diff --git a/docs/analysis_modules.md b/docs/analysis_modules.md index fb591dda..ea4ff758 100644 --- a/docs/analysis_modules.md +++ b/docs/analysis_modules.md @@ -43,6 +43,52 @@ waterfall_plot( ) ``` +### Index Plots + +
+ +![Image title](assets/images/analysis_modules/index_plot.svg){ align=right loading=lazy width="50%"} + +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: + +```python +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", +) +``` + ### Timeline Plot
@@ -73,8 +119,12 @@ from pyretailscience.standard_graphs import time_plot 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)]), + "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, }, ) @@ -149,3 +199,190 @@ pa.df.head() | 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 | + +### Cross Shop + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### Gain Loss + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### Customer Decision Hierarchy + +
+ +![Image title](assets/images/analysis_modules/customer_decision_hierarchy.svg){ align=right loading=lazy width="50%"} + +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: + +```python +from pyretailscience.range_planning import CustomerDecisionHierarchy + +cdh = CustomerDecisionHierarchy(df) +ax = cdh.plot( + orientation="right", + source_text="Source: Transactions 2024", + title="Snack Food Substitutions", +) +``` + +### Revenue Tree + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### HML Segmentation + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### Threshold Segmentation + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### Segmentation Stats + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### Purchases Per Customer + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### Days Between Purchases + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` + +### Transaction Churn + +
+ +![Image title](https://placehold.co/600x400/EEE/31343C){ align=right loading=lazy width="50%"} + +PASTE TEXT HERE + +
+ +Example: + +```python +PASTE CODE HERE +``` diff --git a/docs/assets/images/analysis_modules/customer_decision_hierarchy.svg b/docs/assets/images/analysis_modules/customer_decision_hierarchy.svg new file mode 100644 index 00000000..b3693bcc --- /dev/null +++ b/docs/assets/images/analysis_modules/customer_decision_hierarchy.svg @@ -0,0 +1,2795 @@ + + + + + + + + 2024-08-11T16:44:58.190393 + image/svg+xml + + + Matplotlib v3.9.1.post1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/assets/images/analysis_modules/index_plot.svg b/docs/assets/images/analysis_modules/index_plot.svg new file mode 100644 index 00000000..ee42624a --- /dev/null +++ b/docs/assets/images/analysis_modules/index_plot.svg @@ -0,0 +1,2153 @@ + + + + + + + + 2024-08-11T16:19:18.179184 + image/svg+xml + + + Matplotlib v3.9.1.post1, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/pyretailscience/standard_graphs.py b/pyretailscience/standard_graphs.py index 1d00d343..30fbe5b4 100644 --- a/pyretailscience/standard_graphs.py +++ b/pyretailscience/standard_graphs.py @@ -185,7 +185,26 @@ def index_plot( # noqa: C901, PLR0913 (ignore complexity and line length) include_only_groups: list[any] | None = None, **kwargs: dict[str, any], ) -> SubplotBase: - """Plots the value_col over time. + """Creates an index plot. + + 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. Args: df (pd.DataFrame): The dataframe to plot.