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Create Personalized Micro-moments With MoEngage Smart Recommendations

MoEngage launches Smart Recommendations to help consumer brands build personalized and memorable experiences for their customers.

  • Updated: 20 April 2022
  • 4 min read

The modern consumer wants more personalized and contextually relevant communication. 27% of consumers find irrelevant content and product recommendations to be their most frustrating experience with brands, while 26% of consumers want brands to personalize experiences based on previous shopping history.

Customers no longer question the need for collecting data because they expect you to use this data to create delightful experiences for them. This includes delivering personalized recommendations to them. Although consumer brands are aware of the need for personalized recommendations, most brands are not well-equipped to create memorable experiences for customers.

We’ve built Smart Recommendations to help you build personalized micro-moments for your customers. Using MoEngage Smart Recommendations, you can send the following communication to your customers:

  • Top trending products that have been customer favorites
  • Most loved content that is rated highly by other customers with similar preferences
  • New launches in your customer’s favorite category or brand
  • Highly discounted products that customers browse frequently
  • Products and content that are widely searched by your customers
  • Most profit-generating and highest-selling products
  • Items that complement the products your customers have purchased
  • Related products and content based on your customers’ browsing patterns
  • Alternate products based on the item’s availability

MoEngage’s AI-powered recommendation engine will personalize the recommendations based on customer behavior, buying patterns, and real-time usage, allowing you to send relevant communication at the click of a button.

Why did we build Smart Recommendations?

Personalized recommendations have become necessary for brands to engage with customers, boost sales, and deepen engagement. 49% of customers had purchased a product because of a personalized recommendation they received from the brand. 

We built Smart Recommendations to help brands send personalized, contextually relevant recommendations to customers. Smart Recommendations is a full-fledged recommendation engine powered by Artificial Intelligence, which gets better over time owing MoEngage’s proprietary Machine Learning algorithm.

This new feature enables you to send real-time, contextually relevant recommendations from millions of catalog products to each customer.

With Smart Recommendations, you can:

  • Improve conversion rates
  • Boost loyalty and drive more repeat purchases
  • Increase average order value and the average number of items per order
  • Increase product usage and build predictable buying patterns

How can Smart Recommendations help you?

You can send personalized recommendations to your customers across different touchpoints in your customer's omnichannel journey such as Push Notifications, Emails, WhatsApp, Facebook, Mobile In-app Messages, and Website Banners. You can personalize the recommendations for each stage of the customer journey too.

With Smart Recommendations, you can personalize your website and enable your customers to find what they want easily. Your customers no longer need to click or search multiple times to find their favorite product on your website.

You can use MoEngage’s special date filter to send birthday or anniversary special recommendations to your customers.

Combined with MoEngage’s prediction capabilities, you can predict your customer’s behavior and send them personalized recommendations to improve revenue, increase LTV, prevent churn, win back lost customers, or even drive engagement.

You can use MoEngage Sherpa to A/B test the recommendations to know which one converts better.

Common use cases for Smart Recommendations

Retail and E-commerce brands can suggest or highlight a series of accessories that would complement the product the customer is currently viewing as ‘Relevant Products’. This will encourage customers to add them to the cart and increase the average order value. Brands can also recommend related products during the order confirmation stage or on the cart pages to boost conversions.

Product recommendations on Amazon

Product recommendations on Amazon

Streaming apps can recommend movies, music albums, and other content that the customers can watch or listen to based on their current likes and behavioral patterns. For example, a music streaming app can recommend music to customers based on the types of songs, albums, or artists they heard recently.

Song and music album recommendations by Spotify

Song and music album recommendations by Spotify

Travel aggregator platforms can send hotel recommendations to customers based on their chosen destination. The platform can also recommend alternative options to customers if the hotel they selected has no available rooms.

Travel recommendations from MakeMyTrip

Travel recommendations from MakeMyTrip

What are the types of Smart Recommendations?

There are three categories of Smart Recommendations. You can choose any of them to send recommendations to your customers.

  1. Generic or global recommendations: These are generic recommendations sent based on the general pattern of the product. They are not personalized for individual customers.
  2. Similar products: These identify the similarities between different products based on how the customers have interacted.
  3. Similar customers: These analyze the buying and search patterns between different customers and segments and recommend products accordingly.

How can you get started?

If you're an existing customer, reach out to your favorite MoEngage Customer Success team member. If you're new to MoEngage and want to get started with Smart Recommendations, you can request a demo here.

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About The Author
Pulkit drives growth through Content at MoEngage. His work has been featured in top publications such as Forbes, The Wall Street Journal, World Economic Forum, e27, and more. His experience as an m-shaped B2B marketer comes fueled with a passion for customer-centricity, affinity for data, and a love for technology, movies, comics, and gaming.
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