Simple Matrix for Penalty Point System

Nita Khoirunisa
4 min readJan 4, 2022

As we know Gig Economy become a popular trend lately. Anjali Kumari said a gig economy facilitates an open market of job opportunities and short-term work assignments offered by companies to contractors and freelancers who render their services if or when required.

The “gig economy involves the exchange of labor for money between individuals or companies via digital platforms that actively facilitate matching between providers and customers, on a short-term and payment-by-task basis,” according to the UK government.

In this flexibility and temporary employment, needs a certain regulation in order to build a proper and well supervised working ecosystem.

A penalty system is a system that aimed to regulate the consequences and/or punishment caused by violating the applicable provisions. Furthermore, it encourage Mitra (non-contract-based employees) to provide the best service to the costumer.

In this case, I will share how to create a penalty system based on my experience for a marketplace in property sector.

Step — 1: List of Violations

The first step is make a list of violations. Before determine the list, you should discuss and consider this questions.

Who needs to be regulated?

What needs to be regulated?

What’s the main objective?

How it will affect the business?

The violations are determined by what the company needs and wants to regulate or based on previous experiences that need to be corrected.

Example: Here are some examples of the violations.

After making the list of violations, make it more structured, the list of violations can be grouped by its category or type of violation based on the objectivity.

For example, from the list of violations above, I will categorize it by the business process, which in property agency generally known there are the 3 step to deal the unit namely acquisition (Inquiry), Listing, and Transaction and I add one category for the General violations.

Step — 2: Determine the level or points for each violation.

After grouping the violations then you can determine the level or points for each violation. Determination of levels/points is adjusted by the impact and loss caused. First, you can simply categorize the impact by High Risk, Middle Risk, or Low Risk. Then make a scale to adjust the points in each violation.

For example, I used scale 1 to 5 to determine the point for each violation. The higher the number of points, it means the higher the risks and consequences that company bear.

Point of the violation

Step — 3: Create a penalty scheme

In this step, you must consider carefully what will be the appropriate consequence/punishment if agents break the regulation and how the scheme of it. To decide it, make sure that you sit down with your core team and talk it through.

Let’s start by considering the matrix table!

In order to establish a useful matrix, we must first assess its goals. From there, it is important to find the best outputs that measure the activities related to these goals.

In this case, I use matrix table to determine the punishments/consequences that agent will obtain when they break the regulations. I add 2 variables, how many times and the number of points. In the X-axis I put how many times agents break the regulation and the number of violation points for the Y-axis.

So, here is the matrix…

Table matrix of penalty point system

Which I determine the punishments/consequences; for the lowest point and for the first time violation I decided to warn agents, and for the highest point that it will caused company biggest loss I decide to dismissed agents directly.

In other words, the maximum number of violations agents may commit is as follows:

  • Level I = 5 x Violations
  • Level II = 4 x Violations
  • Level III = 3 x Violations
  • Level IV = 2 x Violations
  • Level V = 1 x Violations

Take 5 seconds out to share it and help me grow! Thank you for reading 😃

--

--

Nita Khoirunisa

Data Science and Data Analyst enthusiast. Experienced in Reporting, Business Development and Ops in prop-tech, e-commerce and p2p lending