Sales Forecasting Examples

sales forecasting examples

However, it requires an advanced analytics solution, meaning it’s not always feasible if you have a small budget. But if anything outside of the ordinary happens, your model won’t hold up. Calculations are subjective and each sales rep can forecast differently.

Armed with this information, your marketing team can decide whether they need to bolster their efforts to promote the product, or scale back and focus their energy elsewhere. So, while a sales forecast is vitally important for determining projections for the month, quarter, or year, you must always be prepared for unplanned events – both positive and negative. If you can’t figure out which method to use, then you can bring in a sales forecasting expert so they can analyse the data themselves, and be able to pick the appropriate method to use. Not only do sales forecasts have sales records and estimates in it, but it also includes events and their possible dates (among other things). It even tells you what your sales performance was in the past, giving you better insights on what would happen in you perform certain actions (like the ones you did in the past).

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Whether numbers come up short of your projections or dramatically exceed them, it can impact your business. This method uses a mathematical approach to studying the factors affecting sales. Similar to Market Factors Analysis, this method also uses statistical analysis to establish a relationship between market factors. This method, however, employs more than just correlation and regression analysis, therefore requiring the availability of complete information.

sales forecasting examples

Access and evaluate your monthly sales reports against the actual performance of your sales forecasts. For example, finance relies on sales forecasts to allocate budgets for hiring and capacity plans. These are just some of the examples of how sales forecasting can help companies with different operations. Getting sales forecasting an accurate picture of qualification, engagement, and velocity for each deal helps sales reps and managers provide data for a reliable sales forecast. Hiring, payroll, compensation, inventory management, and marketing all depend on it. Public companies can quickly lose credibility if they miss a forecast.

Forecasting based on Historical data

Now that you know the importance and the basic factors to take into account when forecasting, let’s look into the nine methods that you can use to create a sales forecast. Regardless of a business’ size and the nature of its operations, it should still run sales forecasts so it can carve out a future direction that has a chance of bringing in business growth. Sales forecasting provides a clear picture of your anticipated sales performance based on the number of opportunities in your pipeline and the industry that your business operates in. Having visibility will help you plan your business correctly—especially when the forecast is downward, and you need to scout for new opportunities to meet your sales targets. To manually calculate churn rate, divide the number of lost customers by the total customers at the start of the time period, then multiply the result by 100. For example, if your business had 200 customers at the beginning of January and lost 12 customers by the end, you would divide 12 by 200.

  • Einstein is your personal data scientist, taking your forecasting and entire sales operations to a new level.
  • A normal lead might take roughly six months to buy, but referrals could typically need only one month, and leads coming from trade shows may require approximately eight months.
  • This is an oversimplification as you need to account for your capacity, customer buying habits, and other factors.
  • Users put in the past year’s numbers with their current sales goals to see the results.
  • Deals that are approaching the 20–day mark can be considered good candidates to close – meaning you can make projections based on that revenue and data.

You anticipate a percentage of the market share and arrive at your forecasted number. You may also compare sales rep predictions with deal stage probabilities to assess accuracy (or maybe your probabilities need updating). For instance, you might start with a pipeline review led by your sales reps, based on their own intuition and understanding of what opportunities are likely to turn into sales. With this method, you compare the age of the deal to the average sales cycle length.

Leverage the historical data

All you need to do is plug in your prospect data and their respective stage in the lead process. Sales cycle length is typically measured in weeks, but it can depend on the type of customer you’re dealing with. For example, if you’re selling to businesses (as opposed to consumers), they may take longer to make a decision since they need approval from higher-ups first. What makes this one truly effective is that combines analytics from all your other methods to create a complete picture with projections that consider a wide range of variables. Using this approach allows you to see trends over time and plan accordingly based on your previous data.

This method makes sense for those businesses that have a lower number of leads. Inside salespeople, for instance, will want to get a clearer picture of every lead within their pipeline. This method isn’t appropriate for SaaS businesses that operate according to volume. This is a useful reference because it helps you to get to grips with seasonality and the outside factors that affect your sales. You might find, for instance, that the holidays are a particularly slow time for your business, and looking at historical data can help you to prepare.