> ## Documentation Index
> Fetch the complete documentation index at: https://docs.usehindsight.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Metrics

> Custom business dimensions for scoring and analyzing deals

## Overview

Metrics are custom business dimensions that let you score and analyze deals beyond standard revenue and stage tracking. Using a 1-5 scale, metrics help quantify qualitative aspects of your sales process and identify patterns that drive success.

## Metric Structure

* **Name**: Clear, specific dimension (e.g., "Champion Strength")
* **Description**: What this metric measures and how to score it
* **Scale Definition**: What each score (1-5) represents
* **Use Case**: When and why to apply this metric

## Scoring Framework

All metrics use a consistent 1-5 scale:

* **5**: Excellent/Ideal state
* **4**: Good/Above average
* **3**: Average/Neutral
* **2**: Below average/Concerning
* **1**: Poor/Major risk

## Common Metric Types

### Sales Process Quality

* **Champion Strength**: How well-positioned and influential your champion is
* **Buying Process Clarity**: How well-defined the customer's evaluation process is
* **Decision Timeline**: Urgency and clarity of purchase timing
* **Budget Authority**: Access to and influence over purchasing decisions

### Technical Fit

* **Solution Alignment**: How well your product matches their requirements
* **Implementation Complexity**: Ease of deployment and integration
* **Technical Champion Buy-in**: Support from technical stakeholders
* **Competitive Positioning**: Strength against alternatives

### Strategic Value

* **Business Impact**: Potential value delivery to customer
* **Strategic Importance**: Priority level within customer organization
* **Executive Sponsorship**: C-level support and involvement
* **Expansion Potential**: Opportunity for future growth

## Metric Analytics

* **Deal Correlation**: Which metrics predict deal success
* **Score Distribution**: How deals perform across different metric ranges
* **Trend Analysis**: How metric scores change throughout sales cycles
* **Segmentation**: Deal performance by metric combinations
* **Forecasting**: Use metric patterns to predict deal outcomes

## Setup Process

1. **Define Dimension**: What business aspect you want to measure
2. **Create Scale**: Define what each 1-5 score represents
3. **Train Team**: Ensure consistent scoring across sales reps
4. **Apply Systematically**: Score deals consistently throughout pipeline
5. **Analyze Patterns**: Use data to identify success factors

## Best Practices

* **Clear Definitions**: Make scoring criteria objective and specific
* **Team Alignment**: Ensure all reps understand and apply metrics consistently
* **Regular Calibration**: Review and adjust metric definitions as needed
* **Actionable Insights**: Focus on metrics that inform specific actions
* **Balanced Portfolio**: Use 3-5 metrics that cover different deal aspects

## Data Integration

Metrics work alongside other Hindsight data:

* **Deal Conversations**: AI extracts evidence supporting metric scores
* **Feature Discussions**: Connect capability mentions to technical fit metrics
* **Decision Driver Analysis**: Link strategic metrics to buyer priorities
* **Competitive Intelligence**: Inform positioning metrics with market data

## Reporting & Insights

* **Metric Dashboards**: Visual summaries of deal performance by dimension
* **Correlation Analysis**: Which metrics most strongly predict outcomes
* **Team Performance**: How different reps score and succeed with metrics
* **Pipeline Health**: Early warning indicators from metric trends
* **Win/Loss Patterns**: Metric profiles of successful vs. unsuccessful deals

## Common Use Cases

* **Deal Qualification**: Identify and prioritize high-potential opportunities
* **Risk Assessment**: Flag deals with concerning metric patterns
* **Sales Coaching**: Help reps improve in specific areas
* **Process Optimization**: Identify stages where deals typically struggle
* **Forecasting Accuracy**: Improve prediction models with qualitative factors