Troubleshooting Salesforce Forecasting Problems

A sales leader told me "Our forecast is always off by 20-30%. I don't know what's real anymore." I looked at his pipeline. Every deal in "proposal stage" had an 80% close probability. I asked him one question: "Has an executive at the buyer's company authorized solving this problem?" He had no idea. Here's the problem: His CRM stages were measuring seller activity. Not buyer commitment. Discovery meant "we had a discovery call." Not "they acknowledged a costly problem." Demo meant "we showed them the product." Not "multiple stakeholders agreed this needs to be solved." Proposal meant "we sent pricing." Not "an executive authorized budget to fix this." So his forecast was always wrong. Because he was tracking the wrong things. Here's what we did: We rebuilt his qualification framework around buyer stages instead of seller activities. The ADVANCED framework: Acknowledged problem Documented issue Validated by team Authorized by executive Narrowed to external Chosen as vendor Established timeline Deal terms finalized These are buyer commitments. Not seller activities. When we ran his pipeline through this framework, reality hit hard. Most of his "80% deals" were actually at 25%. They had acknowledged a problem but nothing was documented. No executive sponsorship. No validation from multiple stakeholders. 𝗪𝗶𝘁𝗵𝗶𝗻 𝗼𝗻𝗲 𝗾𝘂𝗮𝗿𝘁𝗲𝗿, 𝗵𝗶𝘀 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 𝘄𝗲𝗻𝘁 𝗳𝗿𝗼𝗺 65% 𝘁𝗼 93%. Not because his team started working harder. Because they started tracking what actually predicts if deals close. BTW: When you can forecast within 3%, you can predict your income. You can plan for your family. You can budget for that house or wedding or kids' school. When your forecast is always off by 20%, you're guessing. Your compensation is unpredictable. Your future is uncertain. This isn't just about making your boss happy. This is about controlling your financial future. Track buyer commitment. Not seller activity. That's how you build forecast accuracy. — Sales Leaders! Your sales team doesn’t need more training. it needs a revenue operating system: https://lnkd.in/ghh8VCaf

Your sales forecast is a lie. Last month I analyzed 50+ CRM instances and found the average forecast accuracy was just 46%. When I asked sales leaders why deals slipped, the answer was always the same: "The close date was unrealistic." The problem isn't your CRM. It's how it’s being used. Many sales teams are checking boxes and required fields for their leader knowing its not 100% what’s actually going on Here's the simplest CRM hack that has improved forecast accuracy by 40%+ for my clients: Stop using "close date" and start using "customer-voiced impact date." This tiny shift changes everything. When a rep enters a close date, they're guessing when they think a deal will close. When they enter a customer-voiced impact date, they're documenting when the prospect said they'll make a decision. The difference is massive. Here's how to implement this today: 1️⃣ Create a custom field called "Customer Decision Date" This is when the buyer has committed to making a decision. 2️⃣ Require documented evidence for any date "The CFO confirmed they need to decide by June 30th because..." 3️⃣ Track it alongside the rep's forecast date. This creates healthy tension between what the rep hopes and what the customer says. 4️⃣ Make it visible in pipeline reviews "The customer said they're deciding March 15th, but you're forecasting February 28th. Why?" Top sales teams keep these dates separate and review the gap. If there's no customer decision date with evidence, the deal doesn't belong in your forecast.

Don't Be In a Hurry to Update a Forecast Throughout my career in demand planning, I have often been asked about updating forecasts like the one below. The forecast for the sku that hasn't been ordered in several months. My instinctive reaction was to reduce the forecast to some minimal quantity, especially when the expectation was that the issue needed to be resolved immediately. But what I have learned is that updating a forecast for an item like this is the LAST step in a proper analytical process. Without investigating the root cause of the lack of orders, simply changing the forecast may not solve the problem. In fact, it may hide a problem rather then resolving it. Let's look at the possible causes of the lack of orders: * Customer has discontinued the item but not informed the salesperson that it is discontinued. * Customer data is incorrect, so the item does not appear on their side as available to order. * The salesperson and customer have agreed that, due to high inventory levels at the customer's warehouse, no orders are needed. * The salesperson is aware that the item is discontinued in the customer's system, but hasn't yet updated their assortment or the system. * The manufacturer has discontinued the item and not communicated this change. I'm sure you can think of additional reasons why we might see a forecast like this. My point is that until we know what caused the lack of orders, we should not update the forecast by guessing at what numbers might make sense. In this case, the sku had been inactivated in the customer's system, so no orders had been generated. Once the salesperson asked about the lack of orders, the planner for the customer realized that they had not been ordering product and immediately activated the item and placed an order. The salesperson then followed up with the customer to see what additional quantity they might need to fill their shelves. If I had simply updated the forecast and not followed the correct procedure, we could have gone several more months with no orders. In this case, updating the forecast was the last and not the first thing to do. Forecasts should be based on data and business input, not just gut feel or a need to quickly solve a problem. So ignore the urge to update forecasts without following a good root cause procedure first.

If every rep forecasts differently, you don’t have a forecast. You have a guessing game. I’ve seen this too many times: - Some reps pick a number out of thin air and reverse-engineer the deals to match. - Others commit specific deals, but with no clear criteria for why. - Some just lowball their number so they can overdeliver. The result? A forecast that’s less science, more storytelling. When reps set their own rules, you get: - Erratic projections, making it impossible to resource properly. - Lost accountability, because reps can move the goalposts without consequence. - Misalignment with GTM teams, because RevOps, finance, and CS are forced to react to unreliable inputs. Here’s a solution: 1. Define a standard forecasting model Forecasting needs rules. Either reps commit deals based on clear criteria, or they forecast a number with justification. Mixing both = inconsistent rollups. 2. Enforce criteria for Commit deals A deal isn’t commit-worthy unless it checks key boxes: - Multithreaded (or it’s a coin flip at best) - Economic buyer engaged (champions don’t cut checks) - Procurement validated (or it’s stuck in limbo) No criteria, no commit. 3. Use data to call bullshit Reps’ confidence isn’t a data point. If their forecast is wildly different from historical conversion rates and deal stage velocity, they need coaching, not a calculator. 4. Drive home that you’re forecasting is for business planning, not just quota pressure The CFO isn’t asking for fun. If your forecast is off by 30%, you’re not just missing a number - you’re messing up hiring plans, resource allocation, and revenue projections. 5. Make reps own their forecasts Accuracy should be a coaching metric, not just a reporting function. If a rep is always 30% off, fix their deal inspection process, not just their Excel skills. A forecast isn’t a wish list…it’s a commitment. The tighter the system, the better the number. A sloppy forecasting process signals a sloppy sales org. Your forecast is either an operational asset - or a liability. That’s a choice.

One year at Gong this month. Here's what I'm proud of. We had two problems on my team. And we built our way out of both of them. Problem one: discovery was broken. Reps were jumping to solutions before they understood the problem. Calls felt like demos with questions sprinkled in. We were pitching before we were learning. So we built something simple. Before you talk about your product, map the customer's problem. What's causing it. Who it's affecting. What it's costing them. Then talk about product. We called it Problem-Based Discovery. Simple framework, ran it in 1:1s every week, coached it in deal reviews. Made it the default. Win rate up 5 points. Deal size up 40%. Discovery conversion up 20 points. Then it went company-wide. Problem two: forecasting was guesswork. Everyone had conviction about their deals. Nobody had a shared way to translate that into a number leadership could trust. So we built a methodology. Every deal gets pressure-tested the same way. You work from the deal up to the dollar, not the other way around. I call it Deals to Dollars. Forecast variance dropped to about 3% on average. That one is now going company-wide too. A year of building at scale taught me the best systems make the right behavior easier than the wrong one. When discovery has a structure, reps stop winging it. When forecasting has a language, managers stop guessing. The team gets better without just working harder. Not possible without our Enablement and Ops partners (Fiona NicChoiligh, Deb Averett) And look, everyone right now is asking how to plug AI into their sales motion. The key is ensuring you've built clean systems first. AI doesn't fix a broken foundation. It runs on top of one. That's what I'm most proud of. Not the numbers. The fact that the process outlasts the moment.

Last week, the CRO of a $36M ARR SaaS turned to us. They missed their Q4 forecast by 28%. The board wasn't happy. Here's the playbook we used to fix it. 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 I talk to dozens of sales leaders every month. This CRO is not an exception: • Inaccurate forecasts due to poor visibility • Poor visibility due to missing CRM data • No clear process & accountability 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝗽𝗿𝗼𝘃𝗲𝗻 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸 𝘁𝗵𝗮𝘁 𝗵𝗲𝗹𝗽𝗲𝗱 𝟭𝟬𝟬+ 𝗕𝟮𝗕 𝗦𝗮𝗮𝗦 𝗖𝗥𝗢𝘀 & 𝗥𝗲𝘃𝗢𝗽𝘀 𝘁𝗲𝗮𝗺𝘀 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗮𝗰𝗰𝘂𝗿𝗮𝘁𝗲𝗹𝘆: ✅ 𝗙𝗶𝘅 𝟭: 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 The cornerstone of effective deal reviews and visibility into pipeline & forecast health. 1️⃣ Activity data: WHY IT MATTERS: • Emails/meetings not logged = unclear deal velocity • No engagement = high risk of deal slippage • Use activity data, not gut feel E.g. • Last activity date • Next meeting date • Email reply rate  .. SITUATION: The CRO & RevOps team faced 3 issues: 1. Reps forgot to log activities 2. Auto-logging failed (poor opp & contact role mapping) 3. Most opps lacked contact intelligence (who’s involved, decision-maker, multi-threading) No activity/contact insights = no visibility. SOLUTION: Auto-capture emails & meetings with a solution that identifies contact roles. Ideally with an Outlook Add-In/Google Extension to improve opp mapping (e.g. Weflow does this). 2️⃣ Salesforce data entry: WHY IT MATTERS: • Often missing key fields (e.g. MEDDIC) • Bad CRM data = poor deal reviews & forecasts SITUATION: 76% of their MEDDIC fields were not populated. Reps hated updating Salesforce. = managers lacked deal visibility. SOLUTION: An AI notetaker that auto-extracts and updates MEDDIC fields in SFDC from call transcripts (e.g. Weflow). ✅ 𝗙𝗶𝘅 𝟮: 𝗙𝘂𝗹𝗹 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻𝘁𝗼 𝗗𝗲𝗮𝗹 & 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗛𝗲𝗮𝗹𝘁𝗵 WHY IT MATTERS: To improve deal reviews & forecasts, managers need leading indicators of deal health: • Push count • Configurable warnings • Multi-threading & velocity  .. A pipeline coverage dashboard (CQ, Q+1) creates extra visibility. SOLUTION: Embed insights in Salesforce or use revenue intelligence/forecasting tools (like Weflow). ✅ 𝗙𝗶𝘅 𝟯: 𝗖𝗼𝗺𝗯𝗶𝗻𝗲 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗲𝘀 & 𝗠𝗼𝗱𝗲𝗹𝘀 SITUATION: They ... 1. Only forecasted new logos (ignoring expansions/renewals) 2. Used weighted forecasts + spreadsheets (highly inaccurate) SOLUTION: • Opp record types for expansion/renewals • Auto-create renewal opps upon closed-won • Combine models: 1. Deal-by-deal submission & review (+ auto roll-up) 2. Dynamic weighted 3. ML-based (They now run this in Weflow) 💭 𝗖𝗹𝗼𝘀𝗶𝗻𝗴 𝗡𝗼𝘁𝗲 I didn't touch upon revenue cadence/process due to character limits (but we helped fix this too). WHAT WOULD YOU ADD? 👇 ____ PS: We built Weflow to help B2B SaaS revenue teams forecast accurately. Take a product tour (on desktop): https://lnkd.in/eXHt-i6q

79% of sales orgs miss their forecast by more than 10% (Forecastio) At the same time, 70–90% of CRM data is incomplete, outdated or wrong. That’s not a coincidence. It’s cause and effect. We keep talking about “market uncertainty” and “longer cycles”, but a lot of forecast pain is much simpler: Your inputs are a mess. Things I see all the time: ❌Deals in the wrong stages because no one is sure what each stage actually means ❌Deal values showing the original proposal amount even after two scope changes ❌Close dates magically pushed forward month after month to stay on the report ❌Critical information scattered across spreadsheets, Notion docs, emails, and Slack ❌Deal risks living exclusively in your rep's head, not your system Then the end of month hits, and we expect that pile of half-truths to turn into a reliable forecast. So the forecast call turns into: “This is in commit, but it’s soft.” “This amount is wrong, we’re renegotiating.” “I haven’t had time to update this yet, but it should close.” That’s not forecasting. And the fix isn’t a new AI layer or another forecasting tool. It’s getting the operational stuff clear: ✅Clear stage definitions everyone actually follows ✅Guardrails for close dates and amounts (no endless pushing) ✅One single source of truth for deal notes and next steps ✅Simple rules like: “If it hasn’t moved in X days, it’s not commit” Good forecasting is just good data hygiene repeated every week. When your underlying data is clean, the meeting stops being a guessing game. You’re not arguing with the CRM; you’re talking about risk, trade-offs and what to do next. Companies with well-implemented CRM systems see sales forecasting accuracy improve by 42% on average. If your forecast meetings feel like a debate instead of a decision, odds are the market isn’t the main problem. Your maintenance is. #CRM #revops #CRMmaintenance #forecasting

How to fix CRM stage-based forecasting for Q4 CRMs have a default setting for forecasting that matches opportunity stage with forecast probability - visually outlined in slide 2 below. Where the further in the sales process the higher the forecast probability. At first glance this seems to make sense, that is that a deal in the later stages in the process is closer to close and therefore has a higher the probability of win. But what it is not taking into account are the many real-life situations when deals stall, slip, lose prioritization, lose champions, get delayed by holidays, fiscal periods etc. As a results this can create a false sense of confidence in deals and also result in zombie pipe where forecast prioritization are static and don’t change once they have achieved a stage. The default stage-based forecast settings in CRMs are fine, when used as a starting point. Where issues arise is when that is the end of the analysis. What we are going to show in slide 3 is how to use TRAP to identify where this default settings may be hiding risk to help identify opportunities to go resolve issues and drive higher accuracy in forecasting. Here is a quick summary of the process outlined in the slides: 1. Take your pipeline and forecast as is 2. Add Time in Stage to that report 3. Find the Median Sales cycle (opportunity age) of your closed won deals 4. Take Median Sales Cycle and divide by your opportunity stages to find Ideal Time in Stage (ITiS) 5. Take your forecast dashboard or add a conditional field to your excel or google sheet doc 5a. Make the first value anything less than your ideal time in stage, green  = < ITiS +1 5b. Then make another value, orange = > ITiS < ITiS +50-60% 5c. Then make another value, red = >ITiS+50-60% 6. Re-review your forecast and demote or remove forecasted deals with time in stage risk 7. Build an action plan with your rep on how to address these at risk deals 8. Submit your revised forecast #trap #forecasting #managermethodology #crm #salesleadership