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<\/a><\/p>\nTable of Contents<\/strong><\/p>\n A sales forecasting<\/a> method is a systematic approach to understanding future possibilities based on both historical data and human insight. In B2B, this often means combining hard numbers (like pipeline data) with qualitative inputs (like sales rep confidence levels).<\/p>\n All of this can help you know what to expect the next month, quarter, and even fiscal year to look like.<\/p>\n \u201cForecasting feels like having a backstage pass to the future of our market,\u201d says Chris Bajda<\/a>, Managing Partner at Groomsday<\/a>. \u201cBy tapping into data from previous seasons and current trends, we\u2019re able to predict what our customers will need and when.\u201d<\/p>\n I\u2019ll share an example to help make this concrete. Imagine I run a coffee shop. A simple forecasting technique might just look at last year’s sales and add 10% for growth. But a more sophisticated approach would consider:<\/p>\n The impact of forecasting can be dramatic. An analysis by Dgtl Infra<\/a> found that when they used integrated forecasts (combining sales data, usage metrics, and market trends), they closed 31% more revenue<\/strong> than those relying on pipeline data alone.<\/p>\n Source<\/a><\/em><\/p>\n Pro tip<\/strong>: If you\u2019re looking to brush up on your forecasting skills, I recommend checking out these free courses in HubSpot Academy: Forecasting and Analytics in Sales Hub<\/a> and Hubspot Sales Forecasting.<\/a><\/p>\n Forecasting methods<\/a> generally fall into two main categories: qualitative and quantitative approaches. I like to think of them as the \u201cart\u201d and \u201cscience\u201d of forecasting \u2014 both valuable, but used in different situations.<\/p>\n Source<\/a><\/em><\/p>\n Qualitative forecasting methods<\/a> shine when historical data is limited or when you’re venturing into new territory. They rely on expert opinions, market insights, and informed judgment rather than pure numbers.<\/p>\n For example, if you’re launching an innovative product with no direct competitors, you might use:<\/p>\n Best for:<\/strong> New products, innovative industries, or sectors with limited historical data.<\/p>\n Quantitative forecasting is all about the numbers \u2014 using data-driven models to make predictions. Think of it as letting the data tell the story.<\/p>\n For example, a retail chain might analyze:<\/p>\n Examples of quantitative forecasting include:<\/p>\n Best for: <\/strong>Stable, data-rich industries where historical patterns can reliably inform future predictions.<\/p>\n TL;DR? <\/strong>Many successful businesses actually combine both qualitative and quantitative methods, using data to inform decisions while still leaving room for human insight and market knowledge.<\/p>\n <\/a> <\/p>\n In speaking with dozens of experts for this piece, one thing became clear to me: There\u2019s no consensus on what method is \u201cbest.\u201d The options vary widely depending on your end goals, your industry, the data you have available, and much more. It will also greatly depend on which forecasting software<\/a> you choose.<\/p>\n That being said, here are some top forecasting methods<\/a> that you may find helpful.<\/p>\n Time series analysis is widely used for recognizing trends and seasonality in historical data; it\u2019s a heavy hitter in the forecasting world. Many experts that I spoke with use time series as one of their methods.<\/p>\n Bajda from Groomsday explains, \u201cTime series analysis is especially useful for businesses that experience seasonal peaks and valleys, like retail.\u201d This method helps track cyclical patterns, allowing businesses to optimize inventory and marketing strategies for anticipated demand changes.<\/p>\n Below I explain specific types of time series analysis.<\/p>\n This is like taking your business’s temperature over time \u2014 it smooths out short-term fluctuations to show the real trend.<\/p>\n Here\u2019s a simple example:<\/p>\n Q1 Sales: $100,000<\/p>\n Q2 Sales: $120,000<\/p>\n Q3 Sales: $110,000<\/p>\n Q4 Forecast = ($100,000 + $120,000 + $110,000) \/ 3 = $110,000<\/p>\n Exponential smoothing is like your business\u2019s short-term memory. Just as you would remember what happened last week more clearly than last year, this method gives more weight to recent events.<\/p>\n Here\u2018s a real-world scenario: Let\u2019s say I run a downtown lunch spot. My sales might look like this:<\/p>\n Monday: $2,000<\/p>\n Tuesday: $2,200<\/p>\n Wednesday: $1,800 (Unexpected rain)<\/p>\n Thursday: $2,300<\/p>\n Friday: $2,500<\/p>\n A simple average would say I make $2,160 per day. But exponential smoothing might predict closer to $2,400 for next Monday because it:<\/p>\n Auto Regressive Integrated Moving Average (ARIMA) is like having a master analyst who can spot complex patterns. While exponential smoothing is great for clear trends, ARIMA shines when things get messy.<\/p>\n Here\u2018s why it\u2019s powerful. Let’s say I\u2019m running an online fitness equipment store:<\/p>\n ARIMA can handle all these patterns plus:<\/p>\n Machine learning has transformed forecasting by spotting complex patterns humans might miss. Dgtl Infra<\/a> shared compelling results from combining AI with traditional methods.<\/p>\n Their data showed AI models identified enterprise user adoption growing 28% quarter-over-quarter, while sales team insights revealed financial services companies were integrating their API three times faster than other sectors \u2014 a critical pattern that pure data analysis missed.<\/p>\n They\u2019re also the company I mentioned above that closed one-third more revenue when using an integrated forecast rather than just pipeline data alone.<\/p>\n Modern ML approaches include:<\/p>\n In B2B, where single deals can make or break a quarter, scenario planning is essential. This method helps you prepare for different possible futures rather than betting on a single forecast.<\/p>\n \u201cIf we\u2019re promoting a video for a seasonal campaign, like Black Friday, we create multiple outcome scenarios based on varying budget allocations, engagement levels, and ad placement strategies. This way, we\u2019re prepared to pivot as needed,\u201d explains Spencer Romenco<\/a>, Chief Growth Strategist at Growth Spurt<\/a>.<\/p>\n Here\u2019s an example:<\/p>\n Conservative Case<\/strong><\/p>\n – Only deals with 90%+ probability.<\/p>\n – Minimal upsell revenue.<\/p>\n – Standard churn rate.<\/p>\n Base Case<\/strong><\/p>\n – Deals at 70%+ probability.<\/p>\n – Historical upsell rates.<\/p>\n – Normal market conditions.<\/p>\n Upside Case<\/strong><\/p>\n – Additional stretch opportunities.<\/p>\n – Accelerated deal velocity.<\/p>\n – New product adoption.<\/p>\n Understanding the deeper context of customer feedback can be as valuable as tracking pipeline metrics. Sentiment analysis moves beyond basic satisfaction scores to uncover meaningful patterns in customer behavior and market direction.<\/p>\n For example, Kratom Earth<\/a> incorporates feedback from customer reviews, social media comments, and direct interactions in their forecasting process.<\/p>\n \u201cWe pay attention to the words customers use, the benefits or effects they mention, and even any concerns they share. If we notice a trend where people talk about increased stress or a desire for relaxation, this guides us to forecast a higher demand [for certain products],\u201d says Loris Petro<\/a>, Marketing Strategy Lead at Kratom Earth.<\/p>\n \u201cThis allows us to plan inventory and marketing efforts around actual customer emotions and needs, which we believe is extremely accurate.\u201d<\/p>\n <\/a> <\/p>\n To illustrate how you can go through the decision-making process, I\u2019m going to use a fictional example. We\u2019ll call her Hannah and she runs an online pet goods store. Her orders have grown from 100 to 1,000 a month and now she\u2019s facing some headwinds.<\/p>\n \u201cI’m struggling to predict demand. Last month, I ran out of our bestselling cat food. The month before, I had to discount excess dog toys. There has to be a better way than just guessing!\u201d<\/p>\n First ask yourself, what data do you have access to? Most businesses are sitting on more useful information than they realize. (P.S. This is where AI<\/a> can be incredibly helpful!)<\/p>\n This could include:<\/p>\n In Hannah\u2019s assessment of the data, she might find that cat products make 45% of her revenue, dogs make up 40%, and other pets are 15%. In her business, she also sees seasonal trends that cause her products to spike \u2014 things like pet costumes around Halloween and new pet supplies around Christmas.<\/p>\n Pro tip:<\/strong> \u201cIf you have a strong history of data, methods like time series can reveal powerful patterns,\u201d Badja suggests. For industries experiencing rapid shifts, machine learning models that continuously update based on new data are better suited to capturing real-time changes.<\/p>\n The next step is to go one step beyond the data \u2014 find ways to connect the dots.<\/p>\n In Hannah\u2019s example, she might be asking herself:<\/p>\n By looking closely at the patterns over the past few months, you\u2019ll likely spot some key trends. For instance, Hannah could discover that 90% of customers reorder every six weeks, sales spike after email promotions, and the weather doesn\u2019t impact sales.<\/p>\n All of these discoveries offer helpful insight into her customer\u2019s buying patterns and how she can better predict future sales<\/a>.<\/p>\n Now comes the fun part \u2014 choosing your forecasting approach. Let\u2018s look at different methods through Hannah\u2019s lens.<\/p>\n For example, if Hannah calculated the simple average across the last few months, she wouldn\u2019t end up with any results that she could use to predict the future.<\/p>\n Last 3 months sales:<\/p>\n However, a multi-factor method could better account for her business\u2019s growth rate and seasonal patterns.<\/p>\n Product Forecast =<\/p>\n (Base Average)<\/p>\n \u00d7 (Growth Factor)<\/p>\n \u00d7 (Seasonal Factor)<\/p>\n \u00d7 (Marketing Impact)<\/p>\n Example for Premium Cat Food:<\/strong><\/p>\n Base Average: 302 units<\/p>\n Growth Factor: 1.15<\/p>\n Seasonal Factor: 1.0 (non-seasonal)<\/p>\n Marketing Factor: 1.2 (email campaign planned)<\/p>\n June Forecast = 302 \u00d7 1.15 \u00d7 1.0 \u00d7 1.2 = 416 units<\/p>\n Pro tip:<\/strong> Make sure you are factoring in both qualitative and quantitative data.<\/p>\n Start by mapping out sales projections<\/a> for your specific business. Take a piece of paper and draw three columns: this month, this quarter, and this year.<\/p>\n For instance, if you run a software company, your immediate concern might be customer churn rate, while your quarterly view focuses on new feature launches, and your annual picture considers market expansion. A retail business might track daily inventory in the short term, seasonal trends quarterly, and store expansion annually.<\/p>\n Pro tip: \u201c<\/strong>Don’t forecast based on past success,\u201d says Stephen Do<\/a>, Founder of UpPromote<\/a>. You must consider uncertainty. Marketing changes constantly \u2014 new competitors, customer behavior, and affiliate marketing trends can disrupt your models.\u201d<\/p>\n As I mentioned earlier, you\u2019re likely sitting on a ton of valuable data \u2014 let\u2019s put it to use.<\/p>\n To maximize forecasting accuracy, you can pair a CRM like HubSpot with an AI-driven platform, recommends Jeremy Schiff<\/a>, CEO of Salesbot.io<\/a>.<\/p>\n \u201cWhile typical forecasting methods often focus solely on funnel performance, Salesbot.io leverages data across platforms like HubSpot to gain a comprehensive view of the entire sales pipeline \u2014 from lead generation to MQL, SQL, opportunity, and closed-won,\u201d Schiff says.<\/p>\n \u201cBy aggregating insights from HubSpot, we can pinpoint which channels are working best at each stage of the sales journey, enabling smarter investment decisions and optimized resource allocation. This approach allows us to forecast not only future deal closures but also channel-specific effectiveness, helping us maximize impact across the sales process.\u201d<\/p>\n This is where most forecasting efforts succeed or fail. You need a regular rhythm of reviews, but they should fit naturally into your existing workflow.<\/p>\n Forecasts aren\u2019t one-size-fits-all. As Michael Benoit<\/a> from ContractorBond<\/a> says, \u201cWe review our forecasts every quarter to ensure they\u2019re still relevant.\u201d Regularly updating forecasts with current data helps businesses stay agile and maintain alignment with real-time conditions.<\/p>\n Pro tip: <\/strong>\u201cWhen forecasting, especially with a team, you have to strike a balance between being too conservative and too ambitious,\u201d Lexie Smith,<\/a> Founder and CEO at Growth Mode<\/a>, recommends. \u201cSetting goals too conservatively may mean hitting targets sooner, but if they’re too achievable, it risks undershooting potential and can leave you vulnerable to unexpected shortfalls. On the flip side, overly ambitious targets can be unrealistic, leading to slow adjustments and missed opportunities for recalibration if early performance indicates underperformance.\u201d<\/p>\n\n
What Is a Forecasting Method?<\/h2>\n
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<\/p>\nTypes of Forecasting Methods<\/h3>\n
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<\/p>\nQualitative Forecasting Methods<\/h4>\n
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Quantitative Forecasting Methods<\/h4>\n
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Best Forecasting Methods<\/h2>\n
1. Time Series Analysis<\/h3>\n
Moving Average<\/h4>\n
Exponential Smoothing<\/h4>\n
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ARIMA Models<\/h4>\n
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2. Machine Learning Models<\/h3>\n
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3. Scenario Planning<\/h3>\n
4. Sentiment Analysis<\/h3>\n
How to Choose the Right Forecasting Technique<\/h2>\n
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<\/p>\n1. Take stock of your available data.<\/h3>\n
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2. Connect trends from business patterns.<\/h3>\n
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3. Select your method.<\/h3>\n
Simple Moving Average<\/h4>\n
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4. Leverage short-term and long-term projections.<\/h3>\n
5. Build your integration system.<\/h3>\n
6. Adjust on a regular basis.<\/h3>\n
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