"You can't improve what you don't measure" - Lord Kelvin
Key Performance Indicators (KPIs) are metrics that represent how various drivers of the business are performing. These drivers are often both financial and operational in nature. And while there is no one-size-fits-all when it comes to choosing the "right" metrics for your business it is critical that the data used to be consistent and accurate.
What makes an effective set of KPIs?
- KPIs should reflect a mix of strategy and operations
- Effective KPIs require measuring the appropriate data
- Having something to compare this number to
- Having the data in a timely manner while it is still fresh
- Confidence in the accuracy and integrity of the data used
"If you can't measure it you probably can't manage it. Things you measure tend to improve" - Ed Seykota
What KPIs do I need to be tracking?
Unfortunately, there is no default answer because what is important varies not only by industry and maturity of a business, but also by the strategic objectives laid out by the CEO. However, you can't go wrong by examining metrics that provide insight into the short-term viability of the business such as:
- Working capital - this is the amount immediately available cash
- Operating cash flow - OCF is the cash version of net income (net income is on an accrual basis which means it includes non-cash items like depreciation)
- Liquidity ratios such as Current & Quick ratios - measurements to show if the company can pay off short-term debt
- Cash conversion cycle - this is effectively the amount of time is takes to sell inventory and end up with cash in the bank
How should I start collecting KPIs?
Once you identify the appropriate metrics for your business it is imperative that you clearly define what each indicator represents, why it was chosen, where the data will be coming from, how it will be calculated, etc. You must set one standard definition and one "source of truth" as to where the data comes from. There is nominal value without the ability to compare KPIs to previous periods because there is no context.
"Data is only as useful as the context in which it is gathered and presented" - Josh Pigford
How many KPIs should I track?
Again, there is no right answer, but there are wrong ways of going about it. Too many KPIs began to have a dilutive effect on the value of the information being portrayed. Too few metrics leaves you open to potentially missing critical signals and trends within the business. Most CFOs would agree that any number above 20 KPIs is too many and should be trimmed down, potentially requiring you to reevaluate aspects of the business. Less than 5 metrics may be too few depending on the business and amount of transactions that take place throughout the period.
"Metrics are for doing, not for staring. Never measure just because you can, measure to learn, measure to fix."
How often should my KPIs be updated?
This question requires real consideration, specifically by the person(s) making decisions based off the data. One is likely to think that if you had the option between accessing real-time updates to the necessary data or accessing data that gets updated just once per quarter that the former would be a no-brainer. However, as mentioned previously, data has little to no value without context. In this case, the context needed is data from previous periods to compare against. That said, a period can be any duration of time that you choose but is most often refers to a length of time equal to 1 month, 1 quarter, or 1 year.
I have been reporting one of the metrics incorrectly - what should I do?
First, go back to prior periods and identify what the metric would have been if it were correct. Next, understand: how many periods the metric has been incorrect? How large is the variance from the correct number? What caused the error - bad data or a bad calculation? What decisions or actions have been taken either directly or indirectly based on this bad information?
Once you have a better idea around the true impact and potential fallout from reporting the wrong number you can act appropriately. The worst case scenario would be a public company having to restate and lower a metric previously reported to Wall St. If you find yourself in this unfortunate position the course of action to take is clear, start looking for a new job within a new industry.
Read: "Oops, Forecasting Error Slams ServiceNow as Shares Drop 20%" (NYSE: NOW)
How should I present my KPIs?
KPIs are typically organized in the form of a "dashboard", which is analogous to a car dashboard. More often than not, the dashboard is comprised of a series of charts and graphs to help visualize the context. A dashboard should be easy to read, easy to share and comprised of metrics that are bulletproof. Any variance or trend that differs from the expectation should be called out, research, and explained in a commentary section. A dashboard needs to be built in a way that allows for updates to be made with as little human involvement as possible.
How can I get my KPIs faster and still be confident in the accuracy and integrity of the data?
- This is the billion dollar question. We know that inaccurate data can be harmful to the business in many ways. We also know that data begins to lose value the longer it takes to compile and publish creating the necessity for digesting ever-increasing amounts of data in less time.
- One thing a new CFO will often do is implement new systems and controls so that data is being collected properly by all the stakeholders involved. There are countless tools and programs in the market that can help collect data and turn it into easily digested charts and tables; however, this still requires that the root data be input correctly to begin with.
In the end you want it to be simple so that the implications of the data can be understood by all.
"If you can't explain it simply, you don't understand it well enough" - Albert Einstein
Tracking metrics is a necessity for any business to stay focused throughout the year if the objective is to achieve strategic goals. However, bad data has the ability to do far more damage than no data at all. Akin to bad data is the misinterpretation of data, such as: believing correlation implies causation and the confirmation bias.
A Chart of Accounts as an FP&A Framework
In previous articles, I wrote about ways to improve financial reporting through financial planning and financial analysis. A foundation for financial reporting is a chart of accounts. A chart of accounts also can serve as a framework for FP&A.
A chart of accounts serves as a database containing records like account elements, account names, and account numbers. Examples of account elements are assets, liabilities, equity, revenues, and expenses. Examples of account names are cash, accounts payable, common stock, merchandise revenue, and cost of goods sold. Examples of account numbers are assets starting with the number one, liabilities starting with the number two, equity starting with the number three, revenues starting with the number four, and expenses starting with the number five through nine. These records serve as a foundation for the communication of financial statements however these records serve two additional purposes. The first purpose is to establish a framework for financial planning. The second purpose is to establish a framework for financial analysis.
A Framework for Financial Planning through Precision
A chart of accounts establishes a framework for financial planning through precision. Precision within financial planning through a chart of accounts focuses on account names. The use of account names can establish a descriptive manner in how businesses expect to earn income. Earning income from revenues can be described through account names like audit fees, jewelry sales, membership fees, out-of-pocket reimbursements, and smartphone sales. Earning income from expenses can be described through account names like accounting services, fashion show production, freight from drop shipments, LinkedIn advertising, and office stationery. Using precision through account names in financial planning can initiate the proper recording of transactions. Using precision through account names in financial planning also can initiate the proper evaluation of effort toward earning income.
A Framework for Financial Analysis Through the Process
A chart of accounts establishes a framework for financial analysis through the process. The process within financial analysis through a chart of accounts focuses on account names. The use of account names can establish the amount of effort taken. Using account names that describe broadly, for example, miscellaneous expenses will require more effort to learn about the nature of transactions. Using account names that describe narrowly, for example, laboratory equipment, will require less effort to learn about the nature of transactions. Is describing broadly better than narrowly or vice versa? The answer is neither.
Describing Broadly or Narrowly?
Describing broadly establishes a “top-down” approach to solving financial problems. Describing broadly can be used to focus on elements like revenues expenses, assets, liabilities, and equity. Elements like these can establish a starting point for identifying strengths and weaknesses in the financial health of organizations. Describing narrowly establishes a “bottom-up” approach to solving financial problems. Describing narrowly can be used to focus on elements like iPhone sales, Facebook advertising, printer cartridge supplies, California sales taxes payable, and Class A common stock. Elements like these can establish a starting point for making better decisions on specific activities that affect the overall financial health of organizations.
FP&A establishes the foundation for thinking and learning about how processes affect outcomes. These actions can be guided by broad and narrow framing. In order for FP&A to add value toward these actions, it must take an active role in establishing a framework through the chart of accounts.
There is evidence that FP&A interest is growing fast. Each and every day, CFOs feel the pressure building on the finance function to contribute more to business success. Within the CFO’s organization, the responsibility for tracking, assessing and reporting corporate performance normally falls to the Financial Planning and Analysis (FP&A) group.
In reality, FP&A specializes in analyzing and planning for the future, wearing multiple hats and identifying various improvement strategies. A valued FP&A specialist is someone who has the ability to engage with and influence the full breadth of top management – not just CFO – ensuring they have the necessary information. The specialist will explain why the business needs to go towards x, y, z markets and not the a, b, c direction they were planning.
Members of FP&A are the Finance “ambassadors” to business leaders. Embedded across the business, they are a crucial part of decision making in areas such as planning, making resourcing decisions, measuring success, approving investments, and more. Roles include working with the marketing, sales, product, and engineering departments, as well as corporate (which touches on everything, and interacts directly with the CFO). A strong FP&A individual will have the ear of the sales director and can talk to the commercial director and operations director. He/she can sit down with the managing director and also be the right-hand man for the CFO.
FP&A is historically seen as strictly a financial function. There is often confusion regarding the roles of Accounting and FP&A and their differing objectives. Accounting, on the other hand, is very much a science, focused on meeting GAAP standards, instituting controls and shortening the close process. As was previously quoted by Mark Gandy, G3CFO, "The financial controller typically looks backward, the FP&A professional looks forward and sideways, diagonally, upward, downward, multi-dimensionally, and so on".
The role of an FP&A professional is largely a new and evolving one—to be truly great s/he has to be flexible, quick and adaptive. As the primary driver for financial planning, forecasting, reporting, and business analysis, FP&A plays a critical role in the organization and with their business partners.
FP&A moves beyond the traditional budgeting process to link strategic and operational planning. It must focus on high-quality analytics and predictive planning to analyze multiple scenarios and make smart decisions more quickly than ever before. Information delivered quickly, flexibly, in a format most relevant to the issues at hand, is more important than ever. FP&A also has the ability to measure how well Accounting and Operations are collaborating and supporting the company’s long-term goals. An optimized FP&A group, with the direction of executive leadership, has close ties with Accounting and Operations and applies their expertise to facilitate a collaborative business environment.
The definition of FP&A
FP&A generally includes several discrete processes. While these systems can be managed separately, their ownership requires a common skill set. This includes an understanding of accounting, finance theory, data sources and definitions, modeling, creative problem solving and the economics of the business. The processes typically owned by FP&A include: – Budgeting – Forecasting – Strategic Planning – Management Reporting – Financial Analysis – Capital Planning – Business Modeling (e.g., new ventures and investments)
The Skill Sets of FP&A
Ability to communicate with and gather information from business partners - ability to coordinate FP&A tasks with the corporate calendar or the assigned deadline - Ability to prepare reports and/or make presentations - Ability to build budgets, forecasts, annual plans and so on - Ability to receive, analyze, integrate and consolidate assumptions and data from business units - The knowledge of finance principles and processes - Ability to synthesize information to create conclusions, alternatives and recommendations - Technical aptitude - Candidates should have the ability to solve problems utilizing technology- Knowledge of spreadsheet and database structures and functions - ability to perform variance analysis and reporting - Ability to define, incorporate and report on financial and/or non-financial key performance indicators - Intelligence, natural curiosity, and a desire to learn.
Well-designed incentive compensation plans – especially sales commission plans – are an incredibly powerful way to motivate great performance. But designing a great plan is both an art and a science, and prone to design mistakes that are expensive and end up not motivating the desired performance. The commonest and most serious error plan designers make is to lay out the rules before deciding just what it is the enterprise is trying to accomplish. You can avoid that mistake with a simple, straightforward graph that I’ve drawn hundreds of times in my career. Follow these steps:
1. Draw your axes. “Performance” goes on the X-axis (i.e., the horizontal axis) and “Compensation” goes on the Y-axis (i.e., the vertical axis). For now, you don’t have to graph actual dollar amounts… just think in terms of percentage of target amounts:
2. Plot the obvious points. Those would be 0% of target comp at 0% of target performance and 100% of comp at 100% performance. You might also find it useful to draw a reference line through those two points. (That reference line happens to be the graph for a plan with a commission rate that is constant across all levels of performance.)
3. Plot some additional points and connect those points with straight lines or curves. Work with line managers and senior managers to get a sense of how much incentive comp they believe is appropriate at specific levels of performance. For example, here’s a graph for an “accelerated” sales commission plan – that is, one where above-quota performance is rewarded with increasing commission rates, and below-quota performance is penalized, where management has specifically suggested compensation of 40%, 165%, and 250% of target comp for performance at 50%, 150%, and 200% of target, respectively. Note that the compensation curve sits below the reference line for performance below 100% of target, and above the reference line for performance above 100% of target:
4. Plot the same graph, but this time with actual performance levels and comp amounts. Here’s what the graph might look like for the accelerated plan described above, where “Performance” is Sales, with a quota of $2,000,000, and target commission is $100,000:
You’re now ready to flesh out the comp plan with spreadsheets and other formal documentation.
Just for comparison, here’s another example, this time for a typical management MBO plan where no bonus is earned until a specific performance level, such as 60%, is met, and the maximum bonus is the target MBO amount:
In this way, you can visualize any approach to incentive comp, form conclusions about whether that’s the approach you want, and then fill in the blanks to design a comp plan that actually does what you intended.
Sometimes a picture IS worth 1,000 words.
When a multidisciplinary research study group at Princeton University undertook a study of the paired uses of electricity and gas in townhouses, it contacted the residents of Twin Rivers, a nearby planned community in New Jersey. Over a five-year study period, it learned how to eliminate three-quarters of the energy used by the furnace in quite ordinary, reasonably well-built townhouses, as chronicled in Saving Energy in the Home: Princeton's Experiments at Twin River, edited by Robert H. Socolow (Cambridge, MA: Ballinger, 1977).
The purpose of the Princeton study, during a winter in the mid-1970s, was to examine differences in energy use and make comparisons with structural aspects of the 152 individual townhouses and the behavioural aspects of their inhabitants. As a data scientist, I took great delight in being a participant and was intrigued by later looking at the results and the data from the study. I was a resident at Twin Rivers at the time, not realizing that some new analysis techniques used on the data would eventually be published in 1977 in the ground-breaking book Exploratory Data Analysis by data science pioneer John W. Tukey (1915–2000).
The data were gathered automatically through a special device that was hooked up to the landline telephones and the energy sources in the home. There were questions to be answered periodically about our lifestyle, the details of which have long escaped my memory. Nevertheless, some novel uses of graphing techniques with schematic data plots (data visualization) can be found throughout in my new book. These techniques, new at the time, have now become a familiar part of many business statistics books.
Exploring Data Patterns
Studying the patterns in the data improves the forecaster’s chances of successfully modeling data for forecasting applications. Through exploratory data analysis (EDA), a demand forecaster can start the important task of finding factors (drivers of demand) that are generally quantitative in nature.
Tukey likens EDA to detective work: “A detective investigating a crime needs both tools and understanding. If he/she has no fingerprint powder, the detective will fail to find fingerprints on most surfaces. If detectives do not understand where criminals are likely to have put their fingers, they will not look in the right places.” A planned forecasting and modelling effort that does not include provisions for exploratory data analysis often miss the most interesting and important results; but it is only a first step, not the whole story.
Exploratory data analysis means looking at data, absorbing what the data are suggesting, and using various summaries and display methods to gain insight into the process generating the data.
Many business forecasting books describe a variety of classical ways to summarize data. For the practitioner, an entertaining yet informative cartoon guide covering these is Gonick and Smith’s A Cartoon Guide to Statistics, published in 1993. For example, the familiar histogram is widely used in practice. In addition, there are a number of lesser-known techniques that are specifically useful in analyzing large quantities of data that have become accessible as a result of the increased flexibility in data management, computer processing, and predictive analytics. Because of their potential value to demand forecasting, we describe them in some detail. in my new book: Change & Chance Embraced: Achieving Agility with Demand Forecasting in the Supply Chain.
Learning by Looking at Data Patterns
Because most forecasting methods require data, a forecaster analyzes the availability of data from both external (outside the company) and internal (within the company or its industry) sources. For example, one potential source of internal data is a corporate data warehouse or Enterprise Resource Planning (ERP) system, which normally contains a rich history of product sales, shipments, prices, revenues, expenses, capital expenditures, and marketing programs.
The availability of external data is improving rapidly. Most of the required demographic factors (age, race, sex, households, and so forth), forecasts of economic indicators, and related variables can be readily obtained from computerized data sources and from industry and government publications on the Internet.
With the explosion of Internet websites, potential sources of valuable data are becoming limitless. With unstructured data, the need for data mining tools has become a necessity for exploring potential sources of data for consumer analyses and predictive modelling purposes.