Case Study ยท HVAC / Lead Generation SaaS

Automation, AI & CRM Systems for Service Businesses

Multi-State Leads. Manual Process.

Clients Getting Missed.

How we built a fully automated round-robin lead distribution system for Leadzmanager โ€” routing HVAC leads across multiple states in real time, fairly, without anyone on the team lifting a finger.

Company

Leadzmanager (LZM)

Industry

HVAC / Lead Generation SaaS

Platform

Pabbly Connect ยท Round-Robin Routing ยท GHL

Type

Automated Lead Distribution

Multi-State

Lead coverage across multiple states โ€” all routed automatically

Round-Robin

Fair, automated distribution โ€” no client ever skipped

Real-Time

Leads routed instantly upon capture โ€” zero manual review

01 โ€” Context

A Lead Distribution Business Running on Manual Assignment

Leadzmanager (LZM) is a SaaS platform built specifically for the HVAC industry. Their core service: capture inbound leads from paid media, organic search, and partner channels โ€” then route those leads to the right HVAC contractor in the right state.

LZM operates across multiple states, managing a growing network of HVAC clients who rely on a steady, fairly distributed flow of qualified leads to drive their business. With multi-state coverage and leads arriving simultaneously from multiple channels, distribution had to be both geographically intelligent and balanced. Neither was possible with a manual workflow.

02 โ€” The Problem

Manual Assignment Was Breaking Fairness and Speed

LZM was processing every inbound HVAC lead manually โ€” someone on the team reviewed each lead, identified the correct state, found the appropriate contractor in that region, and assigned it by hand. As lead volume grew across states, the process became impossible to scale and impossible to keep fair.

  • โœ•No automated routing logic. Leads arrived from multiple states simultaneously with no system to match them to the right regional contractor automatically.
  • โœ•Delays and backlogs. Manual assignment meant leads sat unworked while the team caught up โ€” especially during volume spikes.
  • โœ•Geographic misroutes. Without a geographic filter, leads were occasionally assigned to the wrong state or wrong contractor.
  • โœ•Broken round-robin fairness. Maintaining equal distribution manually was impossible โ€” some contractors received more leads, others were skipped entirely.
  • โœ•Single point of failure. The process depended entirely on one person's bandwidth and accuracy. Any absence created a backlog and missed distributions.

When lead volume spiked, the bottleneck was always the same: a person manually checking a list and making a judgment call. Some HVAC clients were not getting their fair share โ€” and in a business built on lead delivery, that was a client retention problem.

03 โ€” The Solution

Fully Automated Distribution โ€” State-Matched, Round-Robin, Real-Time

We built an automated lead distribution system inside Pabbly Connect that removes the human bottleneck entirely. Every inbound lead is now captured, validated, state-matched, and routed to the correct HVAC contractor using a round-robin model that ensures no client is ever overlooked.

  • 1Automatic lead capture. All inbound leads from all channels trigger the automation immediately upon submission โ€” no manual review step.
  • 2State-based geographic matching. Lead location is extracted and matched to the correct state group from a centralized client data table.
  • 3Centralized client data table. All HVAC clients organized by state and service region โ€” the live source of truth for routing, monitoring, and updates.
  • 4Round-robin distribution logic. Automated sequencing cycles through eligible clients in each state fairly โ€” every active client tracked in the rotation.
  • 5Edge case validation. Missing address data, unmatched states, and out-of-rotation clients caught before assignment โ€” no misroutes.
  • 6Scalable data architecture. Adding a new state or new client is a data entry โ€” not a process redesign.
04 โ€” How We Built It

Discovery to Live System

๐Ÿ—ฃ

Discovery

Mapped LZM's existing manual workflow end-to-end: how leads arrived, how clients were stored, how assignments were made, and where breakdowns were occurring most often.

๐Ÿ”ฌ

Analysis

Identified three core failure points: no geographic filter, no rotation logic, and no central source of truth for client data across states. Each required a different layer of the solution.

๐Ÿ“‹

Data Structure

Collected and validated every HVAC client's business address and service regions, then organized them into a centralized table structured for automated lookup and state-based grouping.

๐Ÿ”จ

Build

Configured the Pabbly Connect automation: trigger on every new lead, extract state data, query the client table for matching regional providers, apply round-robin sequencing, assign the lead.

๐Ÿ›ก

Edge Case Handling

Built validation logic to catch missing address data, unmatched states, and out-of-rotation clients before assignment โ€” preventing misroutes before they reach a contractor.

โœ…

QA & Handoff

Tested distribution across multiple simulated states and lead volumes, verified rotation accuracy, confirmed client data integrity, and handed off with full documentation.

05 โ€” Results

Before vs. After

Before
After
Leads manually reviewed and assigned one at a time by a team member
Every lead automatically captured and routed in real time without human review
No geographic filter โ€” leads occasionally sent to wrong state or wrong contractor
State-based matching ensures every lead reaches the correct regional HVAC provider
Round-robin fairness maintained only by memory โ€” inconsistent and prone to error
Automated round-robin rotation guarantees equal lead distribution across all eligible clients
Some HVAC clients skipped during high-volume periods โ€” missing their turn in rotation
Every active client tracked in the rotation โ€” no client is ever overlooked
Process dependent on one person's availability โ€” any absence created a backlog
Fully automated system runs 24/7 with no human dependency or single point of failure
Client data scattered โ€” no central source of truth for state groupings or contact info
Centralized client table serves as the live source of truth for routing, monitoring, and updates

The Takeaway

LZM moved from a person-dependent, error-prone manual process to a fully automated distribution engine. Every lead now reaches the right HVAC client in the right state โ€” fairly, instantly, and without anyone on the team lifting a finger. The system scales with lead volume. Adding a new state or a new client is a data entry, not a process redesign.

01 โ€” Context

01 โ€” Context

A Lead Distribution Business Running on Manual Assignment

Leadzmanager (LZM) is a SaaS platform built specifically for the HVAC industry. Their core service: capture inbound leads from paid media, organic search, and partner channels โ€” then route those leads to the right HVAC contractor in the right state.

LZM operates across multiple states, managing a growing network of HVAC clients who rely on a steady, fairly distributed flow of qualified leads to drive their business. With multi-state coverage and leads arriving simultaneously from multiple channels, distribution had to be both geographically intelligent and balanced. Neither was possible with a manual workflow.

02 โ€” The Problem

02 โ€” The Problem

Manual Assignment Was Breaking Fairness and Speed

LZM was processing every inbound HVAC lead manually โ€” someone on the team reviewed each lead, identified the correct state, found the appropriate contractor in that region, and assigned it by hand. As lead volume grew across states, the process became impossible to scale and impossible to keep fair.

  • No automated routing logic. Leads arrived from multiple states simultaneously with no system to match them to the right regional contractor automatically.

  • Delays and backlogs. Manual assignment meant leads sat unworked while the team caught up โ€” especially during volume spikes.

  • Geographic misroutes. Without a geographic filter, leads were occasionally assigned to the wrong state or wrong contractor.

  • Broken round-robin fairness. Maintaining equal distribution manually was impossible โ€” some contractors received more leads, others were skipped entirely.

  • Single point of failure. The process depended entirely on one person's bandwidth and accuracy. Any absence created a backlog and missed distributions.

When lead volume spiked, the bottleneck was always the same: a person manually checking a list and making a judgment call. Some HVAC clients were not getting their fair share โ€” and in a business built on lead delivery, that was a client retention problem.

03 โ€” The Solution

03 โ€” The Solution

Fully Automated Distribution โ€” State-Matched, Round-Robin, Real-Time

We built an automated lead distribution system inside Pabbly Connect that removes the human bottleneck entirely. Every inbound lead is now captured, validated, state-matched, and routed to the correct HVAC contractor using a round-robin model that ensures no client is ever overlooked.

1

Automatic lead capture. All inbound leads from all channels trigger the automation immediately upon submission โ€” no manual review step.

2

State-based geographic matching. Lead location is extracted and matched to the correct state group from a centralized client data table.

3

Centralized client data table. All HVAC clients organized by state and service region โ€” the live source of truth for routing, monitoring, and updates.

4

Round-robin distribution logic. Automated sequencing cycles through eligible clients in each state fairly โ€” every active client tracked in the rotation.

5

Edge case validation. Missing address data, unmatched states, and out-of-rotation clients caught before assignment โ€” no misroutes.

6

Scalable data architecture. Adding a new state or new client is a data entry โ€” not a process redesign.

04 โ€” How We Built It

Discovery to Live System

๐Ÿ—ฃ

๐Ÿ—ฃ

Discovery

Mapped LZM's existing manual workflow end-to-end: how leads arrived, how clients were stored, how assignments were made, and where breakdowns were occurring most often.

๐Ÿ”ฌ

๐Ÿ”ฌ

Analysis

Identified three core failure points: no geographic filter, no rotation logic, and no central source of truth for client data across states. Each required a different layer of the solution.

๐Ÿ—บ

๐Ÿ—บ

Data Structure

Collected and validated every HVAC client's business address and service regions, then organized them into a centralized table structured for automated lookup and state-based grouping.

๐Ÿ”จ

๐Ÿ”จ

Build

Configured the Pabbly Connect automation: trigger on every new lead, extract state data, query the client table for matching regional providers, apply round-robin sequencing, assign the lead.

๐Ÿ›ก

โœ…

Edge Case Handling

Built validation logic to catch missing address data, unmatched states, and out-of-rotation clients before assignment โ€” preventing misroutes before they reach a contractor.

โœ…

โœ…

QA & Handoff

Tested distribution across multiple simulated states and lead volumes, verified rotation accuracy, confirmed client data integrity, and handed off with full documentation.

05 โ€” Results

Before vs. After

Before

After

Before

  • Leads manually reviewed and assigned one at a time by a team member

  • No geographic filter โ€” leads occasionally sent to wrong state or wrong contractor

  • Round-robin fairness maintained only by memory โ€” inconsistent and prone to error

  • Some HVAC clients skipped during high-volume periods โ€” missing their turn in rotation

  • Process dependent on one person's availability โ€” any absence created a backlog

  • Client data scattered โ€” no central source of truth for state groupings or contact info

After

  • Leads manually reviewed and assigned one at a time by a team member

  • Every lead automatically captured and routed in real time without human review

  • Every lead automatically captured and routed in real time without human review

  • State-based matching ensures every lead reaches the correct regional HVAC provider

  • Automated round-robin rotation guarantees equal lead distribution across all eligible clients

  • Every active client tracked in the rotation โ€” no client is ever overlooked

  • Fully automated system runs 24/7 with no human dependency or single point of failure

  • Centralized client table serves as the live source of truth for routing, monitoring, and updates

  • No geographic filter โ€” leads occasionally sent to wrong state or wrong contractor

  • State-based matching ensures every lead reaches the correct regional HVAC provider

  • Round-robin fairness maintained only by memory โ€” inconsistent and prone to error

  • Automated round-robin rotation guarantees equal lead distribution across all eligible clients

  • Some HVAC clients skipped during high-volume periods โ€” missing their turn in rotation

  • Every active client tracked in the rotation โ€” no client is ever overlooked

  • Process dependent on one person's availability โ€” any absence created a backlog

  • Fully automated system runs 24/7 with no human dependency or single point of failure

  • Client data scattered โ€” no central source of truth for state groupings or contact info

  • Centralized client table serves as the live source of truth for routing, monitoring, and updates

The Takeaway

LZM moved from a person-dependent, error-prone manual process to a fully automated distribution engine. Every lead now reaches the right HVAC client in the right state โ€” fairly, instantly, and without anyone on the team lifting a finger. The system scales with lead volume. Adding a new state or a new client is a data entry, not a process redesign.

Project Snapshot

Company

Leadzmanager

Industry

HVAC SaaS

Automation Steps

Multi-State

Manual Steps After

Round-Robin

Uptime

Zero

Alerting

24/7

Tools Used

Pabbly Connect

Round-Robin Logic

GoHighLevel

Geographic Matching

Centralized Data Table

Still Manually Routing Leads?

If your lead distribution depends on someone checking a list, every volume spike is a risk and every absent team member is a backlog.

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