Forecasting Metrics and KPIs

Your dashboards are green but your problems keep getting worse. You're tracking revenue per employee, units produced, and efficiency percentages. All trending upward. But customers still complain about quality. Equipment still breaks down unexpectedly.   Operators still struggle with changeovers. Here's why most metrics miss the mark: They measure what happened yesterday. Not what will happen tomorrow. They focus on outputs. Not the inputs that create those outputs. These 8 KPIs actually predict and prevent problems: 1. OEE (Overall Equipment Effectiveness) Shows equipment reality, not just availability 2. First Pass Yield Reveals true process capability 3. Total Cost of Quality** Captures the real price of problems 4. Employee Suggestion Implementation Rate Measures engagement that drives improvement 5. Setup/Changeover Time Determines your flexibility advantage 6. Supplier Quality Performance Prevents problems at the source 7. Safety Leading Indicators Predicts incidents before they happen 8. Customer Complaint Resolution Time Shows responsiveness that builds loyalty Each metric drives specific behaviors. OEE pushes systematic waste elimination. First Pass Yield forces quality at the source. Cost of Quality makes prevention profitable. The best manufacturing teams measure fewer things. But they measure the right things. And they act on every single number. Stop measuring your past. Start predicting your future. Question for you: If you could only track one KPI for the next 90 days, which would drive the biggest change?

We didn’t fail because the product sucked. We failed because we were looking at the wrong numbers. One of our best-looking product launches quietly started leaking cash within 3 months. Sales were good. Reviews were solid. Site traffic was up. But under the surface? Margins shrinking Return rates rising Repeat purchases… flat Turns out we were too busy watching vanity metrics the ones that make you feel good in a pitch deck and ignoring the ones that actually shape the health of the business. So we rebuilt our dashboard. And I now swear by these 4 KPIs 👇 1. Product-Specific NPS Not general CSAT. Not site feedback. We track NPS per product, every 90 days. If it dips, we investigate. FAST. 2. Warranty Claims per 1,000 Units It’s the quietest indicator of product quality. We aim for <5%. Above that, your cost of support and margin pain kicks in. 3. 60-Day Repurchase Rate 20–40% is solid in most DTC categories. We’ve seen how this drives word-of-mouth, not just retention. If people love it, they’ll buy again (or send friends). 4. Checkout Completion % by Device This helped us uncover a massive drop-off on mobile. Fixing that UX bump raised conversions by 14% in a week. These aren’t always the sexiest metrics. But they tell the truth. And when you're scaling, the truth is more useful than dopamine. What 3–4 KPIs do you actually look at every week? ♻️Repost if you think more founders should obsess over the right metrics, not just the pretty ones.

The Best Forecast Metric is the One Everyone Understands The goal of a successful S&OP process is a shared reality – a common view of the potential future demand along with the risks and opportunities that exist within the planning horizon. A common obstacle to creating this shared reality is KPI’s that not all S&OP team members understand. Unless everyone sees how often and how much an item’s demand varies from its forecast, it is nearly impossible for the team to work together to find and resolve any issues. It is also important to understand that forecasts will always vary from actual demand, and how much variation is acceptable for your organization. This is why I am a huge fan of using forecast bias as a common metric for S&OP discussions. It is easy to calculate and explain, and it gives a clear picture of recent forecast performance. As a demand planner, I am supposed to be the expert in forecasting; expecting everyone on the S&OP team to be equally versed in the complexities of forecasting demand is, to me, not realistic. I think of it this way: everyone can understand if an item is over- or under shooting the forecast. And if an item consistently exceeds or falls short of the planned demand, it needs attention. The example below is the bias model I use. It looks at the last 3 months forecast performance and calculates a bias percent for the 3 months in total. (I’m actually allergic to measuring bias month-by-month, as this only shows how much noise the demand contains.) It also shows the variance by month, which allows for examining and explaining one-off large variations in specific periods – variations which can often be explained by unexpected purchases or promotions that were unplanned. The goal here is to quickly find and show the items with the largest consistent variation, so that the team can analyze these items and see if the forecast needs to be adjusted. This helps create the shared reality that everyone can understand and if necessary, agree that action is required.

The Pipeline Problem: 4 KPIs to Increase Pipeline Predictability and Revenue 80% of sales orgs miss forecasts by over 10%. Why? It’s not lack of lead volume — it’s the wrong GTM metrics. First Principles tell us predictable pipeline comes from measuring what drives revenue, not chasing (vanity) metrics like MQLs. The solution? Hyper-aligned Sales, Marketing, and Revenue Operations on KPIs that identify inefficiencies and drive unified GTM execution. Below, I share 4 KPIs that IMHO increase pipeline predictability, which I define as the ability to accurately forecast volume, quality, and timing of opportunities that will convert into revenue. Yes, there are many roads to Rome regarding ideal B2B recurring revenue KPIs, but here are four that I like to ameliorate a pipeline problem. 1) Bowtie Funnel Conversion Rates: In addition to lead volumes, track the % of opportunities moving from MQL to SQL to SAL and the rest of the opportunity funnel. A good benchmark for scaleups? 20-25% MQL-to-SQL. Below 15% — find the leakage — likely misaligned lead scoring or weak ICP fit. 2) Cost Per Qualified Opportunity: Measure the cost of generating a mid-stage opportunity. Look at this over a trailing-twelve-months. Start by benchmarking against yourself. If you feel your measurement is high, your demand gen or ABM may be burning cash on low-intent or non-ICP prospects. 3) Active Open Opportunities (AOOs): Identify opportunities with a meeting in the last 30 days and buyer communication in the last 14 days (thank you, Mark Kosoglow). Where possible, I like to centrally help reps identify targets. AOO keeps them focused on high-intent pursuits. 4) CAC Payback Period: How long to recover acquisition costs? Best-in-class is 12-18 months. Over 24 months? Your pipeline’s too thin, or deals are stalling. By adopting these four buyer-centric KPIs, SaaS scaleups can transform unpredictable pipelines into more reliable revenue engines, aligning teams, optimizing spend, and more effectively hitting forecasts — ultimately driving sustainable growth and greater board confidence. Measuring your GTM organization with these KPIs is a starting point to driving aligned execution and improved pipeline predictability. What’s your go-to KPI for pipeline predictability? What’s your biggest forecasting challenge? I share Winning by Design's Bowtie Funnel below. One of my favorite tools to drive alignment on GTM investment. #firstprinciples #winningbydesign #revops

I used to think forecasting was all about crunching numbers. Boy, was I wrong. The game-changer? Integrating non-financial data. Here's what I've learned: 1. Employee satisfaction scores → predict productivity trends 2. Website traffic patterns → indicate future sales 3. Supplier performance metrics → forecast potential disruptions By combining these with traditional financial data, we've improved forecast accuracy by 35%. It's not just about better numbers. It's about making smarter decisions. What non-financial data has surprised you with its predictive power? #FinancialForecasting #CFOStrategy #FractionalCFO #StartupFinance #Growth #CFOInsights #CFOServices #Strategy #SMBgrowth #StrategicFinance #SmallBusinessSupport #StartupFinance #SMBfinance #ScalingUp

Want to use machine learning for time series forecasting? The best models will identify the drivers of trends. I once worked with a KPI like the image below. My ML model identified a serious problem. First, let's establish a working definition of "trend" when it comes to time series forecasting: The tendency of the KPI to increase/decrease over time. Like the image above, my real-world KPI exhibited a strong upward trend. Additionally, as shown in the image above, the trend was linear (i.e., a straight line). Finance loved this KPI because it could be easily forecasted with high accuracy. Executives loved this KPI because it kept going up and up. I didn't like it all. The problem was that traditional forecasting techniques rely only on the historical KPI values. These forecasting techniques may implement additional calculations (e.g., moving averages) to enhance accuracy. However, these calculations are based solely on historical KPI values. So, it's no wonder that Finance was able to easily forecast the KPI. However, I wanted to know what the drivers of the KPI were. Enter machine learning forecasting models. Machine learning forecasting models can not only use historical KPI values, but can also include any other data that might impact KPI values: Month of the year Day of the week Economic data Promotions Weather Etc. In the case of my KPI, I was examining activities originating from the marketing team (e.g., promotions and digital ads). That's when my ML forecasting model uncovered a serious problem. The ML model identified that the primary external driver of KPI values was the marketing team's digital advertising spend. I dove into the data and found that digital ad spend increased over the same time period as the KPI. However, the digital ad spend was increasing at a higher rate. The digital ads were experiencing diminishing returns. We were burning budget to prop up the KPI. That's the power of ML forecasting models. BTW - Millions of professionals now have access to the tools to craft powerful ML forecasting models. Python in Excel is included with M365 subscriptions and provides access to libraries such as scikit-learn and statsmodels. Everything you need to go far beyond Microsoft Excel's forecast worksheet.

Many FP&A teams have forecast accuracy as a KPI so let's check out six ways to improve your forecast accuracy... First, let's highlight the six ways and share some insights into each of them: ✅ Data quality ✅ Rolling forecasts ✅ Advanced analytics ✅ Scenario planning ✅ Collaboration ✅ Monitoring ---------- 1️⃣ Data quality Establish robust data management processes to collect, cleanse, and validate financial and operational data. Implement systems and tools that enable efficient data integration and analysis. Regularly review and improve data collection methods. 2️⃣ Rolling forecasts Instead of creating forecasts once a year, update them regularly throughout the year to incorporate the latest information. Rolling forecasts provide more agility and enable the FP&A team to react quickly to market dynamics and internal shifts. 3️⃣ Advanced analytics Utilize statistical methods, trend analysis, regression analysis, and predictive modeling to identify patterns and forecast future outcomes. Incorporate external factors, industry trends, and macroeconomic indicators into the forecasting models. 4️⃣ Scenario planning Develop scenario planning capabilities to forecast multiple potential outcomes. Create multiple scenarios, such as best-case or worst-case. Assess the impact of different scenarios on financial performance and evaluate risk mitigation strategies. 5️⃣ Collaboration Collaborate with business units, department heads, and other key stakeholders to gather input and insights for the forecasting process. Collaborative input enhances the accuracy of the forecast by incorporating diverse perspectives and domain expertise. 6️⃣ Monitoring Monitor actual performance against forecasts and conduct variance analysis. Identify and analyze the root causes of deviations between forecasted and actual results. Regularly review and update forecasts based on the insights gained from variance analysis. ---------- Personally, I don't think forecast accuracy is a goal in itself. But we should indeed measure it, understand the variances, and improve our models. This exercise will be highly insightful, however, let's not penalize people for not hitting a number we know will be wrong at the time of forecasting. Do you agree? What are other techniques to use to improve forecast accuracy? #finance #cfo #accountingandaccountants #careers ---------- 🎧 Listen to our #FinanceMaster Podcast here: https://bit.ly/3NLSt73 📰 Sign up for our newsletter here: https://bit.ly/TrendsInFnA 🧑🎓 Learn how we can help your finance team here: https://bit.ly/3prsWXH 🤝 Book a discovery call with me here: https://lnkd.in/eJWAub9r