Load geo-level data with 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:

Map the column names to the variable types. The required variable types are time, geo, controls, population, kpi, and revenue_per_kpi. For media channels that don't have reach and frequency data, you must assign their media exposure and media spend to the categories of media and media_spend, respectively. Conversely, for media channels that do possess reach and frequency data, you must map their reach, frequency, and media spend to the categories of reach, frequency, and rf_spend correspondingly. 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',
    ],
    media_spend=[
        'Channel0_spend',
        'Channel1_spend',
        'Channel2_spend',
        'Channel3_spend',
    ],
    reach =['Channel4_reach', 'Channel5_reach'],
    frequency=['Channel4_frequency', 'Channel5_frequency'],
    rf_spend=['Channel4_spend', 'Channel5_spend'],
)

Map the media exposure, reach, frequency, 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. Additionally, Channel4_reach, Channel4_frequency, and Channel4_spend are connected to the same channel, Channel4.

correct_media_to_channel = {
    'Channel0_impression': 'Channel0',
    'Channel1_impression': 'Channel1',
    'Channel2_impression': 'Channel2',
    'Channel3_impression': 'Channel3',
}
correct_media_spend_to_channel = {
    'Channel0_spend': 'Channel0',
    'Channel1_spend': 'Channel1',
    'Channel2_spend': 'Channel2',
    'Channel3_spend': 'Channel3',
}

correct_reach_to_channel = {
    'Channel4_reach': 'Channel4',
    'Channel5_reach': 'Channel5',
}
correct_frequency_to_channel = {
    'Channel4_frequency': 'Channel4',
    'Channel5_frequency': 'Channel5',
}
correct_rf_spend_to_channel = {
    'Channel4_spend': 'Channel4',
    'Channel5_spend': 'Channel5',
}

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,
    reach_to_channel=correct_reach_to_channel,
    frequency_to_channel=correct_frequency_to_channel,
    rf_spend_to_channel=correct_rf_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:

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.

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, media_channel, and rf_channel, where rf_channel designates the channels having reach and frequency data. The required data variables names are kpi, revenue_per_kpi, controls, population, media, media_spend, reach, frequency, and rf_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',
        'reach': 'reach',
        'frequency': 'frequency',
        'rf_spend': 'rf_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:

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',
    ],
    media_spend=[
        'Channel0_spend',
        'Channel1_spend',
        'Channel2_spend',
        'Channel3_spend',
    ],
    reach =['Channel4_reach', 'Channel5_reach'],
    frequency=['Channel4_frequency', 'Channel5_frequency'],
    rf_spend=['Channel4_spend', 'Channel5_spend'],
)

Map the media exposure, reach, frequency 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. Additionally, Channel4_reach, Channel4_frequency, and Channel4_spend are connected to the same channel, Channel4.

correct_media_to_channel = {
    'Channel0_impression': 'Channel0',
    'Channel1_impression': 'Channel1',
    'Channel2_impression': 'Channel2',
    'Channel3_impression': 'Channel3',
}
correct_media_spend_to_channel = {
    'Channel0_spend': 'Channel0',
    'Channel1_spend': 'Channel1',
    'Channel2_spend': 'Channel2',
    'Channel3_spend': 'Channel3',
}

correct_reach_to_channel = {
    'Channel4_reach': 'Channel4',
    'Channel5_reach': 'Channel5',
}
correct_frequency_to_channel = {
    'Channel4_frequency': 'Channel4',
    'Channel5_frequency': 'Channel5',
}
correct_rf_spend_to_channel = {
    'Channel4_spend': 'Channel4',
    'Channel5_spend': 'Channel5',
}

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,
    reach_to_channel=correct_reach_to_channel,
    frequency_to_channel=correct_frequency_to_channel,
    rf_spend_to_channel=correct_rf_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.

Load geo-level data without reach and frequency

Load geo-level data with organic media and non-media treatments

Load geo-level data with reach and frequency Stay organized with collections Save and categorize content based on your preferences.

CSV

Xarray Dataset

Other Data Format

Load geo-level data with reach and frequency
Stay organized with collections Save and categorize content based on your preferences.