Revenue Tree

Overview

The Revenue Tree is a hierarchical breakdown of factors contributing to overall revenue, allowing for detailed analysis of sales performance and identification of areas for improvement. It is also know as a KPI tree or sometimes as a driver tree.

In this notebook, we'll look at a quick overview of how to generate a revenue tree.

Setup¶

Here we'll create some simulated data to demonstrate the code. You do not need to know this to perform segmentations.

In [ ]:

import pandas as pd

df = pd.read_parquet("../../data/transactions.parquet")
df.head()

import pandas as pd df = pd.read_parquet("../../data/transactions.parquet") df.head()

Out[ ]:

	transaction_id	transaction_date	transaction_time	customer_id	product_id	product_name	category_0_name	category_0_id	category_1_name	category_1_id	brand_name	brand_id	unit_quantity	unit_cost	unit_spend	store_id
0	16050	2023-01-12	17:44:29	1	15	Spawn Figure	Toys	1	Action Figures	1	McFarlane Toys	3	2	36.10	55.98	6
1	16050	2023-01-12	17:44:29	1	1317	Gone Girl	Books	8	Mystery & Thrillers	53	Alfred A. Knopf	264	1	6.98	10.49	6
2	20090	2023-02-05	09:31:42	1	509	Ryzen 3 3300X	Electronics	3	Computer Components	21	AMD	102	3	200.61	360.00	4
3	20090	2023-02-05	09:31:42	1	735	Linden Wood Paneled Mirror	Home	5	Home Decor	30	Pottery Barn	147	1	379.83	599.00	4
4	20090	2023-02-05	09:31:42	1	1107	Pro-V Daily Moisture Renewal Conditioner	Beauty	7	Hair Care	45	Pantene	222	1	3.32	4.99	4

Some details about the data

In [ ]:

print(f"Number of unique customers: {df['customer_id'].nunique()}")
print(f"Number of unique transactions: {df['transaction_id'].nunique()}")

print(f"Number of unique customers: {df['customer_id'].nunique()}") print(f"Number of unique transactions: {df['transaction_id'].nunique()}")

Number of unique customers: 4250
Number of unique transactions: 25490

In this example we compare February 2023 with March 2023 to see how revenue changed month on month and which factors drove it. Each row is labelled with its month; RevenueTree keeps only the rows whose label matches p1_value or p2_value.

In [ ]:

from openretailscience.analysis import revenue_tree

df["transaction_date"] = pd.to_datetime(df["transaction_date"])
df["month"] = df["transaction_date"].dt.strftime("%Y-%m")

rev_tree = revenue_tree.RevenueTree(
    df=df,
    period_col="month",
    p1_value="2023-02",
    p2_value="2023-03",
)

from openretailscience.analysis import revenue_tree df["transaction_date"] = pd.to_datetime(df["transaction_date"]) df["month"] = df["transaction_date"].dt.strftime("%Y-%m") rev_tree = revenue_tree.RevenueTree( df=df, period_col="month", p1_value="2023-02", p2_value="2023-03", )

The default view of the KPIs can be difficult to view. Use the draw_tree method to visualize the data as a tree.

In [ ]:

rev_tree.draw_tree()

rev_tree.draw_tree()

Out[ ]:

<Axes: >