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Gain Loss Analysis

This module performs gain loss analysis (switching analysis) on a DataFrame to assess customer movement between brands or products over time.

Gain loss analysis, also known as switching analysis, is a marketing analytics technique used to assess customer movement between brands or products over time. It helps businesses understand the dynamics of customer acquisition and churn. Here's a concise definition: Gain loss analysis examines the flow of customers to and from a brand or product, quantifying:

  1. Gains: New customers acquired from competitors
  2. Losses: Existing customers lost to competitors
  3. Net change: The overall impact on market share

This analysis helps marketers:

  • Identify trends in customer behavior
  • Evaluate the effectiveness of marketing strategies
  • Understand competitive dynamics in the market

GainLoss

A class to perform gain loss analysis on a DataFrame to assess customer movement between brands or products over time.

Source code in pyretailscience/analysis/gain_loss.py 
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class GainLoss:
    """A class to perform gain loss analysis on a DataFrame to assess customer movement between brands or products over time."""

    def __init__(
        self,
        df: pd.DataFrame,
        p1_index: list[bool] | pd.Series,
        p2_index: list[bool] | pd.Series,
        focus_group_index: list[bool] | pd.Series,
        focus_group_name: str,
        comparison_group_index: list[bool] | pd.Series,
        comparison_group_name: str,
        group_col: str | None = None,
        value_col: str = get_option("column.unit_spend"),
        agg_func: str = "sum",
    ) -> None:
        """Calculate the gain loss table for a given DataFrame at the customer level.

        Args:
            df (pd.DataFrame): The DataFrame to calculate the gain loss table from.
            p1_index (list[bool]): The index for the first time period.
            p2_index (list[bool]): The index for the second time period.
            focus_group_index (list[bool]): The index for the focus group.
            focus_group_name (str): The name of the focus group.
            comparison_group_index (list[bool]): The index for the comparison group.
            comparison_group_name (str): The name of the comparison group.
            group_col (str | None, optional): The column to group by. Defaults to None.
            value_col (str, optional): The column to calculate the gain loss from. Defaults to option column.unit_spend.
            agg_func (str, optional): The aggregation function to use. Defaults to "sum".
        """
        # # Ensure no overlap between p1 and p2
        if not df[p1_index].index.intersection(df[p2_index].index).empty:
            raise ValueError("p1_index and p2_index should not overlap")

        if not df[focus_group_index].index.intersection(df[comparison_group_index].index).empty:
            raise ValueError("focus_group_index and comparison_group_index should not overlap")

        if not len(p1_index) == len(p2_index) == len(focus_group_index) == len(comparison_group_index):
            raise ValueError(
                "p1_index, p2_index, focus_group_index, and comparison_group_index should have the same length",
            )

        required_cols = [get_option("column.customer_id"), value_col] + ([group_col] if group_col is not None else [])
        missing_cols = set(required_cols) - set(df.columns)
        if len(missing_cols) > 0:
            msg = f"The following columns are required but missing: {missing_cols}"
            raise ValueError(msg)

        self.focus_group_name = focus_group_name
        self.comparison_group_name = comparison_group_name
        self.group_col = group_col
        self.value_col = value_col

        self.gain_loss_df = self._calc_gain_loss(
            df=df,
            p1_index=p1_index,
            p2_index=p2_index,
            focus_group_index=focus_group_index,
            comparison_group_index=comparison_group_index,
            group_col=group_col,
            value_col=value_col,
            agg_func=agg_func,
        )
        self.gain_loss_table_df = self._calc_gains_loss_table(
            gain_loss_df=self.gain_loss_df,
            group_col=group_col,
        )

    @staticmethod
    def process_customer_group(
        focus_p1: float,
        comparison_p1: float,
        focus_p2: float,
        comparison_p2: float,
        focus_diff: float,
        comparison_diff: float,
    ) -> tuple[float, float, float, float, float, float]:
        """Process the gain loss for a customer group.

        Args:
            focus_p1 (float | int): The focus group total in the first time period.
            comparison_p1 (float | int): The comparison group total in the first time period.
            focus_p2 (float | int): The focus group total in the second time period.
            comparison_p2 (float | int): The comparison group total in the second time period.
            focus_diff (float | int): The difference in the focus group totals.
            comparison_diff (float | int): The difference in the comparison group totals.

        Returns:
            tuple[float, float, float, float, float, float]: The gain loss for the customer group.
        """
        if focus_p1 == 0 and comparison_p1 == 0:
            return focus_p2, 0, 0, 0, 0, 0
        if focus_p2 == 0 and comparison_p2 == 0:
            return 0, -1 * focus_p1, 0, 0, 0, 0

        if focus_diff > 0:
            focus_inc_dec = focus_diff if comparison_diff > 0 else max(0, comparison_diff + focus_diff)
        elif comparison_diff < 0:
            focus_inc_dec = focus_diff
        else:
            focus_inc_dec = min(0, comparison_diff + focus_diff)

        increased_focus = max(0, focus_inc_dec)
        decreased_focus = min(0, focus_inc_dec)

        transfer = focus_diff - focus_inc_dec
        switch_from_comparison = max(0, transfer)
        switch_to_comparison = min(0, transfer)

        return 0, 0, increased_focus, decreased_focus, switch_from_comparison, switch_to_comparison

    @staticmethod
    def _calc_gain_loss(
        df: pd.DataFrame,
        p1_index: list[bool],
        p2_index: list[bool],
        focus_group_index: list[bool],
        comparison_group_index: list,
        group_col: str | None = None,
        value_col: str = get_option("column.unit_spend"),
        agg_func: str = "sum",
    ) -> pd.DataFrame:
        """Calculate the gain loss table for a given DataFrame at the customer level.

        Args:
            df (pd.DataFrame): The DataFrame to calculate the gain loss table from.
            p1_index (list[bool]): The index for the first time period.
            p2_index (list[bool]): The index for the second time period.
            focus_group_index (list[bool]): The index for the focus group.
            comparison_group_index (list[bool]): The index for the comparison group.
            group_col (str | None, optional): The column to group by. Defaults to None.
            value_col (str, optional): The column to calculate the gain loss from. Defaults to option column.unit_spend.
            agg_func (str, optional): The aggregation function to use. Defaults to "sum".

        Returns:
            pd.DataFrame: The gain loss table.
        """
        cols = ColumnHelper()
        df = df[p1_index | p2_index].copy()
        df[cols.customer_id] = df[cols.customer_id].astype("category")

        grp_cols = [cols.customer_id] if group_col is None else [group_col, cols.customer_id]

        p1_df = pd.concat(
            [
                df[focus_group_index & p1_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[comparison_group_index & p1_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[(focus_group_index | comparison_group_index) & p1_index]
                .groupby(grp_cols, observed=False)[value_col]
                .agg(agg_func),
            ],
            axis=1,
        )
        p1_df.columns = ["focus", "comparison", "total"]

        p2_df = pd.concat(
            [
                df[focus_group_index & p2_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[comparison_group_index & p2_index].groupby(grp_cols, observed=False)[value_col].agg(agg_func),
                df[(focus_group_index | comparison_group_index) & p2_index]
                .groupby(grp_cols, observed=False)[value_col]
                .agg(agg_func),
            ],
            axis=1,
        )
        p2_df.columns = ["focus", "comparison", "total"]

        gl_df = p1_df.merge(p2_df, on=grp_cols, how="outer", suffixes=("_p1", "_p2")).fillna(0)

        # Remove rows that are all 0 due to grouping by customer_id as a categorical with observed=False
        gl_df = gl_df[~(gl_df == 0).all(axis=1)]

        gl_df["focus_diff"] = gl_df["focus_p2"] - gl_df["focus_p1"]
        gl_df["comparison_diff"] = gl_df["comparison_p2"] - gl_df["comparison_p1"]
        gl_df["total_diff"] = gl_df["total_p2"] - gl_df["total_p1"]

        (
            gl_df["new"],
            gl_df["lost"],
            gl_df["increased_focus"],
            gl_df["decreased_focus"],
            gl_df["switch_from_comparison"],
            gl_df["switch_to_comparison"],
        ) = zip(
            *gl_df.apply(
                lambda x: GainLoss.process_customer_group(
                    focus_p1=x["focus_p1"],
                    comparison_p1=x["comparison_p1"],
                    focus_p2=x["focus_p2"],
                    comparison_p2=x["comparison_p2"],
                    focus_diff=x["focus_diff"],
                    comparison_diff=x["comparison_diff"],
                ),
                axis=1,
            ),
            strict=False,
        )

        return gl_df

    @staticmethod
    def _calc_gains_loss_table(
        gain_loss_df: pd.DataFrame,
        group_col: str | None = None,
    ) -> pd.DataFrame:
        """Aggregates the gain loss table to show the total gains and losses across customers.

        Args:
            gain_loss_df (pd.DataFrame): The gain loss table at customer level to aggregate.
            group_col (str | None, optional): The column to group by. Defaults to None.

        Returns:
            pd.DataFrame: The aggregated gain loss table
        """
        if group_col is None:
            return gain_loss_df.sum().to_frame("").T

        return gain_loss_df.groupby(level=0).sum()

    def plot(
        self,
        title: str | None = None,
        x_label: str | None = None,
        y_label: str | None = None,
        ax: Axes | None = None,
        source_text: str | None = None,
        move_legend_outside: bool = False,
        **kwargs: dict[str, any],
    ) -> SubplotBase:
        """Plot the gain loss table using the bar.plot wrapper.

        Args:
            title (str | None, optional): The title of the plot. Defaults to None.
            x_label (str | None, optional): The x-axis label. Defaults to None.
            y_label (str | None, optional): The y-axis label. Defaults to None.
            ax (Axes | None, optional): The axes to plot on. Defaults to None.
            source_text (str | None, optional): The source text to add to the plot. Defaults to None.
            move_legend_outside (bool, optional): Whether to move the legend outside the plot. Defaults to False.
            kwargs (dict[str, any]): Additional keyword arguments to pass to the plot.

        Returns:
            SubplotBase: The plot
        """
        green_colors = [COLORS["green"][700], COLORS["green"][500], COLORS["green"][300]]
        red_colors = [COLORS["red"][700], COLORS["red"][500], COLORS["red"][300]]

        increase_cols = ["new", "increased_focus", "switch_from_comparison"]
        decrease_cols = ["lost", "decreased_focus", "switch_to_comparison"]
        all_cols = increase_cols + decrease_cols

        plot_df = self.gain_loss_table_df.copy()
        default_y_label = self.focus_group_name if self.group_col is None else self.group_col
        plot_data = plot_df.copy()

        color_dict = {col: green_colors[i] for i, col in enumerate(increase_cols)}
        color_dict.update({col: red_colors[i] for i, col in enumerate(decrease_cols)})

        kwargs.pop("stacked", None)

        ax = bar.plot(
            df=plot_data,
            value_col=all_cols,
            title=gu.not_none(title, f"Gain Loss from {self.focus_group_name} to {self.comparison_group_name}"),
            y_label=gu.not_none(y_label, default_y_label),
            x_label=gu.not_none(x_label, self.value_col),
            orientation="horizontal",
            ax=ax,
            source_text=source_text,
            move_legend_outside=move_legend_outside,
            stacked=True,
            **kwargs,
        )

        for i, container in enumerate(ax.containers):
            col_name = all_cols[i]
            for patch in container:
                patch.set_color(color_dict[col_name])

        legend_labels = [
            "New",
            f"Increased {self.focus_group_name}",
            f"Switch From {self.comparison_group_name}",
            "Lost",
            f"Decreased {self.focus_group_name}",
            f"Switch To {self.comparison_group_name}",
        ]

        if ax.get_legend():
            ax.get_legend().remove()

        legend = ax.legend(
            legend_labels,
            frameon=True,
            bbox_to_anchor=(1.05, 1) if move_legend_outside else None,
            loc="upper left" if move_legend_outside else "best",
        )
        legend.get_frame().set_facecolor("white")
        legend.get_frame().set_edgecolor("white")

        ax.axvline(0, color="black", linewidth=0.5)

        decimals = gu.get_decimals(ax.get_xlim(), ax.get_xticks())
        ax.xaxis.set_major_formatter(lambda x, pos: gu.human_format(x, pos, decimals=decimals))
        ax.grid(axis="x", linestyle="--", alpha=0.7)

        gu.standard_tick_styles(ax)

        return ax

__init__(df, p1_index, p2_index, focus_group_index, focus_group_name, comparison_group_index, comparison_group_name, group_col=None, value_col=get_option('column.unit_spend'), agg_func='sum')

Calculate the gain loss table for a given DataFrame at the customer level.

Parameters:

Name Type Description Default
df DataFrame

The DataFrame to calculate the gain loss table from.

 required
p1_index list[bool]

The index for the first time period.

 required
p2_index list[bool]

The index for the second time period.

 required
focus_group_index list[bool]

The index for the focus group.

 required
focus_group_name str

The name of the focus group.

 required
comparison_group_index list[bool]

The index for the comparison group.

 required
comparison_group_name str

The name of the comparison group.

 required
group_col str | None

The column to group by. Defaults to None.

 None
value_col str

The column to calculate the gain loss from. Defaults to option column.unit_spend.

 get_option('column.unit_spend')
agg_func str

The aggregation function to use. Defaults to "sum".

 'sum'
Source code in pyretailscience/analysis/gain_loss.py 
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def __init__(
    self,
    df: pd.DataFrame,
    p1_index: list[bool] | pd.Series,
    p2_index: list[bool] | pd.Series,
    focus_group_index: list[bool] | pd.Series,
    focus_group_name: str,
    comparison_group_index: list[bool] | pd.Series,
    comparison_group_name: str,
    group_col: str | None = None,
    value_col: str = get_option("column.unit_spend"),
    agg_func: str = "sum",
) -> None:
    """Calculate the gain loss table for a given DataFrame at the customer level.

    Args:
        df (pd.DataFrame): The DataFrame to calculate the gain loss table from.
        p1_index (list[bool]): The index for the first time period.
        p2_index (list[bool]): The index for the second time period.
        focus_group_index (list[bool]): The index for the focus group.
        focus_group_name (str): The name of the focus group.
        comparison_group_index (list[bool]): The index for the comparison group.
        comparison_group_name (str): The name of the comparison group.
        group_col (str | None, optional): The column to group by. Defaults to None.
        value_col (str, optional): The column to calculate the gain loss from. Defaults to option column.unit_spend.
        agg_func (str, optional): The aggregation function to use. Defaults to "sum".
    """
    # # Ensure no overlap between p1 and p2
    if not df[p1_index].index.intersection(df[p2_index].index).empty:
        raise ValueError("p1_index and p2_index should not overlap")

    if not df[focus_group_index].index.intersection(df[comparison_group_index].index).empty:
        raise ValueError("focus_group_index and comparison_group_index should not overlap")

    if not len(p1_index) == len(p2_index) == len(focus_group_index) == len(comparison_group_index):
        raise ValueError(
            "p1_index, p2_index, focus_group_index, and comparison_group_index should have the same length",
        )

    required_cols = [get_option("column.customer_id"), value_col] + ([group_col] if group_col is not None else [])
    missing_cols = set(required_cols) - set(df.columns)
    if len(missing_cols) > 0:
        msg = f"The following columns are required but missing: {missing_cols}"
        raise ValueError(msg)

    self.focus_group_name = focus_group_name
    self.comparison_group_name = comparison_group_name
    self.group_col = group_col
    self.value_col = value_col

    self.gain_loss_df = self._calc_gain_loss(
        df=df,
        p1_index=p1_index,
        p2_index=p2_index,
        focus_group_index=focus_group_index,
        comparison_group_index=comparison_group_index,
        group_col=group_col,
        value_col=value_col,
        agg_func=agg_func,
    )
    self.gain_loss_table_df = self._calc_gains_loss_table(
        gain_loss_df=self.gain_loss_df,
        group_col=group_col,
    )

plot(title=None, x_label=None, y_label=None, ax=None, source_text=None, move_legend_outside=False, **kwargs)

Plot the gain loss table using the bar.plot wrapper.

Parameters:

Name Type Description Default
title str | None

The title of the plot. Defaults to None.

 None
x_label str | None

The x-axis label. Defaults to None.

 None
y_label str | None

The y-axis label. Defaults to None.

 None
ax Axes | None

The axes to plot on. Defaults to None.

 None
source_text str | None

The source text to add to the plot. Defaults to None.

 None
move_legend_outside bool

Whether to move the legend outside the plot. Defaults to False.

 False
kwargs dict[str, any]

Additional keyword arguments to pass to the plot.

 {}

Returns:

Name Type Description
SubplotBase SubplotBase

The plot
Source code in pyretailscience/analysis/gain_loss.py 
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def plot(
    self,
    title: str | None = None,
    x_label: str | None = None,
    y_label: str | None = None,
    ax: Axes | None = None,
    source_text: str | None = None,
    move_legend_outside: bool = False,
    **kwargs: dict[str, any],
) -> SubplotBase:
    """Plot the gain loss table using the bar.plot wrapper.

    Args:
        title (str | None, optional): The title of the plot. Defaults to None.
        x_label (str | None, optional): The x-axis label. Defaults to None.
        y_label (str | None, optional): The y-axis label. Defaults to None.
        ax (Axes | None, optional): The axes to plot on. Defaults to None.
        source_text (str | None, optional): The source text to add to the plot. Defaults to None.
        move_legend_outside (bool, optional): Whether to move the legend outside the plot. Defaults to False.
        kwargs (dict[str, any]): Additional keyword arguments to pass to the plot.

    Returns:
        SubplotBase: The plot
    """
    green_colors = [COLORS["green"][700], COLORS["green"][500], COLORS["green"][300]]
    red_colors = [COLORS["red"][700], COLORS["red"][500], COLORS["red"][300]]

    increase_cols = ["new", "increased_focus", "switch_from_comparison"]
    decrease_cols = ["lost", "decreased_focus", "switch_to_comparison"]
    all_cols = increase_cols + decrease_cols

    plot_df = self.gain_loss_table_df.copy()
    default_y_label = self.focus_group_name if self.group_col is None else self.group_col
    plot_data = plot_df.copy()

    color_dict = {col: green_colors[i] for i, col in enumerate(increase_cols)}
    color_dict.update({col: red_colors[i] for i, col in enumerate(decrease_cols)})

    kwargs.pop("stacked", None)

    ax = bar.plot(
        df=plot_data,
        value_col=all_cols,
        title=gu.not_none(title, f"Gain Loss from {self.focus_group_name} to {self.comparison_group_name}"),
        y_label=gu.not_none(y_label, default_y_label),
        x_label=gu.not_none(x_label, self.value_col),
        orientation="horizontal",
        ax=ax,
        source_text=source_text,
        move_legend_outside=move_legend_outside,
        stacked=True,
        **kwargs,
    )

    for i, container in enumerate(ax.containers):
        col_name = all_cols[i]
        for patch in container:
            patch.set_color(color_dict[col_name])

    legend_labels = [
        "New",
        f"Increased {self.focus_group_name}",
        f"Switch From {self.comparison_group_name}",
        "Lost",
        f"Decreased {self.focus_group_name}",
        f"Switch To {self.comparison_group_name}",
    ]

    if ax.get_legend():
        ax.get_legend().remove()

    legend = ax.legend(
        legend_labels,
        frameon=True,
        bbox_to_anchor=(1.05, 1) if move_legend_outside else None,
        loc="upper left" if move_legend_outside else "best",
    )
    legend.get_frame().set_facecolor("white")
    legend.get_frame().set_edgecolor("white")

    ax.axvline(0, color="black", linewidth=0.5)

    decimals = gu.get_decimals(ax.get_xlim(), ax.get_xticks())
    ax.xaxis.set_major_formatter(lambda x, pos: gu.human_format(x, pos, decimals=decimals))
    ax.grid(axis="x", linestyle="--", alpha=0.7)

    gu.standard_tick_styles(ax)

    return ax

process_customer_group(focus_p1, comparison_p1, focus_p2, comparison_p2, focus_diff, comparison_diff) staticmethod

Process the gain loss for a customer group.

Parameters:

Name Type Description Default
focus_p1 float | int

The focus group total in the first time period.

 required
comparison_p1 float | int

The comparison group total in the first time period.

 required
focus_p2 float | int

The focus group total in the second time period.

 required
comparison_p2 float | int

The comparison group total in the second time period.

 required
focus_diff float | int

The difference in the focus group totals.

 required
comparison_diff float | int

The difference in the comparison group totals.

 required

Returns:

Type Description
tuple[float, float, float, float, float, float]

tuple[float, float, float, float, float, float]: The gain loss for the customer group.
Source code in pyretailscience/analysis/gain_loss.py 
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@staticmethod
def process_customer_group(
    focus_p1: float,
    comparison_p1: float,
    focus_p2: float,
    comparison_p2: float,
    focus_diff: float,
    comparison_diff: float,
) -> tuple[float, float, float, float, float, float]:
    """Process the gain loss for a customer group.

    Args:
        focus_p1 (float | int): The focus group total in the first time period.
        comparison_p1 (float | int): The comparison group total in the first time period.
        focus_p2 (float | int): The focus group total in the second time period.
        comparison_p2 (float | int): The comparison group total in the second time period.
        focus_diff (float | int): The difference in the focus group totals.
        comparison_diff (float | int): The difference in the comparison group totals.

    Returns:
        tuple[float, float, float, float, float, float]: The gain loss for the customer group.
    """
    if focus_p1 == 0 and comparison_p1 == 0:
        return focus_p2, 0, 0, 0, 0, 0
    if focus_p2 == 0 and comparison_p2 == 0:
        return 0, -1 * focus_p1, 0, 0, 0, 0

    if focus_diff > 0:
        focus_inc_dec = focus_diff if comparison_diff > 0 else max(0, comparison_diff + focus_diff)
    elif comparison_diff < 0:
        focus_inc_dec = focus_diff
    else:
        focus_inc_dec = min(0, comparison_diff + focus_diff)

    increased_focus = max(0, focus_inc_dec)
    decreased_focus = min(0, focus_inc_dec)

    transfer = focus_diff - focus_inc_dec
    switch_from_comparison = max(0, transfer)
    switch_to_comparison = min(0, transfer)

    return 0, 0, increased_focus, decreased_focus, switch_from_comparison, switch_to_comparison