FranVV-Ecu / Telcom_users_data

Research of the users data to adjust adevrstiment budget

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Determination of a prospective tariff for a telecom company

Project Description

"Megaline" is a federal operator of cellular communication. Customers are offered two tariff plans: "Smart" and "Ultra". To adjust the advertising budget, the commercial department wants to understand which tariff brings more money.

We need a preliminary analysis of tariffs on a small sample of clients. At the disposal of the data of 500 users of "Megaline": who they are, where they come from, what tariff they use, how many calls and messages each sent in 2018.

We need to analyze customer behavior and make a conclusion - which tariff is better.

Description of tariffs

Smart tariff.

Monthly fee: 550 rubles Included 500 minutes of talk time, 50 messages and 15 GB of Internet traffic Cost of services over and above the tariff package: one minute of talk time: 3 rubles message: 3 rubles 1 GB of Internet traffic: 200 rubles

Tariff "Ultra

Monthly fee: 1950 rubles Includes 3000 minutes of talk time, 1000 messages and 30 GB of Internet traffic Cost of services over and above the tariff package: one minute of talk time: 1 ruble message: 1 ruble 1 GB of Internet traffic: 150 rubles WARNING: "Megaline" always rounds up the values of minutes and megabytes. If the user spoke for only 1 second, the whole minute is counted in the tariff.

Data description

Table users (information about users):

  • user_id - unique user identifier
  • first_name - user's first name
  • last_name - user's last name
  • age - user's age (years)
  • reg_date - date of tariff connection (day, month, year)
  • churn_date - date of tariff termination (if the value is missing, the tariff was still valid at the moment of data upload)
  • city - city of user's residence
  • tariff - name of tariff plan

Table calls (information about calls):

  • id - unique call number
  • call_date - date of call
  • duration - duration of the call in minutes
  • user_id - identifier of the user who made the call

Table messages (information about messages):

  • id - unique message number
  • message_date - message date
  • user_id - identifier of the user who sent the message

Table internet (information about internet sessions):

  • id - unique session number
  • mb_used - amount of Internet traffic spent during the session (in megabytes)
  • session_date - date of the Internet session
  • user_id - user ID

Table tariffs (information about tariffs):

  • tariff_name - tariff name
  • rub_monthly_fee - monthly subscription fee in rubles
  • minutes_included - number of minutes of conversation per month included in the subscription fee
  • messages_included - number of messages per month included in the subscription fee
  • mb_per_month_included - amount of Internet traffic included in the subscription fee (in megabytes)
  • rub_per_minute - cost of a minute of talk time in excess of the tariff package (for example, if the tariff includes 100 minutes of talk time per month, then 101 minutes will be charged)
  • rub_per_message - cost of sending a message in excess of the tariff package
  • rub_per_gb - cost of additional gigabyte of Internet traffic over the tariff package (1 gigabyte = 1024 megabytes).

Note. If merging tables using merge method causes dead kernell error, apply join method - it will ease the load on Jupyter Notebook.

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Research of the users data to adjust adevrstiment budget


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