How 6 Mobile Brands Use Machine Learning & AI to Build Better Marketing Campaigns
Today’s marketers are buried in user data: device preferences, social posts, browsing and content history, hobbies and interests, and much more.
In theory, mobile marketers should have a near perfect understanding of their users. In practice? All that data adds up to a lot of confusion.
But with machine learning and artificial intelligence, marketers have the power to actually access and apply all that customer information to make meaningful connections with users.
1. Segmenting Customers with AI
Fitness brand Under Armour uses machine learning in their Record fitness app to automatically identify precise user segments. They then match those segments to successful customers to create highly customized fitness recommendations.
The app pulls a user’s health data from third-party apps, smart watches, and data entered by users. It also factors in details like nutrition, sleep patterns, and workout stats to get a complete user profile. It then matches users to others with similar health and fitness profiles to offer coaching recommendations based on what’s proven to work.
Image: UA Record
The more users engage with the app, the better its machine learning algorithms get. It can match users to provide customized — and super effective — fitness recommendations.
2. Predicting Customer Behavior
Luxury luggage brand Tumi has also embraced machine learning to predict purchases and optimize the in-store experience. Store employees can now access a customer data platform that shows detailed browsing and purchase history, email activity, and recent search behavior.
Charlie Cole, Tumi’s chief digital officer, explains the power of AI to Digiday: a store manager who had worked with a specific customer for years tried out a new product recommendation tool that shows items the customer is most likely to buy. The tool suggested all women’s products. The manager assumed this was wrong, since the customer had only purchased men’s accessories. So he called the customer to ask what he was currently shopping for, only to discover he was looking for a gift for his wife.
“We knew recent browser behavior, email open rates, search behavior — it’s far more predictive than past purchase,” said Cole. “What I bought yesterday isn’t always going to predict what I buy today. To get to that layer, you have to combine his purchase history with his browser behavior and email open rates. That’s what AI can do.”
3. Fine Tuning Cross- and Upsell Strategies
The Hyatt hotel brand uses machine learning to analyze a guest’s travel history and accommodation preferences. Desk agents then automatically get an alert when the guest they’re checking in is likely to want to an upgrade or a room with a view. Or be interested in hotel amenities like spa or laundry services.
According to Hyatt’s SVP for Strategy and Analysis Chris Brogan, the program has increased the average incremental room revenue, post-reservation, by 60%.
4. Using RFM to Identify the Right Engagement Channel
RFM (Recency, Frequency, Monetary) Analysis is a powerful tool for identifying the best channel and time to engage key user segments.
Beauty brand Sephora uses this strategy to perfect its customer engagement campaigns. By analyzing members of their Beauty Insider loyalty program, they discovered that loyal clients make up 20% of their core customer base and spend the most money. They’re also the most active on social media, making them potentially valuable brand advocates.
Sephora also uses an AI-powered tool to pull detailed information about a customer’s profile and purchase history. This helps the company give shoppers a personalized experience that’s consistent online, on the app, or in-store. Product recommendations and tutorials can be tailored to skin type and shade, beauty routine, and past purchases. And Sephora can notify customers immediately via email, push notification, or SMS when their favorite products are back in stock or on sale.
5. Identifying and Acquiring High-Value Prospects
Using identification models, machine learning applications can find the prospects who are most likely to become customers. And not only that, by identifying prospects with attributes similar to existing customers, it can help identify those likely to become your most valuable customers.
Mazda used this type of identification model to find the perfect social media influencers to promote the new CX-5 at the SXSW festival in Austin. Using IBM’s Watson AI technology to scan posts across major social media networks, the automotive brand identified artistic extroverts — the ideal persona to connect with the festival’s attendees.
Mazda selected four people to drive the CX-5 around Austin and hang out in the Mazda Studio, then share their experience with their followers. This data-centric approach to influencer marketing allowed Mazda to effectively connect with a highly creative audience.
6. Engaging Users with Intelligent Chatbots
Popular language learning app Duolingo created a chatbot for users to practice their conversational language skills, judgement-free.
The company originally paired users with native speakers for conversational sessions. But according to co-founder and CEO Luis von Ahn, 75% of them were too anxious to practice with another person. The chatbot offers a friendly practice partner, available 24/7, with no fear of embarrassment.
It’s an effective tool to keep power users coming back to the platform to practice more advanced language skills in a convenient way.
Power Up Your Marketing Campaigns with Machine Learning
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Our advanced data science models help you group users by interest, identify the optimal channel and time to engage users, and deliver a personalized user experience.
This post originally appeared on the CleverTap blog for mobile marketers.