Implementing AI in Your Demand Forecasting — Tips and Tricks You Need to Know

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I was recently assigned the task of forecasting demand for a project. I set to work using my usual methods, but I've not explored AI in demand forecasting. My recent project got me thinking about AI's role and whether AI could (a) aid the demand forecasting process and (b) save time.

I needed expert advice to help me with this, so I connected with 23 professionals, including sales professionals, directors, and heads of growth and marketing, to hear how AI is revolutionizing demand and sales forecasting. The best responses made it into this article.

If you want to sophisticate your demand forecasting with AI, you're in the right place. We're on a journey to discover why we should use AI and its key benefits, with tips from professionals throughout.

Table of Contents

• Why Use AI for Demand Forecasting?
• How AI Can Be Used for Demand Forecasting
• Tips for AI Demand Forecasting
• Enhancing Your Demand Forecasting With AI

Why Use AI for Demand Forecasting?

To create this article, I interviewed 23 professionals and analyzed their responses to understand common use cases for AI's role in demand forecasting.

The three main reasons for using AI in demand forecasting are:

• Enhanced analysis, particularly around competitor analysis and customer behavior.
• Improved accuracy generally.
• Data-driven decision making.

I'm going to look into these three in a little more detail.

Other reasons for using AI in demand forecasting include:

• Continuous learning.
• Real-time data.
• Integration capabilities.

Enhanced Analysis

For the AI pioneers in demand forecasting, enhanced analysis is one of the main benefits of using AI.

Enhanced analysis is mainly considered a benefit in conjunction with predictive analysis, customer behavior analysis, and competitor analysis.

AI in predictive analysis identifies demand fluctuations and market trends so businesses can proactively respond to changes, reducing risks like stockouts or overproduction.

When it comes to competitive insights, AI pioneers are using tools to analyze competitors, identify market shifts, and take action based on the findings.

In an article for TrueProject, Tom Villani, TrueProject's CEO, credits AI's enhanced analysis as offering:

• Unbiased analysis.
• Real-time insights.
• Excellent extraction of meaningful patterns.
• Remarkable accuracy.

Enhanced analysis should be taken seriously. According to an IBM report, poor data quality costs businesses worldwide.

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Improved Accuracy

According to the experts I connected with, AI helps businesses anticipate trends and predict future sales more accurately than humans. However, it can't be ignored that a common side note to improved accuracy with AI in demand forecasting is that your input has to be good.

Many experts recommend keeping the AI updated to get accurate results.

Simplilearn compared AI with human intelligence to see where AI shines and where humans do. When it comes to improved accuracy, AI takes the trophy.

On "perfection," Simplilearn raises the possibility of "human mistakes" missing nuances, whereas AI's capabilities are credited with being "updated" to "deliver accurate results."

Data-Driven Decision Making

I like the quote, "Without data, you're just another person with an opinion."

Data allows businesses to make decisions that have the best chance of succeeding. Data can challenge what we think will work. I've certainly been in situations where the data has proven the opposite of my expectations.

Gathering data is time-consuming, and your data analysis can be incorrect or biased when relying on a human.

While I don't want to ignore that AI can also be biased, AI can reduce reliance on guesswork and increase forecast accuracy, leading to better data. In addition, AI can analyze data faster and in much larger datasets than humans.

In his Forbes article, Leveraging AI For Data-Driven Decision-Making While Safeguarding Privacy And Security, Neil Sahota, an AI advisor, writes incredibly positively about the role of AI in decision-making.

Sahota writes, "AI algorithms, fueled by machine learning and advanced analytics, can process colossal datasets at speeds unimaginable for humans. This capability enables organizations to extract valuable insights, identify patterns, and make decisions with unprecedented accuracy."

In HubSpot's Smarter Selling with AI research, a quarter of salespeople believe AI helps employees make data-driven decisions.

How AI Can Be Used for Demand Forecasting

From the experts I connected with, I got many use cases for AI. Experts generously shared how they're using AI and what tools so that you can get started.

To Manage Stock and Inventory

You simply couldn't research AI in demand forecasting without examining its value in managing stock and inventory. I had a lot of responses about AI and its role in managing stock and inventory.

Here are some of the best.

Tomasz Borys, senior VP of marketing and sales at Deep Sentinel, is using different data streams to build a wider picture of what the market wants so he can manage stock.

Borys says, "We noticed that our AI system predicted a 30% increase in demand for our outdoor cameras in certain regions during specific months. Upon investigation, we found this correlated with seasonal increases in property crimes in those areas. This insight allowed us to adjust our inventory and marketing strategies accordingly, resulting in a 25% increase in sales during those periods."

To get this data, Borys is using tools like Salesforce's Einstein Analytics, which Borys credits for its "ability to analyze historical sales data alongside external factors like seasonal trends, economic indicators, and even local crime rates (which is particularly relevant for our security products). This comprehensive analysis allows us to predict demand with much greater accuracy than traditional methods."

Another company using AI to manage inventory is All Filters. Shu Saito, the CEO and founder, recommends Prophet by Facebook. Saito uses this AI to predict seasonal demand.

Saito says, "Prophet allows me to model sales data with built-in flexibility, accounting for irregular trends like sudden surges or dips. This helps me optimize inventory levels and adjust marketing strategies ahead of key selling periods, ensuring I can meet customer demand without overstocking."

Finally, but by no means least, HubSpot CRM is used by Jason English, entrepreneur and CEO of CG Tech.

English says, "I have discovered that AI has significantly impacted the way we predict sales demand. We utilize platforms such as HubSpot and Microsoft Dynamics 365 to analyze past data, market trends, and customer behavior trends. The accuracy of AI assists in improving our ability to predict demand, resulting in smoother inventory management and resource allocation."

What I like about this: HubSpot's CRM makes forecasting simple. Instead of importing and analyzing data in spreadsheets, sales teams or trend forecasting teams can get a seamless picture directly from the CRM. You can instantly view sales revenue by month or quarter to make data-driven divisions based on sales trends.

Request a demo for HubSpot's sales forecasting software today.

To Make Marketing Decisions That Bolster Revenue

I've already mentioned the value of accurate data and how AI in demand forecasting helps teams make better decisions. Julie Ginn, vice president of global revenue marketing at Aprimo, can illustrate this point with an example of how Aprimo uses AI in demand forecasting.

Ginn shares how AI and machine learning generate sales forecasts and customer insights, "We use tools like Amazon Forecast and Microsoft Azure to analyze three to five years of a customer's historical sales data to identify trends and patterns. For a major CPG company, Forecast predicted a 10% uptick in seasonal product demand. We adjusted marketing spend and saw sales jump 18%.

"For customers, AI-driven forecasts have cut excess inventory and boosted sales by 15% to 20% annually. Integrating predictive insights into business processes and using them to make timely decisions is key. Companies leveraging predictive analytics will gain a competitive edge."

When asked for a tip about having sizable, high-quality data, Ginn shared that they refresh models quarterly with new data. "While AI is accurate, human judgment remains critical, especially for events impacting demand. AI improves human insights."

What I like about this: I love how Aprimo has used AI, demand forecasting, and marketing to understand what customers want. Increasing marketing spending would've put the right product in front of the right audience, resulting in increased revenue.

To Conduct Competitor Audits

As mentioned above, competitor analysis was one of the top use cases for AI in demand forecasting.

Jessica Bane, director of business operations at GoPromotional, provides an example of how GoPromotional does it.

Bane recommends pairing competitive intelligence with internal sales data to create a powerful forecasting tool.

"Internal sales data provides the historical context, while competitive insights offer an external perspective," she explains. "Merging these can refine predictions, offering a clearer view of where market demand might head. Integrating these data streams ensures forecasts aren't just educated guesses but are grounded in comprehensive, multifaceted analyses. This integrated approach allows sales teams to remain agile and responsive to market changes."

When asked where to start, Bane recommends conducting regular competitor audits and tracking the following:

• Competitor strategies.
• Pricing decisions.
• Market entries.

Bane says, "Combining these findings with sales performance data can paint a detailed picture of future demand trends. This kind of strategic review not only sharpens forecasts but also prepares the team to pivot quickly, optimizing both sales strategies and resource allocation."

To conduct competitor audits with AI to aid demand forecasting, Bane recommends Crayon and Klue.

She says, "[These tools] are transforming how sales teams view the competitive landscape. These platforms gather valuable insights about competitors, like pricing and new product launches, and highlight market trends that could affect demand. Knowing what competitors are up to helps us anticipate shifts in the market, allowing us to adjust strategies proactively. It's akin to having a window into future market dynamics, which is vital for staying ahead."

What I like about this: Bane is taking a holistic approach to demand forecasting combining competitive research with owned sales performances. It could be tempting to rely only on sales data but I like how the competitor audit would bring another layer of data.

To Analyze Past Orders

Joanneke Schuurman, sales executive at Custom-Lanyards.net, also finds HubSpot CRM an essential tool for sales demand forecasting.

Schuurman says, "One way I use AI for sales-demand forecasting is by integrating tools like HubSpot alongside a platform like Clari. These tools help track real-time data trends, historical sales patterns, and customer behaviors."

For example, they implemented AI-driven forecasts when launching a new lanyard product line and saw a 15% improvement in predicting peak demand, allowing them to optimize production scheduling.

"By analyzing patterns from past orders and customer preferences, AI helps us adjust marketing efforts and stock levels," she concludes.

If you're also using HubSpot's CRM and want to get more sophisticated with your forecasting, you can access data seamlessly within the CRM itself - no more exports into spreadsheets!

Check out HubSpot's sales forecasting software.

To Offer Recruitment and Training

In my opinion, this is an interesting use case.

Daniel Meursing, founder of Premier Staff, uses sales-demand forecasting to establish security staffing needs. Knowing the demand for security staff helped Premier Staff where to invest in recruitment and training.

Meursing said, "We use Anaplan's AI-driven platform for sales-demand forecasting. The tool analyzes historical data, market trends, and external factors to predict future demand for our staffing services. For example, Anaplan's AI helped us accurately forecast a 25% increase in demand for security staff at tech events, allowing us to proactively recruit and train personnel."

Tips for AI Demand Forecasting

Before we close this research piece, I wanted to share some invaluable tips experts provided.

Tip 1: "Train" and update your AI.

The tips for updating AI came in various formats: keep the data clean, update regularly, train the AI, etc. I received this tip so many times that it's taking the top spot in this shortlist of tips.

Outside of it being very true - you do need to keep AI updated and data clean and fresh in order to get the best out of it - I wonder how many might fail to integrate AI into their demand forecasting because their data input isn't quite there yet.

Tomasz Borys, mentioned above, updates AI models monthly and credits this with improving forecast accuracy by 15%.

Tip 2: Start with basic tools.

Victor Santoro, founder & CEO of Profit Leap, uses AI tools Amazon Forecast and Tableau for predictive analysis.

Santoro says, "Start with a basic tool like Google Sheets AI or Amazon Forecast. Connect them to your sales data and ask questions about patterns, risks, and opportunities. The more you use them, the smarter they'll get, tuning into the nuances of your business. If demand seems volatile, don't be afraid to make adjustments based on the forecasts."

For those intrigued by Santoro's use of these tools, he says, "Amazon Forecast studies our past sales to anticipate seasonal fluctuations and demand spikes for our consulting services. By understanding these patterns, my team can optimize marketing spend, resource allocation, and new business development.

"Tableau helps us visualize complex sales data, identifying trends that would otherwise remain hidden. A few months ago, Tableau revealed an unexpected drop in sales from one of our major client segments. We were able to diagnose the issue and implement changes to reverse the trend, recovering over $200,000 in projected revenue."

Tip 3: Leverage AI for customer behavior.

Elia Guidorzi, marketing executive at Techni Waterjet, uses AI for predictive analysis. Guidorzi's main tip is to "ensure your AI tool integrates with your CRM for real-time data, which enhances the accuracy of sales forecasts and allows your team to make more informed decisions."

This type of analysis helps forecast sales demands.

Guidorzi continues, "We leverage AI-powered tools like HubSpot's predictive analytics to forecast sales demand. These tools analyze historical data and market trends, providing insights into future customer behavior. AI helps us optimize inventory levels, tailor marketing efforts, and identify sales opportunities."

Enhancing Your Demand Forecasting With AI

When I started looking into demand forecasting and AI, I wasn't expecting to get input from so many professionals with such valuable insights.

Throughout this research, I've been impressed with the use of AI in demand forecasting, and I hope you are, too. Hopefully, this article has given you everything you need to get rolling with AI to enhance your forecasting.

Topics: Sales Forecasting

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