Data Chef ETL Battles. What can be prepared with today’s data?

Do you remember the Iron Chef battles? 

It was a televised series of cook-offs in which famous chefs rolled up their sleeves to compete in making the perfect dish. Based on a set theme, this involved using all their experience, creativity and imagination to transform sometimes questionable ingredients into the ultimate meal.

Hey, isn’t that just like data transformation? Or data blending, or data manipulation, or ETL, or whatever new name is trending now? In this new blog series requested by popular vote, we will ask two data chefs to use all their knowledge and creativity to compete in extracting a given data set's most useful “flavors” via reductions, aggregations, measures, KPIs, and coordinate transformations. Delicious!

Want to find out how to prepare the ingredients for a delicious data dish by aggregating financial transactions, filtering out uninformative features or extracting the essence of the customer journey? Follow us here and send us your own ideas for the “Data Chef Battles” at datachef@knime.com.

Ingredient Theme: Customer Transactions. Money vs. Loyalty.

Author: Rosaria Silipo
Data Chefs: Haruto and Momoka

Ingredient Theme: Customer Transactions

Today’s dataset is a classic customer transactions dataset. It is a small subset of a bigger dataset that contains all of the contracts concluded with 9 customers between 2008 and now.

The business we are analyzing is a subscription-based business. The term “contracts” refers to 1-year subscriptions for 4 different company products.

Customers are identified by a unique customer key (“Cust_ID”), products by a unique product key (“product”), and transactions by a unique transaction key (“Contract ID”). Each row in the dataset represents a 1-year subscription contract, with the buying customer, the bought product, the number of product items, the amount paid, the payment means (card or not card), the subscription start and end date, and the customer’s country of residence.

Subscription start and end date usually enclose one year, which is a standard duration for a subscription. However, a customer can hold multiple subscriptions for different products at the same time, with license coverages overlapping in time.

What could we extract from these data? Finding out more about customer habits would be useful. What kind of information can we collect from the contracts that would describe the customer? Let’s see what today’s data chefs are able to prepare!

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