For this test we propose a gzipped dataset please download it : cz_orders.csv.gz

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

Data

For this test we propose a gzipped dataset please download it : cz_orders.csv.gz

Is contains the aggregated sales informations for Czech Republic during the month of May 2022. The monetary informations are in the local currency CZK The file is a CSV format with the following fields:

merchant_id category_id tracking_id offer_tier device date source lead_target_type is_cpa leads unique_orders grouped_orders unique_returns grouped_returns

Sum_original_order_value sum_order_return sum_earnings sum_earnings_return sum_order_value

We want to focus our attention on the Value per lead (VPL). This value is not present in the file, but can be obtained by dividing the order value by the number of leads.

 

 

Tools

To simplify the test we have used a small dataset so you can use any tool you feel comfortable with to analyse it, but we recommend the following:

 

  Python / Pandas (Jupyter notebook)

Questions

Using the provided data please answer the following questions:

  What is the distribution of the VPL in CZ. Please provide an analysis across the following dimensions: merchant_id, category_id, tracking_id, device, offer_tier, is_cpa and source. What are your conclusions?

 

  Variation of VPL over time. Please provide insights of the VPL variation during the month. What are your conclusions?

 

An attentive look to the data will show that one merchant is a clear outlier. Can you see the impact of this merchant on your analysis and present the figure with and without this merchant?

Many lines have a order_value=0. How do you analyse this fact? Can we really deduct that the VPL=0 for all these lines? Any ideas and suggestions?

Presentation

We expect the results to be shared and presented using a powerpoint file using Office 360. To keep it short this presentation should not exceed 10 slides.

Attachments:

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