Predicting customer churn in insurance using SPSS Modeler


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0:00 begin this insurance example in cartons workspace an easy to use unified 0:04 workspace which empowers the typical business user 0:06 with the self service experience 30 footprint web-based infrastructure 0:10 with my customized dashboard a combined historical customer information for my 0:14 data warehouse 0:14 with data exported from my predictive model created in SPSS modeler 0:18 or consuming a state in my car was portal I can specifically see 0:22 which of my customers are most at risk defecting from their insurance policies 0:25 what's up for renewal 0:26 I can also see what the impact of losing his customers will be 0:29 to my overall business based on their customer lifetime value as well as 0:33 which retention offers they're most likely to accept in my data mining and 0:36 analytical work bench 0:37 known as SPSS modeler I can see the model is comprised of a series of 0:41 interconnected nodes 0:42 which visually shows the floor the data starting on the left 0:45 we're looking at the days or snowed which in this case contains my 0:48 transactional 0:49 and demographic customer information like policy type marital status 0:52 and vehicle size as well as customer lifetime value ourself like to find 0:56 which indicates whether customers current her has churned in this field is 1:00 what will use the target heart shaped remodel algorithm 1:03 they were using this model with the library almost forty different 1:05 algorithms to appease even the expert modelers 1:08 it can be difficult to know which algorithm to use so to help the less 1:11 experienced users get started 1:12 there are even some auto modeling notes which will run and compare several 1:16 veterans 1:16 once the open at the modeling algorithm is the predictive model 1:20 which is the Golden Nugget you see here the graphing allows me to view the 1:23 results of the model 1:24 as a graphical output I can go to the end the stream 1:27 where the output is generated and run it where can see the two columns were added 1:30 by scoring model 1:32 active at the customer's predicted remain a customer and canceled if the 1:35 customer is likely to churn 1:37 the column side it shows the confidence are accuracy of this prediction 1:41 finally now that completed the model I wanna share this analysis for 1:44 consumption by my business community 1:46 in my comments be I portal their number of methods of exporting the data 1:50 which include publishing the results as a package which can be consumed by 1:53 report authors in Report Studio 1:55 using the IBM Cognos BI expert note I can use this information to focus on 1:59 those individual customers who are predicted to churn 2:02 I can see this particular customer has a high probability 2:05 and his customer satisfaction is low but he does have a strong customer lifetime 2:09 value 2:10 will want to make your attention offer in order to retain its customer