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HML Segmentation

Heavy-Medium-Light (HML) Segmentation for Customer Value Classification.

Business Context

The 80/20 rule dominates retail customer behavior: typically 20% of customers generate 80% of revenue. HML segmentation formalizes this insight by classifying customers into Heavy, Medium, Light, and Zero spenders, enabling targeted strategies for each value tier.

The Business Problem

All customers are not equal, but many retailers treat them the same way. Marketing budgets are wasted on low-value customers while high-value customers don't receive appropriate attention. Without clear customer value classification, businesses struggle to: - Allocate marketing spend effectively - Design appropriate service levels - Create relevant offers for different customer types - Identify at-risk high-value customers

Real-World Applications

Heavy Spenders (Top ~20%)

  • VIP programs with exclusive access and premium support
  • Personalized shopping experiences and dedicated account management
  • Early access to new products and sales
  • Higher-value promotional offers and loyalty rewards

Medium Spenders (Middle ~30%)

  • Growth-focused marketing to move them toward Heavy tier
  • Category expansion offers to increase wallet share
  • Loyalty programs designed to increase purchase frequency
  • Targeted promotions based on purchase history

Light Spenders (Lower ~50%)

  • Cost-effective digital marketing channels
  • Basic loyalty programs and promotional offers
  • Automated email campaigns for reactivation
  • Focus on retention rather than acquisition costs

Zero Spenders

  • Win-back campaigns for previously active customers
  • Low-cost reactivation offers
  • Analysis for churn prevention insights
  • Potential customer file purging for database hygiene

This module extends ThresholdSegmentation to implement the standard HML classification using Pareto-based percentile thresholds for consistent, business-relevant segments.

HMLSegmentation

Bases: ThresholdSegmentation

Segments customers into Heavy, Medium, Light and Zero spenders based on the total spend.

Source code in pyretailscience/segmentation/hml.py 
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class HMLSegmentation(ThresholdSegmentation):
    """Segments customers into Heavy, Medium, Light and Zero spenders based on the total spend."""

    def __init__(
        self,
        df: pd.DataFrame | ibis.Table,
        value_col: str | None = None,
        agg_func: str = "sum",
        zero_value_customers: Literal["separate_segment", "exclude", "include_with_light"] = "separate_segment",
    ) -> None:
        """Segments customers into Heavy, Medium, Light and Zero spenders based on the total spend.

        HMLSegmentation is a subclass of ThresholdSegmentation and based around an industry standard definition. The
        thresholds for Heavy (top 20%), Medium (next 30%) and Light (bottom 50%) are chosen based on the pareto
        distribution, commonly know as the 80/20 rule. It is typically used in retail to segment customers based on
        their spend, transaction volume or quantities purchased.

        Args:
            df (pd.DataFrame): A dataframe with the transaction data. The dataframe must contain a customer_id column.
            value_col (str, optional): The column to use for the segmentation. Defaults to get_option("column.unit_spend").
            agg_func (str, optional): The aggregation function to use when grouping by customer_id. Defaults to "sum".
            zero_value_customers (Literal["separate_segment", "exclude", "include_with_light"], optional): How to handle
                customers with zero spend. Defaults to "separate_segment".
        """
        thresholds = [0.500, 0.800, 1]
        segments = ["Light", "Medium", "Heavy"]
        super().__init__(
            df=df,
            value_col=value_col,
            agg_func=agg_func,
            thresholds=thresholds,
            segments=segments,
            zero_value_customers=zero_value_customers,
        )

__init__(df, value_col=None, agg_func='sum', zero_value_customers='separate_segment')

Segments customers into Heavy, Medium, Light and Zero spenders based on the total spend.

HMLSegmentation is a subclass of ThresholdSegmentation and based around an industry standard definition. The thresholds for Heavy (top 20%), Medium (next 30%) and Light (bottom 50%) are chosen based on the pareto distribution, commonly know as the 80/20 rule. It is typically used in retail to segment customers based on their spend, transaction volume or quantities purchased.

Parameters:

Name Type Description Default
df DataFrame

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

 required
value_col str

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

 None
agg_func str

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

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

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

 'separate_segment'
Source code in pyretailscience/segmentation/hml.py 
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def __init__(
    self,
    df: pd.DataFrame | ibis.Table,
    value_col: str | None = None,
    agg_func: str = "sum",
    zero_value_customers: Literal["separate_segment", "exclude", "include_with_light"] = "separate_segment",
) -> None:
    """Segments customers into Heavy, Medium, Light and Zero spenders based on the total spend.

    HMLSegmentation is a subclass of ThresholdSegmentation and based around an industry standard definition. The
    thresholds for Heavy (top 20%), Medium (next 30%) and Light (bottom 50%) are chosen based on the pareto
    distribution, commonly know as the 80/20 rule. It is typically used in retail to segment customers based on
    their spend, transaction volume or quantities purchased.

    Args:
        df (pd.DataFrame): A dataframe with the transaction data. The dataframe must contain a customer_id column.
        value_col (str, optional): The column to use for the segmentation. Defaults to get_option("column.unit_spend").
        agg_func (str, optional): The aggregation function to use when grouping by customer_id. Defaults to "sum".
        zero_value_customers (Literal["separate_segment", "exclude", "include_with_light"], optional): How to handle
            customers with zero spend. Defaults to "separate_segment".
    """
    thresholds = [0.500, 0.800, 1]
    segments = ["Light", "Medium", "Heavy"]
    super().__init__(
        df=df,
        value_col=value_col,
        agg_func=agg_func,
        thresholds=thresholds,
        segments=segments,
        zero_value_customers=zero_value_customers,
    )