Machine learning and digital personalization

Digital personalization based on customer data has become critical in digital marketing, but marketers are still using outdated methods for running their personalization initiatives today. Machine learning systems can help these marketers in correctly understanding human emotions and facilitating superior personalization-driven products and services.

Consumers are spending a lot of time in exploring and analyzing suitable information such as what books to read, what songs to play, which news articles to read, which movies to watch, what clothes to wear, and so on. Imagine, what their experience would be like, if they were not required to pick anything on their own, but were given options of their liking, be it in education, food, or entertainment through digital personalization. Such an advanced and personalized recommendation can be generated by implementing machine learning algorithms.

Machine learning reduces the overall time spent on information discovery and thereby, increases the scope of effective information consumption. Let’s take a look at four use cases that put machine learning for personalization into context.

Digital Personalization by Replenishing Recommendations

By utilizing historical consumer purchasing data, machine learning systems can predict when certain customers are likely to purchase certain types of products. For example, consider a customer who is identified as physically active and who buys new sport shoes at approximately the same time every six months. This rule is identified and validated with several other similar customers, and companies can send all these individuals with related content or offers for new sport shoes models.

Digital Personalization by Providing Product Recommendations based on Customer Interactions

Say there is one customer who performs multiple interactions across different channels, such as a mobile app, online search, and location data indicating store visits. Based on this interaction history, a machine learning system can analyze the customer’s behavioral patterns and map these patterns to the behavior of similar customers. After this, it can predict the next action this customer is most likely to take, and provide the right product to that person on the right channel.

Digital Personalization by Giving Discount Offers on Abandoned Searches

Several marketers today are creating offers around abandoned searches. For example, say a customer searches for a hotel in Dubai. But instead of making her reservation online, she calls the hotel to book a stay. Then, even though she has already made her choice, she receives unwanted content and offers for Dubai hotels for the next several weeks. In such situations, machine learning systems can identify her true context and provide more relevant content and offers.

Digital Personalization by Presenting Opportunistic Promotions through Machine Learning

Some of the most effective business rules are based on the combination of third-party data and customer behavior data. Machine learning systems can help in drawing correlations between these types and thereby help in revealing patterns that target content, offers, or products to customers based on their specific or current context. For instance, during a long string of winter snowstorms on the East Coast of the USA, a home improvement company launched a campaign for discounted snow removing equipment that was targeted at homeowners.

Therefore, machine learning facilitates increases revenue through more effective personalization, thereby leading to customer acquisition, retention, and conversion. It also provides better service and higher customer satisfaction due to personalized engagements. The bottom line is that businesses and online retailers require a data-science system that can offer timely insights to help them take better and more informed decisions. Implementation of machine learning systems can, therefore, drive personalization and help in moving your digital initiative forward.