Football season is finally back, and that 75” HD flat screen you’ve had your eye on for ages is on sale. Score!
You’ve entered your payment details and are all ready to hit “Complete checkout”… but then you notice the delivery options. With order processing time, will your new TV arrive before the kickoff game?
You spot a chat icon in the corner of the screen and click to ask about your potential delivery window. In just a few minutes, you find out the TV will definitely arrive in time, with no extra rush shipping fees. You place the order and start daydreaming of taking in all that pulse-pounding gridiron action in stunning HD.
If you’ve ever had a similar shopping experience, chances are you’ve interacted with an AI-powered chatbot.
Now before you start thinking Skynet, AI is transforming the way computers assist humans to make better decisions, faster.
Machine learning, a subset of AI, makes business processes amazingly efficient. For instance, a bot answering simple customer queries saves time for support staff so they can focus on more complex issues or high-priority tickets. And since bots don’t need to sleep, they can provide instant support to customers 24/7. And through automation, it eliminates the need to hire additional resources for doing the same work.
More and more applications of ML and AI are rapidly being discovered and adopted. We’re already seeing the popularity of Voice-Enabled Home Assistants that use machine learning algorithms to understand user preferences. All in the name of delivering an increasingly relevant, personalized user experience that keeps customers engaged.
The Challenge of Context, Relevance, & Timeliness
No matter what kind of user you serve, they all have one thing in common: sky-high expectations.
They want to receive the right information at exactly the right time. They expect brands to remember what they like or dislike, and serve a personalized user experience based on their habits and interests. If that doesn’t happen, they’re sure to say sayonara.
Knowing everything you can about your customers and their preferences is the foundation to delivering a seamless customer experience. But when you’re dealing with millions of user data points, that’s far from easy — and it’s downright impossible without using intelligent systems and technology. For this reason, marketers have to embrace machine learning to deliver the app experience users have come to expect, across all channels and touchpoints.
With High Customer Expectations Comes Valuable Opportunities
Customer expectations are never static. They always go up.
The new of yesterday becomes the mundane norm today. More than ever, marketers need to stay ahead of evolving customer expectations. With the power of machine learning, marketers can match and even exceed them. Here’s how:
1. Defining Precise Customer Segments
Through Machine Learning algorithms, marketers can group similar users together based on interests, actions, habits, behaviors, demographics, or any other characteristic.
For instance, you can determine the dominant film category preferred by top customers of your streaming app, and the time of day they prefer to watch. This will allow you to engage these users with personalized recommendations just before their preferred viewing time.
Such precision in defining user Colombia Phone Numbers List segments acts as a foundation to run hyper-targeted campaigns that resonate with users and prompt them to act. And that’s not all:
# Vertical ML-Powered Segmentation Example
OTT (Media & Entertainment)
Who to target: People who predominantly like comedy movies but haven’t watched any comedy content in the last 90 days.
What to send: Recommend the latest comedy releases to this user group.
Who to target: Users who prefer to browse products in the evenings, between 6 – 10 pm.
What to send: Time sensitive offers or promotions to encourage more purchases and higher order values.
Who to target: People who prefer to fly on weekends.
What to send: Special offers on weekend flights, or updates on discounted tickets for advanced bookings.
Who to target: Users who predominantly order food between 6 – 9 PM on weekdays.
What to send: Promotional offers or repeat order reminders during that time.
CleverTap’s psychographic segmentation uses the power of Machine Learning to group users according to their predominant interests. Contextual messages using psychographic segmentation has proven to increase conversions by 5X over sending non-contextual messages.
2. Predicting Churn
By studying customer data and extrapolating paths for new customers, ML-based churn prediction algorithms can pinpoint users who are in danger of churn before they uninstall.
Advanced segmentation methods create automated user segments, grouping your user base so you can see your most loyal customers, most profitable customers, customers at risk of churning, users most lik