Sales Forecasting For Subscription-Based Models

In a usage based pricing model, forecasting deals is really hard. There are more variables than in a traditional sale. Not only are we trying to understand close likelihood and date, but in many cases how much revenue will come in and how quickly. With API products like Daily (current company) and Twilio (previous company), the build or migration stage is really critical, because the API must be built into an app or website for usage to begin, and this is rarely a smooth and quick process. Typically customers want to test their implementation live before committing to a partner. This means that we have to invest in and support a potential customer through a build or migration, often before we get any commitment. The sales process works a bit backwards in this model. Once a customer has successfully built on your API, then they either start trying to: 1. gain adoption of their product or feature if it's a new build, which means predicting usage without historical data is impossible or 2. they start a true migration from their current partner, and there are a lot of variables that could impact how quickly or how much traffic they can move to your platform. The UBP model is incredible for keeping sales closely aligned to customer value. Customers are only paying for usage and will only keep using the product if there is value. However, it adds a lot of complexity for the sales person and for the business. To help, we've adopted a model where: - Sales predicts eMRR (expected monthly recurring revenue) upon closing a deal. This is the average MRR that we expect the customer to grow to at steady state within the first year. - Customer Success and Sales work together to determine a quarterly MRR forecast which leverages any historical data and knowledge of the customer to estimate usage in each quarter, at least 2 quarters out from today. This is particularly useful for customers who are ramping up their traffic, and/or have seasonality of usage. - Customer Success has an MRR Goal for each key customer, which is the goal for customer MRR within a particular time period. This ensure we're all aligned on where we're trying to help a customer to go within that period. I'd love to hear other tips for UBP forecasting!

I've spent 10 years figuring out how to predict repeat customer purchases online. Here’s how to do it right and get to 95%+ accuracy: If you want to understand your repeat customers and predict their behavior, it all starts with cohort analysis. This sounds fancy, but it’s just grouping customers based on the date they made their first purchase. From there, you can build a clear picture of what’s happening in your business. Here’s the step-by-step process: 1. Assign customers to cohorts. Start by grouping customers by the month (or week, depending on your volume) of their first purchase. This will be the starting point for tracking retention and repeat purchase behavior. 2. Establish a baseline retention curve. Most customer behavior follows a predictable pattern: orders gradually taper off over time. Plot this out to create a baseline curve—a starting point to measure future cohorts against. 3. Weight for recent behavior. Here’s the thing: the customers you acquired last month are much more relevant to forecasting than the ones you acquired three years ago. Weight your analysis to focus on recent cohorts to get a more accurate picture of what’s next. 4. Segment by customer type. Not all customers behave the same way. You might notice early customers were all over the place—some subscribing, some buying once. Breaking this down by type (e.g., subscribers vs. one-time buyers) makes the data a lot more actionable. 5. Adjust for seasonality. Timing matters. A customer you acquire in October is probably going to shop again in November because… Black Friday. That doesn’t mean they’re inherently “better,” but you need to account for these factors when predicting future behavior. 6. Predict orders, not people. Instead of predicting how many customers will come back, focus on the total number of orders a cohort will generate. Then multiply that by your average order value to get to revenue. Trying to count subscribers, then adjust for churn, reschedules, or payment failure will create lots of inputs to manage and ultimately leads to precision without accuracy. 7. Keep it fresh. The most accurate forecasts come from constantly updating your data. Monthly refreshes are usually the sweet spot—they let you capture new trends without bogging you down with constant updates. Sounds like a lot of work? It doesn’t have to be. Drivepoint does all of this out of the box. Want to see how it works? We can ingest your Shopify and Amazon data into actionable retention and revenue forecasts and show you the results. Link in the comments to book time if you want to learn more. 🚀 #CohortAnalysis #Forecasting #Shopify #Amazon #DTC

Headcount doesn’t equal revenue. And one person doesn’t own sales... I’ve gone from forecasting 𝘩𝘶𝘯𝘥𝘳𝘦𝘥𝘴 𝘰𝘧 𝘵𝘩𝘰𝘶𝘴𝘢𝘯𝘥𝘴 of cases of soap to forecasting 𝘩𝘶𝘯𝘥𝘳𝘦𝘥𝘴 𝘰𝘧 𝘵𝘩𝘰𝘶𝘴𝘢𝘯𝘥𝘴 of dollars in SaaS revenue. Today, I dug into the mechanics of SaaS forecasting with Stephanie Valenti to figure out what’s changed and what’s stayed the same. 𝗕𝗶𝗴 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 #1: B2B forecasting is two different games with different methods. 1️⃣ New Business Forecasting ✅ Waterfall Method: Map key outcomes across each GTM stage (MQL, SQL, Opportunity, Closed Won) while tying inputs to marketing, finance, HR, and the exec team. (The secret most miss? Stage the revenue timing based on each part of the sales cycle.) ❌ Sales Capacity: One human ≠ revenue. This method ignores the entire GTM motion. Use it for setting quotas, not for forecasting. 2️⃣ Recurring Revenue Forecasting ✅ Cohort Analysis: Divide customers into groups (typically by acquisition date or segment) and track behavior over time. This reveals retention trends and churn risks. (This is where I live now.) ✅ Baseline Plus: Start with your mature revenue, then factor in: • New investments: Marketing, sales, or GTM activities. • New activities: Expansion, upsell, or reactivation. • Headwinds: Churn, economic shifts, or competition.    (Minus the new math, this is where I used to live.) 𝗕𝗶𝗴 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆 #2:  Some things are n͟o͟t͟ different a͟t͟ ͟a͟l͟l͟: 1. Forecasting isn't the destination.  It's the map. 2. Numbers don’t appear by magic. They’re built. 3. Challenge every assumption. If you don’t, it’ll challenge you later. 4. Revenue is a team sport - not an individual one. 5. Forecasting is leadership. You have to drive the bus. Alright, off to handle the charts and graphs. 🚀 P.S. Thoughts on the poll question?!