Pipeline Prioritization
Use real-time visitor behavior to rank deals and focus rep time on opportunities most likely to close.
Who This Is For
Step-by-Step Workflow
Follow these six steps to build a data-driven pipeline prioritization system that keeps your team focused on the deals that matter.
Connect ClearView to Your CRM
Integrate ClearView with your CRM to automatically match website visitors against existing contacts, leads, and open opportunities. This creates a live bridge between anonymous web activity and your pipeline, giving you behavioral context on every deal in your forecast.
Ensure your CRM integration maps visitor identity to the account level, not just the contact level. A single opportunity may have multiple stakeholders visiting your site independently, and you want all of that activity rolling up to the deal.
Build a Composite Intent Score
Combine first-party signals (pages visited, visit frequency, recency) with CRM engagement data (emails opened, meetings held, proposal views) and optionally third-party intent data. Weight each signal based on its historical correlation with closed-won outcomes.
Start simple: page views on pricing and case studies weighted heavily, blog visits weighted lightly. Add CRM signals like email reply rates and meeting attendance. Layer in third-party intent only after your first-party scoring model is validated.
Create Scoring Tiers
Segment your pipeline into actionable tiers. Hot (80-100): multiple decision-makers visiting plus pricing page views equals immediate outreach. Warm (50-79): single visitor from an open opportunity equals follow up within 48 hours. Monitoring (below 50): occasional visits equals continue nurture sequences.
The tier thresholds matter less than consistency. Pick numbers your team understands and commit to the response playbook for each tier. Adjust thresholds quarterly based on conversion data from each tier.
Configure CRM Dashboards
Build a dedicated "Hot Accounts" view in your CRM sorted by intent score. This becomes the rep's daily priority list, replacing gut-feel pipeline management with data-driven focus. Include columns for intent score, last visit date, pages viewed, and deal stage.
Create separate views for each rep's territory and a rolled-up view for managers. Add filters for deal stage so reps can quickly toggle between prospecting priorities and closing priorities.
Set Deal-Risk Alerts
Configure real-time alerts for risk signals: a contact from an open opportunity visits your competitor comparison page, cancellation content, or downgrade documentation. These are early warning signs that a deal is at risk and requires immediate AE attention.
Route deal-risk alerts directly to the owning AE and their manager via Slack DM. Include the specific pages visited and the deal value at stake. Speed matters here because the window to save an at-risk deal is measured in hours, not days.
Run Weekly Pipeline Reviews with Intent Data
Transform your weekly pipeline review from a subjective exercise into a data-driven conversation. Classify deals as healthy (active website engagement in the last 14 days) or at risk (zero engagement in 14+ days). Challenge reps to explain why at-risk deals should stay in the forecast.
Track intent-based forecast accuracy over time. Deals with active multi-stakeholder engagement close at 3-4x the rate of deals with no recent website activity. Use this data to build more accurate forecasts and identify coaching opportunities.
ClearView Integrations Used
This playbook connects ClearView with the following tools in your stack.
Expected Outcomes
Teams running this playbook consistently report these results.
Reps focus on opportunities with active engagement instead of chasing ghosts
Deals move faster when reps prioritize the right opportunities at the right time
Early churn warnings let AEs intervene before deals slip away silently
Common Mistakes
Avoid these pitfalls that undermine pipeline prioritization efforts.
Over-indexing on single signals
One pricing page visit does not make a deal hot. Intent scoring must aggregate multiple signals over time. A single data point is noise; a pattern is a signal. Build scores that reward sustained, multi-page engagement.
Not recalibrating scores quarterly
Buying behavior shifts with market conditions, product changes, and competitive dynamics. A scoring model built six months ago may be promoting the wrong signals today. Review conversion rates by score tier every quarter and adjust weights accordingly.
Building scores too complex for reps to trust
If a rep cannot explain why a deal is scored hot in one sentence, the model is too complex. Reps will ignore scores they do not understand. Keep the logic transparent and the tiers simple: hot means act now, warm means follow up soon, monitoring means keep nurturing.
Ignoring negative signals
Most scoring models only add points. But a contact visiting your cancellation page, help documentation for downgrading, or competitor comparison content should subtract points or trigger risk alerts. Negative signals are often more actionable than positive ones.
Pro Tips
Start with just three to five signals in your scoring model and add complexity only when you have enough closed-won data to validate each new signal's predictive power.
Create a "multi-threading score" that tracks how many unique contacts from an account visit your site. Deals with three or more stakeholders engaged close at significantly higher rates.
Use intent score trends, not just absolute values. A deal that went from 30 to 65 in two weeks is more urgent than one sitting steady at 70 for a month.
Build a weekly Slack digest that shows each rep their top five deals by intent score change, highlighting which deals are heating up and which are going cold.