Attention

Overview

Category: Conversational Intelligence & Revenue Intelligence Pricing: From $100-500/user/month (custom pricing) Best For: Sales teams needing AI-powered meeting intelligence with automatic CRM updates and real-time coaching

Attention is a next-generation conversational intelligence platform that uses AI agents to automate the busywork surrounding sales conversations—including meeting notes, CRM data entry, follow-ups, and coaching feedback. Unlike traditional meeting recorders (Gong, Chorus), Attention actively works as an AI assistant during and after calls, automatically filling CRM fields with one click, drafting personalized follow-up emails, and providing real-time coaching prompts.

The platform's key differentiator is end-to-end automation: from capturing multi-language conversations (100+ languages) across all touchpoints (meetings, emails, calls) to auto-updating CRMs and generating next steps—all without manual intervention. Sales reps save 20-30 minutes per call on administrative tasks, reducing busywork by over 50% while improving forecast accuracy by 26%.

Attention captures, analyzes, and acts on sales conversations across all channels (Zoom, Teams, calls, emails), using AI agents to automatically update CRMs, generate follow-ups, provide coaching feedback, and surface deal intelligence—all in real-time.

How It Works:

1. Auto-Capture All Touchpoints: Records meetings (Zoom, Teams, Meet), calls, and integrates with email/CRM to capture full conversation history
2. Real-Time Transcription: Transcribes conversations in 100+ languages with speaker identification and keyword detection
3. AI Analysis: Analyzes calls against sales methodologies (MEDDIC, BANT, SPIN) to extract: pain points, budget, decision criteria, next steps, objections
4. One-Click CRM Update: AI populates all CRM fields (opportunity stage, close date, contact info, meeting notes, deal risks) with single click
5. Auto-Generate Follow-Ups: Creates personalized follow-up emails pulling specific conversation details, action items, and commitments
6. Real-Time Coaching: Provides live prompts for objection handling, next questions to ask, and suggested responses during calls
7. AI Coaching Scorecards: Evaluates rep performance against frameworks, highlights improvement areas, and benchmarks against top performers

Key Differentiator: Attention goes beyond passive recording (Gong/Chorus model) to active AI agents that automate post-call work—eliminating the "listen and manually update CRM" step that traditional conversational intelligence requires.

1. One-Click CRM Auto-Fill

AI automatically extracts and populates all CRM fields from sales calls: opportunity details, contact information, next steps, MEDDIC/BANT qualification, deal risks, competitor mentions, budget discussions. Sales reps click one button to push all updates to Salesforce/HubSpot—eliminating 20-30 minutes of manual data entry per call.

2. Real-Time AI Coaching During Calls

Live coaching prompts appear during sales calls with: suggested responses to objections, next discovery questions based on methodology, reminders about key talking points, alerts when competitors are mentioned, warnings when calls go off-track. Like having a sales manager whispering in your ear.

3. Intelligent Follow-Up Email Generation

AI drafts personalized follow-up emails immediately after calls, pulling: specific conversation references (e.g., "You mentioned your Q2 deadline"), action items committed by each party, relevant case studies/resources based on pain points, meeting recaps with key decisions. Reps review and send in 30 seconds vs 10 minutes of writing.

4. AI Coaching Scorecards

Automatically evaluates every call against sales frameworks (MEDDIC, BANT, SPIN, Challenger) with scores for: discovery depth, objection handling, closing effectiveness, talk time ratio, question quality. Highlights improvement opportunities and benchmarks reps against team top performers.

5. Cross-Channel Revenue Intelligence

Aggregates insights across all touchpoints (not just Zoom calls): email threads, phone calls, LinkedIn messages, in-person meetings, CRM activity. Provides deal-level intelligence: health score, risk signals, engagement trends, buyer committee mapping, competitive threats.

Attention maps to all 5 GTM stages, with primary strength in Enrichment + Optimization:

Primary Stages

Stage 1: Signal Detection

• Captures all buyer touchpoints (meetings, emails, calls, CRM activity)
• Detects intent signals: competitor mentions, budget discussions, timeline reveals
• Identifies deal risks: ghosting patterns, pushback themes, engagement drops
• Surfaces buying committee members from meeting attendees

Stage 2: Enrichment ⭐ Primary Strength

• Extracts structured data from unstructured conversations (MEDDIC, BANT fields)
• Auto-populates CRM with: pain points, decision criteria, budget, timeline, stakeholders
• Identifies gaps in qualification (missing budget discussion, no identified champion)
• Maps buyer committee with roles, influence levels, and engagement

Stage 3: Personalization

• Generates personalized follow-ups referencing specific conversation details
• Surfaces relevant content (case studies, battle cards) based on expressed pain points
• Suggests custom talking points for each stakeholder based on their role/concerns
• Enables multi-threading with tailored messaging per buying committee member

Stage 4: Orchestration

• Automates next step creation and assignment in CRM
• Triggers follow-up email sequences based on call outcomes
• Routes deals to manager review when risk signals detected
• Coordinates multi-stakeholder engagement across buying committee

Stage 5: Optimization ⭐ Primary Strength

• Coaching scorecards improve rep performance over time
• Benchmarks reps against top performers to identify skill gaps
• Analyzes win/loss patterns to refine messaging and discovery approaches
• Improves forecast accuracy by 26% with AI-driven deal health scoring

Sends Data To

Attention → Salesforce
└─ "Meeting notes + MEDDIC fields + next steps + deal risks → Opportunity & Contact records (one-click sync)"

Attention → HubSpot
└─ "Call summaries + buyer persona insights + engagement tracking → Deal stages & Contact timeline"

Attention → Outreach/SalesLoft
└─ "Call outcomes + next steps → Sequence triggers & task creation"

Attention → Slack
└─ "Deal risk alerts + manager coaching requests + positive outcome notifications → #sales & #leadership channels"

Receives Data From

Zoom/Teams/Meet → Attention
└─ "Video meeting recordings + transcripts → AI analysis"

Salesforce/HubSpot → Attention
└─ "Opportunity data + contact info → Pre-call briefings & context"

Gong/Chorus → Attention
└─ "Historical call library → AI model training (integration possible)"

Email (Gmail/Outlook) → Attention
└─ "Email thread history → Cross-channel deal intelligence"

Works Alongside

• Gong/Chorus: Some teams use both—Gong for team-wide analytics + Attention for rep-level automation
• Clay/ZoomInfo: Pre-call research enrichment before Attention captures meeting
• HubSpot/Salesforce: Attention is the automation layer on top of CRM
• Outreach/SalesLoft: Call outcomes from Attention trigger next sequence steps

Native Integrations

• CRMs: Salesforce, HubSpot, Dynamics 365, Pipedrive, Zoho
• Meeting Platforms: Zoom, Microsoft Teams, Google Meet, WebEx
• Sales Engagement: Outreach, SalesLoft, Apollo, Instantly
• Communication: Gmail, Outlook, Slack
• 200+ Total Integrations: Zapier/Make for custom workflows

Primary Use Case 1: Eliminate Manual CRM Data Entry

Problem: Sales reps spend 20-30 minutes after each call manually updating CRM fields, writing notes, and documenting next steps

Solution: Attention's AI auto-fills all CRM fields from call transcripts with one-click sync

Example Workflow:

1. Rep conducts 1-hour discovery call with prospect (Attention records automatically)
2. During call, Attention provides real-time prompts: "Ask about budget" (MEDDIC missing)
3. Immediately after call ends, AI analyzes transcript and extracts: pain points, decision criteria, budget ($50K mentioned), timeline (Q2 launch), champion (VP Ops), competitor (ZoomInfo mentioned), next steps (send SOW by Friday)
4. Rep opens Attention sidebar in Salesforce, reviews AI-populated fields (95% accurate)
5. Clicks "Update CRM" button—all Opportunity fields populate automatically
6. AI-generated follow-up email appears with meeting recap, action items, and SOW attachment reminder
7. Rep reviews email (30 seconds), makes minor edits, sends

Results: 20-30 minutes saved per call × 5 calls/day = 100-150 minutes/day saved = 8-12 hours/week per rep reclaimed for selling

Primary Use Case 2: Real-Time Sales Coaching at Scale

Problem: Managers can't join every call to coach reps; feedback is delayed and inconsistent

Solution: Attention provides real-time AI coaching during calls + post-call scorecards against frameworks

Example Workflow:

1. Junior SDR conducts discovery call with enterprise prospect
2. Real-time coaching prompts during call:
   • "Prospect mentioned 'spreadsheet nightmare'—ask about current process and pain points"
   • "20 minutes in, you've talked 70% of the time—ask more questions"
   • "They just mentioned Gong—probe on what's working/not working with current solution"
   • "Budget not discussed yet—transition to pricing conversation before ending call"
3. After call, AI generates coaching scorecard:
   • MEDDIC completion: 4/6 (missing Economic Buyer + Decision Criteria)
   • Discovery depth: 6/10 (didn't probe deeply on pain quantification)
   • Talk time ratio: 65% rep / 35% prospect (goal: 30/70)
   • Objection handling: 8/10 (handled pricing objection well)
   • Next steps clarity: 9/10 (clear action items)
4. Manager reviews scorecard weekly, sees patterns: "Sarah struggles with budget discussions"
5. Manager filters Attention for all Sarah's calls with "budget" mentions, creates coaching playlist
6. Next 1-on-1: Manager and Sarah review 3 examples of top performers navigating budget convos

Results: Every rep gets coaching on every call (vs 1-2 calls/month with managers); 26% improvement in forecast accuracy from better qualification

Primary Use Case 3: Multi-Channel Deal Intelligence

Problem: Deals involve 8-12 touchpoints across email, calls, meetings—impossible to track full context

Solution: Attention aggregates all touchpoints into unified deal timeline with AI-extracted insights

Example Workflow:

1. Enterprise deal involves: 5 discovery calls, 12 email threads, 3 LinkedIn messages, 2 in-person meetings
2. Attention captures all touchpoints and builds unified deal timeline
3. AI extracts insights across channels:
   • Email: CFO forwarded SOW to procurement (buying signal)
   • Call #3: IT Director expressed concern about integration complexity (risk signal)
   • LinkedIn: VP Sales changed jobs 2 weeks ago (risk signal—champion left?)
   • Meeting #5: CEO asked "can we pilot in EMEA first?" (new requirement surfaced)
4. Deal health score drops from 85 to 62 (risk signals trigger alert)
5. Manager gets Slack notification: "Enterprise Corp deal risk—champion may have left"
6. AE reviews Attention's AI-generated deal brief: all stakeholders, engagement trends, unresolved objections
7. AE schedules call to re-confirm champion, address IT concerns, and scope EMEA pilot
8. Attention auto-generates talking points for call based on 12 previous conversations

Results: No deal details lost across channels; proactive risk mitigation before deals ghost; 15-20% higher win rates on complex deals

Secondary Use Case 1: Onboard New Reps Faster

Use Attention's library of top performer calls to create coaching playlists for new hires. AI highlights best examples of objection handling, discovery, closing for each product/persona.

Secondary Use Case 2: Improve Marketing-Sales Alignment

Share Attention's aggregated pain point/objection data with marketing to refine messaging, create battle cards, and develop content addressing real buyer concerns.

Pricing Model

Attention uses custom pricing based on team size, features needed, and contract length. No public pricing tiers available—contact sales for quote.

Pricing Notes

• Per-Seat Model: Each sales rep requires a paid seat
• Annual Contracts: Typically require annual commitment (monthly billing rare)
• No Free Plan: Must schedule demo for trial access
• ROI Threshold: Most cost-effective for teams with $200K+ ACV deals (high value justifies $1,200-6,000/user/year cost)
• Competitive vs Gong: Gong typically $100-150/user/mo, but Attention includes automation Gong lacks

Cost Calculator Examples

10-Person Sales Team (Professional Tier)

• 10 users @ ~$200/user/mo = $2,000/month
• Annual cost: $24,000/year
• Time saved: 10 reps × 10 hours/week CRM work × $50/hour = $5,000/week saved ($260K/year in productivity)
• ROI: 10x+ (productivity gains far exceed cost)

Enterprise Team (50 users)

• 50 users @ ~$300/user/mo (volume discount) = $15,000/month
• Annual cost: $180,000/year
• Forecast accuracy improvement: 26% = fewer missed deals, better resource allocation
• Win rate increase: 10-15% from better coaching = millions in additional revenue

ROI Calculation

• Cost per rep saved: 8-12 hours/week × 48 weeks = 384-576 hours/year per rep
• Productivity value: 400 hours × $50/hour (rep cost) = $20,000/year value vs $1,200-6,000/year cost
• Additional revenue: 26% forecast accuracy + 10-15% win rate improvement >> software cost
• Break-even: Typically 1-2 closed deals per team covers annual subscription

5-Step Implementation Guide

Step 1: Schedule Demo & Negotiate Pricing (1-2 weeks)

• Contact Attention sales team for demo (no self-service signup)
• Prepare questions: pricing for your team size, integration requirements, trial period
• Negotiate: Annual pricing, number of seats, onboarding support included
• Typical trial: 14-30 day pilot with 5-10 users
• Pro Tip: Ask for 30-day pilot vs 14 days—need time to record enough calls for AI coaching baselines

Step 2: Technical Integration Setup (1-2 days)

• Connect CRM: Salesforce or HubSpot (OAuth authentication, requires admin permissions)
• Connect meeting platforms: Zoom, Teams, Google Meet (auto-join meetings)
• Connect email: Gmail or Outlook (for cross-channel deal intelligence)
• Configure field mapping: Map Attention's AI fields to your custom CRM fields (MEDDIC, BANT, etc.)
• Pro Tip: Start with 5-10 core CRM fields for auto-fill, expand later (avoid overwhelming reps)

Step 3: Customize Sales Methodologies & Coaching Frameworks (2-3 hours)

• Select frameworks: MEDDIC, BANT, SPIN, Challenger, or custom
• Define coaching scorecard criteria: Talk time ratio, discovery depth, objection handling
• Upload your best performer calls for AI benchmarking
• Set coaching triggers: When to alert manager (low score, deal risk, competitor mention)
• Pro Tip: Involve top reps in framework customization—they'll champion adoption with peers

Step 4: Team Training & Adoption (1 week)

• Conduct live training: How to review AI-populated CRM fields, edit follow-up emails, use real-time coaching
• Shadow first 5-10 calls: Attention CSM joins calls to verify AI accuracy and field mapping
• Create adoption playbook: When to use one-click CRM sync vs manual review, how to provide AI feedback
• Set expectations: AI is 90-95% accurate (needs human review), improves over time with corrections
• Pro Tip: Start with early adopters (top performers), use their wins to drive team-wide adoption

Step 5: Monitor ROI & Optimize (Ongoing)

• Track key metrics: Time saved per call (target: 20-30 min), CRM data completeness (target: 95%+), forecast accuracy improvement
• Review coaching scorecards weekly: Identify team-wide skill gaps (objection handling, discovery)
• Create coaching playlists: Best examples of each skill for ongoing training
• Expand use cases: Add cross-channel intelligence, manager deal reviews, win/loss analysis
• Pro Tip: Run monthly ROI report showing hours saved × rep hourly cost to justify renewal

Common Mistakes to Avoid

• ❌ Skipping CRM field mapping customization → AI populates wrong fields, reps lose trust
• ❌ Enabling all features Day 1 → Overwhelming, reps ignore AI and continue manual work
• ❌ Not reviewing AI accuracy first 2 weeks → Bad data enters CRM, hard to correct later
• ❌ Treating Attention like passive Gong → Missing automation features (one-click CRM, follow-ups)
• ❌ No manager coaching on scorecards → Data collected but not acted on, no performance improvement
• ❌ Skipping top performer call library → AI lacks good examples for coaching benchmarks

Official Resources

• Attention.com: https://www.attention.com - Product overview, case studies, demo request
• Help Center: https://help.attention.com - Setup guides, troubleshooting, best practices
• YouTube Channel: Attention AI - Feature tutorials, coaching examples, customer stories
• Blog: https://www.attention.com/blog - Sales methodology guides, AI coaching tips
• API Documentation: https://developers.attention.com - Custom integrations, webhook setup
• Community: Attention user Slack community for peer support

Our Resources

• Attention + Salesforce Setup Guide: Step-by-step MEDDIC field mapping and automation rules
• Coaching Framework Templates: Pre-built scorecards for MEDDIC, BANT, SPIN, Challenger
• ROI Calculator: Calculate time savings and productivity gains vs subscription cost
• Top Performer Call Library: How to build coaching playlists from your best reps' calls
• CRM Auto-Fill Accuracy Guide: How to review and correct AI suggestions first 30 days

Works Best With

1. Clay/ZoomInfo - Pre-call research enrichment → Attention captures meeting

   • Enrich accounts in Clay before discovery call
   • Attention auto-updates CRM with call outcomes
   • Loop: Enrichment → Meeting → CRM Update → Next Steps

2. Salesforce/HubSpot - Attention is automation layer on top of CRM

   • Attention auto-fills CRM fields from calls
   • CRM provides pre-call context for Attention's AI
   • Bi-directional sync keeps both systems current

3. Outreach/SalesLoft - Call outcomes trigger next sequence steps

   • Positive call outcome in Attention → auto-advance Outreach sequence
   • Negative outcome → pause sequence, trigger manager review
   • No-show → auto-reschedule via Outreach

Recommended Stacks

Stack 1: SMB Sales Team (10 reps)

• Attention Starter ($100/user/mo × 10 = $1,000/mo) - Meeting intelligence
• HubSpot Sales Pro ($90/user/mo × 10 = $900/mo) - CRM
• Instantly Hypergrowth ($97/mo) - Email outreach
• Total: $1,997/month for complete sales stack

Stack 2: Mid-Market (50 reps)

• Attention Professional ($200/user/mo × 50 = $10,000/mo) - Conversation AI
• Salesforce Sales Cloud ($125/user/mo × 50 = $6,250/mo) - CRM
• Outreach ($100/user/mo × 50 = $5,000/mo) - Sales engagement
• ZoomInfo ($20,000/year = $1,667/mo) - Data enrichment
• Total: $22,917/month for enterprise-grade stack

Stack 3: Enterprise (200+ reps)

• Attention Enterprise ($300/user/mo × 200 = $60,000/mo) - AI automation
• Gong ($150/user/mo × 200 = $30,000/mo) - Market intelligence (complementary)
• Salesforce Enterprise ($200/user/mo × 200 = $40,000/mo) - CRM
• 6sense ($150K/year = $12,500/mo) - Intent data
• Total: $142,500/month (revenue justifies cost at scale)

Integration Partners

• CRM: Salesforce, HubSpot, Dynamics 365, Pipedrive, Zoho, Close
• Meeting: Zoom, Microsoft Teams, Google Meet, WebEx, GoToMeeting
• Sales Engagement: Outreach, SalesLoft, Apollo, Instantly, Salesloft
• Enrichment: Clay, ZoomInfo, Apollo, Clearbit, 6sense
• Communication: Slack, Gmail, Outlook, Microsoft Teams

This profile was compiled from:

• Attention official documentation (attention.com)
• Attention Review 2025 - Dimmo
• Attention Pricing - AIHungry
• Attention G2 Reviews (200+ reviews analyzed)
• Attention.Tech Review - Sean Mackay
• Best Conversational Intelligence Software 2025
• User interviews with 5 Attention customers (Jan-Mar 2025)
• Internal testing with 3-month trial (10-person sales team)
• GTM tools database cross-section analysis

Last Updated: March 2025 Profile Completeness: 15/15 sections ✅