Load geo-level data without reach and frequency

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Simulated data is provided as an example for each data type and format in the following sections.

CSV

To load the simulated CSV data using CsvDataLoader:

1. Map the column names to the variable types. The required variable types are time, geo, controls, population, kpi, revenue_per_kpi, media, and media_spend. For the definition of each variable, see Collect and organize your data.

   coord_to_columns = load.CoordToColumns(
       time='time',
       geo='geo',
       controls=['GQV', 'Discount', 'Competitor_Sales'],
       population='population',
       kpi='conversions',
       revenue_per_kpi='revenue_per_conversion',
       media=[
           'Channel0_impression',
           'Channel1_impression',
           'Channel2_impression',
           'Channel3_impression',
           'Channel4_impression',
           'Channel5_impression',
       ],
       media_spend=[
           'Channel0_spend',
           'Channel1_spend',
           'Channel2_spend',
           'Channel3_spend',
           'Channel4_spend',
           'Channel5_spend',
       ],
   )
   

2. Map the media variables and the media spends to the designated channel names that you want to display in the two-page output. In the following example, Channel0_impression and Channel0_spend are connected to the same channel, Channel0.

   correct_media_to_channel = {
       'Channel0_impression': 'Channel0',
       'Channel1_impression': 'Channel1',
       'Channel2_impression': 'Channel2',
       'Channel3_impression': 'Channel3',
       'Channel4_impression': 'Channel4',
       'Channel5_impression': 'Channel5',
   }
   correct_media_spend_to_channel = {
       'Channel0_spend': 'Channel0',
       'Channel1_spend': 'Channel1',
       'Channel2_spend': 'Channel2',
       'Channel3_spend': 'Channel3',
       'Channel4_spend': 'Channel4',
       'Channel5_spend': 'Channel5',
   }
   

3. Load the data using CsvDataLoader:

   loader = load.CsvDataLoader(
       csv_path=f'/{PATH}/{FILENAME}.csv',
       kpi_type='non_revenue',
       coord_to_columns=coord_to_columns,
       media_to_channel=correct_media_to_channel,
       media_spend_to_channel=correct_media_spend_to_channel,
   )
   data = loader.load()
   

   Where:

   • kpi_type is either 'revenue' or 'non_revenue'.
   • PATH is the path to the data file location.
   • FILENAME is the name of your data file.

Xarray Dataset

To load the simulated Xarray Dataset using XrDatasetDataLoader:

1. Load the data using pickle:

   import pickle
   with open(f'/{PATH}/{FILENAME}.pkl', 'r') as fh:
     XrDataset=pickle.load(fh)
   

   Where:

   • PATH is the path to the data file location.
   • FILENAME is the name of your data file.

2. Pass the dataset to XrDatasetDataLoader. Use the name_mapping argument to map the coordinates and arrays. Provide mapping if the names in the input dataset are different from the required names. The required coordinate names are geo, time, control_variable and media_channel. The required data variables names are kpi, revenue_per_kpi, controls, population, media, and media_spend.

   loader = load.XrDatasetDataLoader(
       XrDataset,
       kpi_type='non_revenue',
       name_mapping={'channel': 'media_channel',
                     'control': 'control_variable',
                     'conversions': 'kpi',
                     'revenue_per_conversion': 'revenue_per_kpi',
                     'control_value': 'controls',
                     'spend': 'media_spend'},
   )
   
   data = loader.load()
   

   Where:

   • kpi_type is either 'revenue' or 'non_revenue'.

Other Data Format

To load the simulated other data format (such as excel) using DataFrameDataLoader:

1. Map the column names to the variable types. The required variable types are time, geo, controls, population, kpi, revenue_per_kpi, media, and media_spend. For the definition of each variable, see Collect and organize your data.

   coord_to_columns = load.CoordToColumns(
       time='time',
       geo='geo',
       controls=['GQV', 'Discount', 'Competitor_Sales'],
       population='population',
       kpi='conversions',
       revenue_per_kpi='revenue_per_conversion',
       media=[
           'Channel0_impression',
           'Channel1_impression',
           'Channel2_impression',
           'Channel3_impression',
           'Channel4_impression',
           'Channel5_impression',
       ],
       media_spend=[
           'Channel0_spend',
           'Channel1_spend',
           'Channel2_spend',
           'Channel3_spend',
           'Channel4_spend',
           'Channel5_spend',
       ],
   )
   

2. Map the media variables and the media spends to the designated channel names that you want to display in the two-page output. In the following example, Channel0_impression and Channel0_spend are connected to the same channel, Channel0.

   correct_media_to_channel = {
       'Channel0_impression': 'Channel0',
       'Channel1_impression': 'Channel1',
       'Channel2_impression': 'Channel2',
       'Channel3_impression': 'Channel3',
       'Channel4_impression': 'Channel4',
       'Channel5_impression': 'Channel5',
   }
   correct_media_spend_to_channel = {
       'Channel0_spend': 'Channel0',
       'Channel1_spend': 'Channel1',
       'Channel2_spend': 'Channel2',
       'Channel3_spend': 'Channel3',
       'Channel4_spend': 'Channel4',
       'Channel5_spend': 'Channel5',
   }
   

3. Read the data (such as excel) into DataFrameDataLoader, and then load the data:

   df = pd.read_excel(f'/{PATH}/{FILENAME}.xlsx')
   loader = load.DataFrameDataLoader(
       df=df,
       kpi_type='non_revenue',
       coord_to_columns=coord_to_columns,
       media_to_channel=correct_media_to_channel,
       media_spend_to_channel=correct_media_spend_to_channel,
   )
   data = loader.load()
   

   Where:

   • kpi_type is either 'revenue' or 'non_revenue'.
   • PATH is the path to the data file location.
   • FILENAME is the name of your data file.

Next, you can create your model.