Hi everyone, I’m looking for advice on streamlining our lead/contact lifecycle in Salesforce. We’re encountering two main challenges:
Re‑engaging Churned Contacts from Existing Clients
Once a contact churns or a deal falls through, they remain as a Contact record—not a Lead—so they aren’t part of our nurture or re‑prospecting campaigns.
What’s the best approach to convert or tag these contacts so they can be reintroduced as Leads at the appropriate time?
Handling Unclosed New Sales Opportunities
We convert Leads into Contacts when New Opportunities are created when we’re working with a decision‑maker, but not all Opportunities close. Often we’re told to “circle back in six months.”
After conversion, those records are locked as Contacts/Opportunities and can’t be reactivated as Leads for follow‑up, complicating marketing automations and rep outreach plans.
This disconnect is causing confusion for both Marketing and Sales:
Marketing can’t easily include churned or unclosed prospects in drip campaigns.
Reps aren’t sure which list to reference for next‑touch planning.
What we’re hoping to achieve:
A clear, repeatable process (or technical solution) for moving contacts back into the active Lead lifecycle when appropriate.
Seamless integration with our marketing automations so no one falls through the cracks.
A simple, user‑friendly setup for reps to know exactly who to reach out to and when.
Has anyone tackled a similar scenario? I’d love to hear about recommended business processes, Salesforce configurations (e.g., person accounts, custom object/status workflows, automation tools), or third‑party apps that can help bridge the gap. Thanks in advance for your insights!
Yes, the end goal is to have the title bucket stored in Salesforce as a custom field on the contact or lead record. Ideally, the classification would happen before or during the import process, so that when we bring in lead lists (from sources like ZoomInfo, events, etc.), the job title is automatically mapped to the appropriate bucket. We’re open to where the AI processing happens — whether it’s part of a data pipeline (e.g., in Python or via a middleware like Workato, Tray.io, etc.), or potentially using Salesforce integrations like Flow, Apex, or a connected tool — as long as the final result is a clean, bucketed title field in Salesforce.
👋 Hey RevOps friends — looking for some AI/data advice! We’re working on a project to normalize job titles into consistent “title buckets” for our CRM, and would love to tap into your collective wisdom on how to approach it. Here’s the idea: We have ~30,000 unique job titles from various lead sources, with many small variations pointing to the same kind of role. For example:
Dir. Career Counselling → Decision Maker
Director Career Counselling → Decision Maker
Dir, Career Counselling → Decision Maker
Career Counselling Director → Decision Maker
Our goal is to train an AI model that can automatically assign each new title we import to the right bucket, without relying on exact matches. Ideally, something like “Dir. Career Counsel” would still correctly map to “Decision Maker” based on the model’s training. We’re stuck on where to start:
Is there a preferred approach for this kind of title classification using AI?
Should we be looking at custom models, fine-tuning existing LLMs, or is there a more practical way?
What would the step-by-step process look like to build and train this, especially for a small team with limited ML experience?
Appreciate any pointers, similar projects you've tackled, or resources we should check out! 🙏
