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Threshold Segmentation

Threshold-Based Segmentation for Flexible Business Classification.

Business Context

Many retail segmentation needs don't fit standard models like RFM. Businesses often need custom segments based on specific metrics and thresholds - whether segmenting customers by spend percentiles, stores by performance quintiles, or products by sales velocity. This module provides flexible threshold-based segmentation for any business dimension.

The Business Problem

Retailers need custom segmentation rules for different business scenarios: - Create spend-based customer tiers (Bronze, Silver, Gold, Platinum) - Classify stores into performance bands (A, B, C, D stores) - Segment products by velocity (Fast, Medium, Slow movers) - Define custom categories based on business-specific thresholds

Standard segmentation approaches are too rigid, while manual classification is inconsistent and doesn't scale across large datasets.

Real-World Applications

Customer Classification

  • Create VIP tiers based on total spend percentiles
  • Segment by transaction frequency for different service levels
  • Classify customers by recency for retention campaigns

Store Performance Tiers

  • Classify stores by sales per square foot into performance bands
  • Segment locations by customer conversion rates
  • Create store tiers for investment prioritization

Product Categorization

  • Segment SKUs by sales velocity for inventory management
  • Classify products by margin contribution for pricing strategies
  • Create ABC analysis categories for supply chain optimization

Technical Features

  • Flexible percentile-based thresholds for consistent segment sizing
  • Custom aggregation functions for different business metrics
  • Configurable handling of zero-value entities
  • Efficient execution using Ibis for large datasets

ThresholdSegmentation

Segments customers based on user-defined thresholds and segments.

Source code in openretailscience/segmentation/threshold.py 
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class ThresholdSegmentation:
    """Segments customers based on user-defined thresholds and segments."""

    def __init__(
        self,
        df: pd.DataFrame | ibis.Table,
        thresholds: list[float],
        segments: list[str],
        value_col: str | None = None,
        agg_func: str = "sum",
        zero_segment_name: str = "Zero",
        zero_value_customers: Literal["separate_segment", "exclude", "include_with_light"] = "separate_segment",
        group_col: str | list[str] | None = None,
    ) -> None:
        """Segments customers based on user-defined thresholds and segments.

        Args:
            df (pd.DataFrame | ibis.Table): A dataframe with the transaction data. The dataframe must contain a
                customer_id column.
            thresholds (List[float]): The percentile thresholds for segmentation.
            segments (List[str]): A list of segment names for each threshold.
            value_col (str, optional): The column to use for the segmentation. Defaults to
                ColumnHelper().unit_spend.
            agg_func (str, optional): The aggregation function to use when grouping by customer_id. Defaults to "sum".
            zero_segment_name (str, optional): The name of the segment for customers with zero spend.
                Defaults to "Zero".
            zero_value_customers (Literal["separate_segment", "exclude", "include_with_light"], optional): How to handle
                customers with zero spend. Defaults to "separate_segment".
            group_col (str | list[str] | None, optional): Column(s) to group by when calculating segments. When
                specified, segments are calculated within each group independently. For example, setting
                group_col="store_id" calculates Heavy/Medium/Light segments within each store. Defaults to None.

        Raises:
            ValueError: If the dataframe is missing the columns option column.customer_id or `value_col`, or these
                columns contain null values.
        """
        self._group_col: list[str] | None = None
        if len(thresholds) != len(set(thresholds)):
            raise ValueError("The thresholds must be unique")

        if len(thresholds) != len(segments):
            raise ValueError("The number of thresholds must match the number of segments")

        # Initialize column helper
        cols = ColumnHelper()

        df = ensure_ibis_table(df)

        self._group_col = ensure_columns(df, group_col, "group_col") if group_col is not None else None

        value_col = cols.unit_spend if value_col is None else value_col

        # group_col is already validated above; only the function's hard-coded requirements remain.
        ensure_data_has_columns(df, [cols.customer_id, value_col])

        # Build group_by columns: customer_id + optional group columns
        group_by_cols = [cols.customer_id]
        if self._group_col is not None:
            group_by_cols.extend(self._group_col)

        df = df.group_by(*group_by_cols).aggregate(
            **{value_col: getattr(df[value_col], agg_func)()},
        )

        # Separate customers with zero spend
        zero_df = None
        if zero_value_customers == "exclude":
            df = df.filter(df[value_col] != 0)
        elif zero_value_customers == "separate_segment":
            zero_df = df.filter(df[value_col] == 0).mutate(segment_name=ibis.literal(zero_segment_name))
            df = df.filter(df[value_col] != 0)

        # Create window function, partitioned by group columns if specified
        # Order by value_col first, then customer_id to ensure deterministic ordering when values are tied
        window = (
            ibis.window(order_by=[ibis.asc(df[value_col]), ibis.asc(df[cols.customer_id])])
            if self._group_col is None
            else ibis.window(
                order_by=[ibis.asc(df[value_col]), ibis.asc(df[cols.customer_id])],
                group_by=self._group_col,
            )
        )
        df = df.mutate(ptile=ibis.percent_rank().over(window))

        case_args = [(df["ptile"] <= quantile, segment) for quantile, segment in zip(thresholds, segments, strict=True)]

        df = df.mutate(segment_name=ibis.cases(*case_args)).drop(["ptile"])

        if zero_value_customers == "separate_segment":
            df = ibis.union(df, zero_df)

        self.table = df

    @functools.cached_property
    def df(self) -> pd.DataFrame:
        """Returns the dataframe with the segment names."""
        cols = ColumnHelper()
        index_cols = [cols.customer_id]
        if self._group_col is not None:
            index_cols.extend(self._group_col)
        return self.table.execute().set_index(index_cols)

df cached property

Returns the dataframe with the segment names.

__init__(df, thresholds, segments, value_col=None, agg_func='sum', zero_segment_name='Zero', zero_value_customers='separate_segment', group_col=None)

Segments customers based on user-defined thresholds and segments.

Parameters:

Name Type Description Default
df DataFrame | Table

A dataframe with the transaction data. The dataframe must contain a customer_id column.

 required
thresholds List[float]

The percentile thresholds for segmentation.

 required
segments List[str]

A list of segment names for each threshold.

 required
value_col str

The column to use for the segmentation. Defaults to ColumnHelper().unit_spend.

 None
agg_func str

The aggregation function to use when grouping by customer_id. Defaults to "sum".

 'sum'
zero_segment_name str

The name of the segment for customers with zero spend. Defaults to "Zero".

 'Zero'
zero_value_customers Literal['separate_segment', 'exclude', 'include_with_light']

How to handle customers with zero spend. Defaults to "separate_segment".

 'separate_segment'
group_col str | list[str] | None

Column(s) to group by when calculating segments. When specified, segments are calculated within each group independently. For example, setting group_col="store_id" calculates Heavy/Medium/Light segments within each store. Defaults to None.

 None

Raises:

Type Description
ValueError

If the dataframe is missing the columns option column.customer_id or value_col, or these columns contain null values.
Source code in openretailscience/segmentation/threshold.py 
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def __init__(
    self,
    df: pd.DataFrame | ibis.Table,
    thresholds: list[float],
    segments: list[str],
    value_col: str | None = None,
    agg_func: str = "sum",
    zero_segment_name: str = "Zero",
    zero_value_customers: Literal["separate_segment", "exclude", "include_with_light"] = "separate_segment",
    group_col: str | list[str] | None = None,
) -> None:
    """Segments customers based on user-defined thresholds and segments.

    Args:
        df (pd.DataFrame | ibis.Table): A dataframe with the transaction data. The dataframe must contain a
            customer_id column.
        thresholds (List[float]): The percentile thresholds for segmentation.
        segments (List[str]): A list of segment names for each threshold.
        value_col (str, optional): The column to use for the segmentation. Defaults to
            ColumnHelper().unit_spend.
        agg_func (str, optional): The aggregation function to use when grouping by customer_id. Defaults to "sum".
        zero_segment_name (str, optional): The name of the segment for customers with zero spend.
            Defaults to "Zero".
        zero_value_customers (Literal["separate_segment", "exclude", "include_with_light"], optional): How to handle
            customers with zero spend. Defaults to "separate_segment".
        group_col (str | list[str] | None, optional): Column(s) to group by when calculating segments. When
            specified, segments are calculated within each group independently. For example, setting
            group_col="store_id" calculates Heavy/Medium/Light segments within each store. Defaults to None.

    Raises:
        ValueError: If the dataframe is missing the columns option column.customer_id or `value_col`, or these
            columns contain null values.
    """
    self._group_col: list[str] | None = None
    if len(thresholds) != len(set(thresholds)):
        raise ValueError("The thresholds must be unique")

    if len(thresholds) != len(segments):
        raise ValueError("The number of thresholds must match the number of segments")

    # Initialize column helper
    cols = ColumnHelper()

    df = ensure_ibis_table(df)

    self._group_col = ensure_columns(df, group_col, "group_col") if group_col is not None else None

    value_col = cols.unit_spend if value_col is None else value_col

    # group_col is already validated above; only the function's hard-coded requirements remain.
    ensure_data_has_columns(df, [cols.customer_id, value_col])

    # Build group_by columns: customer_id + optional group columns
    group_by_cols = [cols.customer_id]
    if self._group_col is not None:
        group_by_cols.extend(self._group_col)

    df = df.group_by(*group_by_cols).aggregate(
        **{value_col: getattr(df[value_col], agg_func)()},
    )

    # Separate customers with zero spend
    zero_df = None
    if zero_value_customers == "exclude":
        df = df.filter(df[value_col] != 0)
    elif zero_value_customers == "separate_segment":
        zero_df = df.filter(df[value_col] == 0).mutate(segment_name=ibis.literal(zero_segment_name))
        df = df.filter(df[value_col] != 0)

    # Create window function, partitioned by group columns if specified
    # Order by value_col first, then customer_id to ensure deterministic ordering when values are tied
    window = (
        ibis.window(order_by=[ibis.asc(df[value_col]), ibis.asc(df[cols.customer_id])])
        if self._group_col is None
        else ibis.window(
            order_by=[ibis.asc(df[value_col]), ibis.asc(df[cols.customer_id])],
            group_by=self._group_col,
        )
    )
    df = df.mutate(ptile=ibis.percent_rank().over(window))

    case_args = [(df["ptile"] <= quantile, segment) for quantile, segment in zip(thresholds, segments, strict=True)]

    df = df.mutate(segment_name=ibis.cases(*case_args)).drop(["ptile"])

    if zero_value_customers == "separate_segment":
        df = ibis.union(df, zero_df)

    self.table = df