09.01 - Customer Data Analytics (Very Basic)

A smaller Project of the initial Idea 09 Customer Data Analytics

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.

Title

Title

Event finished

25.09.2021 15:00

Now serving beer!

24.09.2021 17:27 ~ benjamin_sawicki

Worked on the pitch

24.09.2021 15:39 ~ nicola_ruch

benjamin_sawicki has joined!

24.09.2021 15:34

Worked on the pitch

24.09.2021 15:33 ~ nicola_ruch

PeterJanes has joined!

24.09.2021 15:13

Other than that, the data is too bad for further analitics. But more data we cannot provide (monopole). Challenge aborted...

24.09.2021 15:07 ~ nicola_ruch

Results were not new: High contacts result in a high chance of bad bonity. The best paying customers use e-Bill or auto-payment.

24.09.2021 15:05 ~ nicola_ruch

The most valuable attributes were the amount of contacts to customer center, the Bonity-Index and the Billing-Type.

24.09.2021 15:01 ~ nicola_ruch

The original file (anonymized customer data) was analyzed on BigML using random forest.

24.09.2021 15:00 ~ nicola_ruch

Since the original challenge 05 and 09 was not accepted, we sat together thinking of what we can do with the ressources we had.

24.09.2021 14:49 ~ nicola_ruch

Worked on the pitch

24.09.2021 14:47 ~ nicola_ruch

nicola_ruch has joined!

24.09.2021 14:42

Project started

Initialized by nicola_ruch 🎉

24.09.2021 14:42

Event started

24.09.2021 08:30
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Energy Data Hackdays 2021