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AI Use Cases
synvert Accel­er­at­ors
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Suc­cess stories

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

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About

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.

Categories
All
Tech
Banking
Communications, Media & Telecom
Consumer Goods & Retail
Energy & Resources
Health & Life Science
Industrials
Insurance
Public Sector

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Retail Com­mis­sion Dia­gnostics Anom­aly Detection

Set­ting up a Reli­able and Power­ful Platform

Pre­dic­tion of cus­tomer prob­ab­il­ity to churn

Early Detec­tion of Mech­an­ical Issues

NLP​ in Auto­mot­ive Reporting

Oper­a­tional Data Lake

Real-Time Mar­ket­ing

Auto­matic clas­si­fic­a­tion of cars by their model

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Data Recon­cili­ation

Cent­ral­ized Data Warehouse

Data as a Ser­vice – BI & Big Data CC

Oper­a­tional Work/Support in Data Warehousing

Erosion Detec­tion with Com­puter Vision

Devel­op­ment and Imple­ment­a­tion of a Fully Auto­mated MLOps Platform

Pre­dic­tion and Alloc­a­tion Optim­iz­a­tion for sta­tion­ary Channels

Build­ing a Near Real-Time Recom­mend­a­tion System

Migra­tion AWS data plat­form to Google Cloud

Design and Devel­op­ment of a Google Cloud-Based Data Platform

Refact­or­ing, shift and new devel­op­ment of Data Ser­vices in Google Cloud

Estab­lish­ment of a Mas­ter Data Man­age­ment Foundation

Cent­ral Data Lake

Replace­ment of a com­plex IBM Data­stage structure

CRM ana­lyt­ics and lead management

Stand­ard­iz­a­tion of eval­u­ation processes

Devel­op­ment of an ERP data hub

Estab­lish­ment and fur­ther devel­op­ment of a DWH

Devel­op­ment of a group-wide BI platform

Estab­lish­ment of a new sales report­ing system

Source data migra­tion with auto­ma­tion of the source data connection

Cloud self-ser­vice BI for data-driven cor­por­ate management

Devel­op­ment of vari­ous ML use cases with AWS

Replace­ment of an in-house soft­ware development

Com­pany-wide data integration

Imple­ment­a­tion of real-time data links

Smart Data Catalog

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