> ## Documentation Index > Fetch the complete documentation index at: https://support.affinity.co/llms.txt > Use this file to discover all available pages before exploring further. # Tutorial 18: Advanced Deal Flow and Forecasting > Score deals by close likelihood, organize complex deal flow with hierarchies, build probability-adjusted forecasts. *** ## Immediate Value After this tutorial, you'll rank deals by close likelihood using a multi-signal framework, structure deal flow across multiple lists, and build a probability-adjusted forecast in Affinity Analytics. ## Prerequisites * Tutorial 12: Advanced Pipeline Management * Tutorial 14: Analytics and Reporting * Tutorial 17: Executive Dashboards * Recommended: enough closed-deal history to calibrate conversion rates (typically 6+ months) ## Quick-Start Roadmap 1. Score deals by close likelihood 2. Organize deal flow across lists 3. Build a forecasting report *** ## Why Forecasting Matters Forecasting converts pipeline data into a planning tool — for LPs, partners, and analysts. Accuracy improves with more historical data in Affinity, so the forecast becomes more useful over time. > **Affinity guide:** [2025 VC's Guide to Fundraising](https://affinity.co/guides-2/the-vcs-guide-to-fundraising-in-2025) and [Best Practice Guide for Private Equity](https://affinity.co/guides/affinitys-best-practice-guide-for-private-equity). *** ## Task 1: Score Deals by Close Likelihood ### Context Combine stage progression speed, relationship strength, activity recency, and champion identification into a simple multi-signal score for each open deal. ### Action **Identify the signals to track:** 1. **Stage velocity** — In your Pipeline list, compare average days-in-stage for Closed Won vs. Closed Lost. Deals currently moving faster than the historical average are stronger candidates. 2. **Relationship strength (RI)** — Check RI on each deal's key contacts. Strong team connections generally correlate with better access for diligence and references. 3. **Activity recency** — Filter for deals with no contact in 30+ days. These are your highest-risk deals. 4. **Champion identified** — Note whether there's a clear internal advocate at the target. **Combine into a scoring framework:** \| **Signal** | **Strong (✅)** | **Weak (⚠️)** | \| --- | --- | --- | \| Stage velocity | Faster than average | Slower than average | \| RI strength | 🟢 Strong team connection | 🔴 Weak/no connection | \| Last contact | Within 14 days | 30+ days ago | \| Champion identified | Yes, active advocate | No clear champion | **Use the scoring to focus attention** — high-signal deals are candidates for more investment of partner time; low-signal deals need either intervention or a pass decision. Recalibrate the framework as you accumulate closed-deal data. Track which signals actually predict closings in your portfolio. ### Expected Outcome You can rank your active deals by close likelihood using a consistent multi-signal framework. *** ## Task 2: Organize Deal Flow Across Lists ### Context As deal flow grows, splitting pipeline across multiple lists (by fund, stage, or workflow) keeps each list focused. Cross-list Analytics reports stitch the full funnel back together. ### Action **Design a list structure:** | **Level** | **List** | **Purpose** | | --------------- | ------------------- | ----------------------------------- | | Top-of-funnel | Sourcing | All inbound and outbound leads | | Active pipeline | Fund III Pipeline | Deals being actively evaluated | | Deep diligence | Due Diligence | Deals in active DD | | Closed | Portfolio | Completed investments + monitoring | | Passed | Archive / Re-engage | Declined deals for future reference | **Connect with automations** (see Tutorial 13): * Sourcing → Status = "Qualified" → add to Active Pipeline * Active Pipeline → Status = "DD" → add to Due Diligence * Due Diligence → Status = "Closed Won" → add to Portfolio **Automations are capped at 10 rules per account.** Prioritize the transitions that happen most often. **Use cross-list Analytics** (Tutorial 14, Task 6) to see the full funnel across all lists. Individual lists stay focused; analytics provides the unified view. ### Expected Outcome You have a clear list structure for your deal flow, with automated movement between stages. *** ## Task 3: Build a Forecasting Report ### Context Once you have closed-deal data, you can build a probability-adjusted forecast that reflects expected deployment based on current pipeline and historical conversion rates. ### Action **Step 1: Pull your historical benchmarks** from Analytics: * Average deals entering pipeline per month * Conversion rate per stage (from funnel analysis) * Average time from entry to close * Average deal size at close ### Step 2: Build the forecast 1. In **Analytics**, create a forecasting report. 2. Use **probability-adjusted pipeline**: * Each deal's Amount × Stage Probability = Expected Value * Sum across all open deals = Weighted Pipeline 3. Apply historical conversion rates to each stage: \| **Stage** | **Deals (example)** | \| --- | --- | \| Initial Review | 25 | \| Partner Meeting | 10 | \| Due Diligence | 4 | \| Term Sheet | 2 | \| **Total Expected Deployment** | **Sum of (deals × probability × deal size)** | **Step 3: Compare forecast to fund targets.** Surface the gap between expected and target deployment so partners can decide whether to source more or focus on closing. **Win-ratio and goals settings** in Affinity Analytics let you configure stage probabilities and deployment targets that flow into forecast reports. ### Expected Outcome You have a probability-adjusted forecast that updates with current pipeline data. ### 📚 Help Center * [How To Create Probability Adjusted Values For Forecasting](/s/article/How-to-create-probability-adjusted-values-for-forecasting-with-Affinity-Analytics) * [Basic Reporting: Forecast Reports](/s/article/Basic-Reporting-Forecast-Reports) ## Common Questions More closed deals give you more reliable conversion rates. With limited history, treat the forecast as directional and recalibrate as you accumulate closings. Yes. Export your forecast each quarter, then compare predicted vs. actual closings. This is how you calibrate probability assignments. Both views have uses. Fund-level forecasts inform LP reporting and deployment planning; cross-fund forecasts show firm-level activity. If your account uses opportunity-level access control (Restricted/Redacted Opportunities), users without access won't see those entries in forecasts. Confirm with the opportunity owner if numbers look incomplete. *** ## See It In Action