Purpose

The Metric Drivers analysis tool helps users identify the key drivers of performance for a specific KPI. It pinpoints the metrics (e.g., sales, volume, units) and dimensions (e.g., brand, region, segment) that are most responsible for overall performance changes. This analysis is ideal for understanding which factors are driving growth or decline.

Key Features

Driver Identification

The tool identifies the key metrics driving performance changes. For example, in a sales analysis, it highlights which metrics—like volume, units, or price—are contributing to sales growth or decline.

Dimensional Drivers

The tool reveals which dimensions (e.g., brand, region, or segment) have the most significant impact on performance. Users can rank these dimensions to see which are contributing most to growth or decline.

Detailed Hierarchical Drill-down

The analysis allows you to drill down across multiple hierarchical levels. For example, you can begin with a high-level view like Brand, then drill down to more granular levels such as Sub-brand or SKU for deeper insights.

Growth and Impact Calculation

Using growth calculations, the tool measures the contribution or change of each metric and dimension. This helps users understand both the magnitude and drivers behind changes.

Flexibility with Filters and Breakouts

Users can apply filters to focus on specific data, such as time periods or product types. The tool also supports breakouts across multiple dimensions, providing an in-depth analysis.

Support for Multiple Metrics

The analysis supports various metrics such as sales, volume, units, and pricing. However, it is not designed for share metrics (analyze market share drivers using the Market Share Analysis Skill)

How It Works

Initial Setup

Users define the dimensions and metrics they want to analyze. The tool supports default and customized configurations, enabling users to focus on specific metrics such as sales growth by brand or volume decline by region.

Processing Filters

The tool processes filters to ensure that only relevant data is included. If filters are too broad, it provides guidance to help users refine their analysis.

Handling Breakouts

When no breakouts are specified, the tool automatically applies the dimension hierarchy, enabling users to explore data at different levels without manual configuration.

Visual Output

Pivot Tables

The analysis results are presented as pivot tables, displaying the breakdown of each metric and dimension. This allows users to compare different dimensions and metrics side by side.

Impact and Growth Indicators

The output includes impact and growth columns, showing how each dimension is affecting the overall metric. Growth rates are calculated based on selected time periods, and the tool highlights significant performance changes.

Interactive Elements

Users can drill down into specific dimensions for deeper insights, clicking on a brand to view performance by region or category.

Warnings and Limits

Row Limits

The tool has a row limit to maintain efficiency. If the limit is reached, users are notified that the results may be incomplete, and they are provided suggestions for refining the analysis.

Filter Guidance

If there are filter or breakout issues, the tool provides error messages or guidance. For example, it alerts users if multiple filters are applied to a dimension that only supports one filter at a time.

Example Questions It Can Answer

Can you explain the sales decline for Barilla in March 2023?
How is the rice category performing in terms of sales growth?
Why is whole grain pasta experiencing a decline in volume?

Skill Setup

Ensure a metric hierarchy and dimension hierarchy are properly setup within your dataset.

Dimension & Metric Hierarchy Example:

Notes on metric hierarchy:

{  
  "metric_hierarchy": [  
    {  
      "metric": "sales",  
      "parent_metric": "value_share"  
    },  
    {  
      "metric": "volume",  
      "parent_metric": "sales"  
    },  
    {  
      "metric": "price",  
      "parent_metric": "sales"  
    },  
    {  
      "metric": "TDP",  
      "parent_metric": "volume"  
    }  
  ],  
  "dimension_hierarchy": \[  
    {  
      "col": "market",  
      "children": \[  
        {  
          "col": "category",  
          "children": \[]  
        }  
      ]  
    },  
    {  
      "col": "manufacturer",  
      "exclude_in_mkt_size": true,  
      "children": \[  
        {  
          "col": "brand",  
          "children": \[]  
        }  
      ]  
    }  
  ]  
}

Notes on metric hierarchy:

[{  
      "metric": "volume",  
      "parent_metric": "sales"  
    }]

Notes on dimension hierarchy:

{  
      "col": "market",  
      "children": \[  
        {  
          "col": "category",  
          "children": \[]  
        }  
      ]  
    }