5 Ways Machine Learning Can Impact Marketing ROI

Machine learning as a part of Artificial Intelligence uses statistical techniques that give computers the ability of self-learning without the explicit need of programming.

Machine learning analyzes huge amount of data from different sources such as website analytics, mobile apps, user interaction, social media insights, digital advertising among others and personalizes content, and offers incentives for different customer segments.

Let’s look at 5 ways how machine learning can impact marketing ROI.

Better Personalization

For ages, marketers are striving to personalize the user journey, but with the advent of machine learning, marketers are now able to scale it. With the abundant availability of data, marketers can learn more about users, their preferences, behaviors and usage patterns.

As the users interact with ads or content, with the help of machine learning algorithms, marketers can now personalize the experience through email marketing campaigns and other types of content. This creates a coherent customer journey geared towards increasing the revenue.

Customer Service and Support

Customers prefer connecting with brands via online chat than calling their customer support. Chatbots are being widely implemented in recent times. Natural Language Processing (NLP) helps to understand how to respond to customer queries. Based on the past engagement, machine learning algorithms can decide when to escalate the conversation.

Based on this, marketers can scale the low-effort work by assigning remote tasks to machines while boosting ROI, customer retention, and overall productivity.


Customer Churn Prediction

Customer churn quantifies the number of customers who discontinued their relationship with a company. For a SaaS-based company, it is when customers unsubscribe or cancel services. Online businesses consider churn after a stipulated time has passed since the customer’s last interaction with them. 

Churn prediction is crucial because it helps gauge customer satisfaction. To predict churn, you need to consider their usage behavior, how they interact with your app or product, what was the last time they had logged in, when was the last purchase etc. With the help of machine learning, a model is trained to predict customer churn based on such previous data.

When the model is implemented on a larger scale, it predicts the risk and timing of the churn and in turn, helps marketers take proactive measures to prevent the churn through customer retention activities. This is crucial because churn results in lost revenue and non-recoverable customer acquisition cost.


Also Read: Future of AI for B2B Marketing


Forecast Customer Profitability

Customer lifetime value helps segment the users and predict growth. Machine learning based predictive customer lifetime value models define the purchase behavior of customers to determine their future actions.

With the help of such models, you can find out the customers who can bring in the maximum revenue. You can further target such strata to deliver personalized customer service to boost sales.

Digital Advertising                                                                       

The proliferation of opportunities and avenues have left the marketers ill-equipped to manage ad campaigns. It requires voluminous creatives to deliver the right message at the right time to your audience. To tackle this, machine learning is implemented in digital advertising including measurement and attribution, cross-device association, intent prediction, audience insights etc. The more these solutions are used, the better they learn and adjust themselves to deliver optimized results. All of this leads to reduced manual efforts and errors.

Google recently released 4 new feature sets armed with machine learning that maximize relevance with search ads, YouTube ads, local campaigns and shopping campaigns. According to Google, advertisers who experiment with multiple creatives see upto 15% more clicks.

Started off in the 1960s, machine learning has finally made its way into marketing. Despite the potential, it’s still not widely used due to the complexity of the field and required the involvement of data scientists and proficient programmers. To solve this issue, many applications are available in the market that tackles various issues.

In the coming time, machine learning will no longer be a competitive advantage, but rather a basic building block of the marketing stack.

Zoomd Trends


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