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Patterns, Predictions & Personalization

Posted on: November 29, 2018 | Posted by: Sona Sharma

Machine Learning Use Cases for Optimized Customer Engagement

While it may seem contradictory, advancements in technology are giving customer engagement a human touch—and it’s all thanks to Machine Learning (ML) capabilities. ML applies Artificial Intelligence (AI) to computers in order to give them the ability to learn from experience instead of being explicitly programmed. By analyzing customer data to predictively enhance and personalize engagements with increasing accuracy over time, ML models can drive both acquisition and loyalty; in fact, McKinsey reports that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and 6 times as likely to retain these customers.

In this blog, we will explore the potential use cases for ML models in Customer Engagement Hubs (CEH), evaluating the personalization benefits of data-driven intelligence across industries.

Drive Traffic to Store with ML-Powered Customer Engagement

Retailers must engage customers not only on their digital channels, but also within stores, where the majority of sales happen. They need to strategically leverage their stores by seamlessly connecting their customers’ online journey with the offline store journey to offer an outstanding experience. How? With the personalized power of Machine Learning.  For example:

Jamie browses her favorite store’s website, searches for boots, puts them in the cart, but does not purchase them. The next day, she goes to the mall and is near the store. At this point, any communication to influence Jamie should be based on her past behavior pattern and current context. An ML model can easily determine that after how many days of putting an item in the cart does Jamie typically makes a purchase? Or does she usually buy online or during a store visit? Is she usually price sensitive or just the availability and the proximity of an item she likes can nudge her to buy.

In this case, the ML model determines that the best action would be inform Jamie, through a push notification, that the boots in her online cart are available in this store in her size at a 10% discount. Excited about the good deal, Jamie picks boots and heads toward the checkout counter.

By using the ML model, the retailer was able to decide the next best action for Jamie that is relevant to her based on how she has reacted in the past and where she is right now. Such a personalized interaction can greatly enhance customer experience.  

Optimize ‘Everywhere Banking’ with Advanced Machine Learning

When it comes to online banking, financial institutions offer a number of loan forms, account applications, and other long-form interactive elements. They require an intelligent ML solution to keep customers moving forward across all channels.

Sam is heading to college and needs to apply for a student loan. He logs into his online banking portal and opens the virtual application while working in the library, but does not have a chance to finish the form. In fact, between classes and other activities, Sam forgets the form for a number of days. When he logs into his banking app to check his account balance, an ML model springs to life, immediately analyzing Sam’s past activity, banking history, and current context. Suddenly, Sam receives a notification: “Hi Sam: Complete your student loan form today and receive a $5 discount on monthly service fees.” As a student, Sam is incentivized by the prospect of long-term savings; he re-opens the form on his phone and picks up where he left off in the library.

Machine Learning helps financial institutions drive loyalty and satisfaction by catering to customers’ individual needs in real-time—and across every touchpoint. ML models take ongoing, cross-channel customer activity into account when crafting engagement, ensuring that no form is forgotten.

Differentiate Guest Experiences with Predictive Booking

Hospitality providers are always striving to improve their guests’ on-site experiences—a task that should originate with the ML optimization of online booking processes.

Karen is booking a trip to a resort she has visited before. During Karen’s first stay at the resort, she enjoyed the spa amenities with her girlfriends—now, she logs in to book a family vacation. The resort’s ML model analyzes Karen’s past itinerary (facials, massages, and nail appointments) in the context of her current on-site clickstream activity (waterparks, mini golf, and shows), determining that children are likely accompanying her for this stay. While filling out her final online itinerary, Karen receives a pop-up notification: “Karen: Travelling with kids? Book a massage now and receive 20% off resort childcare services!” Remembering her amazing spa experience, Karen adds a massage to her itinerary and finalizes her booking to take advantage of the deal.
pring line selection, confident that she has found a new place to shop for her family through more than just the winter season.

By recognizing patterns in individual customer behavior and across guests’ browsing activity, ML models can help resorts deliver the personalized experiences customers are looking for in real-time, even before they arrive on-site.

Machine Learning is at the heart of the ZineOne platform. Our AI-powered Customer Engagement Hub continually ingests individuals’ historic, real-time, and environmental data, predicting their intent with increasing accuracy and triggering the most relevant actions for personalized customer engagement. Discover more about the powerful ML models in ZineOne’s next-gen CEH.