Introduction

The popularity of analytics has led to a slew of new employment openings, increased interest in education and training, and widespread availability of data analytics related services and products from the world’s IT service providers. Fear of Missing Out is a concept that many businesses insist they employ. However, this doesn’t stop many people who haven’t seen it from offering their professional judgment on it. 

 The most forward-thinking enterprises are either already using Analytics to improve their operations or are making (or aspire to make) strategic progress in this direction. These businesses see returns on their analytics investments in the double-digit percentage range. The question of how to calculate return on investment (ROI) for an Analytics deployment and when that investment will break even is on the minds of many businesses. Leadership disapproval and advancement are stymied by the ROI being so hard to see. 

Return on investment (ROI) calculations are notoriously tricky, and it appears that quantifying ROI for data analytics and AI projects is particularly difficult. Since companies engage in data teams, infrastructures, and technologies for a wide variety of reasons and execute a wide range of projects at varying degrees of maturity, there is no universal formula to determine how much each should spend. Where do you even begin?   

Measuring the ROI of data teams and projects, and especially data tools and technologies, is notoriously difficult in practice. Determining the return on investment (ROI) for data analytics requires first defining what “success” means for a certain firm and then analyzing the many ways in which data or a data department has contributed to that success. Since there are various means by which data may contribute to success, it is necessary to consider all of them. 

Here are the top five ways to measure ROI for data analytics: 

• Adoption 

No amount of money spent on Data & Analytics (or Digital) can ever pay off if nobody buys your products or uses your services. Using this strategy, you may reliably gauge the satisfaction of your data products and services among your staff. Analytics on how software or a SaaS is used are now standard. Putting a monetary value on each user/visit is essential. Using the cost of the program and the firm to get a value per user is a reasonable approach, however it can be difficult to calculate. 

• Adjustments Based on Analyses of Past Performance 

An enhancement that is “data-driven” is one that is prompted by some sort of data, such as a report, study, or new understanding. A little extra effort is needed for data-driven projects because you have to stick to the procedure in order to evaluate the impact of the adjustments you make. Create a two-way line of communication to track both incoming and outgoing offers. After implementing the necessary changes, new measurements must be taken and the results recorded. 

• Client Satisfaction 

Tracking the results of our market research and customer happiness efforts can be approximately determined by observing the effects of our customer analysis and recommendations, much as we do with Data-Driven projects. Together, customer value and a formula like Bain & Company’s Net Promoter Score (NPS) allow us to put a monetary value on improvements in customer happiness. 

• Increases in the Quality of the Product 

These days, products are the main focus for most businesses. Looking at the specifics of a product, whether it be digital or tangible. Can help you determine what needs improving or expanding. Calculating the potential financial benefit of adding a new feature or version of a product requires retrieving data on the previous versions’ financial success. 

• Capability with Data 

Your company’s data literacy can be measured using a few different frameworks. You can hire an agency to produce it for you, or you can make one yourself. That is tailored to your business’s needs. A data maturity evaluation provides a more comprehensive method for calculating the return on investment in Data & Analytics. Throughout the company, conduct a data maturity evaluation. Then put a monetary value on the various stages of development. 

Conclusion  

Finding the Return on Investment (ROI) for Data & Analytics can be difficult. None of the approaches presented here are foolproof.  But some of them may be more applicable to your situation than others. However, they do provide a ballpark figure for how much your Data & Analytics initiatives are likely to affect the bottom line.  

Over time, you’ll learn to hone your data collection techniques, allowing you to deliver ever-more precise estimates. The evaluation of return on investment (ROI) following implementation aids in guiding corporate decisions and making the most of Analytics. A key point to keep in mind is that. The value derived from Analytics is contingent on human beings actively seeking it out. 

To become a “insights-driven organization,” businesses can take use of SG Analytics’ expertise as a top Data Analytics Consulting firm. Data that you may not have even realized you possessed can be mined with the help of our data analytics services and solutions to yield valuable insights.