Clari AI Pipeline Health: Predict Deal

AI Pipeline Health Scoring: Predict Deal Closure with Clari AI is a powerful tool designed to streamline workflows and boost productivity.

Key Takeaways (TL;DR)

• Implement Clari's AI-driven pipeline health scoring to gain proactive insights into deal risk and opportunity.
• Configure custom risk factors and scoring parameters within Clari to align with your sales methodology.
• Utilize AI-powered forecasting recommendations to refine your revenue predictions with greater accuracy.
• Track key metrics like engagement, sentiment, and deal progression to identify early warning signs and critical opportunities.
• Integrate Clari insights into your weekly forecast calls to drive data-backed strategic decisions and improve deal outcomes.

Who This Is For & Prerequisites

This tutorial is designed for Intermediate to Advanced Sales Professionals, Sales Managers, and Revenue Operations specialists involved in forecasting, pipeline management, and deal strategy. You should have:

• An active Clari account with appropriate administrative or power user access.
• Basic familiarity with your CRM (e.g., Salesforce) and its integration with Clari.
• A foundational understanding of sales forecasting methodologies and pipeline stages.
• Estimated time: 2-3 hours for initial setup, configuration, and practice run-throughs.

What You'll Build/Achieve

You'll learn to effectively use Clari's AI capabilities to generate sophisticated pipeline health scores and highly accurate deal closure predictions. By the end of this guide, you will be able to:

• Understand how Clari's AI evaluates deal health.
• Customize Clari to reflect your unique sales process and risk indicators.
• Proactively identify at-risk deals and top opportunities.
• Empower your forecasting with AI-driven objectivity, reducing manual bias and improving predictability.

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1. Understanding Clari's AI-Powered Pipeline Health

Clari's AI pipeline health engine is a pivotal tool for any sales professional looking to move beyond gut feelings and into data-driven forecasting. It utilizes machine learning to analyze a multitude of signals from your CRM, email, calendar, and other integrated systems to provide an objective health score for each deal in your pipeline. This score isn't just a number; it's a dynamic assessment of a deal's likelihood to close successfully.

At its core, Clari's AI analyzes patterns in historical data – both wins and losses – to understand the characteristics and activities that tend to lead to successful outcomes. It then applies this learning to your active pipeline, continuously updating deal health scores based on real-time engagement, deal progression, and identified risk factors. This predictive capability directly impacts your ability to predict deal closure with significantly higher accuracy, transforming your sales forecasting AI capabilities.

Why a health score? A numerical health score democratizes insights. It quickly tells you if a deal needs attention before it becomes a problem, allowing for targeted coaching and intervention. This shifts forecasting from reactive reporting to proactive pipeline management.

This goes beyond simple stage progression. Clari considers factors like:

• Engagement: Recipient titles, meeting frequency, email response rates, multi-threading.
• Deal Progression: Time in stage, adherence to sales process, movement between stages.
• Sentiment: Keywords in emails, meeting sentiment indicators (if integrated).
• Historical Data: How similar deals (size, industry, product) have performed in the past.
• Custom Risk Factors: Specific criteria unique to your sales cycle, which we'll cover in customization.

The Problem with Traditional Forecasting

Traditional sales forecasting often relies heavily on individual reps' subjective assessments or rigid stage-based probabilities. This can lead to:

• Sandbagging: Reps intentionally lowering their forecasts to exceed expectations.
• Over-optimism: Overestimating deal likelihood, leading to missed targets.
• Lack of Visibility: Deals progressing through stages without real engagement or clear next steps.

Clari's AI addresses these challenges by providing an objective, data-backed assessment, offering a consistent and scalable approach to evaluate your Clari AI pipeline health.

2. Initial Setup: Connecting Your CRM and Activating AI Features

Before diving deep into customization, ensure your Clari instance is properly integrated with your CRM and that core AI features are enabled. For the most accurate sales forecasting AI, a robust data foundation is non-negotiable.

Step 1: Verify CRM Integration Status

Your Clari platform should already be integrated with your primary CRM (e.g., Salesforce, Microsoft Dynamics).

1. Log in to Clari: Access your Clari account.
2. Navigate to Settings: Look for a gear icon or "Admin Settings" in the navigation bar.
3. Check Integrations: Under "Data Sources" or "Integrations," confirm that your CRM is listed as "Connected" and "Active."
   • Expected Result: A green checkmark or "Connected" status next to your CRM.
   • Troubleshooting: If disconnected, contact your Clari administrator. Data sync issues will severely impact pipeline health accuracy.

Step 2: Ensure Data Sync and Refresh

Clari relies on up-to-date information for accurate predictions.

1. Review Last Sync: Also within the "Data Sources" section, check the "Last Synced" timestamp for your CRM.
2. Initiate Manual Sync (if necessary): If the last sync is unexpectedly old, your admin may have an option to trigger a manual refresh or verify automated sync schedules.
   • Best Practice: Ensure daily, ideally hourly, data synchronization between your CRM and Clari. This guarantees that recent activities (meetings, emails, status changes) are immediately factored into your Clari deal risk analysis.

Step 3: Activate Core AI and Forecasting Features

While many AI features are active by default, it's good practice to confirm.

1. Access Forecasting Settings: Within Admin Settings, locate "Forecasting" or "AI & Machine Learning."
2. Confirm Feature Enablement: Look for toggles or checkboxes related to:
   • "AI Forecast / Deal Scoring"
   • "Deal Risk Assessment"
   • "Revenue AI Insights"
   • Expected Result: These features should be enabled. If not, toggle them on and save changes. (Note: This may require admin privileges.)
   • Why this matters: Enabling these lays the groundwork for Clari to actively analyze your pipeline for health scores and predictive closure probabilities. Without them, you're missing out on the core value proposition.

3. Customizing Pipeline Health Scoring Parameters

This is where you tailor Clari's powerful AI for sales forecasting to your unique business context. Out-of-the-box settings are a good starting point, but every sales process has nuances. Customizing ensures Clari accurately reflects your definition of a healthy or at-risk deal.

Step 1: Define Your Custom Risk Factors

Think about what truly indicates risk or momentum in your deals.

1. Brainstorm Key Indicators:
   • Are there specific product lines or industries that traditionally have longer cycles or higher churn?
   • Do you require a minimum number of stakeholders involved for a deal to be healthy?
   • Is competitor presence a significant risk factor?
   • What constitutes a critical "red flag" at each stage?
2. Navigate to Custom Health Factors: In Clari's Admin Settings, find "Pipeline Health" or "Deal Scoring" configurations.
3. Add New Risk Factors:
   • Click "Add Custom Factor."
   • Example 1: "Lack of Multi-threading"
     • Condition: "Less than 3 unique contacts engaged in the last 30 days." (Clari can often pull this from email/calendar data.)
     • Severity: High (deals often stall without executive buy-in).
     • Stage Applicability: Mid to Late Stages (e.g., "Discovery," "Proposal," "Negotiation").
   • Example 2: "Time in Stage Exceeded"
     • Condition: "Opportunity has been in [Stage X] for > [Y] days."
     • Severity: Medium/High (stalled deals rarely close).
     • Configuration: For each stage, define an appropriate threshold (e.g., 30 days for "Discovery," 45 days for "Proposal").
   • Example 3: "Meeting with Economic Buyer Not Scheduled"
     • Condition: "No meeting scheduled/completed with contact role 'Economic Buyer'"
     • Severity: High
     • Stage Applicability: Late Stage (e.g., "Commit," "Close Plan").

Tip: Involve your top-performing sales reps and sales managers in this brainstorming. They have invaluable insights into common deal fall-outs and success indicators. Their input will make the custom factors highly relevant and actionable.

Step 2: Adjust Scoring Weighting for Different Factors

Not all risk factors are created equal.

1. Access Weighting Settings: Within the "Pipeline Health" configuration, you'll find options to adjust the impact of various factors (both default and custom).
2. Modify Weights:
   • For factors that critically impact deal success (e.g., sponsor change, lack of executive engagement), assign a higher weight.
   • For less critical factors (e.g., minor delay in a document exchange), assign a lower weight.
   • Consider a 1-5 scale or percentage breakdown, depending on Clari's UI.
   • Example: Lack of Economic Buyer Meeting might be weighted at 25%, while "Minor Delay in Information Exchange" might be 5%.
   • Expected Outcome: Your deal health scores will now dynamically adjust based on the specific risk profiles you've prioritized, giving accurate Clari deal risk assessments.

Step 3: Define "Healthy," "At-Risk," and "Unhealthy" Thresholds

Clari often uses a red, yellow, green (RYG) system to visualize deal health.

1. Set Score Ranges: Determine the score ranges that correspond to each health status.
   • Green (Healthy): e.g., 80-100
   • Yellow (At-Risk): e.g., 50-79
   • Red (Unhealthy): e.g., 0-49
   • Guidance: These thresholds should be refined over time as you see actual deal outcomes mapping to scores. Start with Clari's defaults, but be prepared to iterate.
   • Why Iterate: The objective is to ensure that deals flagged "Red" genuinely represent those that reliably slip or are lost, and "Green" deals align with those that close as expected. This feedback loop strengthens your pipeline health scoring.

4. Leveraging AI for Deal Closure Prediction and Forecasting

Beyond individual deal health, Clari's AI provides aggregate predictions and forecasts that significantly enhance your sales forecasting AI capabilities. This allows you to combine individual rep insights with objective, data-driven probabilities.

Step 1: Analyze AI-Generated Forecasts

Clari generates its own forecast based on its AI models, providing an objective benchmark.

1. Navigate to the Forecast View: In Clari, go to the "Forecast" or "Revenue" tab.
2. Review AI Forecast Category: Look for the "AI Forecast" or "Clari AI Commit" number.
3. Compare with Rep/Manager Forecasts:
   • How does the AI forecast compare to your team's current commit?
   • Significant variances (e.g., AI forecast significantly lower than rep commit) are immediate flags for review. Clari's AI is often more conservative, as it's not subject to human bias.
   • Expected Result: A clear comparison between human-generated forecasts and Clari's AI-generated projection. This helps you identify blind spots and over-optimism.

Step 2: Utilize Deal-Level Probability Scores

Each deal in Clari typically has an AI-generated probability of closing.

1. Access Opportunity Grid: In the "Pipeline" or "Opportunity" view, ensure the "Clari Probability" or "AI Win Rate" column is visible.
2. Sort by Probability: Sort your pipeline by this column to quickly identify:
   • High-Probability Deals: These are your strong contenders; ensure they have no outstanding risks.
   • Low-Probability Deals: These deals might be consuming time without a realistic path to close. Consider qualifying them out or re-engaging strategically.
   • Insight: A deal might be in the "Commit" stage (high human probability) but have a low Clari probability due to a lack of recent engagement or key stakeholder involvement. This is a critical Clari deal risk signal.

Step 3: Explore "AI Recommendations"

Clari often provides specific recommendations to improve deal health.

1. Open Individual Opportunity: Click on a specific opportunity in your pipeline.
2. Review AI Insights Panel: Look for a section labeled "AI Insights," "Recommendations," or "Prescriptive Actions."
   • Examples of Recommendations:
     • "Schedule a meeting with the Economic Buyer."
     • "Re-engage with [Contact Name], engagement has dropped."
     • "Identify additional stakeholders – deal lacks multi-threading."
     • "Update Close Date, current date seems unrealistic based on typical sales cycle."
   • Actionable Advice: These aren't just observations; they are data-backed suggestions to predict deal closure more effectively. Incorporate these into your coaching sessions.

Step 4: Leverage Pipeline Change Intelligence

Clari's AI in sales forecasting excels at highlighting pipeline changes that impact your forecast.

1. Review Pipeline Flow: Use Clari's "Pipeline Flow" or "Changes" view weekly.
2. Identify Key Shifts:
   • Deals pushed out: Understand why they were pushed out. Was it predictable?
   • Deals pulled in: Celebrate these, but also understand the accelerators.
   • Deals added/lost: Track the volume and impact.
   • Clari's AI will often flag "Why a deal moved" based on detected activities or lack thereof. This helps refine your understanding of what drives your Clari AI pipeline health.

5. Interpreting and Acting on Clari's Insights

Data is only valuable if you act on it. Interpreting Clari's scores and recommendations effectively turns insights into improved sales performance and more accurate forecasts.

Step 1: Prioritize "At-Risk" Deals

The red and yellow indicators are your call to action.

1. Filter Pipeline by Health Score: In your Clari pipeline view, filter to show only "Red" and "Yellow" deals.
2. Deep Dive into "Red" Deals: For each red deal:
   • Review AI Risk Factors: What specific issues is Clari flagging? (e.g., lack of engagement, stalled stage, no economic buyer).
   • Review Activity History: Confirm the lack of activity or problematic interactions.
   • Formulate an Action Plan:
     • For Reps: What specific action can you take this week to mitigate the risk? (e.g., "Send re-engagement email with new resource," "Request intro to CFO," "Schedule internal strategy review.")
     • For Managers: What coaching or intervention is needed? (e.g., "Call prospect with rep," "Bring in technical expert," "Re-qualify deal.")
   • Goal: Move red deals to yellow, and yellow to green. This directly impacts your ability to predict deal closure.

Step 2: Optimize "Healthy" Deals

Don't neglect your green deals; they represent your highest probability of closing.

1. Filter by "Green" Deals: Focus on the deals Clari deems healthy.
2. Accelerate and Protect:
   • Are there opportunities to accelerate the sales cycle?
   • Are all the necessary proof points and stakeholders engaged?
   • What could still go wrong, even on a healthy deal? Proactively address these (e.g., competitor emerges, budget freeze).
   • Use these deals as examples for coaching other reps. What activities drove their health score up? This reinforces best practices for Clari AI pipeline health.

Step 3: Coach Based on AI Recommendations

Clari provides an objective basis for coaching conversations.

1. Weekly 1:1s: During your weekly sales 1:1s, open Clari.
2. Review Rep's At-Risk Deals: Focus on deals flagged yellow or red.
3. Discuss AI Recommendations: "Clari is suggesting we need to get the VP of Finance involved here. What's our plan to do that?" or "This deal has been in 'Value Proposition' for 40 days, and Clari thinks it's stalled. What are the specific next steps to move it forward?"
4. Track Follow-Up: Use Clari's activity tracking to see if recommendations were acted upon and if deal health improved. This demonstrates the tangible impact of AI in sales forecasting.

6. Integrating Clari Insights into Your Forecasting Cadence

The ultimate goal of leveraging Clari's AI for pipeline health and deal prediction is to create a more robust and reliable forecasting process. Make Clari an indispensable part of your weekly, monthly, and quarterly forecast reviews.

Step 1: Start Forecast Calls with Clari Overview

Shift from qualitative updates to data-driven discussions.

1. Display Clari Dashboard: Begin each team forecast call or 1:1 by sharing your Clari dashboard.
2. Highlight Key Metrics:
   • Overall Clari AI pipeline health (e.g., percentage of pipeline in green, yellow, red).
   • Pipeline coverage ratios (actual vs. target).
   • Trend of AI forecast vs. rep forecast.
   • Focus on Change: Clari excels at showing what's moved since the last review. "Where did our pipeline change? What deals moved in, out, or slipped?"
   • Expected Result: A team that quickly aligns on the objective state of the pipeline, spending less time on data aggregation and more on strategic problem-solving.

Step 2: Drive Strategic Conversations with Clari's "Why"

Clari's power lies not just in what changed, but why.

1. Address Forecast Discrepancies: If Clari's AI forecast differs significantly from a rep's commit, probe deeper using Clari's deal-level insights.
   • "Alex, Clari is showing your EastTech deal as red, yet you have it in commit. Can you walk us through the specific activities and next steps that give you confidence, especially given the lack of recent executive engagement Clari is flagging?"
2. Review At-Risk Deals as a Team: Focus collective intelligence on problematic deals.
   • "Team, we have 5 deals over $100K flagged red. Let's take 10 minutes to brainstorm how we can collectively support these. What have we done successfully on similar deals?" This is a direct application of targeting Clari deal risk.

Step 3: Measure and Refine Over Time

Ongoing measurement is key to continuous improvement.

1. Track AI Forecast Accuracy: Compare Clari's AI forecast against actual closed-won revenue each quarter.
2. Monitor Health Score Correlation:
   • How many "Green" deals closed as expected?
   • How many "Red" deals were lost or slipped?
   • This feedback helps you refine your custom risk factors and their weighting (back to Section 3) to improve your pipeline health scoring.
3. Adjust Sales Processes: Use aggregated Clari insights to identify systemic issues.
   • If many deals stall at a particular stage, perhaps your qualification criteria need adjustment or your sales enablement for that stage needs a boost.
   • If a specific custom risk factor consistently correlates with lost deals, consider making it a mandatory qualification gate. This iterative process is crucial for effective AI in sales forecasting.

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Expected Results

Upon successful implementation and consistent use of Clari's AI pipeline health scoring and deal prediction features, you can expect:

• Improved Forecast Accuracy: A reduction in forecast variance and fewer end-of-quarter surprises, thanks to objective, data-driven estimates.
• Proactive Risk Mitigation: The ability to identify and address at-risk deals much earlier in the sales cycle, leading to higher win rates.
• Enhanced Coaching Effectiveness: Data-backed coaching conversations that focus on specific, actionable improvements, rather than subjective opinions.
• Increased Sales Productivity: Reps spend less time manually updating CRM fields for reporting and more time selling, as Clari automates data capture and provides actionable insights.
• Strategic Pipeline Management: A clear understanding of your overall Clari AI pipeline health and where to allocate resources for maximum impact.

How to Verify It Worked:

1. Compare Forecasts: Overlay Clari's AI forecast with your team's actual closed revenue over 2-3 quarters. You should see increasingly close alignment.
2. Win Rate Improvement: Track the win rates of deals that were proactively managed based on Clari's "at-risk" flags versus those that were not (if you can segment this in your CRM).
3. Qualitative Feedback: Conduct interviews with sales reps and managers. Do they feel Clari's insights are helping them prioritize and win more deals?

Troubleshooting

Common Issue 1: Clari's AI Forecast is Consistently Too Low/High

Solution:

1. Review CRM Data Quality: Clari's AI is only as good as the data it processes. Ensure your CRM data is clean, up-to-date, and consistent. Inaccurate close dates, amounts, or irrelevant activity logs will skew results.
   • Specific Steps: Conduct a CRM data audit. Ensure mandatory fields are completed, activities are logged accurately, and close dates reflect realistic timeframes.
2. Adjust Custom Risk Factors and Weighting: If Clari's AI is too conservative, or too optimistic, your custom risk factors might not be perfectly aligned with your business nuances.
   • Specific Steps: Revisit Section 3. Evaluate if certain factors are being over-weighted (making deals seem riskier) or under-weighted (missing true risks). Iterate on thresholds. For example, if Clari is consistently too low, it might be over-penalizing "Time-in-Stage" for deals that genuinely have longer sales cycles.
3. Historical Data Cleanliness: For the AI to learn effectively, historical closed-won and closed-lost data must also be accurate. If past successful deals had poor data quality, the AI might misinterpret success patterns.
   • Specific Steps: Work with your RevOps team to ensure historical deals are correctly categorized as won/lost and associated with relevant activities.

Common Issue 2: AI Recommendations Seem Irrelevant or Redundant

Solution:

1. Verify Integration Scope: Ensure Clari is integrated with all relevant data sources (email, calendar, communication platforms like Slack/Teams if supported). If it can't see all activities, its recommendations will be incomplete.
   • Specific Steps: Check your Clari Admin settings for all connected data sources. If you use a new communication tool, explore its integration capabilities with Clari.
2. Refine Custom Risk Factors: If Clari keeps recommending actions that are already being done or aren't actually critical, your custom factors might be too broad or misaligned.
   • Specific Steps: Review the conditions you set for custom risk factors (Section 3, Step 1). Are they precise enough? For instance, if it keeps saying "Re-engage contact," but you had a meeting last week, perhaps the definition of "engagement" needs to be more nuanced for that stage, or the CRM activity isn't syncing properly.
3. Provide Feedback to Clari: Clari's AI models continuously learn. Many platforms allow users to provide feedback on the accuracy or helpfulness of an AI recommendation. Use this feature to improve model performance over time.
   • Specific Steps: Look for "thumbs up/down" icons or feedback forms next to AI recommendations within the Clari UI.

AI Pipeline Health Scoring: Predict Deal Closure with Clari AI is ideal for teams that need faster execution and measurable outcomes.