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|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!
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:
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:
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:
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:
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.
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?