July 13, 2023
Understanding the impact and value of your digital transformation and technology initiatives has never been more important. Increasingly, technology leaders are seeing Return on Investment (ROI) as a better tool to do so. In our view, leveraging ROI provides a more holistic and accurate view of the impact and value of technology investments. And with ROI as a decisioning tool, leaders and their organizations can make better strategic choices, optimize resource allocation, and enhance operational efficiency.
However, while many technology leaders are excited about ROI, many struggle with the challenge of identifying and collecting the right data and metrics to build a meaningful and actionable ROI framework. In this article we want to share our view of what the high-level data and metrics should be, where you find them in your organization, and why they are important for assessing ROI. This is not intended as a definitive list. There are more data and metrics that can be included. However, these datasets will provide ROI insight and a framework to build on.
Technology ROI is a metric or a set of metrics that seeks to quantify the value generated by technology investments relative to the costs incurred. To be truly useful, ROI needs to go beyond simply measuring the data of monetary gains and, instead, encompass data around productivity, quality, process efficiency, innovation and design, and customer value. ROI should, in effect, measure and monitor all those activities that help to create value for your customers or stakeholders.
Your organization already holds all the key data you will need to make better, more cost effective decisions:
Engineering data includes information about software development processes, code quality, and performance metrics. This data typically resides in your software repos, version control systems, and engineering management tools, The value of this data and metrics is that it provides insights into development delivery efficiency, time-to-market, and quality. It answers the questions of how well, how fast, and at what quality your teams deliver code.
Release data encompasses information related to software releases, deployment frequency, release cycles, and version updates. This data is found in release management systems and deployment tracking tools. Analyzing release data gives visibility on the speed and frequency of product/feature releases. It allows you to understand how long change takes and when value is realized by your customers.
Project management data includes information on project timelines, milestones, resource allocation, and task completion rates. This data is found in project management and collaboration tools like Jira, Trello, Azure DevOps, etc. This data gives time-based insight on investment. It not only reveals the time associated with each project or outcome, but also time associated with non-strategic projects, BAU, bottlenecks, and poor code.
Error data shows the level of errors and issues your customers experience. It can be collected through bug tracking systems, error monitoring tools, and customer feedback channels. Analyzing this data helps identify how to improve customer experience, while also reducing the cost of supporting systems, and ultimately contributing to a positive ROI.
Product lifecycle data encompasses the data around the various stages of a feature or product, from ideation to retirement. It can be collected through project and product management tools, CRM systems, and sales databases. Analyzing product lifecycle data helps assess how well your organization discovers customer value. It gives insight on vision, strategy, customer discovery & research, business case, and design for technology initiatives – and ultimately how well your efforts drive revenue.
Success metrics encompass key performance indicators related to business outcomes, customer satisfaction, and user experience. This can be collected through customer surveys, feedback forms, and analytics tools and provides data such as Product Engagement Scores, Net Promoter Scores, and Customer Satisfaction Scores. Analyzing this data helps gauge the effectiveness of technology investments in achieving desired outcomes, customer satisfaction, customer loyalty, and brand reputation.
Support request data includes information on customer inquiries, tickets, and support interactions. It can be collected through customer support platforms and ticketing systems. Analyzing support request data helps assess the impact of technology investments on customer support efficiency, issue resolution time, and customer satisfaction. It also enables organizations to identify opportunities for process and product improvement, ultimately impacting ROI.
Cost data includes information on technology investments, operational expenses, maintenance costs, and infrastructure expenditures. It can be gathered from financial records, accounting systems, and procurement data. Analyzing cost data is essential for calculating the financial aspects of ROI. It helps organizations assess the efficiency of technology investments in reducing costs, optimizing resource allocation, and maximizing financial returns.
Revenue, CRM, and sales data encompass information on sales performance, customer acquisition, and revenue generation. This data resides in CRM systems, sales databases, and financial records. Analyzing revenue and sales data helps evaluate the impact of technology investments on revenue growth, customer acquisition costs, and sales pipeline efficiency. It enables organizations to optimize sales strategies, improve customer targeting, and maximize revenue potential, contributing to ROI.
This is far from a complete detailing of what metrics are required for ROI of technology initiatives. Rather, this is meant as an introduction to the datasets that will inform any ROI framework. These datasets are the ones we see as common to high-performing teams we work with. There may be more areas, but for technology ROI these are a good starting point. And, further, this is data that nearly all teams and orgs already have available.
But how do you access and meanigfully analyze all this data? Talk to your data team or talk to us about how we can help you use data you already have to drive ROI.
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