How Can You Measure ROI in Test Automation Vs Productivity Gains?

23 Sept 2024

15 Min Read

Table Of Contents

What is ROI?

What are some common Test Automation ROI Metrics?

Test What do you mean by Productivity Gain?

How do we Measure ROI through Productivity Gains?

How do we conduct a cost benefit analysis?

ToggleTraditional ROI Metrics vs Productivity gain as ROITitle

Toggle Comparing In-House Test Automation Tools and Low-Code Test Automation Tools

Hyper Automation

Toggle Key Components of Hyper Automation

Toggle Future Trends in HyperAutomation

Introduction

Automation testing has significantly evolved over the past decade to adapt to the pace of technological advancements and shifting business needs. In today’s dynamic environment, automation testing is vital for improving efficiency, scalability, and adaptability of products.

By automating test processes, companies can achieve faster test execution, less manual errors, and consistent quality which can be maintained across diverse platforms. This accelerates the development cycle and also helps organizations to respond swiftly to market changes and technological innovations. Thus, ensuring their products remain competitive and resilient in a fast-evolving landscape.

What is ROI?

Return on investment (ROI) in automation tools helps organizations to determine and justify whether investment on tools is adding any value to the organization. Further, in future, will it help in cost and time saving.

What are some common test automation ROI metrics?

To accurately calculate the ROI of test automation, businesses should not rely solely on comparing the time it takes to manually test a case versus automating it.

Instead, it’s important to consider additional factors such as the time required for implementation, ongoing maintenance, and analyzing test failures. These elements significantly impact the overall return on investment.

Moreover, while tangible metrics are important, some benefits are harder to quantify, such as reduced time to market, enhanced platform performance, and increased confidence in the software. These advantages may not be immediately visible at the beginning of your automation project but become more apparent as the project advances.

Some important aspects of test automation ROI metrics to look at are:

Image credit :Chris Schwartz

What do you mean by productivity gain?

Productivity gain measures how the investment in automation tools lead to improved processes and faster delivery of the product. How positively it has impacted the overall productivity of the team.

How do we Measure ROI through Productivity Gains?

Measuring ROI (Return on Investment) through productivity gains involves calculating how investment on Automation tools and learning has translated into increased productivity and financial gains.

The Return on Investment can be calculated based on below parameters:

1. Increased Productivity –

Time saving – It can be calculated by Subtracting the hours deployed to achieve a task pre and post automating it.

Error reduction – Less manual work and a planned automation cycle to perform a task result in lesser errors and save a lot of debugging time.

Employee satisfaction – Automating a process involves a lot of learning, followed by implementation, and bringing out the desired result. This entire process is a roller coaster ride. But, it boosts confidence and brings job satisfaction to the employees.

Increased efficiency – Automating the process reduces miscellaneous error and re-work, increasing overall efficiency.

2. Financial Gains –

Cost saving – Automation saves a lot of time. If we calculate on the basis of hourly wages of employees, it would result in a lot of cost saving.

Higher monetary gains – Automation helps in faster delivery resulting in higher revenues.

Less cost of bug fixes – With Automation, bugs are mostly detected at early stages. Early detection of bugs reduces the cost of fixing.

3. Cost of Investment –

One time cost – It is the cost incurred in purchasing tools, their installation and training.

Recurring cost – It is the amount to be spent on tools subscription and maintenance.

4. Calculation of ROI –

The formula for ROI calculation is:

Where:

Net Benefits = (Total Financial gains from increased Productivity) – (Cost of the Automation Tool)
Cost of Investment includes both initial and ongoing costs.

How do we conduct a cost benefit analysis?

We are taking a simple example below for calculating cost benefit which concludes whether incorporation of automation was a hit for the company or a miss.

1. Let’s assume, time saved by automation is = 100 hours/month
2. Hourly wage of employee = Rs. 2,000
3. Cost of tool including maintenance yearly = Rs. 600,000
4. Cost of Employee saving = 100 * 2,000 = 200,000

Tool cost per month (600,000/12) = 50,000

Traditional ROI Metrics vs Productivity gain as ROI

Traditional ROI metrics are essential for understanding the financial return of investments. Whereas, productivity gain as ROI offers a broader perspective, emphasizing mainly on efficiency and effectiveness improvements that may not quickly translate into direct financial gains but contribute to long-term success. The key difference between both are,

Traditional ROI: follows quantitative approach. Primarily focuses on Financial returns. Traditional ROI in software testing evaluates the profit on the investment made in testing activities. This is typically measured by the budgets saved by identifying and fixing defects early, as compared to the potential cost of those defects if they were found in later stages of development or in production.

Productivity ROI: follows qualitative approach, focusing on improvements in processes or efficiency that will in long course yield better financial returns. It focuses on

Speed of Delivery: Time saved in the development process by early bugs detection.
Improved Quality: Decrease in defects per release, leading to less rework and better quality code.
Team Productivity: Exponential increase in productivity of development and testing teams due to automation, better tools, and proper pipelines.

Aspects	Traditional ROI metrics	Productivity gain as ROI
Formula		There is no standardized formula. based on time savings, quality improvements, etc.
Focus	Financial outcomes. The cost saved from early defect detection and prevention.	Efficiency, effectiveness, and long-term process improvements in Automation testing implementation and approaches.
Application	Justifying testing budgets and demonstrating financial risk mitigation.	Enhancing development process efficiency, reducing rework, and improving overall product quality.
Short vs long term Impact	Mostly focused on short-term financial gains.	Focuses on long-term process improvements and strategic benefits
Direct vs In-direct benefits	Direct financial benefits, such as reduced costs.	Indirect benefits, such as improved team productivity and quick time-to-market.
Relevance in Agile/DevOps	It is less emphasized as these environments prioritize speed and efficiency.	Highly relevant due to the focus on continuous integration and delivery.
Example	Saving $150,000 by preventing defects, with testing costs of $50,000 results in 200% ROI.	Reducing testing time by 50% and increasing feature release rate by 20%.

Comparing In-House Test Automation Tools and Low-Code Test Automation Tools

In-house tools vs low code tools have always been a topic of debate in organizations.

What are in-house tools?

In-house tools are the software, developed and maintained within the organization.

Building In-house tool requires more money and time. But as they are developed by the company itself, software customization becomes very easy.

Pros:

Customization: They can be developed as per demand and unique need of the software to be tested. Companies can also prioritize, develop and implement specific features to ensure an uninterrupted and seamlessly functioning pipeline.

Better control: As you have complete control over the tool, technical aspects such as system updates, options for users, and features can be develop as per your desired requirements

Sensitive data is also secured, as third-party services simply cannot access your data, code and config files.

Cons:

1. Cost: Building a customizable, reliable, sacable and featured equipped tool requires a lot of research, development and expertise. This would incur a lot of cost.

Time: Rome was not built in a day. Building an in-house lab with all desired functionality can take months, even years, especially if you don’t have a big team to get the job done. The size and complexity of the tool and the technical abilities of the team, should also be taken into account. Any software/feature to be tested ,dependent on this tool will be delayed and can negatively impact time-to-market.

Expertise: Building a tool requires a lot of developers and testers with considerate experience in relevant fields. Without the right people involved, the tool will not be built, or will not be able to deal with the many errors and anomalies that will inevitably show up during day-to-day functions.

What are low-code test automation tools?

Low cost tools require bare minimum coding experience for you to automate the testing process, test and ship the product to the market. These tools are user-friendly, provide faster solutions, are easy to learn, and can be made cost effective depending on purchasing features as per you need.

Pros:

Low Cost: As you do not have to spend on research, maintenance and operational costs, the relative cost of tools purchase is reasonably low.

Faster time-to-market: The time involved in development is nil. Hence, the product can be tested faster and delivered at a quick pace to the market.

Easier to use: Low code tools offer drag and drop functionality. This makes it easy for a wider audience to understand, test and deliver the product in a timely fashion.

Cons:

Limited customization: As the product is a third party tool which was not built by your own company, you will not be able to modify each aspect of it as per your needs. You might also have to test a few features manually that are not supported by the product.

Security risk: The ease of use of low-code tools can lead to development of applications without any proper security measures. This can introduce security vulnerabilities.

While selecting a tool, the question to be asked to derive a conclusion should be:

How much budget do we have?
How much time do we have?
Does our team have enough expertise to carry out entire development successfully?
What constraints do we have that require us to develop in-house tools?
If constraints are taken care of, will buying a low-code tool solve our problems?

Criteria	In-House Tools	Low-Code Tools
Customization	High – Tailored to specific needs	Moderate – Limited by platform capabilities
Development Speed	Slow – Longer development cycles	Fast – Rapid development with drag-and-drop interfaces
Expense	High – Significant initial and maintenance costs	Low – Lower development costs
Technical Expertise	High – Requires skilled developers	Low – Minimal coding expertise needed
Control	Full – Complete control over development and security	Limited – Dependent on third-party platforms
Integration	Seamless – Easily integrates with existing systems	Variable – May have integration limitations
Scalability	High – Can handle complex and large-scale projects	Moderate – Potential limitations for complex projects
Maintenance	High – Ongoing technical support required	Low – Vendor handles most maintenance
Accessibility	Low – Limited to technical staff	High – Accessible to non-technical users

Hyper Automation and Future Trends

Hyper automation refers to the use of advanced technologies such as Artificial Intelligence, Machine Learning and Robot Process automation and other emerging technologies to develop more comprehensive and dynamic automation solutions.

Key Components of Hyper Automation

Robotic Process Automation (RPA):

Can automate repetitive tasks and workflows.
Provides faster and more efficient operations.

Artificial Intelligence (AI) & Machine Learning (ML):

Helps in predictive analytics, decision-making capabilities, and the ability to learn from data.

Natural Language Processing (NLP):

Automates tasks that require intervention of human language.
Tasks related to customer service, document processing, and data extraction can be easily automated using NLP.

Process Mining and Discovery:

It is used to analyze the existing processes in organization and helps in identifying automation opportunities to optimize and enhance workflows.

Integration Platforms:

Helps in connecting different systems and applications.
Provides seamless data flow and coordination across automated processes.

Future Trends in HyperAutomation

End-to-End Process Automation:

Future hyperautomation will focus on automating entire business processes from scratch, rather than just focusing on a single task. This will involve integrating different technologies and developing more cohesive and smart workflows.

AI-Driven Decision Making:

As AI models become more precise. hyperautomation will depends on AI to provide real-time decisions, respond and adapt quickly to changing conditions, and optimize processes.

Increased Adoption Across Industries:

Healthcare, finance, manufacturing, and logistics Industries will in future be seen to use hyperautomation. It can help streamline operations, better customer experiences and reduce costs.

Low-Code/No-Code Platforms

creating and managing automation solutions with no deep technical expertise will help its adoption faster to a wider range of industries and audiences.

Conclusion

To remain competitive in a fast-paced market, organizations must acquire the latest trends and technologies, moving beyond traditional and conservative approaches. The ability to swiftly develop, test, and deploy products has become a critical factor in maintaining relevance and staying ahead of the competition.

When evaluating the return on investment (ROI) in test automation versus productivity gains, it’s essential to realize that the true value lies not just in immediate cost savings, but in the long-term benefits of delivered product quality. Moreover, automated testing increases productivity by reducing manual testing efforts. This allows teams to focus on more strategic tasks like innovation and continuous improvement. The improvements in software quality lead to fewer defects, reduced rework, and higher customer satisfaction, all of which contribute to a stronger market position.