How to run a forecast review
Running a forecast review helps revenue leaders evaluate whether the current pipeline is sufficient to meet revenue targets and whether any deals may impact forecast accuracy. Forecast reviews typically involve analyzing pipeline composition, deal progression, engagement activity, and operational signals that may influence whether opportunities close as expected. The Alysio platform helps teams run forecast reviews by aggregating pipeline data from connected CRM systems, analyzing deal progression and engagement patterns, and surfacing signals that may indicate forecast risk. This guide explains how revenue teams can use Alysio to conduct an effective forecast review.Understanding Forecast Reviews
A forecast review is a structured evaluation of the current pipeline to determine whether projected revenue outcomes are realistic. Forecast reviews typically focus on questions such as: Which deals are most likely to close this quarter?Which opportunities may slip beyond their expected close date?
Where is forecast risk concentrated within the pipeline?
Do we have sufficient pipeline coverage to meet targets? These questions help revenue leaders understand whether the pipeline supports the current forecast.
Step 1: Ask a Forecast Review Question
Forecast reviews often begin with a query submitted through the Alysio conversational interface. Examples of forecast review queries include: What deals are driving our forecast this quarter?Which deals are most likely to slip?
Where is forecast risk concentrated?
Do we have enough pipeline coverage to hit our target? When a question is submitted, the platform retrieves pipeline data from connected systems.
Step 2: Retrieve Pipeline and Forecast Data
The platform retrieves relevant opportunity and forecast information from connected CRM systems. Examples of retrieved data include: Opportunity stage and forecast categoryOpportunity close dates
Opportunity owners and associated accounts
Pipeline coverage relative to targets
Stage progression history This information forms the foundation of the forecast analysis.
Step 3: Evaluate Pipeline Coverage
Pipeline coverage analysis evaluates whether the total pipeline value supports the expected revenue targets. Coverage is typically evaluated by comparing: Total pipeline valueForecasted revenue targets
Stage distribution across the pipeline If coverage is too low, the forecast may depend heavily on a small number of deals.
Step 4: Analyze Deal Progression
Deal progression analysis evaluates how opportunities are moving through the pipeline. The platform evaluates: Stage movement across the pipelineAverage deal velocity compared to historical patterns
Opportunities remaining stagnant in late stages Deals that remain stalled or move slower than expected may affect forecast outcomes.
Step 5: Review Engagement Signals
Forecast outcomes are often influenced by engagement activity between the sales team and the customer. The platform evaluates engagement signals such as: Recent meetings with key stakeholdersCommunication frequency with the account
Participation from executive decision makers
Recent activity across communication channels Declining engagement may indicate that a deal is losing momentum.
Step 6: Detect Forecast Risk Signals
After analyzing pipeline composition, deal progression, and engagement activity, the platform identifies signals that may indicate forecast risk. Examples of forecast risk signals include: Late-stage deals with declining engagementLarge forecast commitments concentrated in a small number of opportunities
Deals approaching close dates with limited recent activity
Unusual delays in stage progression These signals help identify which deals may impact forecast reliability.
Step 7: Review Forecast Insights
Once the analysis is complete, the platform generates a structured forecast summary. This summary may include: Deals that are most likely to closeOpportunities at risk of slipping
Accounts requiring additional engagement
Forecast concentration risks These insights allow revenue leaders to evaluate whether the forecast is supported by the current pipeline.
Step 8: Coordinate Forecast Actions
After reviewing forecast insights, revenue teams can take action to address potential risks. Examples of actions include: Prioritizing engagement with late-stage opportunitiesEscalating deals that require leadership support
Reallocating resources across accounts
Adjusting forecast projections based on pipeline conditions AI Revenue Agents can also assist by generating alerts, summaries, or recommended follow-up actions.