Case Study — Fintech Company · Indonesia

Boosting Lead Contact Performance with Cost-Efficient Vicidial Contact Center Suite

A fintech company firm launched their outbound sales operation with no existing dialer infrastructure. We designed, deployed, and managed a full ViciDial environment — growing the team from 19 t0 34 agents and increasing qualified lead conversions by 4.6× while keeping licensing costs at zero.

Industry Financial Services

Project Period Jul 2025 – Feb 2026

Team Growth 19 -> 34 Agents

Platform ViciDial + GHL Integration

Executive Summary

The client — a fintech company serving individuals with consumer debt obligations — had zero outbound calling infrastructure when this engagement began in July 2025. They were handling leads manually, with no dialer, no call tracking, and no visibility into agent activity. The goal was to build a scalable outbound operation from the ground up, fast, and without the per-seat costs of commercial platforms.

Over 8 months, we deployed and managed a full ViciDial environment integrated with GHL (GoHighLevel) as the CRM. The system was configured for predictive outbound dialing against MQL (Marketing Qualified Lead) lists, with dispositions feeding directly back into the sales pipeline. The results speak through the data: contact rates climbed from 5% to a peak of 57%, sales qualified lead conversion grew from 4.6% to 21%, and total agent talk time scaled 65× — all with no licensing fees as the team grew.

5% → 57%

MQL Contact Rate (Jun 2025 → Feb 2026)

4.6×

SQL Lead Conversion Growth (Oct 2025 → Feb 2026)

65×

Agent Talk Time Growth (3.74h → 241.88h/month)

Per-Seat Licensing — across all 34 agents

The Numbers, Month by Month

All data below is sourced directly from ViciDial logs and the GHL (GoHighLevel) CRM dashboard. No figures are projected or estimated — these are the actual metrics recorded during the engagement from July 2025 through February 2026.

MQL Contact Rate — Weekly Progress

The single most important metric: how often we were actually reaching a live person. Starts near zero in the system ramp-up phase, crosses 30% by December as dialing patterns were optimised, and peaks at 57% in February 2026. Source: GHL · Contacts With Corrected Date

Pre-Sales Talk Time per Agent (Weekly)

Shows how individual agent utilisation grew as the system matured. Low early figures reflect agents learning the workflow. By January 2026 agents were consistently hitting 5–7 hours of live talk per week. Source: ViciDial Log

SQL Conversion Rate (Monthly)

Of every MQL contacted, what percentage became Sales-Qualified Leads. Grew steadily from 4.6% in October 2025 to 21% by February 2026 — a 4.6× improvement driven by better lead targeting and improved agent call scripts. Source: GHL

Total Monthly Talk Time vs. Active Agent Count

The clearest picture of operational scale. Talk time grew 65× over 7 months — from 3.74 hours in July 2025 to 241.88 hours in January 2026 — even though agent headcount only grew 2×. This reflects a dramatic improvement in per-agent productivity, not just headcount. Source: ViciDial Log

MQL Contact Rate by Operational Phase

Averaging weekly contact rates by phase reveals a clear growth story: early ramp (4%), growth (12%), scale-up (34%), and mature operation (48%). Each phase reflects deliberate system tuning decisions.

Pre-Sales Team Headcount (Weekly)

Team grew from 2 agents in October 2025 to 8 active pre-sales agents by January 2026 — each additional agent added with zero incremental licensing cost. Source: ViciDial Log

The Situation

When this project began in July 2025, the company had no automated dialing mechanism in placed. Sales reps were working with a basic CRM list, dialling numbers one by one, with no visibility into who had been called, no call recordings, and no way to measure performance. Out of 12 agents, 7 were handling the entire outbound effort.

The debt restructuring market is competitive and time-sensitive — leads go cold fast, and the first company to make live contact typically wins the client. With a manual process, the team was reaching less than 5% of their MQL list in any given week. They needed automation, speed, and structure.

The Starting Point (Jul 2025)

No dialer — agents dialling numbers manually
Zero quality control
Difficult to monitor agent activity or talk time
No lead recycling — attempted leads were often abandoned
MQL contact rate below 5% per week
7 agents handling all outbound activity
No defined MQL → SQL pipeline tracking
Scaling the team meant manually adding to a spreadsheet process

After ViciDial Deployment (Feb 2026)

Predictive dialer reaching up to 57% of the MQL list per week
100% of calls recorded and accessible via agent/supervisor login
Real-time dashboards showing live agent status and talk time
Automated lead recycling — unreached leads re-queued by priority
SQL conversion rate at 21% — up from 4.6%
34 agents operating on the same infrastructure at zero extra cost
Full GHL CRM integration — dispositions synced automatically
New agents onboarded within hours of account creation

How It Unfolded

The deployment wasn't a single big-bang launch — it was a managed build-up. Here's what actually happened, month by month.

Jul 2025 — Month 1

Server Setup & First Calls

ViciDial installed and configured on a dedicated server. SIP trunk connected and tested. First campaign created with the initial MQL list imported from GHL. Two agents onboarded. Talk time for the month: 3.74 hours — low, but the infrastructure was live and working.

Aug – Sep 2025 — Months 2–3

Workflow Refinement & GHL Sync

Built the GHL integration — leads now flowing into ViciDial automatically as they hit MQL status. Disposition codes configured. Lead recycling rules set up for first time. Talk time reached 64 hours/month by September. Contact rates still in the 4–6% weekly range — system was working but list quality needed attention.

Oct – Nov 2025 — Months 4–5

Dialing Optimisation & Team Growth

Adjusted the dialing ratio and answer machine detection settings after analysing call logs. Contact rate broke through 10% for the first time in October, then climbed to 23% by late November. Team grew to 5 pre-sales agents. SQL conversion tracking introduced — initial rate: 4.6%.

Dec 2025 — Month 6

Scale-Up — The Inflection Point

The biggest single month of growth. Agent headcount expanded to 13. Contact rates crossed 33% and peaked at 44% in a single week. Monthly talk time jumped from 67 hours to 154 hours. SQL rate hit 15%. This was the month the system proved it could scale.

Jan – Feb 2026 — Months 7–8

Mature Operation — Consistent 50%+ Contact Rates

System reached steady-state performance. 14 active agents. Weekly MQL contact rates consistently between 50–57%. SQL conversion rate at 21% by February. Total talk time: 241.88 hours in January alone. Ongoing focus shifted from setup to coaching, reporting, and campaign-level optimisation.

Results

Eight months of sustained improvement across every metric that matters for an outbound sales operation. No projections — all figures are drawn directly from ViciDial logs and the GHL dashboard.

By the Numbers

Jul 2025 → Feb 2026 · Source: ViciDial Log + GHL Dashboard

57%

Peak weekly MQL contact rate — up from 5%

21%

SQL conversion rate — up from 4.6% in Oct 2025

241.88h

Monthly talk time in Jan 2026 — up from 3.74h

Per-seat licensing cost at 34 agents

"Before this system, our agents were spending most of their day waiting on calls that never connected. Now they're in live conversations almost immediately. The difference in what the team can do in a day is hard to overstate."

— Operations Manager, Fintech Company Client

What Drove the Results

The Contact Rate Story

Predictive dialing eliminated dead wait time — agents only connect when someone picks up
Multi-attempt recycling logic kept unreached leads in the queue instead of abandoning them
Answering machine detection tuned to minimise wasted connections on voicemail
Contact rate crossed 30% once dialing ratios were calibrated in December — and held above 50% through January–February

The Conversion Rate Story

Call recordings gave supervisors the data to coach agents on specific call moments
Custom disposition codes revealed which lead segments converted at higher rates
GHL sync meant sales advisors could follow up on warm leads within minutes of a positive disposition
SQL rate grew every single month — 4.6% → 12% → 15% → 18% → 21%

What We Learned

Honest observations from 8 months of building and running this operation — including what we'd do differently.

What Made the Biggest Difference

Dialing ratio tuning in December was the inflection point. A single configuration change drove a week-over-week contact rate jump from 23% to 33% — the biggest single-week gain in the entire project.
Call recordings changed agent behaviour. Once agents knew their calls were being reviewed, script quality improved noticeably within two weeks — without formal retraining.
Disposition data is the most underused asset in most outbound operations. Mining which dispositions correlated with eventual SQL conversion helped the team stop wasting time on lead segments with low potential.
Starting with 2 agents was the right call. It allowed the workflow, integration, and recycling logic to be stress-tested before scaling — avoiding failures that would have been much costlier at 34 agents.

What We'd Do Differently

Set up the GHL integration in week one, not week four. The manual period before the sync was built created data inconsistencies that took time to clean up later.
Introduce AMD tuning earlier. We ran with default settings for the first two months. The contact rate improvements in Q4 suggest we left gains on the table in August and September.
Build the supervisor dashboard before the first agent logs in. Operating blind for early weeks meant decisions were reactive rather than proactive.
Document the disposition logic in a shared runbook. As the team grew, agents were using disposition codes inconsistently — a defined guide from the start would have kept the pipeline data cleaner.

Building Something Similar?

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