Business Intelligence

How can AI enhance business forecasting in 2023?

by
Jon Reilly
,
April 19, 2022

Have you ever wished you could see into the future? To know how your product will be perceived by your audience before you launch it? Or to identify who is most likely to convert?

Business forecasting is an important part of any business. It helps you make better decisions about your future by predicting customer behavior, commodity prices, revenue, and more. 

In the past, business forecasting has been limited to traditional methods like surveys and market research. In this article, we’ll explore how AI can be used to enhance business forecasting in 2023.

What is AI forecasting and why should you use it? 

AI forecasting is the process of using artificial intelligence and machine learning algorithms to make predictions about future events. It can be used to predict customer behavior, trends, and market movements.

Screenshot of Akkio AI Flow used for a forecasting application
Screenshot of Akkio AI Flow used for a forecasting application

AI-based forecasting is more accurate than traditional methods because it can take into account a wider range of data points. For example, a traditional market research survey might ask customers about their age, gender, and location. It can consider factors like social media activity, web browsing history, and purchase history to make predictions.

Machine learning forecasting is also faster than traditional methods. Surveys and market research can take weeks or months to complete, while AI approaches can be done in real-time, meaning you’ll have up-to-date information on what’s happening in your industry.

This kind of data-driven forecasting can help you make better decisions about your business by giving you insights into the future. If you’re looking to stay ahead of the competition, AI is the way to go.

How can AI enhance business forecasting in 2023?

Uncertainty has marked the last couple of years for businesses around the world. The pandemic has forced many companies to change the way they operate, and economic swings have exacerbated existing challenges. In this environment, forecasting has become more important than ever.

AI can help businesses forecast by providing real-time data and insights. For example, an AI system can track social media activity to identify early indications of a product launch. This information can be used to adjust marketing plans and messaging in real-time, ensuring that you’re always ahead of the competition.

AI can also be used to predict customer behavior. By analyzing past behavior patterns, AI systems can identify trends and make recommendations about future actions. For example, an AI system might recommend that a company offer a discount to customers who are likely to purchase a competitor’s product.

In 2023, AI will continue to enhance business forecasting by providing accurate and up-to-date insights into the future. If you want to stay ahead of the curve, you need to start using AI today.

Top AI forecasting applications

AI is an incredibly versatile tool, as it seeks to find patterns in any type of data. This makes it particularly well-suited for accurate forecasting, as businesses need to identify patterns in customer behavior, market trends, and more. Here are four of the top applications of AI-powered forecasting.

1. Predicting revenue and sales

The implications of AI for revenue forecasting are far-reaching. By providing accurate predictions of future sales, AI can help businesses optimize their operations and better meet customer demand. In turn, this can lead to increased profits and more efficient use of resources.

Businesses can connect to a wide range of business applications to get data for sales forecasting, such as Salesforce.

Screenshot of a Saleforce dataset used as an Akkio data input for sales forecasting
Screenshot of a Saleforce dataset used as an Akkio data input for sales forecasting

AI is already being used by some companies to great effect. Retail giant Amazon relies on AI to predict consumer trends and manage stock levels accordingly. This has allowed Amazon to stay one step ahead of the competition and maintain its position as the world’s largest online retailer.

These models can be trained to look at a wide range of data sources, including public datasets, market trends, and economic conditions. This makes them well-suited for forecasting purposes.

The Covid-19 pandemic has been a trigger for many companies to invest in AI forecasting. This is because traditional methods of forecasting have been shown to be woefully inadequate in the face of such a global crisis and complex supply chain challenges. To give a simple example, toilet paper sales surged during the early days of the pandemic, caught companies off guard, and resulted in shortages across the country.

Amazon's AI-enabled forecasting quickly responded to this surge in demand. By understanding consumer behavior patterns, AI can predict trends with greater accuracy than ever before. This demand forecasting is crucial for businesses that need to be able to scale up or down quickly.

In a world where unforeseen crises are becoming more and more common, the ability to respond quickly and efficiently will be crucial for survival. Companies that don’t embrace AI-driven forecasting will be at a disadvantage when the next crisis hits.

2. Forecasting customer churn rate

Customer churn has been called the "silent killer" of businesses, and for good reason. It's estimated that it costs businesses billions of dollars every year in lost revenue. 

And yet, many businesses still don't have a clear understanding of what causes customer churn, or how to prevent it.

One of the most famous examples of customer churn is when Pandora lost its lead to Spotify in the streaming music market. Part of the reason Pandora lost was because it didn't evolve as quickly as Spotify did. Spotify was able to offer new features that appealed to customers, while Pandora lagged behind.

It's essential for businesses to stay ahead of the curve and continually innovate in order to prevent customer churn. This means being willing to experiment and take risks. It also requires a deep understanding of your customers and what they want.

Only by prevention can businesses hope to avoid the devastating impacts of customer churn. That's where AI comes in. AI can analyze data much faster and more accurately than humans can, and it can spot patterns that humans might miss. For example, AI can look at a customer's past behavior, such as whether they log in regularly or not, and make predictions about whether they're likely to churn. AI can also take into account demographic factors like age or location.

Armed with this information, businesses can take steps to prevent churn before it happens. They can send personalized emails or offers to customers who are at risk of churning, for example. Or they can give customers who are likely to churn priority when it comes to customer service.

3. Forecasting commodity prices

If you're involved in commodities trading or if your business is impacted by commodity prices, then you know how volatile and unpredictable they can be. For example, oil prices have been highly volatile in recent years, swinging from around $100 per barrel to less than $30 and back again.

Factors driving commodity price volatility

There are a number of factors that contribute to commodity price volatility. First, commodities are often subject to supply and demand fluctuations. A change in weather patterns can have a big impact on crops, for example, while an increase in demand from China can drive up prices for metals.

Second, commodities are often traded on futures markets, which means that prices can be driven by speculation as well as by actual supply and demand. When speculators believe that prices will go up, they may buy futures contracts, driving prices up even further.

Finally, commodity prices can be affected by currency fluctuations. For example, if the US dollar weakens against other currencies, it takes more dollars to buy the same amount of oil. This can lead to higher fuel costs for US consumers and businesses.

The volatility of commodity prices has major implications for businesses. For one thing, it makes planning and budgeting difficult. It's hard to predict how much you'll need to spend on raw materials or fuel six months or a year from now when prices could swing wildly in either direction.

What's more, commodity price volatility can have a big impact on your bottom line. If you use commodities in your business (say you're a manufacturer that uses steel), then an increase in steel prices will eat into your profits. On the other hand, if you're able to sell commodities when prices are high, you could see a windfall profit.

Of course, not all businesses are directly impacted by commodity price swings. But indirect effects can still be significant. For example, if transportation costs go up because of higher fuel prices, that could make goods more expensive to ship and lead to inflationary pressures in the economy as a whole.

Clearly, commodity price volatility is something all businesses need to be aware of and take into account when making decisions about pricing, production levels, and so forth. It's not always easy to deal with but it's an important part of doing business in today's world

But what if we told you that there's a way to predict commodity prices with more accuracy than ever before?

It's all thanks to AI. By analyzing historical data on commodity prices and applying statistical models, AI has been able to identify patterns in price fluctuations. This enables businesses to make better decisions about when and where to purchase commodities from suppliers.

For businesses that sell products whose raw materials are affected by commodity prices, this can be a game-changer. Restaurants, for example, can use AI to predict meat prices and adjust their menu prices accordingly. This helps them keep costs down while still maintaining profitability. Below, we can see an example of an AI model being used to forecast avocado prices.

Screenshot of an Akkio web app being deploying to forecast avocado prices
Screenshot of an Akkio web app being deploying to forecast avocado prices

So if you're looking for an edge in the commodities market, don't underestimate the power of AI. It just might be the key to success.

4. Forecasting market growth

As the world economy continues to grow and evolve, businesses are increasingly turning to AI for help in forecasting market growth. AI provides a powerful tool for understanding historical data and identifying patterns that may not be apparent to the naked eye. This ability to predict market trends can help businesses make better decisions about where to focus their efforts and resources.

AI can be used to improve our understanding of complex systems and make better predictions about future trends. As businesses continue to grapple with an ever-changing marketplace, AI will play an increasingly important role in helping them navigate these waters successfully.

How can you use AI for business forecasting? 

The rapidly advancing field of artificial intelligence presents both opportunities and challenges for businesses looking to use it for AI forecasting. On the one hand, AI can provide more accurate predictions than ever before. On the other hand, implementing AI can be difficult and expensive, requiring businesses to hire data scientists and engineers to build scalable systems.

Fortunately, there is a way to overcome these challenges and reap the benefits of AI without spending a fortune: no-code AI. No-code AI platforms like Akkio make it possible for businesses of all sizes to build and deploy AI models quickly and easily, without the need for expensive data science resources.

With no-code AI, businesses can automatically generate predictions based on historical data, updating forecasts in real time as new data comes in. This means that businesses can get the benefits of AI without having to invest in costly infrastructure or staff. In addition, no-code AI platforms provide transparency into how models are generated, so businesses can be confident in the accuracy of their predictions.

Finally, these AI forecasting models can be deployed anywhere. With Akkio, businesses can tap into better forecasting to augment business decisions, whether for demand planning, forecasting market growth, or any other metric.

Using big data, businesses can improve forecast accuracy compared to traditional forecasting, which has important implications for business planning. The legacy forecasting process comes with a number of forecasting challenges, such as inability to handle large amounts of external data, which can result in lost sales and poorer metrics across the board.

The bottom line is that no-code AI provides an opportunity for businesses to get ahead of the curve in forecasting, without spending a fortune on data science resources. By taking advantage of machine learning models, businesses can improve their decision-making.

Conclusion

So should you use AI in your business for forecasting? Absolutely! No-code AI platforms like Akkio make it easy and affordable for businesses of all sizes to take advantage of this powerful technology. With Akkio, you can get up and running with AI quickly and easily, without the need for expensive data science resources.

While legacy AI platforms like Google Cloud can be costly and difficult to use, Akkio provides an easy-to-use platform with free training and fast results. In just 10 minutes, you can create an accurate predictive model without writing any code.

As highlighted on our applications page, there are a myriad of use-cases for no-code AI, beyond forecasting. But whatever your specific needs may be, Akkio can help you achieve them quickly and easily, without sacrificing accuracy or transparency.

If you’re ready to take your business to the next level with AI, sign up for a free trial of Akkio today.

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