Back in June, we presented a sort of thought experiment based on a labor productivity calculation launched by the U.S. Bureau of Labor Statistics (BLS) this year. Our hypothesis was that new metrics could be innovated and used in the staffing industry. With that, we introduced a way to measure productivity, in terms of output, for contingent talent across an enterprise workforce program. Our industry has no deficit of metrics by which to assess the performance of staffing providers and MSPs, but have they kept pace with the evolving economy, nature of work, and technological advances that have dramatically altered the fabric of our modern world? It certainly wouldn’t hurt to revisit them, and the potential for new metrics should be explored. The solution, however, isn’t merely creating a slew of fresh key performance indicators (KPIs); it’s learning to embrace smarter, more dynamic, and more adaptive ways to utilize KPIs.
Securing Strategic Value Through KPIs
Writing for MIT Sloan Management Review, research fellow Michael Schrage offered some thought-provoking insights into developing smarter business strategies with smarter KPIs. As Schrage pointed out, digital processes, platforms, and predictive algorithms have transformed the strategic role and purpose of key performance indicators: “For data-driven disruptors like Alibaba, Amazon, Airbnb, and Uber, KPIs don’t simply monitor enterprise success; they proactively drive it. This shift creates innovative opportunities for ambitious leadership.”
Leveraging MIT’s research into the subject, Schrage envisioned increasingly sophisticated business environments where the purpose and role of KPIs are turned “inside out.” So rather than viewing analytics as outputs for humans, digital organizations are beginning to use them as inputs for machine learning models. “Smart KPIs,” Schrage explained, “literally learn to improve their performance and the performance of the organization. This emergent capability creates novel value-added relationships between management, metrics, and machines.”
Put in simpler terms, the next iteration of KPIs won’t serve as reflections of past actions, they will become predictive, learning, strategic, and forward-looking drivers that shape upcoming decisions. In many ways, current KPIs provide a rearview mirror glance at missed targets or untapped opportunities that need to be improved in the next round.
KPI Virtuous Cycle
As more of our industry tools incorporate artificial intelligence (AI) and machine learning, KPIs become capable of “learning.” Big data moves to the fore instead of just showing us where we’ve been, delivering prescriptive and anticipatory values that align machines, data scientists, and decision makers in future strategies. This process brings tremendous benefits to service industries like staffing where analytics depend on, as Schrage stated, “data quality, quantity, timeliness, and lineage.”
We can define data governance as the exercise of control and authority over monitoring, planning, and enforcing the management of information. But governance now also includes the “collection of processes and practices which help ensure the formal management of data assets within an organization.” KPI datasets no longer present static numbers to evaluate, they are now dependent on data quality, volume, timing, and historical connections.
Unlike a lot of KPIs in different industries, particularly those that distribute products or tech, smart KPIs aren’t necessarily focused on financials. “Customer, supplier, channel, and partner data are integral to performance parameters,” Schrage wrote. “For example, self-declared customer-centric or customer-obsessed organizations put their data governance initiatives in the service of customer-focused KPIs, including Net Promoter Score (NPS) and customer lifetime value (CLV). Data governance here is a mechanism to facilitate a KPI end.”
Decision rights allow organizations to identify what decisions need to be made for operational and strategic alignment. They determine who is involved and lay out the framework for how decisions are made and supported through business processes and tools.
“As a process becomes more automated, for example, when ― and why ― does a data-driven algorithm get the “right” to make a business decision instead of a human?” Schrage said. “If sophisticated predictive analytics suggests likely risks or desirable opportunities would significantly affect KPIs, what decision rights might people be granted to intervene appropriately?”
While no easy answer exists, smart KPIs force companies to constantly revisit and reallocate decision rights. All related processes must frequently be designed and tested with the automation of decision making at the forefront of the effort. The goal is to understand and develop processes where machines can take over vital decision recommendations through algorithms that are faster, better, cheaper, and more scalable:
As new data sets and analytic techniques become available, established KPIs may need to become more dynamic. As people and algorithms become more capable, perhaps they should be empowered in ways that will meaningfully improve KPIs. Indeed, the very act of exercising decision rights and discretion may generate data that can ― and should ― change how performance is perceived and measured.
Bluntly, identifying the lags and latencies between KPIs, data, and decision ― how tightly or loosely coupled those elements may be, or how carefully or assiduously they are mapped and monitored ― matters enormously. Siloed decision-making would be anathema; there are no virtuous KPI cycles without cross-functional executive collaboration.
The analytics themselves would suggest which parties should best undertake interactions—machines or humans. So if the KPI predicted upcoming customer attrition, an automated customer service chatbot may not be suggested as a fix. The KPI would likely favor getting a human from client services or sales involved to engage with at-risk customers.
Customer Churn Example
Schrage cited customer churn (negative client turnover or attrition) as a primary example of how smart KPIs solve organizational challenges. Losing customers, we all know, has detrimental impacts on our business. According to studies by Harvard Business Review, the cost of acquiring new customers can be 25% more expensive than keeping existing ones. Because customer retention affects the sustainability of profits and revenue flow, it’s a core concern. And today, our aim is to predict and proactively prevent it, not just understand it. This is where smart KPIs come into play.
- Data governance allows for distinctions between “presumed churn” (customers who become less engaged or experience lulls in activity) and “absolute churn” (customers who have discontinued service).
- The latter can be deduced by examining presumed churn and analyzing the catalysts to reactive churn, which generally include bad experiences, poor customer service, missed SLAs, waning performance, unfilled requisitions, lackluster candidate submittals, and others.
- Still, traditional analytics often fail to differentiate high-value customers enjoying strong results versus customers that switched providers to find lower pricing or better service. By aligning customer lifetime value (CLV) data with predictive churn KPIs, companies can more effectively assign the proper resources and intervention strategies to prevent turnover.
“Smarter metrics align the immediacy of situational awareness with longer term strategic aspiration,” concluded Schrage. “Process architectures achieve this by digitally linking KPIs, data, and decision-making into virtuous cycles. In effect, digital transformation empowers smarter KPIs; smarter KPIs compel digital transformation.”