How do you predict the ROI of innovation?

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Feb 09, 2024

In a world where challenging the status quo is no longer a choice, innovation has become almost everyone's job description.

We should know better than most - our clients come to us from the public and private sectors with some of the most ambitious projects in the world:

  • Addressing a mental health epidemic
  • Creating solutions for a housing crisis
  • Improving K12 curriculum choices across the US
  • Designing products that can push hundreds of millions of users to exercise

...and all of that is just in the last few months!

If you've been following us for a while, you'll know that we use a healthy mix of scientific thinking and pragmatism to turn really hard problems like these into opportunities for a better world (if not, read more here). This puts us in a unique position, where we regularly talk to some of the most ambitious (and more importantly, kindest) leaders in the world.

Something almost all of them face is predicting and demonstrating the ROI of innovation-focused projects. This is perhaps the toughest problem of all, as organizational and social change only happens when you have large-scale buy-in. All too often, we’ve seen scenarios where an ambitious leader at an organization pitches some (often no-brainer) breakthrough idea to the CFO, presenting a tight business case with five-year projections for costs and returns. And all too often, we see that the CFO is unimpressed. They are thinking about the case as an optimization problem, not a zero-to-one opportunity. Their job is to exploit, whereas the pitch is focused on exploring.

This is why a core part of our practice is focused on exactly that - predicting and demonstrating ROI, as a tool for behavior change.

Case in point - a financial services company came to us to launch an entire suite of innovation initiatives related to trust, customer satisfaction, HR, and even risk audits. The definition of ROI in this case was different for each of these initiatives - with different stakeholders caring about different things, and different parts of the customer journey (or employee journey) being targeted. After an intense research & ideation phase, a large part of the project, then, became centered around proving that the proposed solutions had legs (or, as science goes, finding out they don’t) - i.e. predicting the ROI they could deliver in each domain.

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So, how do you predict innovation ROI?

The answer, of course, varies depending on the exact type of ROI you are looking to predict (e.g. is it cash-on-cash returns? Is it an increase in mental well-being? Is it the number of workplace accidents averted? Carbon captured?). However, there are a few basic tools that we use:

1. Balancing Time Horizons

Innovation comes in different flavors - some might be focused on gaining efficiency in a very specific context, others might allow a company to build a new impact stream within its existing model, and others yet might be completely transformative to the business model. Understanding the short-, medium-, and long-term impacts of innovation is critical in order to predict true ROI. While the short-term impact might be easiest to figure out, the long-term downstream effects are typically the most dramatic. 

2. Cost-Of-Doing-Nothing

It might be a bit strange to predict ROI by predicting what happens if you DON’T innovate, but this is an often overlooked area. It can be easy to focus on costs associated with investing in R&D, the risks of a failed deployment, low uptake, etc. But, given that innovation has become a status quo, the opposite of innovation isn’t stagnation, it’s quite often lag. Understanding the kind of lag that might result is important - for example, a technology company that postpones the exploration of new technology might lose a predictable share of the market, a climate tech funder that fails to invest in a new carbon capture technology could stifle the ecosystem and a government failing to integrate new research in educational tools might start to see a lag in the educational outcomes relative to other governments that do adopt the new findings.

3. Stakeholder Diversification

Instead of relying on stakeholders from a single place (e.g. senior leadership), consult with a diverse range of stakeholders (diverse in all its meanings) to provide different perspectives and reduce the chance of echo chambers. This sounds relatively simple and "goes without saying" yet it's one of the biggest barriers we've seen in our work.

4. Counterfactual Simulation

Engage in counterfactual thinking by imagining alternative scenarios where key assumptions of the project fail. For example, what if the assumed adoption rate is overestimated, or a regulatory change impacts the project? By methodically considering these "what if" scenarios, you can better understand the resilience of the project's ROI to various uncertainties. This technique is grounded in the principle that understanding vulnerability to hypothetical scenarios strengthens strategic planning.

5. Constraint Analysis

Identify and analyze the most critical constraints that could limit the project's success, such as market barriers, technological limitations, and regulatory challenges. By focusing on these constraints, you can apply the first principle that any system's potential is often defined by its bottlenecks. Understanding and addressing these key constraints can provide a clearer picture of the project's viability and potential ROI, as overcoming the primary obstacles often leads to significant value creation. In other words, you estimated the return you get from unlocking these key barriers.

6. Alternative Data

Use unconventional data sources to forecast outcomes. For innovation projects, especially in behavioral science, standard metrics, and past performance data might not exist or be relevant. Instead, leverage alternative data sources such as social media sentiment, web search trends, and even changes in related regulatory environments to predict market receptivity and potential adoption rates. Machine learning models can process these diverse data streams to forecast outcomes with surprising accuracy

7. Agent-Based Modeling

Techniques such as agent-based modeling have been around in academic settings for decades and have contributed to a number of fields (e.g. modeling fire spreading in a forest, money markets, housing crises, and traffic in a city). They have also been used to model the spread of innovation. Innovation uptake rarely occurs in a vacuum (i.e. asking a series of individuals to uptake it), and so finding a way to understand how agents affect each other and when a critical mass might be reached is a helpful tool.

There are of course many other tools and strategies you can use, but these seven methods represent our foundational approach to navigating the unpredictable waters of innovation ROI. They reflect a balance of empirical rigor and creative speculation, designed to equip leaders with the insights needed for making informed, forward-looking decisions.

Inter-sector Innovation ROI

The approach to predicting innovation ROI significantly varies across industries, each with its unique challenges and opportunities. In the technology sector, rapid advancements and the high pace of change necessitate agile methodologies and a keen focus on emerging trends. In contrast, the healthcare industry must carefully consider regulatory compliance and patient outcomes, prioritizing innovations that offer tangible benefits within strict legal frameworks. Manufacturing companies, on the other hand, might emphasize process innovations that enhance efficiency and reduce costs, closely monitoring industry 4.0 technologies to stay ahead. Each sector requires a tailored strategy that considers industry-specific dynamics, from consumer expectations to regulatory landscapes, ensuring that innovation efforts are both impactful and sustainable.

Communicating the ROI of Innovation

We started this post by saying that the proper prediction and measurement of innovation ROI can, itself, serve as a tool for behavior change. If that’s true (and our experience with some of the largest organizations in the world consistently shows it is), then the way ROI is communicated is critical. This means aligning very well with the target audience around what ROI means in that particular context, how it’s being measured and what some of the core assumptions are.

Coming back to that financial services client - communication for each proposed initiative had to be adapted to the KPIs each corresponding team cared about, with the relevant tools used to predict ROI (e.g. while counterfactual simulation might be relevant in a technology context, but agent based modeling could be far more relevant in the context of financial risk). This approach, which went well beyond the typical KPIs the teams might have used but still had a ‘bottom-line’ centered around those KPIs, allowed us to launch initiatives that have been consistently generating over 50M dollars in additional revenue per year.

What does this mean for you?

Predicting the ROI of innovation isn't just about crunching numbers or making educated guesses; it's about understanding the broader impact of these initiatives on society, the environment, and the very fabric of organizational culture. It requires a deep appreciation for the complexities of human behavior, market dynamics, and technological potential. It's about seeing beyond the immediate horizon to the transformative possibilities that lie ahead.

As we continue to support our clients in their journey towards meaningful change, we're reminded of the power of innovation not just as a tool for economic growth, but as a force for societal good. Whether it's addressing a mental health epidemic, creating solutions for a housing crisis, or encouraging healthier lifestyles on a global scale, the real ROI of innovation lies in its capacity to create a more equitable, sustainable, and thriving world.

Are you facing a challenge or looking to push the boundaries of what's possible in your organization but are reluctant because the ROI is unclear?

We'd love to hear from you - send us a message and our team will reach out to you or send some relevant resources your way.

About the Authors

Dan Pilat's portrait

Dan Pilat

Dan is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. Dan has a background in organizational decision making, with a BComm in Decision & Information Systems from McGill University. He has worked on enterprise-level behavioral architecture at TD Securities and BMO Capital Markets, where he advised management on the implementation of systems processing billions of dollars per week. Driven by an appetite for the latest in technology, Dan created a course on business intelligence and lectured at McGill University, and has applied behavioral science to topics such as augmented and virtual reality.

Sekoul Krastev's portrait

Dr. Sekoul Krastev

Sekoul is a Co-Founder and Managing Director at The Decision Lab. He is a bestselling author of Intention - a book he wrote with Wiley on the mindful application of behavioral science in organizations. A decision scientist with a PhD in Decision Neuroscience from McGill University, Sekoul's work has been featured in peer-reviewed journals and has been presented at conferences around the world. Sekoul previously advised management on innovation and engagement strategy at The Boston Consulting Group as well as on online media strategy at Google. He has a deep interest in the applications of behavioral science to new technology and has published on these topics in places such as the Huffington Post and Strategy & Business.

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