Knowing what your customers want is crucial if you’re looking to meet their demands, boost your bottom line, and continuously improve your brand reputation. And there’s no better strategy for staying on top of your customers’ demand than to continuously predict it accurately. It’s demand planning.

Demand forecasting is the key to these accurate predictions.

Before we get into how to optimize your approach to using demand forecasting, we’re going to take a few steps back and provide you with a quick primer on demand forecasting itself. We’ll also go through the ties between demand forecasting and machine learning.

After that, it will be time to consider four of the best ways to ensure the accuracy of your sales forecasts.

Quick background: what’s demand forecasting?

Your business survives by selling products and/or services to customers, but your customers’ demand isn’t a fixed value. It fluctuates depending on lots of variables, from customers’ moods to the weather to the political climate and more.

Here’s a visual representation of the non-linear changes in customer spending (as a result of demand) in recent years:

As you can see, customer demand follows patterns… until it doesn’t. It obeys trends, but those trends can change at any time. In a word, it’s complicated. In other words, it’s a little chaotic and certainly fast-paced.

Demand forecasting is your means of staying on top of these complex changes in customers’ demands, wants, and needs and can make better business decisions.

With demand forecasting software, it’s both possible and feasible to derive meaningful, actionable insights from customer demand data.

How machine learning factors into the equation

It’s becoming more and more important to make sure you’re using AI in your insights program. This is a trend that also applies when it comes to demand forecasting. More specifically, machine learning-based AI is a fantastic tool to enhance your demand forecasting capabilities.

That’s because using machine learning means your tools get smarter and more accurate the longer they’re used, and that’s ideal for demand forecasting.

Forecasting tools that only become more effective as they’re fed more data are a future-proof solution. They’ll become more capable of predicting changes in trends, picking up on emerging patterns, and noticing new changes.

So, machine learning is indispensable for demand forecasting. But what else can you do to make your forecasts as accurate as possible? Let’s take a look at some practical tips.

1. Gather as much data as possible

This point makes more sense when you consider it in the context of machine learning. Giving your analytic tools more data to work with sharpens their abilities and helps them work better, so, of course, you’ll want to give them as much data as you can, right?

Only that’s not all there is to it.

In our digital age, more information is generated every day. In fact, there are hundreds of zettabytes being generated and consumed right now:

While this huge volume of data isn’t all relevant to your sales, nobody can guarantee that it won’t ever be relevant.

For example, let’s say you find yourself needing an electronic signature tool one day. Before the need for it arose, you never thought about how many signatures your company needs in a day or even how many documents your customers sign on average. This makes it very difficult to predict the level of demand for your tool, which makes it harder to choose one.

You can avoid this situation by gathering and using all the information you possibly can on your industry, customers, and business processes.

Any information you can get has the potential to influence your sales in some way, so all of it should go straight into your demand forecasting tool.

2. Consider both short-term and long-term forecasts

Short-term forecasts tend to be more accurate. They’ll usually consider the immediate future, whether that’s a day, a month, or half of a year into the future.

On the other hand, long-term forecasts give you insight into the situation at least one year into the future. Let’s take a look at what a set of longer-term predictions looks like:

Both types of forecasts are essential. Let’s find out why.

When you’re preparing for the future, you need to know what tomorrow looks like. This lets you adjust your predictions and make necessary changes so that you’re fully prepared for the next day. However, planning day-to-day exclusively is both very limiting and highly stressful.

That’s where long-term planning comes in. Long-term forecasting tools give you an idea of what the industry (and your demand) will look like years into the future, so you can outline long-term strategies and establish contingency plans.

For example, if you’re selling proposal-making software, you’ll need to be ready for quicker, short-term proposals, just as you’ve got to offer options for complex long-term proposals.

3. Double-check things manually

Our future with automation and robotics is one of mutual cooperation.

AI and predictive tools can provide you with lots of help and valuable insight, but they can’t replace the value of human input. While it’s no use second-guessing the predictions you’re given, it’s also inadvisable to trust them blindly without critical examination.

So, you can improve your sales forecasting by performing manual checks and allowing for human input.

It’s always important to remember that human minds can come up with new and innovative ways to obtain more accurate results, such as by performing specific tests or feeding forecasting tools new types of data. That’s a valuable contribution to your market research efforts, so make sure you provide space for human experts to provide feedback.

4. Know exactly what you want

It’s all too easy to say that you want accurate sales forecasts and give the matter no further thought. However, this actually works against you.

Instead, it’s a much better idea to clearly identify your business goals, KPIs, sales targets, and other metrics. That’s because knowing what you’re aiming for helps you get the results you want faster and more optimally.

It’s a little like looking for tools. If you’re looking for an esignature free tool and you know what you want it to do, which features you want it to have, and how you plan to use it, you’ll be more likely to find the perfect one than someone who only has a vague idea of wanting an esignature tool.

To put it briefly, you need to know which targets you’re hitting before you can aim for them properly.

What you need to remember to achieve accurate sales forecasts

Demand forecasting is becoming more and more accurate, but it can’t reach 100% accuracy (at least, not yet). It’s vital to optimize your data analytics tools and remember that you can’t rule out the value of human input.

In short, following these tips will help you get more accurate sales forecasts, just as relying on human experts will enhance those forecasts.