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

# Field Sales

> Optimize territory planning and visit prioritization with entity intelligence.

## The Territory Problem

Field sales is expensive. A rep can visit maybe 5-8 accounts per day. Choosing the wrong accounts wastes days of selling time.

Traditional territory planning uses static lists: "Visit all accounts over R\$1M revenue in the South region." But revenue doesn't predict who's ready to buy.

## Intelligence-Driven Territories

Instead of geography + firmographics, plan territories around:

| Factor                   | What Avra Provides                                                   |
| ------------------------ | -------------------------------------------------------------------- |
| **Propensity to buy**    | Which accounts are showing buying signals in their network behavior? |
| **Growth trajectory**    | Who's expanding and likely to need more?                             |
| **Competitive risk**     | Which accounts are connected to competitors' customers?              |
| **Relationship density** | Where do you have warm paths through existing customers?             |

## Daily Visit Optimization

**Territory**: South Region | **Rep**: Maria Santos | **Date**: Monday

| Rank | Account             | Score | Signal                                  |
| ---- | ------------------- | ----- | --------------------------------------- |
| 1    | TechFlow Ltda       | 94    | Growth spike + connected to 3 customers |
| 2    | Indústria Beta      | 89    | Expansion signals, no competitive risk  |
| 3    | Comércio Delta      | 82    | High fit, decision-maker changed        |
| 4    | Serviços Gama       | 71    | Stable, routine check-in                |
| 5    | Distribuidora Omega | 68    | Slight decline, monitor for churn       |

Rep works the list top-down. Highest-value opportunities get attention first.

## Route Optimization + Scoring

Combine visit prioritization with route efficiency:

1. Score all accounts in territory
2. Filter to top 20 by score
3. Optimize route through top 20 by geography
4. Result: Best accounts, efficient path

## Tracking Impact

Measure before/after:

* Meetings per closed deal
* Average deal size from field vs. inside
* Time from first visit to close
* Territory revenue per rep

The goal: Same headcount, more revenue from smarter targeting.

## Powered by two foundations

Field sales ranking composes both Avra foundations. The **Graph Foundation Model** identifies buying propensity through network-level patterns — expansion signals, competitive dynamics, and relationship density that firmographic filters miss. Your **Relational Foundation Model** learns what "ready to buy" looks like for your product and market, drawn from the patterns in your sales outcomes. The downstream model is trained on both, and feeds signal back into your RFM with every retrain.

### Customer Data Needed

| Data               | Purpose                                             |
| ------------------ | --------------------------------------------------- |
| **Sales outcomes** | Won/lost deals, deal size, time-to-close by account |
| **Account list**   | CNPJs of target accounts with territory assignments |
| **CRM activity**   | Visit history, engagement signals, pipeline stage   |

### Output Schema

| Field              | Description                                                                            |
| ------------------ | -------------------------------------------------------------------------------------- |
| `propensity_score` | Probability (0-1) of conversion within the scoring horizon                             |
| `growth_signal`    | Indicator of entity expansion or contraction trajectory                                |
| `network_density`  | Number of warm paths through existing customers                                        |
| `risk_factors`     | Key signals driving the ranking (sector health, competitive exposure, buying patterns) |

### Evaluation Metrics

* **Meetings-to-close ratio** — Primary metric: improvement in conversion rate from scored visits vs. unsorted visits.
* **Lift at top decile** — How much better the model's top-ranked accounts perform vs. random or firmographic ordering.
* **Revenue per rep** — Measures territory-level impact of score-based prioritization.