Tremendous confusion persists about how to successfully implement non-GAAP measures and key performance indicators (KPIs) within an organization for assessing business performance. In this article, we aim to bring more clarity on this topic to CPAs involved in enterprise performance measurement initiatives.
Non-GAAP Measures and KPIs Are Critical
Organizations commonly use both financial and nonfinancial information for their decision-making, valuations, and other purposes. Financial information, particularly GAAP financial data, tends to be backward-looking; non-GAAP measures and KPIs enable organizations to incorporate more forward-looking data in their performance evaluations and decision-making processes.
Non-GAAP measures are numerical measures that adjust the most directly comparable GAAP measure reported on the financial statements, such as earnings before interest, taxes, depreciation, and amortization (EBITDA), adjusted EBITDA, and non-GAAP income, for items that are unusual or nonrecurring. The motivation for the adjustments resides in the fact that these unusual, nonrecurring items are in the past and may not be relevant for forecasting future performance, allowing management to project what the financial performance would be on a run rate or normal course of business basis (i.e., without these unusual items).
KPIs are quantifiable measures that focus on the aspects of organizational performance that are most critical for the current and future success of the organization. By most critical, we mean those that have the highest impact on the firm’s current and future performance.
As pointed out by performance improvement systems expert Gary Cokins,1 the finance and accounting function is evolving from its traditional role of collecting and validating data and reporting information to a more value-adding role of supporting analysis for decision-making. This includes playing a key role in managing the overall performance of the organization as part of an integrated function, referred to as enterprise performance management (EPM, also referred to as business performance management – BPM – or corporate performance management – CPM). Indeed, as Cokins explains, EPM should be considered an umbrella concept that integrates operational and financial information into a single-decision support and planning framework. For him, EPM (including business analytics) will be the next new management breakthrough. As Cokins puts it, “Integrating systems and information is a prerequisite step, but applying business analytics, in particular predictive analytics, may be the critical element to achieve the full vision of EPM.”
Non-GAAP measures and KPIs are used in business planning, forecasting, and reporting for the following reasons:
- They are forward-looking and help predict future outcomes.
- They are needed for decision-making: nonfinancial considerations are often considered critical.
- They may be used to better design, integrate, and align executive compensation schemes.
- * They provide the foundation for continuous business process improvement.
At the same time, caution is needed when using non-GAAP information in external financial reporting. Recently, the Securities and Exchange Commission (SEC) urged CPAs to establish consistency in their delivery of the non-GAAP information they report to investors. Indeed, over the past few years, non-GAAP measures have become a significant area of focus for the SEC, and publicly traded companies are required to follow SEC guidelines in using non-GAAP measures in public filings. These guidelines vary, based on where the non-GAAP measures are presented, and generally include disclosing the management purpose of the non-GAAP measure and its usefulness for investors, providing a reconciliation of the non-GAAP measure to the GAAP measure, and presenting the GAAP measure with equal or greater prominence than that given to the non-GAAP measure.2
Problems with Non-GAAP Measures and KPIs
Organizations face a number of problems when using non-GAAP measures and KPIs. These include inconsistencies in the definition of the measures. For example, one organization may exclude stock-based compensation from its computation of EBITDA, while another will include it. Some will adjust EBITDA for certain restructuring charges, while others will not. Similar inconsistencies exist with KPIs. For example, customer churn (i.e., percentage of customers that stopped using the company’s product or service during a given period of time) will be calculated differently from one organization to another. Likewise, each organization itself can be inconsistent in its non-GAAP measures and KPIs over time as the business changes and management evolves its metrics. Inconsistencies create confusion and frustration among investors and put an organization at risk of investor claims or SEC violations. Caution is a must when using these measures in external financial reporting.
From an internal use perspective, challenges with non-GAAP measures and KPIs include a general misunderstanding of the measures themselves and their purpose. Organizations engaged in EPM initiatives tend to measure and report on indicators that are not key indicators, lacking both adequate centralized oversight and focus on the critical success factors of the organization. As a result, initiatives fail due to the ineffectiveness of the measurement framework, siloed and misaligned measures, unintended behavioral consequences,3 time-consuming data collection and analysis processes, measurement fatigue, and overall lack of decision-making value for the organization.
What Metrics to Use?
Business performance measurement expert David Parmenter suggests that financial measures (regardless of whether or not they are GAAP or non-GAAP financial measures) are not KPIs. He calls them result indicators (RIs) or key result indicators (KRIs). RIs and KRIs are useful summary measures, but he believes they do not help management fix a problem because it is difficult to pinpoint which team or which part of the organization is responsible for the performance or nonperformance. KPIs, on the other hand, are nonfinancial measures that he says are timely, brief, and informative, linking daily activities to the organization’s critical success factors. By monitoring them, the organization is able to significantly increase its performance.
The number of metrics will vary, based on the size and complexity of the organization. However, the more concise they are, the more effective the measures will be: typically, no more than eight to 12 KPIs at the highest level of the organization. The types of metrics will depend on the organization’s industry and strategic focus but should cover multiple perspectives, potentially including customer, process, and learning and growth perspectives, such as in a balanced scorecard approach.4 Although each organization is unique in terms of its own critical success factors, examples of KPIs may include customer churn, pipeline throughput, customer satisfaction, late deliveries, capacity utilization, response time, customer visits (current and/or scheduled), employee engagement, and innovations (current and/or planned).
Implementing Business Metrics
The CEO should start by appointing a member of the C-suite to be the leader of the performance measurement function, and drive the enterprisewide implementation and monitoring of the metrics in line with the organization’s critical success factors. Often the CFO or the COO will be tasked with that role. In larger organizations, a dedicated EPM leader may be appointed by the CEO. In all cases, the CFO and the financial planning and analysis (FP&A) staff should play a key role.5
The value that the finance team brings to performance measurement reporting includes ensuring that the definition of the measures is normalized; the data reported is accurate, consistent, and unbiased; and the financial forecasts are updated in a timely manner based on the latest indicators. The finance team will also formalize and maintain policies on the non-GAAP measures and KPIs used in external financial reporting to ensure compliance with laws and regulations.
Once the line of authority for performance measurement has been established, create and maintain an enterprisewide consensus on what the organization’s critical success factors are, based on input from frontline staff and a bottom-up approach. In this process, staff and management should focus on the critical success factors and consider only the measures that are relevant (eliminate or abandon the measures that are not). A common mistake is including too many performance indicators, which results in a time-consuming and costly reporting process. In larger organizations, the collaborative process is facilitated by FP&A analysts with a clear mandate to focus only on the metrics that matter. Although each company is unique, organizations can research any literature that might exist in their industry about relevant business metrics or competitive intelligence materials to ensure they have not left out an important measure.
Finally, this process is not static or rigid. It is real-time and agile, evolving in line with changes to the business (i.e., changes in the organization’s critical success factors). The frequency of the measures will depend on the measures themselves, with some reported on a daily or weekly basis to allow early detection of potential new trends.
The Role of Technology
Data is critical for developing metrics that can drive decision-making. More affordable data storage combined with advances in technology have enabled organizations to store and analyze vast amounts of data to create dynamic and integrated performance metrics that can lead to better and faster insights and decisions. Amazon Redshift, for example, is a cloud-based data warehouse with storage capacity in exabytes (one quintillion bytes) that integrates with a variety of business intelligence tools. Intuit, the maker of QuickBooks and TurboTax software, migrated to Amazon Redshift and experienced a twofold increase in its data processing performance, thus enhancing metric monitoring. Further, the built-in security access controls and audit trail capabilities in Redshift enable Intuit to maintain compliance with Sarbanes-Oxley regulations.6
There are a variety of business intelligence software solutions capable of generating and tracking non-GAAP measures and KPIs, including traditional spreadsheet programs such as Excel, data visualization tools such as Tableau and Microsoft’s Power BI, and analytic platforms such as Alteryx.
Excel, for example, has the ability to extract data directly from a relational database management system such as Microsoft SQL Server using commands in the query editor. Once the data has been loaded into a worksheet, CPAs can build KPIs using the Data Model and Power Pivot add-in tools available in Excel.
Tableau and Power BI users can create an interactive balanced scorecard dashboard that can be directly connected to the organization’s database to generate non-GAAP measures and KPIs in real time. Charles Schwab, one of the largest publicly traded financial services firms in the United States, has more than 16,000 employees who use Tableau to monitor dashboards of client satisfaction, identify outreach opportunities, and customize offerings with the goal to improve the client experience.7
Alteryx is another popular platform for transforming, analyzing, and visualizing data. Grant Thornton LLP, one of the world’s largest professional services firms, used Alteryx to automatically analyze sales and use tax metrics for clients impacted by the U.S. Supreme Court’s South Dakota v. Wayfair Inc. decision. Prior to the decision, a physical presence within a state was needed to create tax filing requirements. Now, as a result of the decision, more than 40 states have “economic nexus” for state sales tax. Alteryx provided Grant Thornton with visualizations that showed current filing locations, states nearing thresholds, states exceeding thresholds, and other proprietary analytics.8
The next generation of performance metrics will adopt advances from artificial intelligence and machine learning technologies, providing additional value to organizations seeking to gain a competitive advantage. Michael Schrage, a research fellow at the MIT Sloan School of Management, notes that “KPIs are becoming measurably smarter, more dynamic, and more adaptive.”9 Schrage suggests that simply understanding a performance metric such as customer churn will not be sufficient to remain competitive in an environment of big data and advanced business intelligence tools. Instead, organizations should seek to predict churn and then take actions to prevent it, thus making the KPI “smart.”
According to Schrage’s research, the most digitally sophisticated organizations do not simply focus on optimizing known KPIs; instead, KPIs should be used as inputs for machine learning models capable of identifying and recommending new KPIs. Further, machine learning models can be used to predict outcomes and recommend courses of action that optimize performance. For example, Uber, the ride-sharing and food delivery service company, uses machine learning models to estimate accurate arrival times, a critical metric for both riders and drivers. These metrics are then used as inputs into Uber’s business intelligence tools that optimize pricing and driving routes.
Empowering Performance Measures with Data Analytics
One of the most significant challenges in using data analytics for business performance measurement is working with the data itself. First, obtaining the right data requires CPAs to understand exactly what data is needed for hypothesis testing and to answer relevant business questions. Then, CPAs must be familiar with where the data is located (for example, from which ERP application or external data source should the data come from), and from whom or how to request it. Once obtained, the CPA must transform and clean the data prior to loading it into analytical tools such as Excel or Tableau.
When the data is ready, the CPA must determine which data analytics approach is the best one; that is, which modeling technique will be used to analyze the data. For example, a classification model can be used to predict and classify whether firms consistently receive qualified audit opinions or whether a customer belongs to a certain class based on the behavior of other customers. A regression model could be used to predict a performance metric such as the allowance for doubtful accounts based on the end of year accounts receivable balance and age of the accounts.
Perhaps the most challenging part of using data analytics as a performance management tool is communicating gained insights to stakeholders in a clear and understandable way. As noted in a report produced by the AICPA and Chartered Institute of Management Accountants (CIMA),10 CPAs may be comfortable with data analysis, but others in management may need relevant insights communicated visually via a dashboard or in storytelling form to help them clearly grasp the results.
The role of the CPA is evolving from a producer of historical financial statements to a strategic partner who supports decision-making and performance management. CPAs who are well-versed in developing performance metrics using the latest technology will be able to add value to their organizations through successfully implementing EPM initiatives.
1 Gary Cokins, Strategic Business Management: From Planning to Performance (John Wiley & Sons Inc., 2013).
2 Jennifer Biundo, “Non-GAAP Rules Are Needed to Keep Companies Honest,” CFO.com (May 18, 2017). See also “A Roadmap to Non-GAAP Financial Measures” by Deloitte (2019).
3 Such as achieving performance in one business area at the expense of another or achieving short-term performance at the expense of long-term performance.
4 Robert S. Kaplan and David P. Norton, The Balanced Scorecard: Translating Strategy into Action (Harvard Business School Press, 1996).
5 In “Master of All Metrics,” business journalist and author Russ Banham makes the case that CFOs are the best positioned within the C-suite to lead the charge in this area and that their accountability for traditional financial metrics should extend to nonfinancial measurements as well. (CFO.com, Feb. 3, 2017)
6 See video “AWS re:Invent 2018: Modern Cloud Data Warehousing (Intuit): Optimize Analytics Practices” at www.youtube.com/watch?v=owJ-ipdTbko.
7 “Charles Schwab Equips More than 16,000+ Employees with Tableau to Advance Data-Driven Culture,” announcement by Tableau. www.tableau.com/solutions/customer/charles-schwab-equips-more-12000-employees-tableau-advance-data-driven-culture
8 “From a Culture of Worksheets to a Culture of Analytics,” Alteryx’s Grant Thornton use case article. https://community.alteryx.com/t5/Alteryx-Use-Cases/From-a-Culture-of-Worksheets-to-a-Culture-of-Analytics/ta-p/453753
9 Michael Schrage, “Smart Strategies Require Smarter KPIs,” MIT Sloan Management Review (Sept. 16, 2019).
10 Business Analytics and Decision Making – The Human Dimension, CGMA report (July 2016). www.cgma.org/Resources/DownloadableDocuments/business-analytics-briefing.pdf
J.L. “John” Alarcon, CPA, DBA, CGMA, is a principal at Bearn LLC, a business advisory services firm in Philadelphia, and a member of the Pennsylvania CPA Journal Editorial Board. He can be reached at firstname.lastname@example.org.
Cory Ng, CPA, DBA, CGMA, is an assistant professor of instruction in accounting at the Fox School of Business at Temple University and a member of the Pennsylvania CPA Journal Editorial Board. He can be reached at email@example.com.