Tests A/B et segmentation

Analyser des campagnes marketing avec pandas

Jill Rosok

Data Scientist

N'oubliez pas la segmentation !

Test de personnalisation segmenté par langue

for language in np.unique(marketing['language_displayed'].values):
    print(language)

Isoler les données pertinentes

for language in np.unique(marketing['language_displayed'].values):
    print(language)

    language_data = marketing[(marketing['marketing_channel'] == 'Email') & 
                              (marketing['language_displayed'] == language)]

Isoler les abonnées et abonnés

for language in np.unique(marketing['language_displayed'].values):
    print(language)

    language_data = marketing[(marketing['marketing_channel'] == 'Email') & 
                              (marketing['language_displayed'] == language)]

    subscribers = language_data.groupby(['user_id', 'variant'])['converted']\
                                                                      .max()

Isoler témoin et personnalisation

for language in np.unique(marketing['language_displayed'].values):
    print(language)

    language_data = marketing[(marketing['marketing_channel'] == 'Email') & 
                              (marketing['language_displayed'] == language)]

    subscribers = language_data.groupby(['user_id', 'variant'])['converted']\
                                                                      .max()
    subscribers = pd.DataFrame(subscribers.unstack(level=1)) 
    control = subscribers['control'].dropna()
    personalization = subscribers['personalization'].dropna()

Boucle for complète

for language in np.unique(marketing['language_displayed'].values):
    print(language)

    language_data = marketing[(marketing['marketing_channel'] == 'Email') & 
                              (marketing['language_displayed'] == language)]

    subscribers = language_data.groupby(['user_id', 'variant'])['converted']\
                                                                      .max()
    subscribers = pd.DataFrame(subscribers.unstack(level=1)) 
    control = subscribers['control'].dropna()
    personalization = subscribers['personalization'].dropna()  

    print('lift:', lift(control, personalization))
    print('t-statistic:', stats.ttest_ind(control, personalization), '\n\n')

Résultats

Arabic
lift: 50.0%
t-statistic: TtestResult(statistic=-0.58, pvalue=0.58, df=8.0) 

English
lift: 39.0%
t-statistic: TtestResult(statistic=-2.22, pvalue=0.03, df=486.0) 

German
lift: -1.62%
t-statistic: TtestResult(statistic=0.19, pvalue=0.85, df=42.0) 

Spanish
lift: 166.67%
t-statistic: TtestResult(statistic=-2.36, pvalue=0.04, df=10.0)

Passons à la pratique !

Analyser des campagnes marketing avec pandas