The rise of the Key Performance Indicator (KPIs) has largely been heralded as one of the key factors driving business growth. Pushed by an increasing reliance on business intelligence tools and integrated software, KPIs use a variety of inputs and connect them in ways formerly considered impossible.
For the past few decades, this was great. New information to fuel decisions. But what’s next? How will these evolve?
The answer is smarter KPIs—KPIs based on KPIs.
From Insights for Humans to Inputs for Machines
As discussed by MIT, leading organizations are taking new approaches to generating and tracking KPIs, noting that digitally sophisticated organizations have flipped traditional KPI purpose and processes inside out. What does this mean? Put simply, KPIs are no longer the endpoint—they’re becoming a midpoint.
How? It all comes down to artificial intelligence and machine learning. According to the research, KPIs are no longer the final analytic output for decision making, as these indicators are now just a factor used in the training, tuning, and optimization of AI models. Authors David Kiron and Michael Schrage note:
“In data-rich, digitally instrumented, and algorithmically informed markets, AI plays a critical role in determining what KPIs are measured, how they are measured, and how best to optimize them. Optimizing carefully selected KPIs becomes AI’s strategic purpose. Understanding the business value of optimization is key to aligning and integrating strategies for and with AI and machine learning. KPIs create accountability for optimizing strategic aspirations. Strategic KPIs are what smart machines learn to optimize.”
How KPIs Are Becoming Smarter
One of the core selling points for machine learning and AI is its ability to process more data than humans—and do so in seconds. Now, algorithms can run hundreds of simulations, find out how factors connect, and refine the inputs until a decision is made.
Using an example of reducing churn and increasing customer retention, authors note that these factors used to be the end of the line—the goal was to increase customer retention rates so you could minimize the cost of acquisition. But it was still on leaders to answer the “how” of that equation.
But as the tools used to analyze KPIs have evolved, businesses now have the analysis capabilities to become increasingly proactive:
“Customer churn offers the canonical KPI example of how virtual interdependencies between data and decision-making coevolve. […] In big data and AI environments, however, understanding ex post facto churn no longer strategically suffices; organizations seek to predict churn to proactively prevent it. Making churn a more anticipatory and prescriptive KPI requires a virtuous cycle approach. In short, “learning from churning” makes the KPI smart.
In turn, companies can now move from simply understanding ‘churn’ to defining and preventing it. The evolution of smart KPIs and analysis means that a company would be able to capture and chart the gradual disengagement behaviors that reliably lead to disconnection.
Why This Matters for Finance
Churn is just one of the many ways a company can use data and KPIs to become more proactive. For finance, a department whose entire business is based on numbers, the evolution of KPIs means that decisions can become better informed and companies can become even more agile.
Controllers in turn can leverage the decision outputs from thousands of what-if scenarios and models to manage finances even more effectively, understanding when, why, and how to act.
The Controller’s Guide to AI: Free Whitepaper from Controller’s Council
As AI and KPIs continue to evolve, it pays to understand how everything works. We recently released our free guide to AI in the finance department, exploring the significant impacts on the way you work and discussing how to use these tools to remain invaluable. Read it here.
