Lead re-engagement
Re-engage quiet leads with contextual WhatsApp follow-up, reply routing, CRM updates, and activity visibility.
Use this product state to connect the visible UI to the operational decision the lesson is teaching.
Learn the system by following the product states.
Use the screenshots as the primary map: start with the full context, trace the connected workflow, inspect the focused UI, then compare against the completed operating state.
Customer signalWorkflow pathVisible outcomeUse this product state to connect the visible UI to the operational decision the lesson is teaching.
Summary
Re-engage quiet leads with contextual WhatsApp follow-up, reply routing, CRM updates, and activity visibility.
Concepts covered
Step breakdown
- Define the system to deployStart from this Sales operating system and confirm the business outcome, source signals, owners, and review gates.
- Create the Frontline assetsBuild the workflow canvas, agent prompt, channels, CRM records or tables, routing logic, and Max Activity evidence described in the blueprint.
- Launch the narrow versionDeploy the smallest reliable version first, keep human review visible, then use activity and analytics to expand the system.
What you will build
A re-engagement system that detects dormant deals in CRM, drafts a context-aware WhatsApp follow-up using the deal history and person profile, sends it automatically, and routes replies by intent — logging every outcome in Max Activity.
After deployment: deals that have been inactive for your defined window receive a personalized message automatically. Replies trigger qualification or owner handoff. No-replies trigger a second follow-up or mark the deal as cold.
When to use this
Your pipeline has deals that went quiet after initial contact and have not progressed in 14–60 days.
Reps are too busy to manually follow up with every stale opportunity.
You want to test whether a deal is still alive before formally losing it.
You need a consistent re-engagement cadence that does not depend on individual rep discipline.
System components
Studio workflow with a scheduled or CRM-event trigger (deal last-activity date exceeds threshold).
CRM Deal record: stage, last-activity date, lost reason, owner, value.
CRM Person record: name, WhatsApp phone, preferred language, consent state, recent conversation.
CRM Company record: segment, current account context.
AI follow-up agent: drafts a personalized re-engagement message using deal and person context.
Channels: WhatsApp Send Message node for outbound delivery.
Reply classification node: categorizes inbound reply as interested, not interested, or wrong person.
Max Activity: logs message sent, reply received, classification, and next action.
Step-by-step implementation
1. Define the dormancy threshold: how many days without activity triggers re-engagement. Common starting point: 21 days.
2. Create a Studio workflow. Set the trigger to a scheduled check or CRM webhook when deal last-activity date crosses the threshold.
3. Add a Deal lookup node. Filter: stage = Active or Nurture, last-activity > threshold. Retrieve Person and Company linked to the Deal.
4. Add an AI agent node. Pass: Person name, company, deal stage, deal value, last conversation snippet, and language preference.
5. Instruct the agent to draft a short, specific re-engagement WhatsApp message (2–3 sentences, no generic openers).
6. Add a Send Message node (Channels: WhatsApp). Use the AI-drafted message. Set the Person's phone as the recipient.
7. Add a Wait node. Wait up to 72 hours for a reply before branching.
8. Add a Conditional Routing node after the wait. Branch on: reply received vs. no reply.
9. Reply branch: run a classification agent to categorize intent (interested, not interested, wrong person). Route accordingly.
10. No-reply branch: send a second follow-up after 7 more days, or mark deal as Cold and write Max Activity.
11. Write Max Activity on every outcome: message sent, reply classification, deal stage update, and next action.
Agent prompt
You are writing a re-engagement WhatsApp message for a dormant sales opportunity.
Context you will receive: the person's name, their company, the deal stage, the deal value, and any notes from the last conversation.
Write a message that: addresses the person by first name, references something specific from the deal context, asks one clear question to re-open the conversation.
Do not use generic openers like 'Just checking in' or 'Hope you are well'. Be specific and brief — maximum 3 sentences.
Adjust tone for the language preference in the context. Output only the message text, nothing else.
Workflow logic
Reply = interested: update Deal stage to Active, assign follow-up task to owner, write Max Activity with reply summary.
Reply = not interested: move Deal to Lost, record reason in Max Activity, stop re-engagement.
Reply = wrong person: create a task for owner to find the correct contact, write Max Activity.
No reply after first message: wait 7 days, send a shorter second message. No reply after second: mark deal Cold, write Max Activity.
High-value deals (above threshold): after any reply, skip automation and route directly to owner for personal follow-up.
Final operating state
Dormant deals receive a personalized WhatsApp message within 24 hours of hitting the inactivity threshold.
Replies are classified and routed: interested deals re-enter the active pipeline, not-interested deals are closed with reason.
Max Activity on each deal shows: message sent, reply received or not, classification, and the next action taken.
Owner intervention is reserved for high-value deals or ambiguous replies, not every dormant lead.
Troubleshooting
Messages feel generic despite AI drafting: add more deal context to the agent node — include the last conversation message and the specific product or service the deal was about.
Wrong deals are being triggered: double-check the dormancy filter — confirm stage = Active (not Closed/Won/Lost) AND last-activity calculation is correct.
Replies not being classified: verify the inbound WhatsApp reply is linked to the correct workflow session. Check that the Wait node is listening on the same conversation thread.
Deal stage not updating after reply: confirm the Deal update node is on the 'interested' branch and the Deal ID is passed correctly from the trigger.
Operational playbook
Use Lead re-engagement as part of the Frontline Solutions Sales operating loop: inspect the current product state, confirm the source context, and decide what should happen next.
The goal is not to memorize screens. The goal is to understand how the product surface supports repeatable work, AI assistance, and accountable handoff.
Best practices
Start with the operational job before changing configuration. Name the owner, define the trigger or source context, and decide how the result should be reviewed.
Prefer narrow, inspectable setups over broad automation. Teammates should be able to explain why the system took an action from the visible product state.
Platform layers involved
Studio defines the workflow and AI agent behavior. Channels capture the customer interaction. CRM provides customer memory. Max Activity shows what the system did and what needs follow-up.
Use the solution page as the business-facing map, then open the related product tutorials when you need configuration detail.
Outcome metrics
Track a small set of operational signals: response time, handoff rate, completion rate, escalation quality, CRM field completeness, reply rate, and repeated failure patterns.
The metric should reflect the business outcome, not only whether the automation ran.
Agent Builder visual map
FAQs
What does Lead re-engagement teach?
Re-engage quiet leads with contextual WhatsApp follow-up, reply routing, CRM updates, and activity visibility.
How should teams use this lesson?
Use it as an implementation guide: create the assets, connect the systems, verify the completed state, and operate the blueprint with review gates.
Which Frontline products are involved in this solution?
Most solution playbooks connect Studio workflows, Channels, CRM records, AI agents, and Max Activity. The business outcome is the entry point; the platform layers make it operational.
How should we decide whether to automate this use case?
Automate when the path is repeated, has clear source context, needs consistent follow-up, or benefits from AI classification, routing, summaries, or structured capture. Keep human review where judgment or risk is high.
What should be visible before this goes live?
Verify the workflow trigger, CRM context, channel permissions, AI agent instructions, handoff owner, logs, and Max Activity output so the team can trace what happened.
How do we keep the customer experience personal?
Use CRM context, conversation history, and approved message patterns. AI should use relevant customer memory, not generic copy, and workflows should escalate when context is missing.
What is the best first version of this playbook?
Start with one channel, one workflow, one owner group, and a narrow success metric. Expand only after logs, activity, and customer-facing outputs are trustworthy.