Skip to main content

​
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 and 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. A dashboard with a tile per list puts the full funnel back on one screen.

​
Action

Design a list structure: Connect the lists (see Tutorial 13):
  • Sourcing → Status = “Qualified” → a Create Opportunity list trigger prompts an opportunity in your Fund III Pipeline opportunity list
  • Company lists such as Portfolio or Re-engage → select the records and use the bulk Copy to List action
  • Pipeline → Status = “Closed Won” → an automation can set a field such as Fund Stage to “Portfolio” (Advanced and Enterprise)
Keep stages inside one list where you can. A single opportunity list with a Status field is easier to report on than a chain of lists that records have to move between.
Bring each list onto one dashboard (Tutorial 14, Task 6) to see the full funnel. Each tile reads from one list, so put the stages side by side rather than expecting one tile to total them.

​
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

​
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 firm uses Restricted Opportunities (Enterprise), restricted deals still count as rows — so totals stay accurate — but their name and field values are masked for anyone not on that deal’s allow list. Ask an allow-list member or your Enterprise Admin if you need access.

​
See It In Action

​
Congratulations — You’ve Completed the Tutorial Library 🎓

You’ve covered every section of the Foundational Tutorial Library, from your first list to forecasting. Here’s your ongoing toolkit:
  • Daily: Use keyboard shortcuts (Tutorial 8) and search navigation (Tutorial 3 — Cmd+K / Alt+K)
  • Weekly: Review pipeline in Board view (Tutorial 9), update stale deals (Tutorial 12)
  • Monthly: Run analytics reports (Tutorial 14), maintain data quality (Tutorial 11)
  • Quarterly: Update executive dashboard (Tutorial 17), review forecast accuracy (this tutorial) Keep learning:
  • Affinity Help Center — reference for every feature
  • Affinity Masterclass — training videos
  • In-app behavioral prompts — contextual tips that appear as you use Affinity

​
Where to Go Next