquired SPSS , the industry's landscape has become interesting.
Academic publications like "Competing on Analytics" by Harvard Press and the recent survey published in
MIT's Sloan Management Review on BI trends have contributed to the hieghtened interest and investment in this upcoming discipline. Companies like Netflix have grown to a billion dollar by predicting consumer buying patterns based on "clicks".