Skintim Case Study - VentureHub

The Situation

Skintim is a fast-beauty salon network built for speed and high volume. With appointment-free services and many locations, the phone becomes a profit leak if quality slips—even when calls are technically "answered."

Across 17 locations in the network (15 in Hungary + additional locations in the region), Skintim handled ~700–1,000 inbound calls per day. (Skintim.hu)

The problems weren't obvious in dashboards, but they showed up in revenue:

Call quality drift at scale (tone, rushing, incomplete explanations, wrong info)
Lost conversions without a "missed call" (the call happened, but the customer left unconvinced or confused)
No systematic way to identify "bad calls" across dozens of receptionists/shifts
Managers only heard about issues after reviews/complaints, when it was too late
No fast recovery loop (no workflow to call the customer back and save the booking)

In short: Skintim didn't have a "phone availability" problem—it had a phone consistency + quality assurance problem.

The Solution

Venture Hub deployed an AI quality agent that passively monitors inbound calls across the entire network and escalates only the calls that matter.

What the system does:

Listens to every inbound call (from all locations)
Auto-scores each call on:
- correctness of information (pricing, services, location policy)
- tone & professionalism (rushed / dismissive / impatient)
- completeness (did we clarify the need? did we offer next step?)
- conversion intent signals ("I'm coming today", "How much?", "Do you do…?")
Flags "high-risk calls" (where a customer likely churned or was misinformed)
Sends the full call + key highlights to the relevant salon manager
- what went wrong (bullet summary)
- recommended save action (what to say + offer)
- customer context (service requested, urgency, objections)

Recovery workflow:

Manager gets the alert immediately
Manager calls back the same customer and fixes the misunderstanding
Booking is recovered (or customer is routed to the correct service/location)

(This is not a chatbot receptionist. It's a "call QA + recovery engine.")

"We finally see the calls we should care about. Not all calls—just the ones that lose money."

— Skintim Area Manager (anonymized)

The Results

1) ~27,000 calls analyzed per month

Based on ~900 calls/day average across the network (range 700–1,000). This created a complete, searchable operational record of what customers actually ask for—and where conversion breaks.

2) ~150 customers recovered per month (~5/day)

The AI flagged the minority of calls that were most "saveable" (high intent + mishandled). Managers recovered ~5 customers/day across the chain through fast callbacks.

3) Faster fixes, fewer repeat issues

Because every flagged call includes what happened + how to fix it, receptionists improve quickly:

repeated misinformation (price/service/policy) drops
tone issues are coached with real examples
managers spot training gaps per location/shift

4) Estimated €30k–€45k monthly revenue protected (conservative)

A realistic salon conversion recovery is valuable because recovered customers often become repeat clients.

A conservative way to think about it:

150 recovered customers/month
€70–€100 average first visit value (waxing / laser / add-ons varies by market)
€200–€300 "90-day value" for customers who return (even 2–3 visits)

That yields a protected revenue band of roughly €30k–€45k/month, depending on actual ticket size and repeat rate.

"Before, we only learned about problems from complaints. Now we catch them the same day—and we win the client back."

— Skintim Operations (anonymized)

Key Takeaway

Skintim didn't need "more people to answer calls." They needed a system that turns phone mistakes into recoverable opportunities.

By deploying Venture Hub's call-quality AI agent across the network, Skintim created a tight loop:

Every call → scored → problems surfaced → manager saves the customer → team improves.

That's how you stop revenue leaks in a high-volume salon chain—without slowing operations.