Revenue Intelligence & Forecasting Software Buyer's Guide for Sales & CRM

Who This Guide Is For

You need this guide if you are any of the following:

• VP of Sales / CRO
• Your forecast is still built in spreadsheets and manager judgment.
• Commit calls vary by manager, region, or mood.
• You need to know whether deals are truly real or just “pushed” with optimism.

• RevOps Leader / Sales Operations Director
• CRM hygiene is weak and pipeline stages are inconsistent.
• Leadership wants dashboards, but the data behind them is noisy.
• You need better pipeline inspection, activity capture, and process enforcement without becoming the CRM police.

• Sales Enablement Director
• Reps are not using the right behaviors, and coaching is opinion-driven.
• You need evidence of what top performers do differently.
• You care about conversation intelligence, deal risk signals, and play execution.

• Revenue Operations / Business Systems Leader
• You own systems integration across CRM, dialer, meeting tools, and ERP.
• The company wants “one source of truth” for revenue.
• You need a vendor that can actually connect with Salesforce, HubSpot, Dynamics, Zoom, Teams, Slack, Outreach, or billing tools.

Who should NOT read this guide

This guide is not for you if:

• You are a very small team with fewer than 10 sellers and no formal forecasting process.
• You only need a simple CRM dashboard and do not care about forecasting rigor.
• You are shopping for a pure BI tool like Tableau or Power BI, not a revenue intelligence layer.
• You are looking primarily for compensation management, territory planning, or subscription billing—those are adjacent, but not the same problem.

If your team is still deciding whether forecasting discipline matters at all, fix your sales operating cadence first. Software will not rescue a chaotic pipeline.

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What Revenue Intelligence & Forecasting Software Does

Revenue intelligence & forecasting software sits between your CRM and your revenue leadership team. It pulls together activity data, deal signals, pipeline history, and often conversation data to answer five questions:

• What is likely to close?
• What is at risk?
• What should reps do next?
• How accurate is the forecast by segment, manager, and product line?
• What behaviors correlate with winning?

The best platforms do more than display dashboards. They automate data capture, surface deal risk, score pipeline health, predict outcomes, and help managers coach better.

Before vs. after

What it actually changes in the business

Revenue intelligence is not just a “nice dashboard.” It can materially change how a team operates:

• Forecast accuracy improves because leaders stop relying only on rep sentiment.
• Manager effectiveness increases because coaching becomes evidence-based.
• Reps waste less time because activity capture and workflow automation reduce data entry.
• Deal slippage drops because risk is visible earlier.
• Pipeline hygiene gets better because the system exposes stale opportunities and missing next steps.

Dollar-value example

Assume a company with:

• $20M annual new-business ARR target
• 10% forecast error creating bad staffing, marketing, and inventory decisions
• 5 sales managers spending 4 hours per week each in manual forecast rollups
• 50 reps spending 20 minutes per day on CRM admin that could be automated

A conservative value model might look like this:

• Forecast improvement benefit:

If better visibility reduces quarter-end misses by even 2 percentage points, that can protect $400K in ARR on a $20M target.

• Manager time savings:

5 managers × 4 hours/week × 50 weeks = 1,000 hours/year. At a fully loaded manager cost of $100/hour, that is $100,000 in time value.

• Rep admin savings:

50 reps × 20 minutes/day × 220 workdays = 36,667 hours/year. Even if only 25% of that is truly reclaimable, that is 9,167 hours. At $60/hour, that is $550,000 in productivity value.

That is how a platform can justify itself: not by “AI,” but by reducing waste and improving forecast quality.

What it does NOT do

Avoid vendors that pretend the category is magic. Revenue intelligence software does not:

• Fix broken sales strategy
• Repair poor product-market fit
• Clean bad CRM data without effort
• Turn weak managers into great coaches overnight
• Replace human judgment entirely

If your pipeline is garbage, software will give you a better picture of the garbage. That is still useful, but it is not transformation.

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Market Landscape 2026

The market in 2026 is splitting into four clear camps:

1) Revenue intelligence platforms

These are the core category players. They focus on forecasting, deal inspection, activity capture, coaching, and buyer interaction analysis.

Examples from this directory:

• Gong
• People.ai
• Aviso
• InsightSquared
• Revenue.io
• Ebsta
• Salesloft Analytics
• Salesloft Deals
• Engagement Dashboards by People.ai
• Relationship Intelligence by Aviso
• Pipeline Inspection by Aviso
• Revenue Forecasting by Aviso
• AI Agents by Oliv AI
• Katie, Alex, and Luna by Alta

These tools tend to overlap heavily. The real differentiator is not “AI” anymore. It is:

• depth of signal capture
• quality of forecasting
• strength of workflow automation
• usability for managers
• quality of CRM sync
• ability to influence rep behavior

2) Revenue orchestration and execution platforms

These platforms go beyond analytics into guidance, sequencing, and play execution.

Examples:

• Gong Orchestrate
• Revenue.io
• parts of Salesloft Analytics / Deals
• AI Agents by Oliv AI

These are better when you want the system to do something, not just report something.

3) Revenue planning and adjacent systems

These are not pure revenue intelligence products, but they matter because they often own adjacent decisions.

Examples:

• Anaplan Territory & Quota Planning
• Everstage Planning
• Xactly Design
• Xactly Next
• DealHub Subscription Billing
• AdvantageClub.ai Incentive Compensation Management Software
• Catalyst by Totango

These solutions matter when forecasting is broken because the business model, territories, quotas, incentives, or customer expansion motions are misaligned.

4) Data foundation and customer profile platforms

These are enabling layers rather than direct forecasting tools.

Examples:

• Salesforce Data Cloud
• Data Cloud by salesforce
• Salesforce Einstein in some contexts

These are useful when the main problem is fragmented customer data, not forecasting logic.

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The big market trend: AI is now table stakes, but not equally useful

Every vendor claims AI. Most of it falls into one of three buckets:

• Pattern detection: surfacing likely risks based on prior deals
• Workflow assistance: drafting notes, next steps, summaries, or follow-ups
• Prediction: assigning a probability or forecast outcome

The hard truth: Prediction is the most valuable and hardest to do well. Everything else is easier to fake.

What buyers should expect in 2026

• More real-time signals from calls, emails, meetings, and CRM changes
• Better automation of note capture and activity logging
• More opinionated workflows that guide managers and reps
• More vertical and role-specific products
• More bundling from broader CRM or sales engagement vendors
• More pressure on data quality, because AI is only as good as the source system

Why the market is crowded

Because the category sits at the intersection of:

• CRM
• sales engagement
• conversation intelligence
• forecasting
• business intelligence
• RevOps

That creates a lot of overlap. The result is a market with:

• a few large platforms
• a long tail of niche players
• several “feature, not platform” products
• adjacent vendors trying to move into forecasting

How the vendor landscape is structured

Core enterprise leaders

These are the most established names for larger teams:

• Gong
• People.ai
• Aviso
• Revenue.io
• Salesloft Analytics / Deals

Strong SMB/mid-market options

These can be more approachable for smaller teams:

• InsightSquared
• Ebsta
• Revenue.io
• Salesloft
• Oliv AI
• Alta

Planning-heavy adjacent tools

Better when quota, territory, or comp design is the real issue:

• Anaplan
• Everstage
• Xactly Corp.
• AdvantageClub.ai
• DealHub

Customer-success / expansion-oriented tools

Useful for expansion revenue rather than pure new-logo forecasting:

• Catalyst by Totango
• People.ai Engagement Dashboards

Data foundation

When you need unified profiles first:

• Salesforce Data Cloud
• Data Cloud by salesforce
• Salesforce Einstein

Product-specific market notes

• Gong is the category benchmark for conversation intelligence and increasingly a broader Revenue AI platform. It is expensive, but buyers pay for depth and ecosystem gravity.
• Aviso is one of the strongest pure forecasting stories in the market and is especially relevant when leadership wants serious forecast discipline.
• People.ai is strong where activity capture, relationship intelligence, and bottoms-up forecasting matter.
• Revenue.io is compelling for Salesforce-centric teams that want real-time coaching plus pipeline analytics.
• Salesloft is strongest when you already live in the Salesloft ecosystem and want reporting plus deal execution.
• InsightSquared is attractive for mid-market teams that need forecasting and analytics without enterprise bloat.
• Anaplan, Xactly, Everstage, DealHub, and AdvantageClub.ai solve adjacent planning and revenue mechanics. They are not substitutes for revenue intelligence, but they frequently show up in the same buying cycle.
• Salesforce Data Cloud / Einstein can become foundational if your problem is disconnected data and weak customer identity resolution.
• Oliv AI and Alta are worth evaluating when you want AI agent-style automation or lighter-weight execution support.

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The 8 Things That Matter Most

Below are the evaluation criteria that actually separate winners from expensive dashboards.

Weighting summary

\*Pricing is important, but it is embedded across other criteria. If the budget is tight, make it a formal tiebreaker.

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1) Forecast accuracy and methodology — 20%

Why it’s critical: If the forecast is wrong, everything downstream is wrong: hiring, capacity planning, cash expectations, board reporting, and quota setting.

What to evaluate:

• How forecasts are built: rep input, historical trends, weighted pipeline, AI scoring, or hybrid
• Whether the model adapts by segment, rep, product, and stage
• How the system handles slip, push, and stage regression
• Whether the forecast is explainable or a black box

What good looks like:

• Forecast accuracy measured by segment and time horizon
• Clear confidence bands, not just a single number
• Explainable reasons why a deal is in or out
• Historical bias correction over time

Red flag: The vendor says “near-perfect forecast accuracy” but cannot show methodology, benchmark conditions, or segmentation.

Ask the vendor:

• How do you calculate forecast accuracy, and over what time period?
• Can you show accuracy by segment, manager, and deal size?
• How does the model handle late-stage slippage or rep optimism bias?

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2) Data capture and signal quality — 15%

Why it’s critical: Bad signals create fake confidence. The best AI is useless if the platform misses buyer interactions or logs them incorrectly.

What to evaluate:

• Automatic capture of calls, emails, meetings, and CRM updates
• Whether data is captured passively or requires rep action
• Conversation transcription quality
• Activity attribution accuracy
• Relationship graph completeness

What good looks like:

• High capture rate with minimal rep effort
• Reliable contact/account mapping
• Accurate meeting summaries and action extraction
• Strong signal from buying committee engagement

Red flag: The tool depends on rep compliance for basic data capture. That usually fails after the pilot.

Ask the vendor:

• What percentage of activities are captured automatically?
• How do you match activities to the correct account and opportunity?
• What happens when CRM data is incomplete or inconsistent?

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3) CRM and tech stack integration — 15%

Why it’s critical: Revenue intelligence only works if it sits on top of your actual systems of record.

What to evaluate:

• Native integrations with Salesforce, HubSpot, Microsoft Dynamics
• Support for Zoom, Teams, Slack, Gmail, Outlook, Outreach, LinkedIn
• Sync latency and field-level control
• Bi-directional sync vs. one-way exports
• API quality and flexibility

What good looks like:

• Stable, low-maintenance connectors
• Ability to map custom objects and fields
• Reliable sync performance without admin babysitting
• Clear conflict resolution logic

Red flag: Integration is “available,” but only via brittle middleware or manual CSVs.

Ask the vendor:

• Which integrations are native and which require third-party tools?
• How often does data sync, and what is the typical latency?
• Can we control field-level mappings and conflict rules?

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4) Workflow automation and coaching — 12%

Why it’s critical: Insights without action are just expensive reporting. The best systems push the team toward better behavior.

What to evaluate:

• Automated next-best actions
• Deal alerts and risk triggers
• Manager coaching workflows
• Play execution capabilities
• Task automation and nudges

What good looks like:

• Managers get actionable alerts, not noise
• Reps see specific next steps tied to real deals
• Coaching recommendations reflect actual behavior, not generic templates
• Automation reduces admin and follow-up overhead

Red flag: The product generates dashboards but does not change the daily work of reps or managers.

Ask the vendor:

• What actions can be automated based on deal signals?
• Can managers assign coaching tasks from the platform?
• Does the system recommend actions by persona, stage, or deal type?

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5) Ease of adoption for reps and managers — 10%

Why it’s critical: A powerful product that no one uses is dead on arrival.

What to evaluate:

• UI simplicity
• Mobile usability
• Time to understand insights
• Manager workflow fit
• Rep burden

What good looks like:

• Reps do not need extra admin steps
• Managers can use it in weekly pipeline reviews
• Insights are visible where people already work
• Adoption increases without endless training

Red flag: The tool requires a “champion” to interpret every dashboard.

Ask the vendor:

• What does a rep need to do each day, if anything?
• How long does it take a new manager to become productive in the tool?
• What adoption metrics do you track for customers?

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6) Reporting depth and segmentation — 10%

Why it’s critical: Executives need different views than frontline managers. Good reporting answers “what changed?” not just “what is the number?”

What to evaluate:

• Segment-level views
• Cohort analysis
• Rep/manager overlays
• Custom dimensions
• Trend and variance analysis

What good looks like:

• Reporting across product, region, segment, manager, and stage
• Ability to drill from summary to deal-level detail
• Historical trend comparison
• Customizable dashboards without engineering

Red flag: The reporting layer is pretty but shallow.

Ask the vendor:

• Can we create custom dimensions and formulas?
• Can executives and managers see different views?
• How does the tool handle historical snapshots?

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7) Security, privacy, and compliance — 10%

Why it’s critical: These platforms touch calls, email, customer data, and often sensitive internal notes. Security failures are business failures.

What to evaluate:

• SOC 2 Type II and ISO certifications
• GDPR, HIPAA, CCPA support if relevant
• Data retention controls
• Access controls and audit logs
• AI data usage policies

What good looks like:

• Clear security documentation
• Fine-grained permissions
• Strong governance over transcription and recording data
• Transparent AI training/data retention policies

Red flag: The vendor is vague about whether your data trains its models.

Ask the vendor:

• Is customer data used to train shared models?
• What data retention and deletion controls do we get?
• Can we restrict access by role, team, or region?

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8) Implementation speed and admin burden — 8%

Why it’s critical: Long implementations often mean data cleanup, change management, and stakeholder coordination were underestimated.

What to evaluate:

• Time to first value
• Data migration complexity
• Admin workload
• Change management requirements
• Vendor support during rollout

What good looks like:

• A clear implementation plan
• Data cleanup done before go-live
• Rapid proof of value within 30-60 days
• Minimal ongoing admin burden

Red flag: The vendor expects your team to “figure out the process” after go-live.

Ask the vendor:

• What is the typical time to first forecast in production?
• How much internal admin time is required each week?
• What does implementation look like for a 100-user rollout?

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Use-Case Matching: Which Type of Buyer Are You?

1) Growth-stage buyer: scaling from chaos to discipline

Typical profile:

• Series B to Series D
• 20–150 sellers
• Forecasting is inconsistent
• CRM hygiene is mixed
• Leadership wants predictability fast

What matters most:

• Fast deployment
• Simple UI
• Forecast accuracy
• Basic deal inspection
• Activity capture with minimal seller burden

Prioritize these capabilities:

• Automated activity capture
• Deal risk scoring
• Forecast rollups by rep and manager
• Lightweight dashboards
• Strong Salesforce or HubSpot integration

Avoid:

• Overbuying territory planning or compensation tooling before the core forecast is fixed
• Heavy customization that delays value by months

Usually the best fit:

• InsightSquared
• Revenue.io
• Ebsta
• some Salesloft modules
• Aviso if forecasting discipline is the urgent need

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2) Enterprise buyer: complex org, multiple segments, board pressure

Typical profile:

• 200+ sellers
• Multiple regions and product lines
• Board-level forecasting scrutiny
• Complex systems and governance requirements

What matters most:

• Explainable forecast methodology
• Segmented reporting
• Security and compliance
• Customizable workflows
• Integration depth

Prioritize these capabilities:

• Manager and executive forecasting views
• Deal inspection with buying committee signals
• API and custom object support
• Strong governance and auditability
• Role-based permissions

Avoid:

• Tools that only work well in one selling motion
• Systems that hide methodology behind “AI confidence”

Usually the best fit:

• Gong
• People.ai
• Aviso
• Salesforce Data Cloud
• Revenue.io
• Salesloft Analytics / Deals

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3) Industry-specific buyer: recurring revenue, customer expansion, or quota design complexity

Typical profile:

• Subscription, SaaS, services, or complex quota environments
• Revenue depends on renewals, expansions, or multi-product motions
• Compensation and territory alignment matter

What matters most:

• Customer lifecycle visibility
• Expansion and retention signals
• Territory/quota design
• Incentive alignment
• Subscription and billing context

Prioritize these capabilities:

• Expansion opportunity detection
• Territory and quota planning
• Incentive compensation modeling
• Billing and revenue recognition integration
• Customer health and engagement insights

Avoid:

• New-logo-only forecasting tools that ignore customer expansion
• Planning tools used as a substitute for revenue intelligence

Usually the best fit:

• Catalyst by Totango
• Anaplan Territory & Quota Planning
• Xactly Design
• Xactly Next
• DealHub Subscription Billing
• Everstage Planning
• AdvantageClub.ai
• People.ai Engagement Dashboards

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4) Budget-conscious buyer: wants value without enterprise bloat

Typical profile:

• Mid-market
• Limited RevOps staff
• Needs forecasting and visibility but not a giant rollout

What matters most:

• Fast ROI
• Lower admin overhead
• Clear pricing
• Good-enough forecasting
• Minimal implementation risk

Prioritize these capabilities:

• Easy integrations
• Simple forecasting workflows
• Core dashboards
• Basic conversation intelligence if relevant
• Predictable subscription pricing

Avoid:

• Enterprise-only products with opaque pricing and heavy services dependency
• Buying conversation intelligence when all you need is forecasting

Usually the best fit:

• InsightSquared
• Ebsta
• Revenue.io
• Salesloft
• Oliv AI
• potentially Alta

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Vendor Evaluation Questions

Use these as copy-paste RFP questions.

Features

• How do you define and calculate forecast accuracy?
• Can the system forecast by rep, team, region, product, and segment?
• What deal risk signals do you track?
• Do you provide buying committee visibility or relationship maps?
• Can the platform capture calls, emails, meetings, and notes automatically?
• What actions can be automated based on deal signals?
• Can managers coach inside the platform?
• Do you support historical trend analysis and forecast snapshots?
• Can we customize stages, fields, and workflows?
• How do you handle renewals, expansions, and multi-product revenue?

Integration

• Which CRMs do you support natively?
• Which sales engagement tools do you support natively?
• What is your standard sync frequency and latency?
• Do you support custom objects and custom fields?
• Is your API open and documented for production use?
• How do you handle duplicate records and conflicting sources of truth?
• Can you integrate with Zoom, Microsoft Teams, Slack, Gmail, Outlook, and LinkedIn?
• What data import or migration tools do you provide?

Security

• Are you SOC 2 Type II certified?
• Which ISO certifications do you hold?
• Do you support GDPR, CCPA, HIPAA, or industry-specific controls?
• Is customer data used to train shared AI models?
• What role-based access controls are available?
• Do you provide audit logs and admin activity history?
• What are your data retention and deletion policies?
• How do you secure call recordings and transcripts?

Pricing

• What is the full pricing model: per user, platform fee, consumption, or usage-based?
• Are there implementation fees?
• Are there required add-ons or modules?
• What features are included in the base package vs. premium tiers?
• What happens to pricing when we scale user counts?
• Are there multi-year discounts, and what tradeoffs come with them?

Implementation and Support

• What is the typical implementation timeline for a company our size?
• What internal resources do you require from us?
• What does your customer success model look like after go-live?
• What training do you provide for managers and admins?
• How do you support change management and adoption?
• What are your standard SLAs and escalation paths?

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Common Mistakes to Avoid

Mistake #1: Buying “AI” instead of buying a business outcome

If the demo is all buzzwords and no operational outcome, walk away. Consequence: You pay for a fancy dashboard that doesn’t improve forecast quality or rep behavior. Do instead: Define the exact outcome first: forecast accuracy, deal inspection, coaching, or activity automation.

Mistake #2: Letting sales leaders choose without RevOps

Sales leaders feel the pain first, but RevOps lives with the data model and integration burden. Consequence: A tool gets selected that looks great but is impossible to maintain. Do instead: Make RevOps a co-owner of the evaluation.

Mistake #3: Overvaluing conversation intelligence when the real problem is forecasting

Call recordings are useful. They are not a substitute for a forecasting system. Consequence: You learn a lot about calls and nothing about predictability. Do instead: Separate “coaching tool” from “forecasting system” in the requirements.

Mistake #4: Ignoring data cleanup until after purchase

Dirty CRM data is the most common reason implementations underdeliver. Consequence: Bad maps, bad forecasts, bad trust. Do instead: Budget 2–4 weeks for data cleanup before rollout.

Mistake #5: Choosing a tool that needs too much rep behavior change

If the system only works when reps do extra work, adoption will collapse. Consequence: Garbage in, garbage out. Do instead: Favor passive capture and low-friction workflows.

Mistake #6: Assuming forecasting and territory planning are the same thing

They are related, but they solve different problems. Consequence: You buy an Anaplan/Xactly-style planning tool when you need forecasting discipline, or vice versa. Do instead: Fix the operating model first, then the planning layer.

Mistake #7: Not defining forecast governance

If managers and reps all use different rules, the software cannot save you. Consequence: Forecast calls remain political. Do instead: Set definitions for commit, best case, pipeline, and slippage before go-live.

Mistake #8: Ignoring the cost of administration

Some tools are cheap on paper and expensive in labor. Consequence: You end up paying in hidden admin hours every week. Do instead: Ask how much ongoing admin is needed to keep the system accurate.

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Evaluation Timeline & Process

Recommended 7-week evaluation process

Weighted evaluation matrix template

Use this as a simple scorecard.

Scoring guidance

• 5 = Excellent
• 4 = Strong
• 3 = Acceptable
• 2 = Weak
• 1 = Unacceptable

Anything below 3 on forecast methodology, integration, or security should usually eliminate the vendor.

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Implementation Reality Check

Typical implementation timelines by company size

Key success factors

• Clean data before go-live
• Single owner in RevOps
• Executive sponsor who enforces usage
• Clear forecast definitions
• A narrow pilot with real business pain
• Manager training, not just rep training

What vendors won’t tell you

• Your CRM data quality may be the real bottleneck
• Adoption often drops after the pilot unless managers are held accountable
• Integration “support” may still require internal admin effort
• Forecast models can be good in aggregate and bad in your exact segment
• The first 30 days usually reveal governance issues, not product issues
• If reps dislike the tool, it will become another source of shadow work

The honest rule

If the vendor says implementation is “simple,” ask:

• Simple for whom?
• Which data sources?
• How many custom fields?
• How many meetings with admins?
• What happens when the CRM is messy?

The answers matter more than the slide deck.

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Red Flags — When to Walk Away

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How This Guide Was Created

This guide was created using:

• vendor-provided product descriptions and positioning
• publicly available feature and certification information
• category-level market analysis for revenue intelligence, forecasting, sales planning, and adjacent revenue operations tools
• comparison of product positioning across the directory’s 25 products

Review date: September 2026 Update frequency: Quarterly, or sooner if major product, pricing, or market shifts occur.

Independence disclaimer: commercial.ai is an independent enterprise software directory. This guide is written to inform buyers, not to promote any specific vendor. Product mentions are included for comparison and market context only.

Revenue Intelligence & Forecasting solutions in the catalogue (27)

• AI Agents by Oliv AI — Autonomous AI agents for sales coaching, deal intelligence, and workflow automation.
• Aviso by Aviso — The AI Revenue Operating System to Predict, Guide, and Simplify Every Revenue Action.
• Catalyst by Totango — Unify GTM teams to drive recurring revenue growth by identifying expansion and upsell opportunities.
• Data Cloud by Salesforce — Unify customer profiles and enable real-time segmentation from any source.
• DealHub Subscription Billing by DealHub — Automate subscription billing, payments, and revenue recognition to unlock recurring revenue.
• Ebsta by Ebsta — Revenue Intelligence for Predictable Growth and Top Sales Performance.
• Engagement Dashboards by People.ai — Gain 360-degree visibility into customer engagement and pipeline health to pinpoint at-risk accounts.
• Everstage Planning by Everstage — Precision sales planning for territories, quotas, and capacity.
• Forecastio by Forecastio — AI-powered sales forecasting and performance management for HubSpot teams.
• Geo-spatial Mapping by Anaplan — Visualize, optimize, and manage sales territories and quotas with geo-spatial intelligence.
• Gong by Gong — Gong: The Revenue AI OS for Go-to-Market Teams.
• Gong Orchestrate by Gong — AI-driven capability to double rep productivity and accelerate execution for revenue teams.
• Incentive Compensation Management Software by AdvantageClub.ai — Optimize sales incentives, boost performance, and streamline payouts.
• InsightSquared by Insightly — AI-powered revenue intelligence for predictable growth and optimized sales performance.
• Katie, Alex, and Luna by Alta — AI Revenue Workforce: Automate Sales, Optimize RevOps, Drive Growth.
• Oliv AI by Oliv AI — Achieve predictable growth with AI agents.
• People.ai by People.ai — Unlock revenue potential with AI-driven sales activity capture and intelligence.
• Pipeline Inspection by Aviso — AI-powered pipeline inspection to identify risks, optimize deals, and improve forecast accuracy.
• Relationship Intelligence by Aviso — AI-driven Relationship Intelligence for superior sales and revenue outcomes.
• Revenue Forecasting by Aviso — AI-driven platform for precise revenue predictions and predictable growth.
• Revenue.io by Revenue.io — Engage, Coach, and Optimize Every Revenue Moment with AI.
• Salesforce Data Cloud by Salesforce — Unifies customer data from any source to create real-time, actionable customer profiles with AI.
• Salesloft Analytics by SalesLoft — Real-time sales analytics and reporting for data-driven revenue teams.
• Salesloft Deals by SalesLoft — AI insights for sales teams to understand deal status, risks, and accelerate cycles.
• Territory & Quota Planning by Anaplan — An intuitive, AI-driven approach to optimize sales territories and quotas for aligned GTM and financial plans.
• Xactly Design by Xactly Corp. — AI-driven incentive design for optimal sales performance.
• Xactly Next by Xactly Corp. — Composable solutions for sales performance management and unified intelligent revenue.