Want to forecast like a wizard? You don’t need a crystal ball for sales forecasting. But you do need to strike a balance between art and science. Like most things in life and business, sales forecasting lies somewhere between the two.
Since we’re numbers people here at SyncApps, I would say it’s better to err on the side of science and rely more on math when you do sales forecasting. No worries, you won’t need advanced trigonometry to pull this off (sorry, but your high school teacher lied). Basic math and a calculator are all you need.
Let’s take a look at what sales forecasting is, why you need it, and how to do it right.
Sales forecasting is predicting how much a company (or a team, or a department, or an individual) will sell in a year, a quarter, or a month. In other words, a sales forecast is an estimation of expected sales. These sales metrics are key for not just starting a business but growing one as well.
There are two crucial parts to any sales forecast or two questions it needs to answer:
Of course, this is just the gist of it. You can’t make an accurate sales forecast just by pulling a number out of your hat and adding a deadline to it. So, the best sales forecasters take into account more questions and rely on a lot of tools or tactics (more on that later).
Image via Salesforce
Here’s how all the questions above are factored in:
Now, accurate sales forecasting is no easy feat. It’s usually an inter-departmental endeavor that has to be fine-tuned and revised as often as possible.
This begs the question: why should I even bother? I get it; you’ve got plenty to do anyway. But read on to see why this isn’t one of the things to nix from your list.
Allow me to direct your attention to the semiconductor shortage that everyone’s been talking about for two years and that still has to be solved.
How did it happen?
The short answer: a supply and demand issue.
The long answer: before the pandemic, car manufacturers didn’t hold chips in stock. It was expensive to order them in advance and it was expensive to buy the land and build the storage facilities they needed for their chips. Instead, they got monthly contracts with semiconductors manufacturers.
Each car manufacturer had a sales forecast (of course!). Based on that forecast, they knew how much they needed to manufacture and they ordered an adequate number of chips.
Sounds like a good plan, right?
It really was. This allowed them to direct their cash elsewhere and offer relatively cheaper cars (since the buyer didn’t have to pay for chip storage and the risks associated with storing technology for too long). Everybody won.
But then COVID hit. The semiconductor plants (most of them based in Asia), ran into supply chain problems, and…you know the rest.
Car manufacturers, however, pivoted rather quickly — given the behemoth-like size of their operations. They found the “change” to invest in storage facilities and started ordering chips in bulk. Of course, those deliveries didn’t all happen yet — this is a developing story, so please don’t throw stones at me if you’re reading this after 2022.
If you’re still waiting for the car you ordered three months ago, know that this is why it’s not there yet. And that it’s sales forecasting that helped deliver cars on time before the pandemic. Sales forecasting is the strategy that will also get your car there eventually.
As you might have guessed, the first reason to do sales forecasting is to ensure a smooth-running supply chain. A car manufacturer needs to have every single part at the plant assemble and deliver a car.
The same goes for an eCommerce store. How do you know how many laptops to order for Balck Friday? You do a sales forecast to ensure that you will have enough of them to ride off the winter holidays, but not so many that you’ll be forced to sell them at a loss later on when everyone’s looking for a newer model.
Other reasons to engage in math for sales forecasting are:
Here’s a visual of the top benefits of sales forecasting from NSKT Global:
Lastly, forecasting builds a bridge between sales and marketing. It gets them on the same page, fighting for the same goal.
Remember what I said earlier about balance? This is the key ingredient of a good sales forecast, along with accuracy.
Let’s look at the reason to look for a realistic expectation first. When it comes to forecasting (anything, really), we usually tend to be optimistic. If we see a good market evolution, we tend to say we’ll sell far more than usual in the next quarter.
While optimism is great (in general), it can bring about pessimism for your team. If your sales forecast is too optimistic, your sales team will feel discouraged by the goal target.
When you set the bar too high and the forecast is not met, negativity will loom across your departments. Aside from feeling inadequate, your sales team (and everyone else, because every department depends on sales) will have trouble finding motivation again.
What about setting the bar lower? That’s also not a good idea. Setting the bar too low means missed opportunities. As soon as the goal is met, everyone will relax (too much!) and you will leave money on the table.
So, again, balance is key. To determine if your sales forecast was within the acceptable limits, let’s look at these metrics from Forrester.
Image via Forrester
As you can imagine, no one expects you to nail the exact number down to the last digit. You have some rather wide margins. A sales forecast that is within 5% is considered excellent. But if your forecast misses the mark by more than 10% you need to do something about your forecasting techniques.
Of course, major social or economic shifts can render useless even the best of forecasts (see the pandemic and the war that happened within two years). If your forecasts are consistently missing the 10% mark, the problem is not with the world, but with your techniques.
Let’s see what you can do to improve them.
Your usual sales forecasting technique has a very simple formula:
Sales forecast = estimated amount of customers X average value of customer purchases
While the formula in itself is simple, the same can’t be said about obtaining the numbers after the “=” sign. Typically, sales forecasting takes into account sales history and factors in new product launches, market conditions, competition changes, and more.
There is no one-size-fits-all sales forecasting technique, so we won’t dwell on this too much.
What all these techniques have in common is the fact that, no matter how much they rely on tech solutions, they still need a lot of human input, which can make or break accuracy. This leads us to the first way to improve your forecasts.
Modern sales forecasts are CRM-driven, which today also means AI-driven. Salesforce is a powerhouse of forecasting solutions. In Salesforce, you can collaborate with your team members to create realistic expectations and you have a lot of reporting features to give you the grounds for your forecast.
We also love Einstein’s input into forecasting. Sales Cloud Einstein is an AI that provides “an objective, unemotional point of view on what’s actually happening in sales”. Here’s a quick practical example: if Einstein notes that an opportunity has been pushed for too long, it will give the sales rep an alert that he needs to do something about it.
Here’s what it looks like:
Image via Salesforce
Of course, these alerts are coupled with and aligned to your sales goals, so they will always be timely and relevant. The problem lies elsewhere: 100% AI-enabled forecasting is not possible (yet!).
While Salesforce is great at pulling data from various sources within your CRM and generating actionable insights, it can’t know what’s happening in the world. If a new war breaks out, Einstein will be unaware and it will keep pushing alerts that you can’t act on anymore.
However, your other solutions may be more fine-tuned to the outer world. Your email marketing platform, for instance, is one of the first places where you’ll spot that something is amiss. Perhaps your open rate has gone down and your CTR along with it.
Perhaps your best-selling product is no longer your customers’ darling because, unbeknownst to Einstein, your competitors launched a feature-rich alternative. You’ve got all that data that may signal that your sales forecast needs a bit of adjusting.
Now it’s time to put it to good use. If you keep it siloed from the data in Salesforce, you’ll never get the full picture. If, on the other hand, you integrate your email marketing platform with Salesforce, you’ll have access to combined insights and bi-directional data flows.
This means that you’ll forego your dependency on a single solution for forecasting and that your sales forecasts will get more accurate.
If it has “sales” in the name, it has to be about sales, right? Right, your sales department will always be at the forefront of the forecasts. But they are not alone in this.
Your acquisitions department depends on the sales to…make new acquisitions. The reverse is true: if your acquisition department can’t acquire all the parts or products you need to sell, your forecast is moot. They need to be involved at any stage of the forecast process since what you’re selling depends on them.
Your financial department has a different overview of your finances than sales. More importantly, they tend to be less naturally inclined to optimism than your sales reps. Their view over sales projections is crucial and so is their involvement in analyzing the accuracy of forecasts and figuring out how to fine-tune them in the future. If you have a SaaS business they will be the ones taking care of your SaaS spend management so not taking into account their opinion on the matter will be a mistake.
Your marketing department needs the money from sales to fuel its campaigns. Based on the sales forecasts, they can also plan their expenses better and put their budget where it can make a bigger difference. For instance, if the quarter is nearly over and you still have a lot to sell to meet your forecast, your marketers can get involved by switching gears and investing more in promoting the products with the highest margins or those products that are slugging behind.
To do that, though, each of these departments needs access to as much data as possible. When financial, sales, marketing, or eCommerce data is siloed, none of them can see the full picture. Bring them all on board with a unified view of your data.
There’s a saying about data that’s as old as data reliance itself: “garbage in, garbage out”.
This means that, irrespective of how sophisticated your AI-based sales forecasting solutions are, if you feed them junk data, you’ll get a junk forecast.
Start at the beginning: your data entry. Train your sales reps on how to add data to your CRM. For instance, if they don’t know why a certain property in your CRM is important, they might ignore it altogether.
Here’s how to make sure you’re not relying on dirty data:
No matter how accurate your team tries to be, human errors are a constant in manual data entry and migration. Eliminate inconsistencies automatically by removing manual inputs altogether and save dozens of hours of your team’s time in a single swoop.
When you think about sales forecasting, the first thing that naturally springs to mind is quantitative data. While quantity is all that matters in a sales forecast, qualitative data may be just the ingredient that’s missing to add more accuracy to your predictions.
Qualitative data brings new insights to your forecasts and it’s usually the thing that tones down (or up) excessively optimistic or pessimistic predictions, bringing them closer to that sought-after balance.
Let’s say you run an eCommerce shop and, naturally, you want to boost your sales for more than one category of products. The first thing to do is look at quantitative data — how many products can we sell from each category?
Good start!
Now pair this data with the answer to this question: “how many people buy products from more than one category?” This will give you the opportunity to create better-targeted campaigns and to upsell and cross-sell more clients. And, of course, it will bring more accuracy to your sales forecasts as you won’t be looking at each category as an isolated vertical.
Sales forecasting is much easier with the right tools in place. But since we’ve talked so much about balance, I’m going to refer to it once more: you need the right balance between artificial and human intelligence.
AI-powered tools are great, but they can rarely take into account complex geo-political and social realities. This is where your team comes in. Free them from manual tasks like data input and migration and they’ll have the time to tackle the complexities AI can’t tackle.
Use your resources better and your sales forecasts will get better.
If you need additional help, our integration experts are here for you 24/7 and always ready to help you leverage SyncApps to the fullest and get better, more accurate sales forecasts.
