Sales forecasting is a critical element in any demand forecasting process. There is no single method of sales forecasting that will work for every company, and each business employs a forecast process that works for them.
I reached out to several business leaders to understand how they forecast sales in their business. I asked them whether they relied more on historical seasonality, or if they relied on forward-looking sales pipelines.
See below for their responses:
Publishing: Historic Sales Reveals Seasonality
I work at a digital publication for small business owners and
have found that seasonality is a big deal in the SMB space. Our readers’ needs fluctuate throughout the year in predictable ways, and we’ve found that we can use historical sales patterns to help determine which industries will see higher ad conversions (sales) from our readers and which won’t. This allows us to better manage our resources over multiple quarters.
However, if I want to get more granular in my revenue predictions, I can
also look at, for example, how well our advertising partners were able to
convert our publication’s readership in the last two weeks, and apply that conversion percentage to the traffic we send in the next two weeks. This method has worked better for us when predicting our revenue in the short term.
B2B Tools: Rely on Historical Data
We have been selling our product for over half a decade now so we have lots of historical data to rely on. In most cases, we look back at the same period a year or two before and we can pretty accurately predict how many sales we’ll be able to make.
Of course, when we launch a new campaign or prepare a feature that we want to promote heavily, we always expect a significant increase in sales. For the most part, we can make accurate predictions if we focus on the things that we can control.
Expo Sales: Win Probability + Sales History
We forecast sales using the following methods:
Win probability of current sales pitches. We look at each opportunity, see where it is at in the sales cycle and give it a percentage score of converting. This is normally accurate as we know the decision making process, the time frames, and the likely value of the sale. Using this method, we are normally forecasting at 90-95% accuracy.
Sales History. We use the past performance as an indicator for each month as our business can be seasonal but we also factor in our current ongoing pitches. We normally add 15-20% uplift from past performance as we usually increase our sales by this amount each year.
Jewelry: Semi-Annual Forecasting
I forecast our business’s sales only on a semi-annual sales pattern because it is so effective and predictive. This may not be the case for all businesses, but in the case of our business, it works like charm.
On average, during the last 5 years, we have been consistently growing our revenue per semester, each semester. To do so, we focus our marketing on high-end products (jewelry in our case) and we try to offer high-priced items our audience is already looking for.
B2B Service: Align Offerings to Seasonal Trends
I forecast my future sales by comparing patterns in sales around certain times of the years. My business is more of a service business, so it may be different from your typical product business.
For example, I’ve noticed things tend to slow down around November to January, but I think it’s due to the fact that it’s the holiday season and people are more concerned about getting gifts for their loved ones and spending time with family. So during that time of the year, I try to market more towards businesses with products to help them in running a smoother business.
Business for me usually doesn’t start picking up until after the holidays, and when that happens, I don’t have to worry about marketing as much. I just put my faith in history and let the sales roll in.
Durable Goods: Statistical Analysis
The sales forecasting method that we use is multivariable analysis. This takes aspects of various other forecasting methods in order to gain a more accurate result of future sales outcomes.
The reason I like this method so much is because it doesn’t focus solely on one area of forecasting. Taking more factors into consideration will always give you a better result because you’re doing more in-depth research into your potential outcomes. Yes, it takes more time than the other methods, but the results are definitely worth it.
Consumer Goods: Risk-Adjust Sales Leads
We track all of the leads we have and forecast future sales by applying a win-probability to our sales pipeline.
Leads are deemed hot, warm or cold depending on how they respond to the first contact we make (outbound).
- Hot leads have a very high probability of transitioning to a sale.
- Warm leads reply but may need further nurturing.
- Cold leads never reply or follow-up. I don’t count these leads at all.
The fact that we know what proportion of leads are hot, warm or cold
helps us judge which activities are working and which aren’t. I also
keep a close eye on the win-probability by product line (and customer
segment) so I can adjust our forecast based on how each product line
We forecast with a 75% win-probability so we do two sales forecasts:
one with the 50-60% range and one in the 70-80% range.
That way we have a buffer of at least 10%. We seek to have 90-100%
coverage between three months and one year out each quarter (we
forecast five months out). We can then cross check these projections
against actual results to further refine our forecasting process.
I’ve found this kind of forecasting to be reliable. It improves our
accuracy as we can tweak our processes to refine our pipeline
management. We have found that we need high-quality leads to achieve a higher win-probability.
Wrapping It Up
I hope the above feedback is helpful as you consider refining your sales forecasting method at your own business. Remember, there is no one perfect way to forecast sales – While these business leaders provide excellent examples of robust sales forecast techniques, you should deploy a forecasting process that makes sense for your business.