Another Insight That I Learned in GoVenture Kiosk Business Simulation

Hello everyone! I learned another valuable concept while playing the GoVenture Kiosk Business Simulation.

From Slow Service Loss Rate -> Hiring Decision

While going through the “1-Hour Entrepreneur Video Training” here in Skool, I remembered Sir Mathew demonstrating a situation where his kiosk business lost many customers due to slow service after marketing. He said something about considering how will we entrepreneurs know when to decide hiring an employee, so a question came up to my mind:

"Does the extra revenue I gain from hiring someone exceeds the cost of paying them?"

So, with the help of AI and what I’ve been learning in the simulation, I put together a simple, step-by-step way to think about this.

1. Slow Service Loss Rate

First, we determine how many customers are leaving specifically because of slow service.

Formula:

Slow Service Loss Rate (%)= (Avg. Customers Lost due to Slow Service / (Customers Served + Customers Lost due to Slow Service)) × 100

For myself, I set a 10% threshold:

Below 10% -> normal loss, no immediate action needed
Above 10% -> investigate and consider intervention

I think customer loss is unavoidable in business, but the key is knowing at what point it becomes a real problem.

2. Average Revenue Per Customer (ARPC)

If the loss rate is above the threshold, the next step is to understand how much each customer is worth

Formula:

ARPC= Average Total Revenue / Average Customers Served

This tells me how much revenue I earn per customer on average.

3. Recovery Rate

This represents the percentage of lost customers I can realistically recover after hiring since not every lost customer will come back even if services improves.

I decided to focus on two methods only:

Method 1: Simulation-Based Recovery (After Hiring)

Used after hiring, based on actual results.

Formula:

Recovery Rate= (New Customers Served − Old Customers Served) / Customers Lost

This is the most accurate because it uses real outcomes.

It answers:

“How many lost customers did I actually recover after improving service?”

Method 2: Conservative Estimate (Before Hiring)

Used before hiring, when results are uncertain.

Recovery Rate = 50%

Now why use this?

Prevents overhiring
Builds a safety buffer
If hiring works at 50% recovery, it will likely work at higher recovery

It asks:

“If I only recover half of the lost customers, is hiring still profitable?”

4. Recoverable Revenue

This step combines everything.

Formula:

Recoverable Revenue= (Customers Lost × ARPC) × Recovery Rate

This tells me how much revenue I can realistically regain after hiring.

5. Compare Recoverable Revenue vs Employee Cost

Decision Rule:

If:

Recoverable Revenue > Employee Cost

Then hiring is profitable

If not, it’s better to wait or fix other issues first.

Final Thoughts

I’m not claiming this is the perfect or only formula since there are many similar frameworks online. But I like this approach because it:

Uses real data
Reduces guesswork
Prevents emotional decisions
Lowers the risk of overhiring

So would this work exactly the same in real life?Maybe, maybe not. Real businesses have additional variables like training costs, overhead, benefits, and even employee meals. But as a decision-making framework, I find it very useful especially in a simulation where learning how to think matters more than being “perfect.”

Would love to hear your thoughts or how others approach this in the game 👋

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