✅opportunity  win prediction : Solution design on salesforce
1. Business Objective

Predict the likelihood that an Opportunity will close successfully and automatically recommend next-best actions to improve win rate—all using standard Salesforce data and AI features.

2. Data Source (Standard Salesforce Fields Only)

Input data for both the AI model and recommendations are derived from standard fields already available on Opportunities and related objects.

Opportunity Standard Fields Used for Prediction
    Opportunity Health : StageName, Amount, CloseDate, IsClosed, IsWon

    Sales Activity Signals : LastActivityDate, NextStep, Probability, LeadSource

    Account Signals : AccountId, Account.Industry, Account.Rating

    Contact Signals : Primary Contact fields (Title, Email, Phone)

    Deal Metadata : Type, ForecastCategory, OwnerId

3. AI Model: Einstein Prediction Builder Setup
3.1 Dataset for Training

Objects: Opportunity
Record Set Filter:

• Opportunities from the last 3 years

• Stage is either Closed Won or Closed Lost (for binary learning)

3.2 Outcome Field

Use the existing IsWon standard field (boolean).

• True → model learns attributes of won deals

• False → model learns attributes of lost deals

3.3 Predictor Fields (Standard Only)

Einstein auto-selects, but recommended signals include:

Deal Size & Timing

Amount
CloseDate
CreatedDate
StageDuration (auto-calculated by Salesforce)

Sales Cycle Behavior

LastActivityDate
HasOpportunityLineItem
NextStep (text NLP automatically applied)
ForecastCategory
CampaignId

Account Indicators

Account.Industry
Account.Rating
Account.NumberOfEmployees

4. Output from the AI Model

Einstein generates:

4.1 Prediction Field (Automatically Created) : Opportunity_Win_Score__c (0–100) (This is created by Prediction Builder.)

4.2 Top Predictive Factors

Einstein automatically displays factors such as:

Engagement level (LastActivityDate)
Deal size (Amount)
Time-to-close (CloseDate proximity)
Buying power (Account.Rating)