Wrap-up

Analyzing Marketing Campaigns with pandas

Jill Rosok

Data Scientist

Dataset

marketing = pd.read_csv('marketing.csv')
print(marketing.head())

      user_id date_served    channel          variant  conv  \
0  a100000029  2018-01-01  House Ads  personalization  True   
1  a100000030  2018-01-01  House Ads  personalization  True   
2  a100000031  2018-01-01  House Ads  personalization  True   
3  a100000032  2018-01-01  House Ads  personalization  True   
4  a100000033  2018-01-01  House Ads  personalization  True   

  language_displayed language_preferred    age_group  
0            English            English   0-18 years  
1            English            English  19-24 years  
2            English            English  24-30 years  
3            English            English  30-36 years  
4            English            English  36-45 years

Preprocessing

Feature engineering
Resolving errors in the data

Marketing metrics

$$ \text{Conversion rate} = \frac{\text{Number of people who convert}}{\text{Total number of people who we market to}} $$

$$ \text{Retention rate} = \frac{\text{Number of people who remain subscribed}}{\text{Total number of people who converted}} $$

Customer segmentation

marketing.groupby(['channel', 'age_group'])\
                                ['user_id'].count()

Dip in conversion rate?

house_ads = marketing[marketing['channel'] == 'House Ads']

language = conversion_rate(house_ads, 
                           ['date_served', 
                            'language_displayed'])

You analyzed an A/B test

Lift
T-tests

Good luck!

Analyzing Marketing Campaigns with pandas