6 Machine Learning Methods to Simplify And Sharpen Sales Forecasting


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By now, most of us have some idea of what AI is. We know it stands for Artificial Intelligence and we have an idea that it uses robotics to replicate human behavior. But for many of us, that’s the extent of our knowledge when it comes to this machinery.

In reality, AI plays a bigger role in business than you may realize. Chatbots are used to answer customer complaints and queries quickly and efficiently. Biometrics helps verify customer identities, and machine learning assists with sales forecasting.

Maybe you have already cracked asynchronous communication and know all about content management systems (CMS). But, if you want to generate more sales and take your firm into the modern age, then it might be time for you to get to grips with AI—particularly, optimizing the machine learning methods. This will make sales forecasting easier for you.

After all, sales are the driving force behind any for-profit firm. So, wouldn’t it be great to know how a sale will pan out?

What Is Sales Forecasting?

As the name suggests, sales forecasting is estimating the volume of future sales. It’s a good way to boost e-commerce sales as it helps you to identify the strong points of your itinerary. It even lets you know how much that product should sell, at which price, and in which market. It does this by making informed business decisions.

Sales forecasting also involves gathering information on cash flow, resources, and workforces. Doing this helps capitalize on trends and makes more accurate decisions. So, when it comes to scalability in business, you will know what is working and what isn’t. You’ll then know which areas to focus on to get the most income.

Source: Slide Share

Where Does Machine Learning Come in?

A subset of AI, Machine Learning (ML) uses algorithms to improve an experience. It automatically ‘learns’ from information and develops its knowledge on a subject, depending on how it is programmed. It makes predictions and decisions without being told to. It can be a powerful data tool that collects and processes information in ways that humans simply can’t.

Therefore, ML is ideal to use as part of sales forecasting. This is due to its ability to store, process, and use data much more quickly and efficiently than people can.

Using machine learning in this way can help you discover the highest potential earning opportunities.

It also prioritizes leads to a stronger sales approach and forecasts sales for the future. As it can go through years’ worth of data, it can identify patterns that take people a long time. These patterns are turned into predictions and sales forecasts.

The majority of ML information given out is based on probability and tells you if the chance of that happening is impossible or certain. It also takes into account internal and external factors and makes a prediction on sales based on this too.

However, these predictions are best used as a baseline rather than a solid answer. As we know from history, sometimes the financial and sales market can unexpectedly change.

So, just like if you were figuring out your project management solutions, it’s important to assess information. Only then can you combine it with ML predictions and build on that data. Don’t rely solely on predicted ML data.

Source: Clari

1. Use Past And Current Data

One way that ML can be used to predict sales is to use data collected over weeks, months, and years. For the best results, base data on trends, sales in the pipeline, and general sales too. This helps to focus on specific products and understand how well products will sell for each market. Doing this is the simplest—and possibly most effective—way to get data.

Using this method of data gathering also helps you reflect on your company as a whole. It helps you to see the areas in which your workforce is doing well and the areas where they may need a little more help.

For example, it may be that your sales team did a great job of selling computer fax software to marketers. However, data shows it did not sell as well to healthcare professionals. From this, you learned more about your target audience. You have also learned that if you do want to sell outside your target audience, you will have to change your sales pitch.

The more information available, and the longer the data goes back, the more reliable the prediction will be. Companies that don’t have years’ worth of info, should use data based on industry trends and averages.

2. Use Human Intelligence

If anybody knows how well certain products sell, it’s your sales team. Use their knowledge alongside ML to make the best predictions possible.

Whilst a machine goes off data, a human will remember unusual details that machines don’t pick up. They will be able to say whether selling a product took a lot of persuasions or if it was an easy sale. They will also be able to remember the comments customers made or if something was happening at the time that altered sales.

For example, let’s say you are a company that sells socks. After assessing data from the past few years, ML has predicted that thick sock sales will increase by 25% in November. This means you should make sure that you have enough stock in by October to meet these increased sales.

However, a member of the sales team points out that last November, it snowed unexpectedly. And the year before that there was a flood. This year, November is predicted to be mild. Therefore, combining human intelligence with AI in this way will help you make a more informed decision.

But humans aren’t always reliable sources either. They can often over/underestimate data and give data that isn’t concise. This is why combining human intelligence and ML is a great solution. This is a simple way of making sure you have data that covers more than analytical results.

Remember too that people also make mistakes. It may be that you run a company that requires using an online booking system. A member of staff could have put the wrong demographic in. Or put wrong dates in the system. This may alter the outcome of the data and is something that needs to be considered when making decisions.

With this in mind, you will need to make actionable strategies based on information from both ML and human staff. The ML data will probably be more accurate; however, human information can explain any anomalies. By combining the two, you can make better decisions.

Source: Juniper Research

3. Consider External Factors

There may be external factors that could affect potential predictions. ML is very good at dealing with them, but they still need to be considered.

One example is that time of year can affect sales. We know that shorts will make more profit in the summer and scarves more profit in the winter. When using ML to make a prediction, it’s important to compare data and make a prediction based on seasons. There is no point in using winter’s data to buy stock for the summer.

Another point is that the price of products can also change over time. In general, they tend to go up, but things like recession or financial prosperity can cause unexpected inflation or deflation. Let’s say, for example, you sell chocolate. 3 years ago, a chocolate bar sold for $1. Each year, the price of that chocolate bar increased by 1%. However, due to a recession, that item is less in demand and prices have generally gone down. It wouldn’t be fair to sell that chocolate bar for an increased price again even though ML predicts it will sell well.

Even if you did decide to sell for the predicted price, customers would just go with the competition that sold it for a lower price anyway.

Some companies may also need to take into account the weather. Of course, firms who make a profit by selling fax by email software or anti-virus software won’t have to worry about this. But from the Wi-Fi signal to the clothes we wear, the weather can have a big effect on sales. A freak occurrence of a month-long heavy downpour in August may result in a company selling out of boots in a day—whilst also being left with an overstock of sun lotion.

Outside factors can mean that sales fluctuate out of our control. So, even though ML can make a prediction, there may be a few irregularities or inaccuracies because of this.

4. Consider That Things Change

We know that the best type of data is taken over the years. But, if you really want modern and sharp forecasting, you need to take into account that a lot of things change over time. Selling over social media has dramatically increased over the last few years, for example. Customers also spend a lot more online than they do in traditional stores today. So, to get the best prediction possible, you will need to take into account things like social media data. Or make sure that you are accounting for sales from all platforms.

You also need to consider how trends change over time. Fax, for example, keeps fluctuating in popularity. 20 years ago, it was all the rage. 10 years ago, it was a dead means of communication. And today, people are combining old and new tech and asking, “can I fax from my computer”.

If you come across a sales problem similar to this, it won’t be enough to use data from 20 years ago—that tech is now outdated. So, you will have to use the data you can and reflect on industry trends.

However, one of the great things about using ML to forecast sales is that it assimilates a prediction based on all the information it has. So, it will still make good suggestions even if the information isn’t perfect.

Source: Bo Tree Technologies

5. Optimize Marketing Campaigns

Marketing campaigns and ML software intertwine in different ways. The first is that the different campaigns may affect the sales data differently. If you are changing your marketing campaigns regularly, it will be harder for ML software to make an accurate prediction.

But with that thought in place, as ML can spot patterns, it can also help your teams figure out the type of campaign that worked the best. It also means they will be able to spot the campaigns that have the highest potential.

The same goes for helping the sales team spot opportunities. This is because the algorithms from ML will say which products are worth putting the time and effort into. This means when the sales team say they have the best call center software available, they mean it!

6. Record All Data

It’s important to make sure that any prediction is as simple and as sharp as it can be. Therefore, every employee is recording every bit of information they can. This can be a lot of effort at the time—but it will mean that ML can make better predictions for the future.

It’s not to say that the sales team needs to be noting down every time a customer asks, “what is a teleconference?” But if they are selling tickets for a teleconference then they need to record all the details they can. This means the algorithms provided by ML can help you compare with old data and fix any issues that come up with sales.

Source: Bo Tree Technologies

Using machine learning to predict sales is a wonderful way of accessing and processing data that humans simply can’t do alone. But what’s important is that it helps you figure out the success of products. This in turn makes sure you aren’t losing out on income and customers by over/understocking products.

Remember, don’t just rely on ML to make the sales decisions for you. Combining ML with outside methods or factors will provide you with results that cover all grounds. Overall, though, ML is a more effective, speedier, and more precise outcome when it comes to predicting sales.

Samuel O'Brien
Sam O’Brien is the Chief Marketing Officer for Affise—a Global SaaS Partner Marketing Solution. He is a growth marketing expert with a product management and design background. Sam has a passion for innovation, growth, and marketing technology.


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