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Laurel Lightning Service had lead flow, but conversion was constrained by slow response, manual handling, and no clear way to prioritize hot, warm, and cold prospects. Attribution exposed the opportunity; a GHL Conversation AI Lead Qualification Agent converted that insight into faster response, structured qualification, and consultation booking.
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Laurel Lightning Service is an owner-operated lighting company serving homeowners and property-focused clients with seasonal Christmas lighting, permanent roofline lighting, landscape and architectural lighting, and commercial lighting services. Its website is designed around consultation-led sales: prospects request a quote or free consultation, the business reviews the property, and the team determines scope and pricing.
Laurel Lightning Service website showing services, consultation calls to action, and lead-capture form
Leads were primarily arriving through the company website after Meta Ads activity. The Attribution System made the lead-generation environment visible, but visibility revealed a second problem: the business was not converting every opportunity as efficiently as it could.
The company was owner-operated on the CRM and conversation side. New enquiries, questions, appointment requests, rescheduling, and cancellations were handled manually. Response speed was inconsistent, and there was no reliable priority layer to distinguish a hot lead from a warm lead or a cold lead. As a result, the next optimization opportunity was not simply more traffic. It was to respond faster, qualify more consistently, and give every lead a clear next action.
This project demonstrates the broader operating sequence behind the offer:
flowchart LR
A[Lead source visibility] --> B[Attribution analysis]
B --> C[Conversion bottleneck identified]
C --> D[AI lead qualification]
D --> E[Priority and intent captured]
E --> F[Consultation booked]
F --> G[Scope and pricing conversation]
Attribution showed where the leads were coming from. The conversion analysis showed where the system needed improvement. The Lead Qualification Agent was then built to address that bottleneck inside the existing GHL environment.
| Attribution and diagnosis | Action taken | Visible evidence |
|---|---|---|
| Lead sources, mediums, and activity could be inspected in GHL | Built a reporting layer for source visibility and lead-quality review | 19 contacts shown in the visible source/medium dashboard snapshot |
| Lead handling had ownership and data-quality gaps | Added structured qualification and clearer next-action handling | 42 unassigned contacts, 13 contacts without email, and 2 without phone shown in the visible quality snapshot |
| New enquiries required owner-led response and appointment coordination | Activated a bounded Conversation AI Agent across supported channels | 2 appointments booked, 8 actions triggered, 41 unique contacts, and 30 messages shown in the visible AI dashboard snapshot |
The dashboard figures are current snapshots from their displayed filters and periods. They are used here as visible evidence, not as a consolidated before-and-after study.
The GHL attribution environment provided a reporting layer for understanding contact sources, mediums, lead quality, and activity. The dashboard included sections for Overview and Attribution and Lead Quality and Activity, allowing the operator to move from source-level visibility into contact-level quality issues.
The supplied attribution snapshot shows 19 contacts created across displayed source categories, including Other, CRM Workflow, CRM UI, and Organic Search. A separate source/medium view shows 19 contacts distributed across displayed medium categories including Other, Manual, Conversation, and Chat Widget. These are dashboard snapshots from the visible configuration; they are not presented as a complete Meta Ads performance report.
GHL attribution dashboard showing contacts created by source and medium