AI Automation with Make

Building an AI-Powered Lead Nurture Sequence with Make + OpenAI

Traditional email nurture sequences send the same message to every lead. AI-powered nurture adapts the content, timing, and message to each lead’s profile and behaviour — significantly improving conversion rates.

3xHigher engagement vs static sequences
Fully AutomatedAfter initial setup
No-CodeMake + OpenAI + your email tool
How AI Nurture Differs from Traditional Sequences

The Core Upgrade

DimensionTraditional SequenceAI-Powered Sequence
ContentSame email for every leadPersonalised to industry, role, and behaviour
TimingFixed schedule (Day 1, Day 3, Day 7)Adaptive — triggered by engagement signals
ToneOne voice for everyoneAdjusted to lead’s communication style
Subject linesA/B tested manuallyAI-generated variants per lead segment
Call to actionSame CTA for all leadsCTA matched to lead’s stage and interest signals
Follow-upSequence ends regardless of engagementContinues adapting based on opens, clicks, replies
The Architecture

How the System Works

Four connected components that make personalised nurture at scale possible.

🗄️

Lead Database (Airtable, Bubble, or HubSpot)

Stores lead profile data: company, role, industry, ICP score, lead source, pages visited, content downloaded. The richer this data, the more personalised the AI’s output.

Make.com — Orchestration Layer

Watches for trigger events (new lead, email opened, link clicked, day X in sequence), runs the scenario, calls OpenAI, and dispatches the personalised email via your email platform.

🤖

OpenAI GPT-4o — Personalisation Engine

Receives the lead’s profile data and sequence context. Generates a personalised email subject line and body tailored to that specific lead’s industry, role, and behaviour signals.

📧

Email Platform (SendGrid, Mailchimp, ActiveCampaign)

Sends the AI-generated email and reports back engagement data (opens, clicks) to Make via webhook — feeding the next personalisation decision.

Step-by-Step Build

Setting Up the Complete System

1

Configure your lead data model

Ensure your lead database has: first name, company name, job title, industry, lead source, ICP score, and a sequence_stage field (values: new / nurture_1 / nurture_2 / nurture_3 / qualified / unsubscribed). This stage field controls where each lead is in the sequence.

2

Create the Make scenario for Nurture Email 1

Trigger: Watch Records in your lead database where sequence_stage = new. For each new lead, call OpenAI with their profile data. Prompt: ‘Write a personalised nurture email for a [job title] at a [industry] company called [company name]. Our product helps [value prop]. The lead came from [lead source]. Write a subject line and 3-paragraph email body. Be specific to their industry. Do not sound like a template.’

3

Map AI output to email fields

Parse the OpenAI response to extract the subject line and body (use json_object response format for clean parsing). Map to your email platform’s send module. Update the lead’s sequence_stage to nurture_1 after sending.

4

Build the engagement-triggered follow-up

Create a second scenario triggered by email open webhook: if a lead opens Nurture Email 1 within 48 hours, send a follow-up that references the first email’s topic and adds a case study. If they click a link, trigger an immediate high-intent follow-up offering a call.

5

Build the time-based fallback

Create a third scenario triggered by schedule: if 5 days pass and the lead has not opened Nurture Email 1, send a different subject line variant. If they do not open Nurture Email 2 either, move to sequence_stage = low_engagement and reduce frequency.

6

Add the qualification trigger

When a lead clicks a high-intent link (pricing page, case study, schedule a call), trigger an immediate Make scenario: update sequence_stage to qualified, create a task in your CRM for the sales team, and send a personalised email with a direct meeting booking link.

The AI Prompt Engineering

Writing the Nurture Email Prompt

The quality of your nurture sequence depends entirely on your prompt. Here is a template that works.

System prompt stored in Make as a text variable:

You write personalised B2B nurture emails for SA Solutions, a software development agency
specialising in Bubble.io applications and AI automation.

Our ideal customers are: founders and product managers at growing startups who need
to build web applications without large engineering teams.

Writing rules:
- Maximum 150 words for the email body
- One specific, relevant call to action per email
- Reference the lead's specific industry in the first sentence
- Never use filler phrases: 'I hope this email finds you well', 'touching base', 'reaching out'
- Sound like a knowledgeable peer, not a sales rep

Return as JSON only: {subject: string, body: string}

📌 Store this system prompt in a Make data store or a configuration record in your Bubble database — not hardcoded in the scenario. This lets you update it without rebuilding the scenario.

3xHigher email open rate vs generic sequences
2xMore demo bookings from nurtured leads
80%Of sequence runs fully automated
Day 14First meaningful engagement data available

Want an AI Nurture Sequence Built for Your Business?

SA Solutions builds complete AI-powered sales automation systems — from lead capture through nurture to qualified opportunity. Fully automated, personalised at scale.

Build Your Nurture SequenceOur Automation Services

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