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Pre­dic­tion of cus­tomer prob­ab­il­ity to churn

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Com­mu­nic­a­tions, Media & Telecom  - Pre­dic­tion of cus­tomer prob­ab­il­ity to churn

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Com­mu­nic­a­tions, Media & Telecom

With sup­port by synvert, a tele­com­mu­nic­a­tions com­pany developed a pipeline using Azure and Python to pre­dict cus­tomer churn reas­ons and tim­ing, improv­ing tar­geted mar­ket­ing cam­paigns and increas­ing the like­li­hood of identi­fy­ing true churn­ers by 20%. Synvert tuned a clas­si­fic­a­tion model to report churn drivers and provide daily pre­dic­tions for action­able insights.

Ini­tial situation

The cli­ent was faced with the scen­ario that there was a high turnover and many of their cus­tom­ers wanted to ter­min­ate their con­tract after the first year.

The chal­lenge was to pre­dict what reas­ons a cus­tomer might have to ter­min­ate and when.

Archi­tec­ture

Open source and Big Data tech­no­lo­gies (Azure, Python) have enabled the devel­op­ment of the pipeline, which stores the pre­dicted res­ults in an addi­tional database.

Com­pan­ies can query the res­ults there. The pipeline has been developed using Azure Data Fact­ory and the pro­ject has been based on the Scrum methodology.

Gen­er­ated bene­fits

The increased busi­ness under­stand­ing of churn drivers leads to improved and tar­geted mar­ket­ing cam­paign design.

Given a spe­cific tar­get, they are now 20% more likely to identify a true churner.

Ser­vices accom­plished by synvert 

synvert has tuned a clas­si­fic­a­tion model based on spe­cific cli­ent data.

After sev­eral iter­a­tions and through con­tinu­ous busi­ness and tech­nical val­id­a­tion, the model is able to report the most import­ant drivers of churn as well as provid­ing daily pre­dic­tions to the busi­ness that can now take actions on spe­cific customers.

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