In my former career, I managed facilities for a senior living company. Beyond general management and administration duties, my primary responsibility was ensuring that we hit our targeted net operating income (NOI). For those unfamiliar, NOI is essentially EBITDA for real estate.
Many of the numbers on our monthly income statement reflected the decisions, actions, and efforts undertaken in the prior month. Did we overachieve in our sales efforts? We’ll see that on the top line. Did we monitor labor and control overtime consistently? We’ll see that reflected in the “salaries – non-exempt” line.
Shortcomings of traditional financial reporting
But does the income statement tell the story we need? Sure, it provides valuable information, but two things are apparent: it’s full of lagging indicators, and it doesn’t tell a particularly detailed story.
Indeed, these are the shortcomings of financial reporting. While critical, it emphasizes the short-term and only provides managers with a retrospective view of performance. To complement financial reporting, we needed a system that leveraged the available operational and sales datasets so that the management team would have time to make adjustments and focus our collective energy throughout the month on the key cost and revenue drivers that dictated NOI.
In short, we needed reliable leading operational and sales indicators to collectively act as a “canary in the coalmine”, alerting us contemporaneously and giving us time to course-correct – before we reviewed lagging indicators (like NOI) on the income statement.
Surfacing leading indicators
But we couldn’t rely on manual processes to deliver these kinds of leading indicators. What if the key person responsible for providing insights went on leave? What if that person made an error collating data in Excel? What if there was a delay in their process of transforming raw data into actionable insights? Generally, any of these contingencies (and there are many more) would mean our opportunity to act on leading indicators would be lost.
Fortunately, we had automated dashboards featuring key leading indicators for each functional area of the business. Every morning, we reviewed leading indicators taken from sales and operational data, to prioritize how we spent our most precious resource - time. If we saw leading indicators like inquiries and sales interactions dipping, or leading indicators related to cost trending in the wrong direction (e.g., department spending or overtime percentages), we quickly decided which actions to take.
The key word is quickly. Because zero time was spent preparing insights, the management team was able to swiftly make decisions and act. The automated dashboards constantly updated with the most recent data, preserving our time for what really created value – better actions and decisions, based on an informed view of what the data was telling us.
Why companies need automated reporting
Automation is highlighted here, because that’s precisely where we see midmarket companies struggle (along with an overweighting of lagging indicators). Whether it’s a lack of internal resources, slow and repetitive manual reporting processes, or the inability to effectively combine disparate datasets, many midmarket companies stall in trying to prepare the right insights to propel profitable decisions and actions.
Indeed, the only pathway for data initiatives to create value is to enable better, more timely decisions and actions. That’s it. But if what you’re ultimately consuming is full of lagging indicators, or if leading indicators are delivered late, you’ve already lost the ability to take timely action.
Finding a balance of leading and lagging indicators
This is not intended to suggest that lagging indicators aren’t valuable. Rather, it’s to champion the idea that management teams should have a “balanced scorecard” for awareness and decision-making. To achieve that balance, teams need the right mixture of metrics covering the critical activities within their value chain – which will of course be based on the business model itself.
Each primary activity in the value chain should have this balance of leading and lagging indicators, in part to oversee the connection between them and to adjust if necessary.
For example, if we start to see a shift in the historical correlation between the leading indicator of Facebook ad engagement and won sales, we’re spurred to examine the prevailing hypothesis that the correlation still holds. Perhaps another ad platform is now starting to show more of a correlation than in quarters past. The action? Adjusting our ad spend allocations before more dollars are wasted in a channel that has stopped showing the correlation to the lagging metric everyone cares about – won sales.
Or, if a manufacturer is aiming to improve customer experience and retention and sees throughput and OEE improve in the plant (good leading indicators here that could otherwise be lagging indicators in different analyses), but doesn’t see on-time delivery improve, it might suggest that the leading production-related indicators are fine, but another problem exists downstream that’s degrading OTD and the customer experience.
In summary, leading and lagging indicators both have their place, and in some analyses can play both roles. Financial reports like income statements are unquestionably valuable, but absent the appropriate operational reporting, don’t allow teams to leverage data for better actions and decisions. So, there should be balance – not only between financial reporting and automated operational reporting, but within operational reporting across the value chain, a balance between input, process, and output metrics (i.e., leading and lagging indicators).
Adapting to economic pressures with better data intelligence
Why now, more than ever? Because the higher-for-longer interest rate environment is here to stay, and companies that were once able to succeed without sophisticated data intelligence are at-risk of margin compression, lagging revenue growth, cash flow dilemmas, and more. An investment in data and automated reporting that connects leading and lagging indicators is ultimately an investment that serves to safeguard profitability.
Lastly, we should never expect a midmarket company to attain this level of data intelligence with manual processes. It must be automated through business intelligence, to allow for timely adjustments.
After all, what’s the point of distributing leading indicators when you’ve already lost the chance to act?
