Version: 3.20.1

mmm_attribution

Python

D_variables must include prior_weight. The value is applied to each raw input signal before saturation and scoring.

prior_weight: business weight assigned to each raw signal. A common way to estimate it is conversions / touchpoints, but it can also be set from domain knowledge or historical benchmarks.

import pandas as pd
from ChannelAttributionPro import mmm_attribution

token = "yourtoken"

Data = pd.read_csv("https://app.channelattribution.io/data/data_mmm_3.csv")

D_variables = pd.DataFrame({
    "variable": [
        "direct_searches",
        "facebook_impressions",
        "facebook_clicks",
        "google_impressions",
        "google_clicks",
        "tv",
    ],
    "channel": [
        "direct",
        "facebook_ads",
        "facebook_ads",
        "google_ads",
        "google_ads",
        "tv",
    ],
    "measure": [
        "direct_searches",
        "impressions",
        "clicks",
        "impressions",
        "clicks",
        "views",
    ],
    "prior_weight": [
        0.50,   # direct search
        0.015,  # Facebook impressions
        0.21,   # Facebook clicks
        0.015,  # Google impressions
        0.25,   # Google clicks
        0.02,   # TV views / GRP-like signal
    ],
})

target = "conversions"

res = mmm_attribution(
    Data=Data,
    D_variables=D_variables,
    target=target,
    model="linear",  # "linear", "reward", or "copula"
    max_p=12,
    seed=1234567,
    verbose=1,
    password=token,
)

print(res.head())

Using a different internal model:

res_reward = mmm_attribution(
    Data=Data,
    D_variables=D_variables,
    target=target,
    model="reward",
    max_p=12,
    seed=1234567,
    password=token,
)

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

Python​

Python