Leverage the Power of Machine Learning to Retain Users for Life

Leverage the Power of Machine Learning to Retain Users for Life
Last updated on June 13, 2021

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 segments acts as a foundation to run hyper-targeted campaigns that resonate with users and prompt them to act. And that’s not all:

#VerticalML-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.


Food Tech

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 likely to respond to promotions, etc. With these insights, marketers can proactively tackle churn or run targeted promotions that bring better ROI.

For example, studying which users are most at risk for churn can clarify your app’s engagement strategy. If most of your new users are at high risk of churn, for instance, your engagement strategy should focus on activating and retaining them with key feature tips, first-purchase promotional offers, or personalized product/feature recommendations.

3. Identifying the Optimal Time, Device, or Channel to Send Campaigns


Using a mix of data science and machine learning, marketers can deliver messages at the exact time a user is most likely to act, on the most optimal device and channel.

For example, a food tech app can run targeted promotional offers to users who prefer ordering food between 6 – 9 PM on weekdays. That same campaign can target micro-segments of users who are more likely to respond to push messages than other channels.

#VerticalExamples of Intelligently Triggered Campaigns Using ML


OTT (Media & Entertainment)

New content recommendations timed for users’ preferred viewing time and device. For example, night owls vs. afternoon viewers, or weekend vs. weekday viewers.



Time-bound promotions tailored to users’ browsing preferences, device, or time. For example, a time-sensitive coupon code for purchases made through the mobile app.



Last minute travel deals that reach users on their preferred channel (push message, text message, or email) at the optimal time (when users are most likely to respond).


Food Tech

Send nearby meal recommendations at the time the user typically searches for restaurants on the app.

4. Pinpointing the Optimal Time to Nudge Inactive Users


By using machine learning to recognize patterns in user behaviour, marketers can trigger messages based on user inactivity without hindering the user experience.

For instance, an ecommerce app leveraging machine learning can determine the optimal time to nudge users who’ve abandoned their carts. It’s important to trigger these messages at the right time, since sending messages too early can interrupt the user experience for customers who may still be shopping, while sending them too late isn’t effective in prompting purchases.

Adding this level of intelligence to campaigns brings more conversions — and greater business revenue.

#VerticalExamples of Campaigns to Nudge Inactive Users


OTT (Media & Entertainment)

Bring back users to the platform by sending recommendations after n days of inactivity.



Identify the optimal time to send a reminder email or push message after cart abandonment, based on past user behavior.



Search abandonment campaigns triggered at the optimal time to customers who searched for flights but did not book.


Food Tech

Recommend meal options when user does not launch the app for n consecutive days.

The Road to Retaining Users for Life

Even with today’s innovative marketing tools and resources, segmenting users effectively is still a challenge for many marketers since there are so many dimensions to segment on.

They have to analyze hundreds of data points and identify which user belongs in which segment. To be both effective and efficient, data-driven marketers need to apply machine learning capabilities.

CleverTap processes millions of data points across your entire user base to enable marketers to realize relationship marketing at scale. It connects the dots between critical customer interactions, such as purchase history, app activity, browsing behavior, and device preferences to deliver a consistent end-to-end experience.

As conversational, assistive marketing becomes the norm, machine learning is set to revolutionize the mobile analytics and engagement industry. And CleverTap is at the forefront.

With CleverTap’s AI/ML capabilities, marketers can apply their creativity and talents where it’s needed most — in bringing the human touch to customer conversations.

See how today’s top brands use CleverTap to drive long-term growth and retention

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