The Interesting Advantages of Machine Learning Marketing

Consumers generate massive amounts of valuable data daily. How can businesses use it to their advantage? A machine learning marketing approach utilizes a special subset of artificial intelligence to process data, helping professionals quickly make accurate, insightful decisions. Here are some of the most interesting advantages of using this technology.

1. Predict the Future

Predictive analytics is one of the best advantages of using machine learning in marketing. An algorithm can learn independently, adjusting to data changes in real time. It then compares current information with historical behavioral patterns to produce an accurate projection. 

In other words, it effectively lets you predict the future. For example, it can analyze a customer’s purchase history and previous site interactions to estimate when or why they would buy another product. Since a machine learning model can improve on its own over time, it can continuously refine itself through feedback loops, using data collection to enhance each customer’s experience.

With this new insight, the marketing department could tailor advertisements to increase the chances of a sale or conversion. Knowing how to prepare for market changes and shifts in behavior gives them a unique opportunity to cater to the customer before they even verbalize what they want.

2. Get Ahead of Trends

Social media provides massive opportunities for brand awareness and consumer engagement. Younger audiences have the most buying power — Generation Z alone has $360 billion in disposable income — and most grew up using some platform. 

As such, it’s a good thing that machine learning can recognize trends before they go viral. While businesses attempt to engage users by making posts or comments, the oversaturation of commercial content online has desensitized them somewhat. As a result, many only interact when a brand appeals to current trends.

Even then, there’s no guarantee that content will do well, which is why machine learning may be the solution. According to one 2021 study, an algorithm can recognize what will go viral with nearly 70% accuracy on average. Additionally, it can discover hidden trends, allowing marketing professionals to get ahead of the curve.

In response, they can create content that will receive much more attention and engagement. The chance of sales and conversions is much higher as a result. Brand awareness can also skyrocket with consistently viral posts.

3. Understand Customers’ Thoughts

Since segmentation is crucial to effective marketing, professionals would ideally have in-depth knowledge of every customer. Luckily, machine learning gives them a chance to build accurate, data-driven profiles. 

Since it can learn without needing constant retraining, their details stay up to date. The algorithm can rapidly process massive amounts of information, providing real-time insight into consumers’ lives and purchasing behavior — almost like it’s reading their thoughts. 

In response, marketing professionals can customize emails, advertisements, and site interactions. This effort is essential since around 62% of people will only continue doing business with a company if it personalizes their experience. 

4. Outperform the Competition

Competitor analysis is one of the most effective approaches when using machine learning in marketing. The algorithm can monitor their budget, earnings, and other information to track the success of their marketing. As a result, the business gains unique insight into consumer behavior, potentially leading to more conversions.  

Machine learning marketing campaigns often outperform traditional strategies because they provide data-driven insight. Since businesses can get ahead only by using this technology, the knowledge of their competition can ensure their success. 

Plenty of useful information is publicly available. For instance, the United States government has small business and consumer credit information for reference. Businesses can use their new knowledge to identify market opportunities, audience gaps, potential obstacles, and hidden secondary competitors.

5. Incentivize Purchases

Personalized algorithms are foundational for most platforms in the digital age. This pattern has grown to the point where many people expect a business to tailor their shopping experience. For example, they prefer to see related products, similar items others bought, and best-sellers in familiar categories. 

Machine learning can adapt without needing manual input, so it can use a customer’s real-time browsing behavior and purchase history to make accurate recommendations. The sale chance increases by 35% simply if the suggestion is relevant, meaning this approach is effective.

It’s more effective than traditional algorithmic approaches because it can interpret data in real-time instead of relying on old information. Consumers also feel more comfortable with AI personalization because it seems natural, meaning they’re more likely to make a purchase. 

6. Expose Hidden Patterns

Every online interaction generates data. Experts predict people will create over 149 zettabytes by 2024. To put those figures into perspective, a single zettabyte equals 1 billion terabytes. While filtering through a massive amount of information to find something of value may seem challenging, an algorithm can do so easily.

A machine learning model can analyze consumer engagement levels, open rates, and frequency of interactions to find trends. It can evaluate how people interact with a company so staff can see which approaches are effective. Many patterns aren’t visible to the human mind, making it a valuable asset. 

Also, it functions much faster than humans. While it would take someone months — maybe even years — to filter through and find connections between millions of random data points, an algorithm can do it in moments. Marketing professionals can take advantage of the response time improvement to push relevant media out much more quickly.

7. Keep Customers Loyal

A machine learning model can analyze historical churn data and current customer behavior to recognize when someone is about to take their business elsewhere. Retaining them is critical, considering they will spend 67% more than a new customer. Machine learning helps enhance customer care and preventative measures.

For example, it can identify when someone unsubscribes from emails and their engagement drops. It compares these behaviors to past cases to predict how likely they are to leave. With this insight, the marketing team can personalize recommendations, deliver deals, or tailor content to keep them loyal to the brand. 

Utilizing dynamic content can increase retention because it creates a better user experience. Additionally, it could lead to more sales because special offers and personal emails often incentivize people to make purchases.

8. Calculate Customers’ Value

While segmentation exists, marketing attempts are often broad because it’s more cost-effective to appeal to a larger audience. Because of this, businesses often spend money to appeal to consumers who will never open emails or make a purchase.

Being able to accurately calculate the Customer Lifetime Value is one of the most significant advantages of machine learning marketing. Simply multiply the average purchase value and number of transactions, then multiply the resulting figure by the length of their brand relationship. 

A machine-learning model can do this math quickly. Additionally, it can precisely estimate a consumer’s value, number of future purchases, and how long they’ll continue doing business with the company. Because of this, the marketing team can identify the return on investment for each, better informing their choices in the future.

Deploy a Machine Learning Marketing Campaign

A machine learning marketing campaign can personalize content, increase conversions, retain customers, and calculate an accurate return on investment. Every action is data-driven and precise since the algorithm operates on real-time information. Essentially, it’s one of the most reliable means of marketing available.