salestech sales region:us
---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 7416
    num_examples: 100
  - name: test
    num_bytes: 1884
    num_examples: 26
  download_size: 6977
  dataset_size: 9300
tags:
- salestech
- sales
---
# Dataset Card for "salestech_sales_qualification_framework_bant"

---
license: apache-2.0
---

# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** 
- **Repository:** 
- **Paper:** 
- **Leaderboard:** 
- **Point of Contact:** 

### Dataset Summary

The BANT technique is a sales qualifying framework that considers a prospect's budget, internal influence/ability to buy, need for the product, and timeframe for making a purchase when determining whether to pursue a sale.

Because it aids in lead qualification during the discovery call, BANT plays a vital role in the sales process. The sales team may acquire precise information from the prospect about their budget, stakeholders, need, and timescale immediately, rather than waiting days or weeks for leads to be qualified using a score determined from the prospect's behaviour and engagement with marketing and sales materials.

Budget - The prospect's financial capacity to invest in your solution.

Authority - Who has the final say in this transaction? Who gets to make the final call?

Need – Does the potential customer really need my product? Do all members of the team require this?

Timeline -How long will it be before the potential customer makes a decision?

### Supported Tasks and Leaderboards

N.A.

### Languages

ENGLISH

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

There are 2 columns: 
text: text 
label : label (one of four from BANT)

### Data Splits

N.A.

## Dataset Creation

### Curation Rationale

Prospectus text mining

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

This will help SaleTech to better qualify leads.

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information

### Contributions

Made by Author [Scholarly360](https://github.com/Scholarly360) .