How AI Predictions Drive Business Insights and Decision Making

Machine learning is a subset of AI that uses algorithms that mimic human learning by providing machines with datasets. From these datasets, machines can learn various tasks ranging from forecasting to data analysis.

Businesses use machine learning to improve their decision-making processes, making data-driven decisions that impact the entire company. With the help of machine learning, businesses can start understanding their data and making predictions about what might happen if they change some aspects of their business. For instance, what would happen to a business if it stopped selling one of its products? Machine learning can tell them based on previous sales data.

Data is at the heart of any machine learning process, so you might wonder why more companies aren't using it. Machine learning and AI require massive amounts of training data to learn, and with enough data, they become more accurate.

Nevertheless, AI in marketing and business can help businesses learn how to improve sales, enhance the customer experience, and plan for the future.

Let's take a look at a few of the ways machine learning improves decision-making.

Predictive analytics

Predictive analytics is another subset of AI that's based on statics. It makes predictions based on the data it has, providing you with better business intelligence insights. For instance, if you give it sales data, it can make sales projections for the next month, quarter, or year. Predictive analytics uses predictive modeling to use historical data to predict something that may happen in the future.

It can be used to predict anything as long as you have historical data. For instance, financial companies might use it to determine when to sell a stock based on past market behavior.

ML can also predict the performance of a marketing campaign and how likely it is to convert customers based on past purchases and behavior, ultimately measuring the performance of a campaign that hasn't happened yet.

Customer segmentation

Machine learning can also be used to segment customers based on various data points. For instance, AI can group customers with similar characteristics together based on demographics and attitudes. However, when you add in customer data you collect from your online store, it can be used to segment customers based on past purchase behavior.

For instance, Mailchimp uses predictive analytics to analyze past purchase behavior and predict contacts with a high, moderate, or low customer lifetime value (CLV). Then, those predictions are used to segment your customers automatically.

AI technology can automatically find patterns in customer data the human brain can't, allowing you to segment them based on the information you didn't even know existed and create more personalized marketing campaigns.

Using machine learning for customer segmentation increases efficiency and is highly scalable. Manual methods of combing through customer data to find similarities might work for small businesses, but it's not efficient enough when you have tens of thousands of customers.

Fraud detection

Fraud detection refers to IT processes that prevent fraudulent payments. Unfortunately, many fraud protection tools have high accounts of false positives, which prevents real customers from being able to do business with you.

For instance, large orders were considered more likely to be fraudulent, blocking transactions over a certain amount. If your fraud detection system blocks customers automatically based on order quantity or sales amount, you can't determine whether any of those orders were from genuine customers.

AI technology solves some of the problems related to outdated fraud detection programs. Additionally, it works faster than most of those programs, giving you results immediately after receiving an order. ML fraud detection is also more scalable, allowing you to increase transaction volume by providing it with more data. But that's just the beginning.

Using machine learning for fraud detection is more accurate, which means you're not potentially blocking genuine customers. These technologies learn from patterns and can adapt to changes faster than human intelligence. Therefore, it can identify suspicious or fraudulent transactions even faster to protect your business.

Resource optimization

Artificial intelligence and ML use algorithms that outperform humans, easily allocating resources (whether labor or cash) faster. AI models can continue learning once they're trained without human intervention. After model deployment, you can use it to allocate resources by quickly processing large amounts of data and finding free spots for labor or money that are easily overlooked.

Sentiment analysis

AI technologies can assist in almost every aspect of your business because they're more accurate and efficient than the human mind. For example, trained models can use ML to analyze customer or employee sentiment by reviewing texts and categorizing them as positive or negative to create quantitative data that's easy to understand. Such a model can be useful for tracking customer or employee sentiment to help you learn how to improve internal or external processes.

Risk assessment

Your chief technology office might perform a risk assessment to identify potential dangers to your business's IT systems. However, AI investments can help identify hazards and analyze the potential outcome if those hazards occur outside of IT systems.

Instead, you can assess risk for what might happen if your website goes down during the holidays, how much it would cost to replace an employee, or how a specific marketing campaign would impact sales.

Demand forecasting

Demand forecasting allows you to predict future sales based on past sales data, allowing you to make forecasts about anything related to your business.

AI technologies use algorithms that analyze sales patterns and predict future trends. For instance, a brick-and-mortar store owner can determine which day of the week is the busiest without seeing it for themselves.

You can also use demand forecasting throughout the year to determine your budget allocation. For instance, you might find that July is your busiest month, allowing you to spend more on various initiatives like marketing and sales.

Predictive AI can yield more accurate results than human intelligence, but the two must work together. Using AI in business can help you sort and organize data, but it's your responsibility to draw conclusions.

Uncovering hidden patterns

Predictive AI discovers hidden patterns in data. Once it has the data, it can begin to find similarities and effectively group data points together. For instance, you can give it customer data to find hidden patterns among your customers and reveal new information you didn't see before.

AI can find patterns in any data set if there are any to find. For instance, you can give it photos of animals, and it will find a way to categorize them based on appearance.

What does this mean for businesses? Ultimately, AI can uncover new patterns you didn't see before, helping you learn more about your business and its customers.

Streamlining data analysis

AI enhances data analysis by being able to analyze massive amounts much faster than human data scientists. It can identify trends humans can't detect and help businesses predict future outcomes while making more data-driven decisions.

Enhancing predictive modeling

Predictive modeling is a technique used to forecast future behavior, answering the question, "What happens if..." Analyzing historical data can generate a model that predicts future outcomes. AI enhances predictive modeling by analyzing historical patterns faster and more efficiently to detect patterns in new data and use them to predict the future.

For instance, you can use it to determine what might happen to sales if you remove a product from your lineup, invest in a new website, or advertise more during the off-season.

Personalizing customer experiences

Building customer relationships is one of the best ways to promote your business. With AI, you can learn everything there is to know about your customers and personalize their experiences.

For instance, AI can help you find patterns in purchase behavior to tell you how likely someone will purchase a similar product.

Using AI can streamline the decision-making process by helping organize data in an easy-to-understand way. A few advantages of using AI to make business decisions include the following:

Accuracy and precision

AI doesn't make mistakes and isn't prone to human error. A data scientist can insert a number wrong or misread data, leading to inaccurate results. AI can't make those same mistakes because it only has the data you give it to work from.

Speed and efficiency

When you're running a business, you need to be flexible and make decisions quickly. But unfortunately, you won't always have time to collect and analyze data.

AI systems can process and analyze massive amounts of data faster than any human, reducing the time it takes to make important decisions.

Reduction in human bias

Data bias is something only humans can insert into the equation. AI systems only become biased when bias has been entered into them, so when they analyze data, they're not inserting any bias into it. However, human bias can be reintroduced after AI has performed its job.

For instance, an AI model might predict that sales will increase during the holidays based on past sales data. However, your AI system might not have data about what causes the increase in sales, leading business owners to think that they should expect an increase in sales every holiday season.

In this case, AI might analyze data from years when the same business had comprehensive holiday marketing strategies. Therefore, assuming that the business will automatically have increased sales during the holidays is inaccurate.

Challenges and limitations of AI systems

Unfortunately, no small businesses can take advantage of predictive AI just yet. Here are a few challenges you might be faced with if you're considering making the investment:

Data quality and quantity

AI and ML model training require massive amounts of high-quality data. Without it, your system can't learn, yielding inaccurate results.

Interpretability and explainability

AI analyzes data, allowing you to identify patterns faster than before and predict outcomes based on variables. However, it can't interpret those results.

If you're wondering if you should allocate more money to your marketing department, AI can only tell you what can happen if you do; it can't provide advice. Instead, you must interpret the predictions yourself to make the best decision possible.

Human intervention

As we've mentioned, there are currently two popular types of AI: predictive and generative AI. Generative AI creates content and images and is frequently used to drive chatbots, while predictive AI uses statistics to forecast the future.

Both require human intervention. AI chatbots can't respond to all types of customer inquiries, and predictive AI can only analyze data; it can't speculate or interpret the data. Those who interpret the data can make mistakes, even though AI can accurately predict the outcome based on data.

The future looks bright for AI in business workflows

AI can be used throughout your entire business. Many companies already use AI to improve customer segmentation and workflows.

Predictive AI can help you make more informed business decisions. Start using Mailchimp today to take advantage of our predictive e-commerce insights to help you create more effective marketing campaigns.