Version: 3.19

mmm_attribution

Python

import pandas as pd
from ChannelAttributionPro import mmm_attribution

token = "yourtoken"

Data = pd.read_csv("https://app.channelattribution.io/data/data_mmm_2.csv", sep=",")

D_variables = pd.DataFrame({
    "variable": [
        "direct_searches",
        "facebook_impressions",
        "facebook_clicks",
        "google_impressions",
        "google_clicks",
    ],
    "channel": [
        "direct",
        "facebook_ads",
        "facebook_ads",
        "google_ads",
        "google_ads",
    ],
    "measure": [
        "direct_searches",
        "impressions",
        "clicks",
        "impressions",
        "clicks",
    ],
})

target = "conversions"

res = mmm_attribution(
    Data=Data,
    D_variables=D_variables,
    target=target,
    model="reward",
    model_score_target="delta",
    max_p=12,
    seed=1234567,
    verbose=1,
    password=token,
)

print(res.head())

Using a different internal model:

res_copula = mmm_attribution(
    Data=Data,
    D_variables=D_variables,
    target=target,
    model="copula",
    model_score_target="level",
    max_p=12,
    nsim=1000,
    seed=1234567,
    password=token,
)

res_linear = mmm_attribution(
    Data=Data,
    D_variables=D_variables,
    target=target,
    model="linear",
    model_score_target="level",
    max_p=12,
    password=token,
)

To inspect diagnostics:

diag = mmm_attribution(
    Data=Data,
    D_variables=D_variables,
    target=target,
    model="reward",
    model_score_target="delta",
    max_p=12,
    return_diagnostics=True,
    password=token,
)

attribution = diag["attribution"]
print(attribution.head())
print(diag["input_saturation_alpha_by_measure"])
print(diag["model"])

Python​

Python