On Day 8, we’ll integrate AI-driven sales forecasting into the sales pipeline, helping sales teams predict which deals will close and when. Using historical data + GPT insights, AI will assign probabilities to each deal for better revenue projections.
1. Why AI-Driven Sales Forecasting?
✅ Predict Deal Closures → AI assigns win probabilities based on past CRM trends.
✅ Improve Revenue Forecasting → Helps teams prioritize high-probability deals.
✅ Detect Deal Risks Early → AI flags stalled or at-risk deals before they slip away.
2. How Sales Forecasting Works with AI
We’ll use:
- GPT-4 → Analyzes past CRM data + meeting notes to predict deal probability.
- Historical CRM Data → Finds trends from previous closed deals.
- Pipeline Stages → Classifies deals into:
- Cold (0-30%)
- Engaged (31-70%)
- Hot (71-100%)
3. Setting Up Sales Forecasting API
✅ Step 1: Update gptService.js to Predict Deals
Modify src/api/gptService.js:
import axios from 'axios';
import { OPENAI_API_KEY } from '@env';
export const predictDealOutcome = async (meetingNotes, pastCRMData) => {
try {
const response = await axios.post(
'https://api.openai.com/v1/chat/completions',
{
model: 'gpt-4',
messages: [
{
role: 'system',
content: "You are an AI that predicts sales deal outcomes based on CRM history and meeting insights.",
},
{
role: 'user',
content: `Analyze this sales meeting transcript:
${meetingNotes}
Based on the following past CRM data:
${pastCRMData}
Predict the deal outcome:
- Probability of closing (0-100%)
- Expected deal stage (Cold, Engaged, Hot)
- Key risks or concerns
- Suggested next actions`,
},
],
},
{
headers: { Authorization: `Bearer ${OPENAI_API_KEY}` },
}
);
return response.data.choices[0].message.content;
} catch (error) {
console.error('GPT Forecasting Error:', error);
return 'Error predicting deal outcome. Please try again.';
}
};
4. Adding AI Pipeline Tracking to the App
✅ Step 1: Modify HomeScreen.js
import React, { useState } from 'react';
import { View, Text, Button, StyleSheet, ActivityIndicator, Alert } from 'react-native';
import VoiceRecorder from '../components/VoiceRecorder';
import { uploadAudio, transcribeAudio, getTranscriptionResult } from '../api/transcriptionService';
import { generateCRMNotes, analyzeSalesMeeting, predictDealOutcome } from '../api/gptService';
export default function HomeScreen() {
const [recordingUri, setRecordingUri] = useState(null);
const [transcription, setTranscription] = useState('');
const [crmNotes, setCrmNotes] = useState('');
const [salesAnalysis, setSalesAnalysis] = useState('');
const [leadScore, setLeadScore] = useState(0);
const [dealPrediction, setDealPrediction] = useState('');
const [isLoading, setIsLoading] = useState(false);
const handleRecordingComplete = (uri) => {
setRecordingUri(uri);
Alert.alert('Recording Saved', `Saved to: ${uri}`);
};
const handlePredictDeal = async () => {
if (!crmNotes) {
Alert.alert('No CRM Notes', 'Please generate CRM notes first.');
return;
}
try {
setIsLoading(true);
const pastCRMData = "Past 10 deals: 7 closed, 3 lost. Closed deals had strong budget alignment and high urgency.";
const prediction = await predictDealOutcome(crmNotes, pastCRMData);
setDealPrediction(prediction);
} catch (error) {
Alert.alert('Error', 'Failed to predict deal outcome.');
} finally {
setIsLoading(false);
}
};
return (
<View style={styles.container}>
<Text style={styles.title}>AI Sales Assistant</Text>
<VoiceRecorder onRecordingComplete={handleRecordingComplete} />
{crmNotes && (
<>
<Button title="Predict Deal Outcome" onPress={handlePredictDeal} />
{dealPrediction ? <Text style={styles.prediction}>{dealPrediction}</Text> : null}
</>
)}
{isLoading && <ActivityIndicator size="large" color="#0000ff" />}
</View>
);
}
const styles = StyleSheet.create({
container: { flex: 1, justifyContent: 'center', alignItems: 'center', padding: 10 },
title: { fontSize: 24, fontWeight: 'bold', marginBottom: 20 },
prediction: { marginTop: 10, fontSize: 16, fontWeight: 'bold' },
});
5. Example AI Sales Pipeline Predictions
🔹 Sales Meeting Transcript:
“The client is comparing our solution with two competitors. They like our pricing but need to discuss internally before making a decision. They mentioned a budget of $15K per year and a potential rollout by Q3.”
🔹 AI-Generated Sales Prediction:
📌 **Win Probability:** 65%
📌 **Pipeline Stage:** Engaged (Client is evaluating options)
📌 **Key Risks:** Competitor pricing & internal delays
📌 **Next Steps:** Follow up in 1 week, offer case studies
📌 **Expected Close Date:** 3 months
6. AI Pipeline Forecasting Dashboard (Optional)
To display pipeline analytics, use React Native Charts:
npm install react-native-chart-kit
Modify HomeScreen.js:
import { LineChart } from 'react-native-chart-kit';
<LineChart
data={{
labels: ['Jan', 'Feb', 'Mar', 'Apr'],
datasets: [{ data: [30, 50, 80, 65] }],
}}
width={300}
height={220}
yAxisLabel="%"
chartConfig={{
backgroundColor: '#f2f2f2',
backgroundGradientFrom: '#ff9800',
backgroundGradientTo: '#ffcc80',
decimalPlaces: 0,
color: (opacity = 1) => `rgba(255, 255, 255, ${opacity})`,
}}
/>;
7. Preparing for Tomorrow: AI-Driven Sales Coaching & Recommendations
Tomorrow, we’ll:
- Train AI to suggest improvements for sales calls.
- Provide AI-driven coaching tips for sales reps.
8. Key Concepts Covered
✅ AI-powered sales forecasting based on past CRM data.
✅ Pipeline tracking for deal stage classification.
✅ Generated revenue predictions for sales teams.
9. Next Steps: AI Sales Coaching for Sales Reps
Tomorrow, we’ll:
- Analyze past sales calls to improve rep performance.
- AI-driven coaching tips for better sales closing strategies.
10. References & Learning Resources
11. SEO Keywords:
AI sales forecasting, CRM predictive analytics, sales pipeline automation, GPT for deal predictions, AI sales probability tracking.
