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    7 Macro & Micro Models for Sales Forecasting

    We’ve explored what sales forecasting is, its benefits, and what often gets in the way. Today, we’ll identify seven methods for sales forecasting accuracy, as well as each option’s advantages and disadvantages. 

    1. Bottom-up sales forecasting
    2. Top-down sales forecasting
    3. Length of sales cycle sales forecasting
    4. Opportunity stage sales forecasting
    5. Intuitive sales forecasting
    6. Historical sales forecasting
    7. Multivariable analysis sales forecasting

    To produce the most accurate sales forecast, companies perform both macro and micro forecasts, then tweak each one until they produce a similar number.

    Macro Sales Forecasting Models

    From an accuracy perspective, it’s important to understand and use the two macro sales forecasting methods available.

    Macro Model #1: Bottom-up Forecasting 

    Bottom-up forecasts estimate a company’s future sales performance with spending plans by department. It begins with detailed customer and product information, and then works up to revenue. For example, teams will estimate the number of products they will sell and multiply that number by product costs for an overall figure. 


    • A bottom-up forecast is easy to modify if certain variables change, such as cost per item or number of reps.
    • Because this forecast employs historical sales and production data, it provides granular information.


    • Unique departmental projections often rely on individual team members’ competitive knowledge and strategic expertise, which are sometimes lacking.
    • Flawed sales and production data tarnishes the projection. 

    Macro Model #2: Top-down Forecasting 

    Top-down forecasting takes the opposite approach to bottom-up forecasting. It begins by quantifying the total value of the addressable market—often referred to as TAM, or Total Addressable Market—and then estimates the market percentage that the business believes it can capture. In other words, it starts with high-level market data and works down to revenue.


    • Top-down forecasts sidestep statistical outliers, which often appear in low-level details and individual examples. 
    • Teams generally gain insight into sales trends from top-down projections’ wider context.
    • Because top-down forecasts do not rely on real-time point-of-sale data, they are generally faster to produce. 


    • This approach lumps individual products and regions together, so does not accurately describe items or teams at an individual level. 
    • Macro data like GDP, population growth, and urbanization may not incorporate nuances specific to your industry or category.

    While macro forecasts look at revenue holistically, micro forecasting models examine sales performance with a closer lens. 

    5 Micro Sales Forecasting Methods

    Following are the pros and cons of the five most common types of micro sales forecasting. 

    Micro Method #1: Length of Sales Cycle Forecasting 

    This method relies on data for how long a lead typically takes to close. The technique can incorporate data for different deal sources—such as cold leads, referrals, or upsell to existing customers. Doing so typically generates a more accurate picture, provided your data is clean.


    • Calculations are objective, because individual opinions are not involved.
    • You can easily integrate lead sources to better forecast opportunities.


    • Calculations don't always consider the size or type of each opportunity.
    • The output is only as good as the data your team puts into it.

    Micro Method #2: Opportunity Stage Forecasting

    Opportunity stage forecasting accounts for each deal’s stage in the sales process. The general rule of thumb is that the further along deals are in the pipeline, the likelier they are to close. For this calculation, simply multiply each deal's potential value by its probability to close, then add up the total. 


    • Since opportunity stages are already in your CRM, it’s relatively easy to establish a sales forecast.
    • The calculations are objective.


    • Inaccurate data can lead to inaccurate forecasts.
    • Calculations don't consider each opportunity’s size or age.
    • It’s difficult to assign the right percentage to a stage, when deals move through the pipeline for such divergent reasons. 
    • Without a mature customer success organization that’s fully aligned with sales and marketing, existing opportunities may not accurately reflect opportunities within your customer population.

    Micro Method #3: Intuitive Forecasting

    Intuitive forecasting relies on individual sales contributors to estimate their own pipeline by providing the value and close date for each deal. This method can be valuable for early stage companies or products, when historical data doesn’t exist.


    • It relies on your sales team, which works closest to your prospects.
    • It relies on your account management team, which works closest with your customers.
    • You don't need historical data.


    • Calculations are subjective, and each sales rep can forecast differently.
    • You can't scale or replicate this method.

    Micro Method #4: Historical Forecasting

    Historical forecasting assumes future results will be equal to or greater than data of previous sales. Ultimately, historical demand should be used as a benchmark rather than the foundation of your sales forecast. Why? If anything outside of the ordinary occurs, your forecast won't hold up. You can refer to 2020 for a simple example.


    • Proven historical data can be helpful in steady markets.
    • It's quick and easy.


    • Seasonality and market changes aren’t included.
    • Fluctuations in buyer demand are not taken into account.
    • Any forecasts will roll forward historically unleveraged cross-sell and upsell. 
    • Does not account for a faster pace of change, as technological innovation increases.

    Micro Method #5: Multivariable Analysis Forecasting 

    Multivariable analysis, the most sophisticated sales forecasting method, combines the best parts of every forecasting model into one complex, analytics-driven system. 

    While multivariable analysis tends to be the most accurate approach, thanks in part to predictive analytics, it does require an advanced analytics solution and clean data. If your reps aren't dedicated to tracking deal progress and activities, and your account managers don’t effectively identify and log customer opportunities, your results will be inaccurate no matter how great your software is.


    • It's very reliant on data, so is therefore the most accurate.


    • An analytics solution and/or forecasting tool is required.
    • Outputs will be flawed if sales team members do not consistently input clean data.

    As we’ve already said, the best results come from using a combination of several, if not all, of the sales forecasting methods we just discussed. Our next blog shares tips for successfully combining macro and micro models, alongside other forecasting best practices. 

    Curious for more? Download our whitepaper: Sales Forecasting in the Next Normal.



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