5 Use Cases of Machine Learning In Marketing

Use Cases of Machine Learning In Marketing

Marketers today have access to more data than ever before. To a large extent, this is the result of a shift in most organizations to prioritize data storage and use. Additionally, user data before would stop at basic information like name and demographics. But in the present day, it goes a lot further to include user interests, preferences, content and browsing history.

Theoretically, it would seem like marketing today should be easier than it ever was. Marketers have an almost perfect understanding of prospects and customers. However, an abundance of data comes with its own complexities and that’s where machine learning comes in.

Machine learning algorithms make it possible to access the mountains of data marketers come across and make sense of it. They have what it takes to help marketers enjoy higher levels of efficiency and effectiveness in virtually every respect. When they have relevant insight into customer behavior, they can focus their efforts in the right direction to create value.

However, how exactly can the use of machine learning assist marketers to enhance performance? In this article, we explore five use cases of machine learning in marketing.

Top Use Cases of Machine Learning In Marketing

5. Customer Segmentation and Personalized Marketing Efforts

Prospective customers generate countless data points rendering manual segmentation inadequate. But with predictive analytics, it becomes possible to eliminate guesswork and streamline the process. Machine learning-based segmentation has the capacity to analyze user data to identify complex correlations and patterns.

In order to do this, machine learning algorithms could use clustering, which groups observations into groups based on similarities. For instance, in marketing analysis, a machine learning model can group customers according to geographic locations, age, sex and income among other factors.

With such information at hand, marketers can match relevant segments to customers and create custom recommendations. Another marketing strategy based on segmentation is the creation of multiple offers or products for the different target groups.

For example, a company can segment customers based on geographical locations. Each of these locations would have different marketing strategies such as targeted ads. The company could also offer varying pricing packages to appeal to the target market.

Overall, the use of segmentation lets marketers tailor their efforts better to their different audience subsets. The approach holds a higher likelihood of success than blanket marketing efforts.

4. Forecasting Customer Behavior Using Real-Time Data

Imagine what business would be like if entrepreneurs could read minds and predict a prospect’s next move. Well, even though that might seem far-fetched, it is one of the top machine learning applications that marketers can leverage.

Machine learning-powered prediction algorithms can analyze user data to forecast possible future trends. And the best part is that they use real-time rather than historical data, which greatly enhances accuracy.

For example, before initiating a marketing campaign, it would be great to find ways to assess the likely response of the target audience. Similarly, knowing which customer or website visitor might make a purchase in the near future can also enhance performance and effectiveness.

How do machine learning algorithms achieve this? The answer lies in the fact that human beings tend to have repeat patterns. If this is indeed true, predicting potential customer behavior would be as simple as learning buyer patterns.

Neural networks make this precise feat possible by learning the trends of similar inputs and similar responses. They then use this information to draw useful, actionable insights to inform decision-making. By embracing this approach, businesses can forecast product demand, tailor recommendations and optimize inventory accordingly.

3. Predicting and Addressing Churn

More and more companies in high-churn sectors are turning to machine learning to predict potential churn and avert it. Machine learning can identify users who are on their way to churn and help marketers accordingly.

Customer churn is a sure way to tell if customers are enjoying their experience or are dissatisfied and would like to leave. Machine learning algorithms are trained to predict the likelihood of customers leaving using real case scenarios from previous churn data.

Getting customers to convert is just half of the battle for businesses. Ultimately, what really matters is the ability to engage and retain them for the long-term. Failure to do so could have marketers running on the proverbial hamster wheel getting new customers and losing them.

Thanks to its proactive approach, which is vastly different from the traditional retroactive model (read post-mortem), machine learning reduces the risks involved and enhances retention. By grouping together customers who are likely to churn, it allows marketers to uncover common profile attributes. With this information, it is easier to predict the kind of customers who are likely to churn. In turn, it becomes easier to take preventative measures and limit future turnover.

2. Targeted Advertising and Prospect Acquisition

One of the most popular machine learning use cases is ad targeting. Even the best of ads cannot be effective if it does not reach the right audience. Marketers need to make sure that their efforts are worthwhile by reaching the target audience.

Machine learning algorithms can predict what type of promotional strategy would be most effective for each site visitor. As such, it is a necessary prerequisite for personalized advertising and optimized targeting.

Businesses using this approach are better able to anticipate consumer needs and make suggestions before they even initiate contact. For one, machine learning applications can draw correlations from information accessible on social media and other platforms. They generate input that aid advertisers to reach their audience more effectively.

Based on the goals of any given advertising strategy, available funds and available time, machine learning can help marketers adjust their approaches effectively. Hence, they are able to maximize the potential of every person that meets the profile and stand a better chance to achieve their objective.

1. Delivering Excellent Customer Experience

Companies are always looking for ways to improve customer experience and machine learning holds the key to this as well. One of the ways it does this is by facilitating better customer support. When a business takes a long time to handle issues and queries, it often takes a toll on customer retention.

In this regard, machine learning-powered chatbots offer sophisticated engagement opportunities and are likely to deliver high satisfaction. Since they learn from previous interactions, they almost always offer the fastest problem resolution time. They grasp complex requests with ease and are able to personalize responses. Notably too, they are available round the clock keeping the call center fully functional at all times.

Another reason for customer dissatisfaction and churn has to do with running out of stock. But with the opportunity for accurate predictions, this no longer has to happen. Getting accurate forecasts makes it possible to stock up in advance based on likely future trends.

To cap it all up, machine learning algorithms make it possible to analyze customer sentiment and keep track of what they think of you. Having such intelligence from available data at all times makes it easy to adjust your strategies to match prevailing attitudes, countering negative ones and optimizing positivity.

Harnessing the Full Potential of Machine Learning in Marketing

Machine learning is certainly a powerful tool for marketing. These are just a fraction of the potential it holds for allowing marketers to extract actionable insights and drive efficiency. Undoubtedly, the competitive differentiation that drives today’s business model lies in the possibility of leveraging data to understand the customer better.

Thanks to the use of advanced data science models powered by machine learning, it is now possible to process countless data points and connect the dots that inform customer interactions. With the information marketers derive, identifying optimal channels for engaging users and getting the timing right is no longer left to chance. By using predictive rather than reactive approaches and personalizing user experience and automation, marketers can now harness the full potential of machine learning to power their strategies.

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Janica San Juan

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