For past CFOs, determining financial success meant keeping a watchful eye on a few key indicators but today’s CFOs have bigger shoes to fill. Nowhere is this truer than in private equity, where sponsors closely analyze the financial and performance metrics that their portfolio CFOs are tracking across dozens of businesses, stages of growth, and industries. Sponsors and CFOs know there are several value-creation levers at their disposal. They just might not know which ones drive business outcomes.
To answer this question, however, CFOs can’t just rely on “lagging” indicators, or past performance indicators, to show where the company has been.
A shift to “leading” indicators can show where the company is going and is a valuable metric for understanding tangible business impact. However, leading indicators need a stronger data foundation that requires partnering with leaders across the business, which may not come naturally to CFOs whose focus has long been on lagging indicators. Leading indicators weren’t previously on CFOs’ radars: the data wasn’t available, the systems weren’t available, and measuring the future generally fell outside of the expectations of the role.
Making the shift to leading indicators can generate transformative insights. With many companies without a formal chief technology officer or chief information officer role, it falls to the CFO to better understand what is driving the business through better capture and analysis of the company data.
Building a Better Data Foundation
A much-used trope in the digital age is garbage in, garbage out. Data and analytics are critical to value creation, but only if data is properly collected, governed, and organized.
Firms must first define their data strategy as it relates to their goals and objectives. What data is important and what is not? This will vary by company and what is ultimately capturable. CFOs have long been capturing purely financial data, but they are now able to capture and integrate an even wider range of data into their decision-making, in areas spanning operations, product and inventory, and marketing to compliance, customer service, and competitive intelligence.
Firms should design a data architecture that supports their needs and integrates with other systems, establish governance policies to ensure stewardship and access procedures, implement security protocols to prevent unauthorized use, and standardize hygiene and Q&A processes to ensure bad data is cleaned before storage. Data management takes time and expertise, but firms can take even small actionable steps that can pay dividends in the short term.
Harnessing Leading Indicators
Forward-looking metrics, i.e., “leading indicators”, help anticipate future outcomes and trends and enable companies to seize opportunities or identify potential problems. Despite the obvious appeal, CFOs have historically paid them less attention because the data is more difficult to gather and without the systems/technology we have now, impossible to analyze.
New data and analytics capabilities are enabling CFOs to organize, analyze, and action data in a way not possible even a few years ago. By building relationships with other business units and establishing processes to collect and integrate a broader range of data alongside financial data, CFOs can build true driver-based analyses to look not only at what is driving outcomes, but also at what is influenceable, and what is not.
Three examples of these analysis types are cohort analysis, churn analytics, and innovation.
1. Cohort Analysis. A topline statistic might show one thing, but a closer look at the cohorts within the data can yield insights that can feed into improved decision-making. For example, for a fast-food subscription company, the top line was growing at a healthy rate and customer acquisitions were increasing. However, when we segmented customers into acquisition time period cohorts, we noticed that the new cohorts were subscribing to lower-priced products and weren’t sticking around very long. This situation can be tracked as a leading indicator if the high-level metrics are tracked at a more granular (cohort) level, due to the capture of customer acquisition date.
2. Churn Analytics. The ability to assess a group of customers and know which will stay and which will leave can have a transformative effect on a company’s bottom line. “Churn analytics” is particularly impactful for software businesses, which use a recurring revenue model and focus heavily on growing subscriptions from existing customers.
They are also effective for consumer-focused companies, which rely on returning customers. As an example, a nutrition supplements company was struggling with ~60% customer churn, and a flat discount-focused customer retention strategy was not working. By building a more flexible discount strategy that considered historical purchase characteristics such as churn probability, type of customer, and prediction of lifetime value, the company was able to improve client margins, streamline product lineup, and remove low-rated products from listings.
3. Innovation. Tracking innovation can be another game changer for CFOs who may get pushback for “stepping out of their lane” if they incorporate it. Innovation is perceived as the future of the business, and, therefore, above question. There may be a lot of capex going into the innovation, but the efficacy of that spend is rarely part of the financial analysis. This could include a look at how new products are doing vs legacy products, how new locations are performing compared to old locations, or how the business has performed after an innovative change.
Measuring innovation can be tough because it takes time for new initiatives to bear fruit, but it’s important for a key framework to be established upfront and for all stakeholders to be aligned on how and when success will be measured.
Neither cohort analysis, churn, nor innovation are necessarily part of a CFO’s traditional financial statement analysis, but they are examples of operational drivers that will directly or indirectly impact financial outcomes.
Better Value From Lagging Indicators
This doesn’t mean CFOs should not focus on lagging indicators. Enhanced data and analytics can help here too, especially when it comes to benchmarks. An example would be revenue cycle management in the healthcare sector, where companies can compare themselves against industry benchmarks for net collections rate, clean claims rate, cost-to-collect, and more to better understand their performance and uncover opportunities for improved cash collections, margin, and EBITDA.
By building a strong data foundation to better analyze existing data, and pulling in data from across the business, private equity sponsors and their portfolio company CFOs go from looking backward (however real-time it may be) to looking forwards. By identifying the indicators of the future of the business, PE sponsors and their CFOs get a concrete handle on the influenceable factors that are truly making a difference.
Paavan Choudary is Accordion’s global head of data and analytics and Sanjeev Parlikar is Accordion’s head of strategic finance at Accordion.