| title | Analysis Modules | ||||
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| social |
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Waterfall plots are particularly good for showing how different things add or subtract from a starting number. For instance,
- Changes in sales figures from one period to another
- Breakdown of profit margins
- Impact of different product categories on overall revenue
They are often used to identify key drivers of financial performance, highlight areas for improvement, and communicate complex data stories to stakeholders in an intuitive manner.
Example:
from pyretailscience.standard_graphs import waterfall_plot
labels = ["New", "Continuning", "Churned"]
amounts = [660000, 420000, -382000]
waterfall_plot(
labels=labels,
amounts=amounts,
title="New customer growth hiding churn issue",
source_text="Source: PyRetailScience - Sales FY2024 vs FY2023",
display_net_bar=True,
rot=0,
)
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Index plots are visual tools used in retail analytics to compare different categories or segments against a baseline or average value, typically set at 100. Index plots allow analysts to:
Quickly identify which categories over- or underperform relative to the average Compare performance across diverse categories on a standardized scale Highlight areas of opportunity or concern in retail operations Easily communicate relative performance to stakeholders without revealing sensitive absolute numbers
In retail contexts, index plots are valuable for:
Comparing sales performance across product categories Analyzing customer segment behavior against the overall average Evaluating store or regional performance relative to company-wide metrics Identifying high-potential areas for growth or investment
By normalizing data to an index, these plots facilitate meaningful comparisons and help focus attention on significant deviations from expected performance, supporting more informed decision-making in retail strategy and operations.
Example:
from pyretailscience.standard_graphs import index_plot
index_plot(
df,
df_index_filter=df["segment_name"] == "Light",
value_col="unit_price",
group_col="category_0_name",
title="Music an opportunity category for Light?",
y_label="Categories",
x_label="Indexed Spend",
source_text="Source: Transaction data financial year 2023",
sort_by="value",
sort_order="descending",
legend_title="Quarter",
)
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Timeline plots are a fundamental tool for interpreting transactional data within a temporal context. By presenting data in a chronological sequence, these visualizations reveal patterns and trends that might otherwise remain hidden in raw numbers, making them essential for both historical analysis and forward-looking insights. They are particularly useful for:
- Tracking sales performance across different periods (e.g., daily, weekly, monthly)
- Identifying seasonal patterns or promotional impacts on sales
- Comparing the performance of different product categories or store locations over time
- Visualizing customer behavior trends, such as purchase frequency or average transaction value
Example:
import numpy as np
import pandas as pd
from pyretailscience.standard_graphs import time_plot
# Create a sample DataFrame with 3 groups
rng = np.random.default_rng(42)
df = pd.DataFrame(
{
"transaction_datetime": pd.concat(
[pd.Series(pd.date_range(start="2022-01-01", periods=200, freq="D"))] * 3
),
"total_price": np.concatenate(
[rng.integers(1, 1000, size=200) * multiplier for multiplier in range(1, 4)]
),
"group": ["Group A"] * 200 + ["Group B"] * 200 + ["Group C"] * 200,
},
)
time_plot(
df,
period="M",
group_col="group",
value_col="total_price",
agg_func="sum",
title="Monthly Sales by Customer Group",
y_label="Sales",
legend_title="Customer Group",
source_text="Source: PyRetailScience - Sales FY2024",
move_legend_outside=True,
)The product association module implements functionality for generating product association rules, a powerful technique in retail analytics and market basket analysis.
Product association rules are used to uncover relationships between different products that customers tend to purchase together. These rules provide valuable insights into consumer behavior and purchasing patterns, which can be leveraged by retail businesses in various ways:
-
Cross-selling and upselling: By identifying products frequently bought together, retailers can make targeted product recommendations to increase sales and average order value.
-
Store layout optimization: Understanding product associations helps in strategic product placement within stores, potentially increasing impulse purchases and overall sales.
-
Inventory management: Knowing which products are often bought together aids in maintaining appropriate stock levels and predicting demand.
-
Marketing and promotions: Association rules can guide the creation ofeffective bundle offers and promotional campaigns.
-
Customer segmentation: Patterns in product associations can reveal distinct customer segments with specific preferences.
-
New product development: Insights from association rules can inform decisions about new product lines or features.
The module uses metrics such as support, confidence, and uplift to quantifythe strength and significance of product associations:
- Support: The frequency of items appearing together in transactions.
- Confidence: The likelihood of buying one product given the purchase of another.
- Uplift: The increase in purchase probability of one product when another is bought.
Example:
from pyretailscience.product_association import ProductAssociation
pa = ProductAssociation(
df,
value_col="product_name",
group_col="transaction_id",
)
pa.df.head()| product_name_1 | product_name_2 | occurrences_1 | occurrences_2 | cooccurrences | support | confidence | uplift |
|---|---|---|---|---|---|---|---|
| 100 Animals Book | 100% Organic Cold-Pressed... | 78 | 78 | 1 | 0.000039 | 0.0128205 | 4.18 |
| 100 Animals Book | 20K Sousaphone | 78 | 81 | 3 | 0.000117 | 0.0384615 | 12.10 |
| 100 Animals Book | 360 Sport 2.0 Boxer Briefs | 78 | 79 | 1 | 0.000039 | 0.0128205 | 4.13 |
| 100 Animals Book | 4-Series 4K UHD | 78 | 82 | 1 | 0.000039 | 0.0128205 | 3.98 |
| 100 Animals Book | 700S Eterna Trumpet | 78 | 71 | 1 | 0.000039 | 0.0128205 | 4.60 |
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A Customer Decision Hierarchy (CDH), also known as a Customer Decision Tree, is a powerful tool in retail analytics that visually represents the sequential steps and criteria customers use when making purchase decisions within a specific product category. Here's a brief summary of its purpose and utility:
CDHs allow analysts to:
- Map out the hierarchical structure of customer decision-making processes
- Identify key product attributes that drive purchase decisions
- Understand product substitutions and alternatives customers consider
- Prioritize product attributes based on their importance to customers
In retail contexts, CDHs are valuable for:
- Optimizing product assortments and shelf layouts
- Developing targeted marketing strategies
- Identifying opportunities for new product development
- Understanding competitive dynamics within a category
By visualizing the decision-making process, CDHs help retailers align their offerings and strategies with customer preferences, potentially increasing sales and customer satisfaction. They provide insights into how customers navigate choices, enabling more effective category management and merchandising decisions.
Example:
from pyretailscience.range_planning import CustomerDecisionHierarchy
cdh = CustomerDecisionHierarchy(df)
ax = cdh.plot(
orientation="right",
source_text="Source: Transactions 2024",
title="Snack Food Substitutions",
)
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