1. A sales forecast is a prediction of future sales revenue. Sales forecasts are usually based on historical data, industry trends, and the status of the current sales pipeline. Businesses use the sales forecast to estimate weekly, monthly, quarterly, and annual sales totals.
2. New businesses that don’t have much data about their own sales process may need to rely on industry averages or even educated guesses. On the other hand, more established companies can use their historical data to model future performance.
STEP-BY-STEP
1. Document your sales process - Without a clearly documented sales process describing the actions and steps it takes to close a deal, you’ll have difficulty predicting whether any single deal will close.
2. Set your sales goals or quotas - While your forecast may be different from your goals, you won’t know if your forecast is good or bad unless you first have a target. So each rep needs an individual quota, as does the entire sales team. Read more about setting sales goals or quotas here.
3. Set a benchmark or a current average of some basic sales metrics - Having easily accessible measures of the following basic sales metrics will make forecasting much easier
4. Understand your current sales pipeline - Make sure you understand what’s in your current pipeline, and that your CRM is accurate and up-to-date. If you don’t have a CRM, forecasting is more difficult, but not impossible.
METHODOLOGY
1. Relying on sales reps’ opinions - Many sales managers simply ask their reps: “when will this deal close, and how much will it close for?”
2. Using historical data - With this method, you use a record of your past performance under similar conditions to estimate how you’ll perform in the present. For example, you may know that your business typically grows at 15% year over year and that you closed $100k of new business this month last year. That would lead you to forecast $115,000 of revenue this month.
3. Using deal stages - In this forecasting method, you assign a probability of closing a deal to each stage in your sales process. Then, at any given time, you can multiply that probability by the size of an opportunity to generate an estimate of the revenue you can expect.
4. Sales cycle forecasting - As a result, an alternate forecasting method is to use the age of the sales opportunity, rather than the probability, to assess the strength of the pipeline.
5. Pipeline forecasting - This method is much more accurate but still more reliant on a high quality of data. It looks at each opportunity sitting in your pipeline and analyses it based on a number of factors, which could include age, deal type and deal stage.
6. Using a custom forecast model with lead scoring and multiple variables - This forecasting method relies on a combination of all of the above. It has some similarities to the pipeline forecasting method, but it has greater depth and complexity. Usually, you’d need an analytics tool or advanced CRM reports set up to help create these forecasts. You also need extremely good data in the first place, so you’re relying on your reps to enter a lot of accurate information.
EXAMPLE 1 : FORECASTING BASED ON HISTORICAL SALES DATA
1. Let’s say that last month, you had $150,000 of monthly recurring revenue and that for the last 12 months, sales revenue has grown 12% each month. Over the same period, your monthly churn has been about 1% each month.
2. Your forecasted revenue for next month would be $166,500.
3. You multiply last month’s revenue by your expected growth, and subtract your expected churn:
($150,000 * 1.12) – ($150,000 * .01) = $166,500
EXAMPLE 2 : FORECASTING BASED ON CURRENT INTEREST LEADS
1. Let’s say you have three open opportunities this month:
- One where you’ve just had a quick phone call, with an expected value of $1,000.
- One that has received a full demo, with an expected value of $1,500.
- And one with an offer, with an expected value of $1,200.
2. You’ve done your math, and you know that in each of these stages, any given opportunity has the following likelihood of closing:
- “Connect Call” = 30% chance of closing
- “Demo” = 40% chance of closing
- “Offer” = 70% chance of closing
3. You multiply that probability with the forecasted value of the deal, and sum them all up to come up with a total sales forecast.
EXAMPLE 3 : FORECASTING BASED ON LEAD SCORES AND MULTIPLE VARIABLES
1. You’ve really done your research, and have lead scoring set up in your CRM. You group your leads into three groups of varying quality: A, B, and C. These determine how likely an opportunity is to close.
2. You also know that companies with less than 50 employees close at a slightly lower rate, and companies larger than 50 employees are more likely to close.
3. You could then use average opportunity sizes to calculate the forecasted value of any given opportunity, using a table like this:
1. ABC classification is used by inventory management teams to help identify the most important products in their portfolio and ensure they focus on managing them above those less valuable. Using ABC classification inventory is divided into three categories, A (most important), B (fairly important) and C (least important).
- Category A: this is the smallest category and consists of the most important stock items
- Category B: will generally be slightly larger in terms of volumes of SKUs and will usually be made up of products of less value
- Category C: this will typically be the largest category where products will contribute the least to your businesses bottom line.
2. The ABC inventory classification framework, or ABC analysis, is based on the theory that all inventory is not of equal value. Instead it follows the Pareto Principle, where 20% of stock accounts for 80% of the value to the business.
3. Your inventory’s ‘value’ can be defined based on a number of criteria, such as annual sales revenue, profitability or annual consumption value.
EXAMPLE OF ABC INVENTORY CLASSIFICATION
1. Use the formula ‘annual number of units sold x cost per unit’ to calculate the annual consumption value of each item
Annual number of units sold (per item) x cost per unit
2. List your products in descending order, based on their annual consumption value
3. Total up the number of units sold and the annual consumption value
4. Calculate the cumulative percentage of items sold and cumulative percentage of the annual consumption values
5. Determine the thresholds for splitting the data into A, B and C categories. The threshold for determining the ABC split will be unique to your company and your product mix, but typically it’s close to 80% / 15% / 5%.
Source:
https://www.eazystock.com/uk/blog-uk/abc-classification-calculation-inventory-management/
https://www.saleshacker.com/sales-forecasting-101/
