Sales teams are always looking for clearer insights, stronger consistency, and the ability to predict results with confidence. The challenge is how to improve forecast accuracy, turn data into decisions that drive momentum, and create a more reliable foundation for growth.<\/p>\n
This guide offers 12 practical tips that help you build forecasts rooted in clarity, consistency, and confidence.
\nInside, you will find insights from leaders at monday.com and real examples from the field, giving you a clear path forward and a smarter framework to follow.<\/p>\n"}]},{"main_heading":"Meet the Experts","content_block":[{"acf_fc_layout":"text","content":"
Chris Douglass, Senior CRM Manager, monday.com<\/p>\n
Alex Mahoney, Sales Manager, monday.com<\/p>\n
Jonathan Moss, EVP of Growth and Strategy, Experity<\/p>\n"}]},{"main_heading":"1. Focus on actions that drive impact","content_block":[{"acf_fc_layout":"text","content":"
When teams spend hours logging notes, updating fields, or manually managing data, they lose meaningful time for selling and coaching. As Chris puts it, \u201c<\/strong>Being bogged down in busy work isn\u2019t the same as being productive.\u201d He reminds leaders to ask whether the work they\u2019re doing is \u201creally moving the needle.\u201d<\/p>\n Jonathan discovered this firsthand while auditing his team. \u201cA huge chunk of the time was assembly, pulling data from different systems,\u201d he says. \u201cIt\u2019s important work, but it doesn\u2019t require human judgment.\u201d His team used AI to handle that data plumbing<\/a> so reps could focus on revenue-driving work.<\/p>\n Alex saw the same thing. \u201cMy team was spending about 40 percent of their time on administrative tasks,<\/strong>\u201d she says. \u201cOnce we automated the low-signal, repetitive work, they got hours back in their day.\u201d<\/p>\n Forecasts break when teams rely on old assumptions or outdated truths. Chris encourages leaders to ask, \u201cWhere are my blind spots? How can I better anticipate the roadblocks I\u2019m seeing?\u201d<\/strong> He also reminds teams that strong forecasting requires combining what happened historically with what\u2019s happening now.<\/p>\n When Alex examined her team\u2019s pipeline, she found a rep who kept a \u201chero deal\u201d in commit for weeks. \u201cI call these sandcastle deals,\u201d she says. \u201cThey look great from far away, but the second you start poking them, they fall apart.\u201d<\/p>\n Jonathan uses AI to flag these patterns. \u201cEvery rep is different,\u201d he says. \u201cAI helps surface when a deal hasn\u2019t actually progressed, even when the rep still thinks it will close.\u201d<\/p>\n A list of numbers cannot explain why your forecast is trending up or down. Chris emphasized that \u201c<\/strong>your forecast shouldn\u2019t remain in a bubble\u201d<\/strong> because leaders need insight, not just totals.<\/p>\n Jonathan saw this gap clearly. \u201cContext does not travel well in spreadsheets or dashboards,\u201d he explains. \u201cThey tell you the \u2018what.\u2019 What we really need is the \u2018why\u2019 and \u2018what\u2019s next.\u201d His team now attaches brief explanations to every forecast submission.<\/p>\n Alex follows the same approach. \u201cNumbers don\u2019t mean much without context,\u201d she says. \u201cWhen we share the forecast, we also share why the numbers are what they are.\u201d<\/p>\n The most valuable insights often hide inside the deals that slipped or quietly stalled. Chris encourages teams to confront them directly: \u201cDig into what went wrong, not just what went right.\u201d<\/p>\n When Alex reviewed her \u201cno decision\u201d deals, she found a clear pattern. \u201cOnce we dug in, the pattern was obvious,\u201d she says. \u201cNot enough urgency, not enough stakeholders, and we were projecting our timeline onto the customer.\u201d Naming the issue helped her team adjust qualification and urgency validation earlier in the process.<\/p>\n Forecasts go off the rails when optimism overtakes evidence. Chris puts it simply: \u201cHope is not a strategy.\u201d<\/p>\n Jonathan sees this often. \u201cI am a recovering optimist,\u201d he admits. \u201cYou have to build systems that protect you from calling deals based on hope.\u201d His team distinguishes between a rep\u2019s confidence and what truly belongs in commit.<\/p>\n Alex reinforces this rigor by asking for proof. \u201cIf a rep feels confident, I ask why. What is the concrete evidence?\u201d she says.<\/p>\n Forecasts should be flexible because markets, buyers, and organizations change constantly. Chris reminds teams that \u201cno forecast is ever perfect,\u201d which is why adaptability is essential.<\/p>\n Alex builds flexibility into the forecasting process<\/a>. \u201cWe never rely on one forecast,\u201d she says. \u201cWe build a realistic version, a stretch version, and a downside version.\u201d AI adds an extra layer by flagging stale deals before she spots them manually.<\/p>\n Jonathan uses AI for scenario planning as well. \u201cIt tells us what would need to happen for best case, most likely, or worst case.\u201d<\/p>\n Forecasting improves when teams across the company contribute insight. Chris puts it simply: \u201cNo single person can own the entire forecast.\u201d<\/p>\n Alex sees the same benefit. \u201cWhat strengthens teams is shared ownership over the number,\u201d she says, \u201cnot just sales owning it but marketing, ops, CS.\u201d<\/p>\n Inconsistent definitions create forecasting chaos. If \u201cqualified,\u201d \u201ccommit,\u201d or \u201clate stage\u201d mean different things to different people, the story falls apart. Chris captures this risk clearly: \u201cIf everyone uses a different version of commit, the whole forecast becomes fragmented.\u201d<\/p>\n Alex has seen this firsthand. \u201cIf you do not define things up front, your team will make up their own version,\u201d she explains.<\/p>\n Jonathan adds, \u201cDocument it, make it accessible, and review it regularly.\u201d<\/p>\n A forecast is only as trustworthy as the data behind it. Chris tells a story that illustrates this point exactly.<\/p>\n \u201cI once told a seller, \u2018Some of these deals are dead.\u2019 Chris says. \u201cYou need to get rid of them.\u2019 The rep said, \u2018Then my pipeline\u2019s not going to be anything.\u2019 I told them, \u2018That\u2019s like having food in your refrigerator spoiled. You may have a refrigerator full of food, but you can\u2019t eat any of it.\u2019\u201d<\/p>\n Jonathan sees the same challenge operationally. \u201cWillpower alone does not work,\u201d he says. \u201cIf the system makes it hard, reps will skip it.\u201d Once-a-week reviews are not enough to maintain predictable forecasts. Chris emphasizes that teams must \u201cconstantly be challenging and re-validating assumptions\u201d to avoid end-of-quarter surprises.<\/p>\n Alex added a daily element to her team\u2019s cadence. \u201cWe do a deeper review once a week,\u201d she says, \u201cbut we also do quick daily check-ins. Sometimes it is just five minutes.\u201d<\/p>\n Automation<\/a> accelerates forecast prep, but human judgment creates accuracy. Chris reinforces this balance: AI should \u201cenhance your strategy, not replace it.\u201d<\/p>\n Jonathan draws the line clearly. \u201cAutomation handles the \u2018what.\u2019 People handle the \u2018so what,\u2019\u201d he says. AI surfaces signals, but teams must interpret and act on them.<\/p>\n Alex agrees. \u201cAutomation supports the work,\u201d she says. \u201cIt cannot replace the conversations.\u201d<\/p>\n Periodic cleanup sets the foundation for stronger forecasting. Chris encourages teams to use the end of each quarter intentionally: \u201cclean up outdated reports, refresh dashboards, and refocus KPIs.\u201d<\/p>\n Alex runs regular pipeline audits. \u201cWe run a very honest quality sweep of the pipeline,\u201d she says. \u201cA lot of deals that survive the holidays are not actually alive.\u201d She follows it with a re-engagement sprint to validate buyer interest.<\/p>\n Jonathan has seen the payoff of preparation. \u201cThe companies that struggle are the ones that spend the first few weeks every quarter figuring out where they stand.\u201d<\/p>\n Forecasting is more than a monthly exercise. It is a habit: of reflection, of context, of discipline. This 12-step framework can help your team forecast with clarity and agility all while protecting your time, sanity, and results.<\/p>\n If you are ready to put these practices into motion, we invite you to explore monday CRM<\/a>, you can book a free 1:1 consultation below.<\/p>\nChat with an expert<\/a>\n"},{"acf_fc_layout":"testimonials_carousel","testimonial_collection_select":14083,"tc_slide_to_show":"2"}]}]}],"show_sidebar_sticky_banner":false,"lobby_image":false,"post_thumbnail_title":"","hide_post_info":false,"hide_bottom_cta":true,"hide_from_blog":false,"landing_page_layout":false,"hide_time_to_read":false,"sidebar_color_banner":"","custom_tags":false,"disclaimer":"","cornerstone_hero_cta_override":{"label":"","url":""},"show_contact_sales_button":"default","override_contact_sales_label":"","override_contact_sales_url":"","cluster":"","display_dates":"default","custom_header_banner":false,"featured_image_link":"","faqs":false,"activate_cta_banner":false,"banner_url":"","main_text_banner":"","sub_title_banner":"","sub_title_banner_second":"","banner_button_text":"","below_banner_line":"","use_customized_cta":false,"custom_schema_code":""},"yoast_head":"\nHow to make it happen<\/strong><\/h3>\n
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