Customer scoring suite

In a nutshell
Just specifying the key column names, Shimoku will create a whole Customer Scoring Suite in less than 48 hours.
​
​
shimoku.suite.customer_scoring_suite(
    data=data,
    customer='customer_id',
    date='date',
    business_type='transactional',
)
Where business_type could be transactional for Retail-like businesses or subscription for subscription businesses.
For retail, you may want to add also the product column for the recommender system
shimoku.suite.customer_scoring_suite((
    data=data,
    customer='customer_id',
    product='product_id',
    date='date',
    business_type='transactional',
)
On the data
The minimum dataset for Shimoku to make predictions must contain
date column as timestamp
customer_id column as the customer unique identifier
product_id as the product identifier for Retail-like businesses
Nevertheless the more data you add the more will learn the algorithm, in particular, we suggest to add as much data as possible, being the ideal case the Shopify Order object​
Updating
The first time you have to update at least 12 months of data. Nevertheless, after you post the historic dataset you can count on updated predictions just posting the daily lambdas by specifying how='append'
historic_dataframe = pd.read_csv('historic_orders.csv')  # Orders from the last 12 months
​
shimoku.suite.customer_scoring_suite((
    data=historic_dataframe,
    customer='customer_id',
    product='product_id',
    date='date',
    business_type='transactional',
)
​
yesterday_df = pd.read_csv('yesterday_orders.csv')  # orders from yesterday
​
shimoku.suite.customer_scoring_suite(
    data=yesterday_df,
    customer='customer_id',
    product='product_id',
    date='date',
    business_type='transactional',
    # to tell Shimoku SDK that this is a piece of data
    # to be concat to the historic one
    how='append',  
)
Full example
Including the Class instance call
api_key: str = getenv('API_TOKEN')
universe_id: str = getenv('UNIVERSE_ID')
business_id: str = getenv('BUSINESS_ID')
environment: str = getenv('ENVIRONMENT')
​
config = {
    'access_token': api_key,
}
​
shimoku = shimoku.Client(
    config=config,
    universe_id=universe_id,
    environment=environment,
)
shimoku.plt.set_business(business_id=business_id)
​
shimoku.suite.customer_scoring_suite((
    data=data,
    customer='customer_id',
    product='product_id',
    date='date',
    business_type='transactional',
)

Retention suite

Demand planning suite

Last modified 3mo ago

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