Metric Forge

Metric Forge is your ultimate toolkit for measuring and evaluating performance across various business domains.

Disclaimer

Metric Forge provides a collection of calculations across various domains, including finance, mortgage, marketing, and more. While every effort has been made to ensure the accuracy and reliability of the calculations and methods provided, Metric Forge is intended for informational and educational purposes only.

Important Notice:

No Warranty: The calculations and methods provided in this package are offered "as-is" without any guarantees or warranties of any kind, either express or implied. The package's creators do not assume any responsibility for errors or omissions or for any damages resulting from the use of the package.

Not Professional Advice: The results produced by the package should not be considered as professional financial, investment, legal, or any other type of advice. Users should consult with qualified professionals before making any decisions based on the outputs generated by this package.

Use at Your Own Risk: Users of Metric Forge assume full responsibility for the use of the package and its results. The creators of the package shall not be held liable for any decisions made based on the information provided or for any consequences arising from the use of the package.

By using this package, you acknowledge and agree to this disclaimer. If you do not agree, please refrain from using the Metric Forge package.

Getting Started

install the package via pip

pip install metric-forge

Ecommerce

You can use Ecommerce Metrics in two different ways:

• With Polars
• As a Single Value Function (SVF)

Metric Forge extends the Polars expression library; making it possible to perform row-wise-calculations for various metrics. In addition to mass calculations, you can also perform single value calculations that return just one value.

from metric_forge.ecommerce import * 
import polars as pl
data = pl.read_csv('datasets/ecommerce_metrics.csv')
data.head()

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (5, 15)

month	total_revenue	number_of_orders	cost_of_acquisition	new_customers	carts_created	completed_purchases	revenue_from_ads	advertising_spend	num_conversions	num_visitors	revenue_per_customer	average_customer_lifetime	num_customers_lost	total_customers_beginning
str	f64	i64	f64	i64	i64	i64	f64	f64	i64	i64	f64	f64	i64	i64
"2023-01"	87454.011885	991	22958.350559	406	1969	1425	81339.957696	5390.910169	300	13154	202.427329	3.159364	239	3306
"2023-02"	145071.430641	913	18736.874206	234	1506	1421	76875.083402	15585.037018	427	14762	116.173436	1.812245	124	4680
"2023-03"	123199.394181	1305	28355.586842	120	1497	965	116505.482191	14872.037954	367	10056	384.265156	4.771414	177	4675
"2023-04"	109865.84842	885	14184.81582	428	1963	1102	106008.046381	12337.204368	132	19948	144.356328	3.395462	299	2972
"2023-05"	65601.864044	691	18764.339456	266	1009	801	97258.809912	5351.995568	147	13110	275.734601	3.77914	296	3768

Customer Acquisition Cost

data.select(pl.col('cost_of_acquisition'),
            pl.col('new_customers'),
            pl.col('*').forge_ecommerce.customer_acquisition_cost('cost_of_acquisition', 'new_customers'))

Average Order Value

data.select(pl.col('total_revenue'),
            pl.col('new_customers'),
            pl.col('*').forge_ecommerce.average_order_value('total_revenue', 'number_of_orders'))

Cart Abandonment Rate

data.select(pl.col('carts_created'),
            pl.col('completed_purchases'),
            pl.col('*').forge_ecommerce.cart_abandonment_rate('carts_created', 'completed_purchases'))

Return on Advertising Spend

data.select(pl.col('revenue_from_ads'),
            pl.col('advertising_spend'),
            pl.col('*').forge_ecommerce.return_on_advertising_spend('revenue_from_ads', 'advertising_spend'))

Conversion Rate

data.select(pl.col('num_conversions'),
            pl.col('num_visitors'),
            pl.col('*').forge_ecommerce.conversion_rate('num_conversions', 'num_visitors'))

Customer Lifetime Value

data.select(pl.col('revenue_per_customer'),
            pl.col('average_customer_lifetime'),
            pl.col('*').forge_ecommerce.customer_lifetime_value(revenue_per_customer_column='revenue_per_customer',average_customer_lifetime_column='average_customer_lifetime',method='basic'))

<style> .dataframe > thead > tr, .dataframe > tbody > tr { text-align: right; white-space: pre-wrap; } </style> shape: (12, 3)

revenue_per_customer	average_customer_lifetime	customer_lifetime_value
f64	f64	f64
202.427329	3.159364	639.54169
116.173436	1.812245	210.534716
384.265156	4.771414	1833.488253
144.356328	3.395462	490.156408
275.734601	3.77914	1042.039585
…	…	…
290.148089	2.182535	633.258286
325.310229	1.421977	462.583676
378.206435	2.826138	1068.863683
155.732582	1.873762	291.805755
341.766952	2.66604	911.164293