Starting Point

Since the challenge 09 Customer Data Analytics was not accepted, we did together with another not accepted Challenge Owner a small very basic analytics. We tried to find correlations or patterns in the provided File.

Approach

We used the file provided (anonymized 4'000 data from customers) using BigML and tried to find correlations in the data. The most valuable attributes seemed to be "Amount of contacts to customer center", "creditworthiness" and "Invoice-Type". It turned out, that (unfortunately very obvious) people with high contact numbers to the customer center also have a bad creditworthiness (German: Bonity).

Secondly we found a correlation, that the creditworthiness also correlates to the Invoice-Type. E-Bill-Customers pay their bill very fast (Bonity-Number is low whilst for customers with printed bills, it is much more likely to not pay the bills on time.

Other correlations - like between the Amount of energy could not be found.

Conclusion

All in all, the dataset was not very valuable (since it had to be cut down for data protection reasons - we are not allowed to use it internally also). That was probably the reason, why the challenge was not accepted. Learning: We have to think about how we want to deal with DA in the future and how we can use it compliant to data protection.