How GIVA Recorded 120% Uplift in Conversions Using MoEngage’s Newly Launched Smart Recommendations! [Customer Spotlight]

  • UPDATED: 22 November 2023
  • 6 min read

Reading Time: 6 minutes

Editor’s note: Customer Spotlight is an initiative by MoEngage. In these articles, we talk to our customers to understand their customer growth strategy, engagement tactics, and best practices across product and marketing. 

Imagine you’re looking for a funky new shirt to add to your Hawaiian wardrobe.

You go to your favorite store’s app and explore their trendy collections. After scanning through hundreds of shirts from the wide variety of options, you narrow it down to a few and add them to your wishlist. Unable to decide on one after spending hours, you furiously exit the app.

Just then your phone beeps. A notification alert shows up. You get a recommendation for a Hawaiian shirt, based on your preferences (and past interactions). That shirt ends up being the one you’ve been looking for all this while, so you buy it and now you can’t stop getting compliments from everyone!

Giva smart recommendations push

While that might be a very convenient outcome, it can be replicated pretty easily by most consumer brands, irrespective of industry. These brands can now deliver hyper-personalized, contextual recommendations to their customers at every stage of the journey. These manually-curated or AI-driven recommendations can help customers better discover the brand’s catalog through relevant product suggestions at each step, while delivering a personalized 1:1 experience, making the customers feel special and more welcome.

Multiple recent studies show one in three customers quit brands they love after one bad experience, while close to 92% leave after two or three such experiences. 

This should show you the importance of investing in personalized recommendations in 2023!

You might need further reasons or ask yourself:

How do I incorporate personalized Smart Recommendations for my brand?

Well, that’s where GIVA comes into the picture!

Started in 2019 by Ishendra Agarwal, Sachin Shetty, and Nikitha Prasad, the Bengaluru-based D2C brand is committed to making fine silver jewelry accessible to all while providing a varied collection of pendants and necklaces, earrings, rings, bracelets, and anklets.  

Serving over a million customers through website, mobile app, and marketplaces like Amazon, Myntra, Flipkart, and Nykaa, GIVA is now expanding its offline presence, currently available in over 20 Indian cities. 

For a D2C fine jewelry brand, effective communication with customers is key to driving business growth. In the initial stages, understanding customer preferences were pretty straightforward. However, as the brand scaled, manual collection and analyzing customer data became a hassle, which is when the brand opted for a martech platform. 

The fine jewelry brand has now started personalizing communications across various channels (viz., push notifications, WhatsApp, and email, among others). While this drove higher repeat purchases, there was a considerable case to be made for improving conversion rates, increasing average order value and items per order, and reducing cart abandonment, among others.

This is precisely where the D2C fine jewelry brand opted for integrating MoEngage’s Smart Recommendations feature.  

Before we delve into how GIVA achieved a clickthrough rate (CTR) uplift of 122% and a conversion rate (CVR) improvement of 120% using Smart Recommendations, here’s a quick overview:   

What is Smart Recommendation?

Smart Recommendations is an AI-powered recommendation engine from MoEngage. It enables brands to deliver hyper-personalized, contextual product recommendations to their customers.

Powered by AI, the recommendation engine dynamically adapt the recommendations to each customer – their preferences, behavior, and shifting patterns in real-time, suggesting products they are most likely to purchase.

A consumer brand can now seamlessly serve:

  • Item Attributes based recommendations
    Recommend products (items) filtered based on selected attributes
    Ex. Recommend t-shirts “blue” in color and “medium” in size
  • User Actions based recommendations
    Recommend products based on customer interaction i.e, past actions
    Ex. Recommend product the customer added to the cart but didn’t purchase
    Ex. Recommend product the customer added to their wishlist
    Ex. Recommend product the customer viewed or searched for
  • AI- Sherpa powered recommendations
    Sherpa AI-Engine recommends products that best suit your customer preferences.
    AI engine considers users’ past and present interactions in near real-time to suggest recommendations.
    Ex. Recommend the best product for a customer based on their preferences, one they’d be interested in or looking for.

Here’s how GIVA recorded a 122% Uplift in CTR and a 120% Uplift in CVR

GIVA, with the help of the MoEngage team, identified two sets of users having similar engagement and then personalized the campaigns to one group using AI-based recommendations, while the other campaign was sent without personalized recommendations.

Guess what! The CTRs from the campaigns with AI-powered recommendations were significantly higher than the ones without personalized recommendations. 

To put it into context, in a week of running campaigns, the CTR uplift with AI-powered recommendations was 122% and 86% for Day 2 and Day 3, respectively. At the same time, the brand also noticed a 120% increase in conversion rates.

Here’s an example of a push notification being sent:

Giva personalized recommendation

The AI-powered engine keeps track of all the user activities, feeds them to the algorithms, refreshes in hours to adapt to them, and thus provides recommendations that are most accurate and relevant. With the full-fledged recommendations feature, consumer brands can deliver product recommendations in near real-time. Brands can also update recommendations for every user (including anonymous users), thus increasing the audience size that can be reached using these campaigns.

Smart Recommendations can also be combined with other MoEngage capabilities to cater to a multitude of use-cases for your brand like:

  • Serving customers with personalized recommendations across the user journey and any channels, viz. Email, Push, SMS, In-App, On-Site Messaging, Cards, and more
  • Delighting customers who have a birthday (or anniversary) during a particular month and recommending a product best suited to their preferences while offering exclusive discounts.
  • Predicting customer behavior and serve your customers with personalized recommendations. For example, if a customer is likely to buy shoes in the coming week, recommend the new collection of shoes over an email with an exciting offer. 

Smart Recommendations can help consumer brands:

  • Drive seamless product discovery – Using MoEngage’s Smart Recommendations, brands can cut through the noise and offer customers precisely what they’re looking for, when they’re looking for it 
  • Improve customer satisfaction – Not finding a product that one is looking for can be quite frustrating and if it happens multiple times can lead to customer churn. With Smart Recommendations, your brand can delight customers by providing delightful; search experiences. 
  • Offer personalized purchase journeys – Customers take different paths before completing a purchase. You can offer personalized journeys (spanning several channels) for each customer using Smart Recommendations. 
  • Provide richer experience – Reports show close to 49% of customers purchased a product they weren’t interested in, after receiving personalized recommendations. It just shows the role Smart Recommendations can play in building trust in your brand by offering a seamless and rich shopping experience.

What sets smart recommendations apart from other offerings?

  • Deliver impactful recommendations powered by AI: Now, with AI-powered Smart Recommendations, you can recommend products to shoppers that they’re most likely to purchase. This is achieved by our AI engine analyzing customer preferences, interactions, and behavioral patterns, among other metrics, to understand the intent and thus deliver the most relevant recommendations.
  • Deliver real-time recommendations every time: Our recommendation engine not only collates customer interactions but also feeds it to the algorithm. The engine then adapts to the information and refreshes quickly to provide accurate and relevant recommendations in real-time every time!.
  • Reach customers across channels: Smart Recommendations helps brands send relevant recommendations to customers across all the channels they prefer to be engaged at like email, push notifications, in-app, onsite messaging, and more.
  • No technical expertise needed: Smart Recommendations are easy to utilize and don’t require technical expertise in coding or data science, thus eliminating dependencies on data or engineering teams.

 Over the last couple of years, a paradigm shift has occurred in the modern customer’s buying behavior. The changing preferences and spending patterns mean consumer brands must cater relevant recommendations to customers across their lifecycles.

The traditional recommendation models work on a trigger and rule basis, i.e., a user performs a predefined action, and the system sends them a recommendation accordingly, or recommendations are provided based on product attributes. This methodology doesn’t consider and adapts to the changing buying pattern and behavior. 

That’s where an AI-powered recommendation engine comes in handy, tracking all customer interactions in real-time, analyzing their preferences and changing behavior, and feeding it to its algorithm to deliver the right recommendation to the right customer on the right channel every single time! 

So, what are you waiting for?

Still on the fence? Get insights into how Smart Recommendation is making product discovery easy:

Get started today on the path to impactful personalization with Smart Recommendations!