Sales Conversion
Maximize sales conversion by effectively transforming interest into revenue-generating opportunities.
We're in the early alpha stage, this page is constantly evolving, book a call with us to learn more.s
Context
The Sales Engine is a powerful solution that enables organizations to optimize their sales processes, increase revenue, and make data-driven decisions. With features like deal priorization, forecasting, tracking, and automation, businesses can streamline their operations and achieve their sales goals more effectively.
Benefits
CRM Data Visualization and Insights
Visualize and analyze your CRM data, providing a clear overview of your sales pipeline and deal progress.
This kind of system uses data visualization techniques to analyze and display your CRM data, providing a clear overview of your sales pipeline, deal progress, and other important metrics.
Prospect Interaction Scoring
Track and score prospect interactions, allowing you to focus on highly engaged leads and optimize your sales efforts.
The system tracks and scores interactions with prospects, helping sales teams to focus on highly engaged leads and optimize their efforts.
Product Analytics-Driven Signals
Leverage product usage data to identify customer needs and pain points, enabling your sales team to tailor their approach and close deals more effectively.
This system leverages product usage data to identify customer needs and pain points, enabling your sales team to tailor their approach and close deals more effectively.
Meeting assistant & sales process optimization
Transform your notes in actionable items to follow up with your prospect. Transform memos to close you deal and generate data on what works.
Sure, let’s consider a Meeting Assistant & Sales Process Optimization system. This kind of system transforms meeting notes into actionable items for following up with prospects, helps close deals, and generates data on effective practices.
Features
Chat with Your Sales Agent
You can interact with your growth agent to interact with your network and leads. This agent can assist you in crafting a series of messages and action items, fostering new connections between you and individuals within your network.
Receive Automated Email Reports
Receive automated overview of your sales performance, including forecasted revenue, won deals, and the weighted pipeline. It also highlights specific actions needed for deals that require attention, allowing you to prioritize and your sales actions.
Prioritize Deals
Analyze the various factors such as deal size, customer engagement, and probability of closing to identify the deals that have the greatest potential for success.
Integrate Product Analytics Signals
Integrate product analytics signals to tracking of every product-related interaction, such as email sent, open, click, and login, directly into your CRM's deal activities. It ensures that no prospect engagement with your product goes unnoticed, allowing you to capture and respond to important signals promptly.
Create AI-Powered Email Sequences
Use the information collected into your CRM to determine the best strategy and create a custom email sales sequences that will create unique relationships with your prospects.
Transform Meeting Recording into Actionable Memos
Automatically transform meeting recordings into actionable memos, this feature generates a draft memo in your inbox at the end of the recording. It eliminates the need for manual note-taking and ensures important information is captured accurately for easy review and collaboration.
Data Model
INSIGHTS
Dataset
| DEAL ID | DEAL NAME | STAGE | VALUE | PROBABILITY | CLOSE DATE | SALES REP | PRODUCT |
|---|---|---|---|---|---|---|---|
| 1 | Deal A | Proposal | $10,000 | 0.75 | 6/15/2023 | John Doe | Product X |
| 2 | Deal B | Discovery | $20,000 | 0.20 | 7/1/2023 | Jane Smith | Product Y |
| 3 | Deal C | Negotiation | $15,000 | 0.60 | 6/30/2023 | John Doe | Product Z |
| 4 | Deal D | Closed Won | $5,000 | 1.00 | 5/20/2023 | Jane Smith | Product X |
Schema
{
"type": "object",
"properties": {
"DEAL ID": {
"type": "integer",
"description": "A unique identifier for the deal."
},
"DEAL NAME": {
"type": "string",
"description": "The name of the deal."
},
"STAGE": {
"type": "string",
"description": "The current stage of the deal in the sales pipeline."
},
"VALUE": {
"type": "integer",
"description": "The potential value of the deal."
},
"PROBABILITY": {
"type": "number",
"description": "The probability of the deal closing, as a decimal."
},
"CLOSE DATE": {
"type": "string",
"description": "The expected close date of the deal."
},
"SALES REP": {
"type": "string",
"description": "The sales rep assigned to the deal."
},
"PRODUCT": {
"type": "string",
"description": "The product involved in the deal."
}
},
"required": ["DEAL ID", "DEAL NAME", "STAGE", "VALUE", "PROBABILITY", "CLOSE DATE", "SALES REP", "PRODUCT"]
}
SCORING
Dataset
| LEAD ID | NAME | COMPANY | EMAIL INTERACTIONS | CALL INTERACTIONS | MEETING INTERACTIONS | WEB ACTIVITY SCORE | INTERACTION SCORE |
|---|---|---|---|---|---|---|---|
| 1 | John Doe | Company A | 5 (responded 4 times) | 2 (1 incoming, 1 outgoing) | 1 (attended) | 80 | 75 |
| 2 | Jane Smith | Company B | 3 (responded 2 times) | 3 (2 incoming, 1 outgoing) | 0 | 70 | 65 |
| 3 | Bob Johnson | Company C | 4 (responded 3 times) | 1 (1 outgoing) | 2 (attended both) | 60 | 70 |
| 4 | Mary Davis | Company D | 2 (responded 1 time) | 2 (2 outgoing) | 1 (did not attend) | 50 | 55 |
Schema
SIGNALS
Dataset
| CUSTOMER ID | NAME | PRODUCT | USAGE HOURS | FEATURES USED | FREQUENT ISSUES | SATISFACTION SCORE | NEEDS IDENTIFIED |
|---|---|---|---|---|---|---|---|
| 1 | John Doe | Product A | 120 | Feature X, Feature Y | Issue X | 8 | Need X |
| 2 | Jane Smith | Product B | 80 | Feature Y, Feature Z | Issue Y, Issue Z | 7 | Need Y, Need Z |
| 3 | Bob Johnson | Product C | 150 | Feature X, Feature Z | Issue X | 9 | Need X |
| 4 | Mary Davis | Product A | 100 | Feature X | Issue Z | 8 | Need Z |
Schema
{
"type": "object",
"properties": {
"LEAD ID": {
"type": "integer",
"description": "A unique identifier for the lead."
},
"NAME": {
"type": "string",
"description": "The name of the lead."
},
"COMPANY": {
"type": "string",
"description": "The company that the lead is associated with."
},
"EMAIL INTERACTIONS": {
"type": "string",
"description": "The number of email interactions with the lead and their responses."
},
"CALL INTERACTIONS": {
"type": "string",
"description": "The number of call interactions with the lead, broken down by incoming and outgoing."
},
"MEETING INTERACTIONS": {
"type": "string",
"description": "The number of meeting interactions with the lead and their attendance."
},
"WEB ACTIVITY SCORE": {
"type": "integer",
"description": "The score assigned to the lead based on their web activity."
},
"INTERACTION SCORE": {
"type": "integer",
"description": "The overall score assigned to the lead based on their interactions."
}
},
"required": ["LEAD ID", "NAME", "COMPANY", "EMAIL INTERACTIONS", "CALL INTERACTIONS", "MEETING INTERACTIONS", "WEB ACTIVITY SCORE", "INTERACTION SCORE"]
}
MOM
Dataset
| MEETING ID | PROSPECT NAME | DATE | MEETING NOTES | ACTIONABLE ITEMS | DEAL CLOSED | EFFECTIVE PRACTICES |
|---|---|---|---|---|---|---|
| 1 | John Doe | 2023-05-24 | Discussed product features and pricing. | Follow up with pricing details. | Yes | Detailed product discussion |
| 2 | Jane Smith | 2023-05-23 | Discussed product use cases. Prospect showed interest in use case X. | Prepare a demo for use case X. | No | Use case discussion |
| 3 | Bob Johnson | 2023-05-22 | Prospect asked for references. | Provide references. | No | Providing references |
| 4 | Mary Davis | 2023-05-21 | Discussed product features. Prospect showed interest in feature Y. | Follow up with more details on feature Y. | Yes | Feature discussion |
Schema
{
"type": "object",
"properties": {
"MEETING ID": {
"type": "integer",
"description": "A unique identifier for the meeting."
},
"PROSPECT NAME": {
"type": "string",
"description": "The name of the prospect who attended the meeting."
},
"DATE": {
"type": "string",
"format": "date",
"description": "The date of the meeting."
},
"MEETING NOTES": {
"type": "string",
"description": "The notes taken during the meeting."
},
"ACTIONABLE ITEMS": {
"type": "string",
"description": "The actionable items identified from the meeting notes."
},
"DEAL CLOSED": {
"type": "string",
"description": "Whether the deal was closed after the meeting."
},
"EFFECTIVE PRACTICES": {
"type": "string",
"description": "The practices that were found to be effective during the meeting."
}
},
"required": ["MEETING ID", "PROSPECT NAME", "DATE", "MEETING NOTES", "ACTIONABLE ITEMS", "DEAL CLOSED", "EFFECTIVE PRACTICES"]
}
How to Contribute?
Collaborate with Us on our Public GitHub Repo
Accelerate development by working with us on issues in our public GitHub repository: https://github.com/jupyter-naas/engine-content.
Submit Your Ideas and Improvements
Create GitHub Issues or Participate in GitHub discussions with fellow community members. Your ideas and suggestions for improvements are always welcome.
Talk About Naas On Social Media
If you like what we are doing, share your story and case studies on social platforms to inspire others in the community. Your experiences can provide valuable insights for others.