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When it comes to staying relevant in today’s ever-evolving digital world and driving business growth, most roads lead to leveraging artificial intelligence. Artificial intelligence (AI) and machine learning (ML) have already revolutionized many industries but there is still much untapped potential across the board, including in marketing.

Traditionally most companies have looked to data scientists to help gather, aggregate, and synthesize data in order to make decisions and solve problems. But, with the ability to do the job of data science more rapidly and with more accuracy by processing even larger datasets, AI and ML solutions are becoming more popular.

In marketing, using ML can mean more opportunities to achieve better customer relations, sales, growth, and ultimately more revenue. Integrating AI and ML in marketing, for some, may seem like a difficult task with a steep learning curve, but with the right tools and resources, it doesn’t have to be.

Let’s take a look at seven ways your business can benefit from using machine learning in marketing.

Different Ways to Use Machine Learning In Marketing

1. Better Personalized Content

Many brands are already leveraging machine learning to improve customer experience. One area of machine learning that is particularly effective for improving personalization is predictive targeting. Predictive targeting is essentially delivering the best experience to your customer at the best time. To do this, companies use AutoML tools that track and analyze a customer’s behavior. Through its learnings, it recommends how to target and serve them a more personalized experience.

For example, music streaming platform Spotify uses AI and machine learning technology to create customized playlists. It uses AI and ML to analyze its customer’s listening habits based on the day, the week, the genre and more, for an overall better listening experience. For consumers, a more personalized experience means more time on a platform which brings the potential for more subscription or advertising revenue.

2. Improve Lead Scoring

Scoring leads is a form of lead qualification that assigns a numerical value to prospects based on how likely they are to convert. The score helps companies decide which leads to prioritize.

Through AutoML tools, businesses can understand patterns from previous sales and use those factors to determine high-scoring leads, to help increase sales. This also allows marketers to focus on sales-ready customers.

Platforms such as help companies execute lead scoring strategies more easily by offering functionality that goes further than most CRM systems. However, most AutoML tools can still integrate with your CRM, allowing for the information gathered from Auto ML learnings to be sent and stored in your CRM.

3. Personalized Customer Care

Machine learning in marketing can significantly improve customer care by using virtual assistance and chatbots. AutoML constantly learns from the data customers provide to the chatbot assistants to deliver relevant answers. Moreover, the more data ML collects, the more personalized and accurate the answers are.

Overall, it gives the customer a better experience while building your brand a loyal audience.

4. More Accurate Risk Modeling

Risk modeling is a technique used in risk management to assess and quantify risk. It usually involves assessing potential future events and their probable outcomes. To determine these outcomes, a model needs to be fed historical data that contains– the data you’re trying to predict (target data) and the characteristics related to the things you’re trying to predict (features data).

Although machine learning in risk modeling and machine learning in marketing may seem like two different uses of AutoML, they are actually tied to each other. That’s because, as already mentioned above, a machine learning model requires good data and lots of it, to make accurate predictions. Therefore, targeting the best potential customers at the right time through ML-powered marketing strategies is essential in gathering the necessary data to train a good model. So, in a nutshell, you can say better marketing data means better risk predictions.

5. Improve Churn Prediction

Minimizing customer churn has been a common challenge for marketers. However, companies can significantly reduce churn by using AutoML algorithms. These models help predict the customer lifetime value and their possibility of churning.

AutoML analyzes the customer’s behavior and sorts users by high to low churn probability. This helps focus a marketer’s attention on valued customers. Furthermore, it provides marketers insight into what factors contribute to churn and how to decrease the risk of a customer churning.

6. Lower Marketing Costs

Marketing a product is costly. When resources are put into ads without having a complete understanding of your audience, you lose money. Therefore, machine learning in marketing can significantly improve targeted marketing.

AutoML tools identify trends in real-time and respond by presenting relevant ads to the user. This reduces marketing costs on unnecessary ads and helps personalize ads a customer will more likely engage with.

7. Predict Sales Projections Easily

Machine learning in marketing also vastly improves sales prediction. Machine learning uses recency, frequency, and monetary (RFM) models, which provide markets with accurate data on the demographics of their customer bases.

Simply put, it identifies the best customers, biggest spenders, and even lost customers. AutoML uses customer data from different segments and builds sales predictions using RFM-based predictive models. For example, the ML models predict that sales will be higher on a specific date and time. Marketers can target their customers with discounts or limited-time offers to achieve better sales while lowering marketing costs.

What It Comes Down To

Companies that can better target and serve their customer’s needs will always come out on top. To do this, understanding your customers on a deeper level and having the ability to interpret big data is necessary. You can do this one of two ways, hire a data scientist–which can be expensive and hard to find–or by using advanced machine learning tools, like uses no-code AI technology to enable organizations and individuals to gain insights, make predictions and innovate by harnessing the power of data science. From the non-technical managers and executives to the highly technical data scientist, a tool like – with its easy to use interface-makes the benefits of machine learning in marketing or any industry accessible to everyone.

To learn more about how using AnyML can help you or your company take advantage of better marketing insights to solve problems and find solutions visit