Best Practices for Sales Forecasting

"Just curious, how's your forecast looking?" My CEO friend asked me. The weekly forecast review. The monthly pipeline call. The quarterly business review. All centered around one flawed model: Asking reps to predict the future based on gut feeling. "50% chance of closing." "Strong verbal commitment." "Just waiting on final approval." These phrases hide a painful truth: We have no idea what's actually happening inside our deals. I changed how we forecast last quarter: Instead of: "How do you FEEL about this deal?" We now ask: "What have they actually DONE?" - Has the economic buyer viewed pricing? - Have technical stakeholders reviewed security docs? - Have end users looked at implementation plans? - Is the champion actively sharing content internally? Behavior doesn't lie. Words do. We tracked content engagement across 200+ deals: Closed deals: Prospects engaged 7+ times in final two weeks Lost deals: Engagement dropped to 0-1 interactions before going dark The deals your team is most confident about? Often the ones with the least actual buyer engagement. Here's how we transformed our approach: Every opportunity now has a digital space where we can see: - Exactly who is engaging with what content - Which stakeholders are involved (even ones we haven't met) - Where deals are getting stuck - When interest spikes or drops Our forecast accuracy improved INSANELY. Stop asking reps what they "think" will happen. Start measuring what buyers are actually doing. The best indication of deal health isn't what prospects tell you. It's how they behave when you're not watching. Do you know what your buyers are really doing? Or are you still forecasting based on feelings? Agree?

Targeted revenue provides stretch goals for sales teams. But it's also vital for strategic planning. Here's how targeted revenue works and why it matters for FP&A. 1) Start with known and knowable sales This is the core of a sales forecast. Every company should maintain sales activity in a CRM. This may be broken down by customer, channel, product category, SKU, or a combination of all. Customers are known, the stage of the sales process is clear, and the amount of the deals are quantified. If a company is planning using driver-based forecasting, the sales outlook may omit this level of detail since the figures won't tie directly to customer accounts. 2) Layer in a stretch target. Many companies don't know which specific customers will generate revenue a year from now. Even if they do, there’s uncertainty in the amounts. But this shouldn’t stop setting the targets. Revenue targets can be based on forecasts within a sector or revenue channel where sales managers believe there's untapped opportunity, rather than with a specific customer. This brings about a focus on sales strategy, marketing, and other sales initiatives to make inroads in those channels. 3) Quantify the opportunities A vital, but challenging task, is for the sales team to put numbers to those opportunities: • Which channels are most promising? • What the potential deal size? This provides FP&A with a foundation for all-in revenue planning. 4) Cascade the impact Once a revenue target is set, it doesn't stop at the sales forecast. It drives the operating assumptions further down the P&L, for capex, and for financing: • Direct costs • Gross margins • Headcount planning • Compensation • Marketing • Facilities • Debt 5) Build in timing assumptions It's rare for revenue to be forecast in neat, even increments. FP&A needs to decide: • Smooth it evenly throughout the year • Front-load, if sales are aggressive • Back-load, if sales are conservative • Weight it, if seasonality is in play The choice of FP&A or a Controller is not just for revenue recognition. It impacts hiring plans, marketing, cash flow, and especially working capital needs. 6) Apply conservatism discounts Targeted revenue is aspirational and hardly guaranteed. Because of this, the financial model benefits from conservatism or scoring adjustments upon which scenarios can be run. A sale may be all-or-nothing, where it's either won or it's not. Weighted confidence levels can allow for scenario triggers so forecasts adjust dynamically. This helps FP&A and sales create what I call "tiers of planning" -- high, mid, and low confidence. Tiered planning sets optimistic and conservative sales thresholds. 7) Apply the plan With sales targets at various thresholds, FP&A can better plan for the rest of the FP&A and set performance milestones.

Choosing the right chart is half the battle in 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠. This one visual helped me go from “𝐖𝐡𝐢𝐜𝐡 𝐜𝐡𝐚𝐫𝐭 𝐝𝐨 𝐈 𝐮𝐬𝐞?” → “𝐆𝐨𝐭 𝐢𝐭 𝐢𝐧 10 𝐬𝐞𝐜𝐨𝐧𝐝𝐬.”👇 The right chart makes insights stick. The wrong one? Confusion. 𝐇𝐞𝐫𝐞'𝐬 𝐦𝐲 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭 – which chart to use, when, and why: 𝟏. 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 – Compare values across categories • When: Sales by region, product performance • Why: Our brains process length differences instantly 𝟐. 𝐋𝐢𝐧𝐞 𝐂𝐡𝐚𝐫𝐭 – Show trends over time • When: Revenue growth, user adoption curves • Why: Makes patterns and changes obvious 𝟑. 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭 – Display parts of a whole • When: Market share, budget allocation • Why: Works when you have 5 or fewer segments 𝟒. 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭 – Find relationships between variables • When: Price vs. demand, experience vs. salary • Why: Reveals correlations and outliers 𝟓. 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 – Show frequency distribution • When: Customer age ranges, response times • Why: Spots normal vs. skewed distributions 𝟔. 𝐑𝐚𝐝𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 – Compare multi-dimensional data • When: Employee skills assessment, product features • Why: Shows strengths and gaps at a glance 𝟕. 𝐌𝐚𝐩 – Visualize geographic data • When: Sales by state, store locations • Why: Location patterns jump out immediately 𝟖. 𝐇𝐞𝐚𝐭𝐦𝐚𝐩 – Highlight intensity patterns • When: Website clicks, correlation matrices • Why: Color gradients reveal hot spots 𝟗. 𝐁𝐮𝐛𝐛𝐥𝐞 𝐂𝐡𝐚𝐫𝐭 – Display three variables • When: Market cap vs. growth vs. profit margin • Why: Adds a third dimension through size 𝟏𝟎. 𝐃𝐨𝐧𝐮𝐭 𝐂𝐡𝐚𝐫𝐭 – Modern take on pie charts • When: KPI progress, category breakdown • Why: Center space for key metrics 𝐏𝐫𝐨 𝐭𝐢𝐩: Match your chart to your audience's decision. Executives need trends? Line chart. Team needs to compare options? Bar chart. The right visualization = clearer insights, faster decisions, stronger impact. ♻️ Save this guide for your next presentation! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 16,000+ readers here → https://lnkd.in/dUfe4Ac6

A VP just called me about a rep who's been working a "hot lead" for 6 months with zero progress. Here's how our diagnostic conversation went: Me: "Do they have confirmed budget?" VP: "Well, the rep says not exactly confirmed..." Me: "What's their timeline for making a decision?" VP: "They said maybe this year, maybe next..." Me: "What's their decision process?" VP: "Uh, I think the VP has to approve it..." Then I asked the question that exposes every fake deal: "What would have to happen for them to say no?" Complete silence. That's when I knew this "opportunity" was a complete waste of time. Here's the hard truth for sales leaders: If your reps can't answer these basic qualification questions, they're not working real opportunities. They're chasing ghosts. The signs your team has a qualification problem: → Sales cycles that drag on for months with no progress → Forecasts full of "thinks," "maybes," and "hopefullys" → Reps who can't explain why a prospect would reject them → Pipeline inflation with terrible conversion rates Real opportunities have: ✓ Identified budget and clear decision authority ✓ Timeline driven by genuine business need ✓ Defined process with known stakeholders ✓ Specific criteria that could disqualify you The best sales teams I work with qualify aggressively and early. They'd rather have a smaller pipeline of real deals than a bloated forecast of fantasies. Your reps' time is your most expensive resource. Stop letting them waste it on deals that were never real in the first place. — Sales Leaders, want to be a world class sales manager and get your team crushing quota? Go here: https://lnkd.in/ghh8VCaf

Want to use machine learning for better forecasting? Your models must learn whether seasonality exists in your business and successfully predict it. Here's how. First up, we need a working definition of trend: Patterns that appear at regular intervals (e.g., weekly or monthly). Think of seasonality as a factor that modifies the KPI you are trying to forecast: - Retailers make more sales in November and December. - Customer service receives fewer calls on weekends. - Airlines carry more passengers around holidays. - Website visits are higher in the morning. As always, the key to building a powerful machine learning model is knowledge of the business process. For this post, the business knowledge takes on two forms: 1 - Knowing that seasonality is part of the business process. 2 - Understanding the nature of the seasonality. For this post, we'll assume that seasonality exists and that its nature aligns with the calendar year - for example, the classic seasonality of brick-and-mortar retail (i.e., Black Friday). As with any machine learning model, you must provide the algorithm with enough data so that patterns can be learned. I will cover one aspect of this in a later post, when I discuss lagged features. A powerful way to help ML forecasting models learn seasonality is to provide features that explicitly detail seasonal aspects of the business process. This is a bit abstract, so let's explore the scenario of seasonality manifesting within a calendar year. Let's say you're trying to build an ML forecasting model for a monthly KPI (e.g., sales). Since you are aware that the business process exhibits seasonality within each calendar year, providing the month name as a feature often helps the algorithm learn this seasonality. For example, the resulting ML forecasting model can learn: - Sales are highest in November and December. - Sales are lowest in January and February. - Sales bump in August. However, keep this in mind. Months are categorical data, and you need to handle them correctly in your ML forecasting models. While you can use month numbers instead (e.g., January = 1), I prefer to use month names explicitly. Regardless of whether you use month numbers or month names, be sure to encode the data as needed to ensure that the ML algorithm treats it as categorical. For example, when using Python's scikit-learn library, be sure to use a OneHotEncoder on the month data before training your model. 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.

Your forecast accuracy is probably terrible. Here's why and how to fix it. Most sales leaders are forecasting based on rep gut feeling instead of buyer behavior. I analyzed 1,000+ deals last quarter and found the pattern: Traditional forecasting asks "What's your confidence level on this deal?" "When do you think it will close?" "How committed is the buyer?" Buyer-behavior forecasting asks "When did the buyer say they need to make a decision?" "What's their documented evaluation timeline?" "What competing initiatives are they prioritizing?" The difference is massive. Reps guess. Buyers have actual timelines. The best sales leaders I work with have completely separated "rep forecast date" from "customer decision date" in their CRM. This creates healthy tension between hope and reality. If there's no customer decision date with evidence, the deal doesn't belong in your forecast.

Most companies are flying blind when it comes to revenue 📊 "Some months we're closing deals left and right, other months it's crickets. I never know what's coming next month." Every month I meet with business owners who tell me exactly this. Revenue unpredictability kills everything. You can't plan hiring, you can't forecast growth, and you definitely can't sleep well at night wondering where next month's revenue is coming from. Well here's the thing...it doesn't have to be this way. ➡️ THE SOLUTION: PIPELINE DRIVEN FORECASTING Stop guessing at your revenue and start building forecasts based on actual pipeline data. Think about that difference. Instead of hoping deals close, you're working with real data from real prospects. STEP 1️⃣ → STRUCTURE YOUR CRM Track each deal by stage, amount, and expected close date in your CRM system. See every deal needs to move through defined stages that actually reflect how your sales process works. You can't just throw deals in there and hope for the best. STEP 2️⃣ → EXPORT PIPELINE DATA Export your CRM data to Excel for revenue forecasting and analysis. You know what's amazing about this? You get complete control over how you manipulate and model your data. Plus Excel gives you that flexibility that most CRM reporting just can't match. STEP 3️⃣ → FORECAST REVENUE Use weighted pipeline data to predict future revenue with confidence. Apply probability percentages to each stage and calculate realistic monthly projections. That's pretty powerful when you think about it. ➡️ RECOMMENDED CRM TOOLS 🔵 Salesforce → Enterprise grade pipeline management for larger companies 🔴 HubSpot → All in one sales & marketing platform ⚫ Pipedrive → Simple, visual pipeline management for smaller teams Now you may be thinking which one should I choose? Well that depends on your company size and complexity, but any of these will work better than spreadsheets alone. ➡️ BEST PRACTICES FOR PIPELINE MANAGEMENT 📅 Keep data updated weekly 📊 Track conversion rates by stage 📋 Define clear stage criteria 📝 Review forecasts monthly ⚙️ Set up CRM automations 🗓️ Set realistic close dates The key is to export pipeline data monthly to maintain accurate revenue forecasts. This monthly ritual will completely change how you plan and operate your business. === I've seen this transform companies from reactive revenue planning to predictable growth patterns. Instead of crossing your fingers each month, you'll know exactly what's coming and can make strategic decisions accordingly. What's your experience been with pipeline management? Are you still flying blind or do you have a system that works?

I once saw a forecast where a $500K deal had been marked as commit for three straight quarters. Every time the quarter closed, the close date was pushed forward. No real movement, no new champions engaged—just a rep saying, “Still in play, just delayed.” The reality is: every time a deal slips, the probability of it closing drops significantly. Sales Reps spend so much time tracking pipeline coverage, weighted forecasts, and commit numbers, but the real revenue risk hides in slipped deals—the ones that keep getting rolled forward with no clear next steps. 1. A deal that slips once might be a timing issue. 2. A deal that slips twice is already in trouble. 3. A deal that slips three times? That’s not pipeline, that’s a ghost. Yet, most forecasting models don’t adjust for this. They assume a slipped deal is still a deal, just delayed. But if the same revenue keeps rolling over quarter after quarter, your forecast isn’t conservative—it’s broken. This is where Everstage's Crystal comes in. With Advanced Commission Forecasting, sales reps can see exactly how pipeline changes impact their potential earnings. Crystal's payout prediction intelligence allows reps to modify deal attributes and understand commission implications before closing opportunities, naturally steering them away from pursuing "ghost deals" that won't materialize. Everstage builds trust through detailed earning statements, providing real-time visibility into quota attainment and commissions. By aligning compensation visibility with pipeline reality, both reps and finance teams get a clearer picture of what deals are truly worth pursuing. Explore Everstage here - https://lnkd.in/ervpk4p3 Because the biggest risk in forecasting isn’t missing pipeline—it’s believing in deals that should have been written off months ago.

Forecasting off pipeline stages is like using self-tanner before a beach trip. It gives you false confidence, washes off fast, and fools absolutely no one who gets too close. “30 opps in stage 3 × 40% = $1.2M forecasted.” Bueno. Now subtract the 9 deals that haven’t moved in 30+ days. Then subtract the 5 with no economic buyer involved. And the 8 that don’t have next steps or a MAP. Still $1.2M? lol nah...didn't think so. Stage-based forecasting is pretty broken, mainly because pipeline stages are opinions. Velocity and conversion, on the other hand, are facts. Buyers don’t care what CRM column they’re sitting in. They care about friction, fit, and fear. And your forecast should reflect all three. Here’s how to fix it: 1. Pair conversion rate with conversion velocity. - Let’s say Stage 3 deals have a 30% win rate.  - But they take 52 days to close on average. - If it’s day 50 of the quarter, and that deal just hit Stage 3? It’s not real revenue. It’s next quarter’s homework. One RevOps team I know added “days to close by stage” into their forecast model. They realized 63% of late-stage pipeline wouldn’t close in time based on historical cycle length. The result? They re-weighted forecastable revenue by stage age × velocity. Forecast accuracy jumped 21% in two quarters. 2. Use behavioral signals, not just stage tags Stop assuming every Stage 4 opp has a 60% chance of closing. Start tagging based on buyer actions - not rep motion. What to track: - Was an economic buyer involved in the last call? - Did the buyer ask about implementation timeline? - Has procurement been looped in? - Are multiple stakeholders engaged and documented? Deals with 3+ of these signals close 2 - 3x more often. AND they close faster. Build a behavioral scoring model and overlay it on top of your CRM stages. 3. Build pipeline coverage by real math Forget the “3x coverage” rule of thumb. If your conversion rate from Stage 2 to Close is 18%, and your quarterly target is $1M, you don’t need $3M in pipeline. You need $5.56M in qualified opps. Idea: A CRO we work with built a stage by stage conversion model with time-based decay curves. They found that 22% of their pipeline had aged out of viable range, and 19% of Stage 1 deals had <5% chance of conversion. So they cut their pipeline headline by 41% - and finally forecasted accurately for the first time in six quarters. tl;dr = Forecasting isn’t about hope. It’s judgment × math × motion. If you’re still forecasting based on pipeline stage alone, you don’t have a sales process. You have a spreadsheet-shaped fantasy. And fantasy doesn’t hit number.

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!