Delivering Dynamic Personalization

Introduction

The retailer is part of one of India’s largest multinational conglomerate holding with products across various verticals. The customer’s shopper base consists of people who are looking for specific products to suit their needs. The shoppers were keen to discover brands and find products that are relevant.

Vue.ai allows the customer to tailor the e-commerce storefront using personalized recommendations based on each shopper's style preferences. This helps bring shoppers down a guided journey through the online store, reduce bounce rates and ensure shoppers engage with the rest of the site.

Vue.ai users add to cart 3x and buy 4.5x the sitewide average
Users who engage with Vue.ai on the home page contribute 5x more in revenue and view 2x more products

11.3% lift in overall conversion rate
9.4% lift in overall revenue per visit
5.6% of total revenue comes through direct attribution*
101%lift in user engagement rate through Dynamic Personalization

"We introduced these recommendation components about a year back, which were powered by Vue.ai. Most of these recommendations are on our homepage and very clearly one of the positive benefits which we found is the ability to showcase products that are closer to what the customer wants, build a bit of relevance with the customers—and those components actually have given us the ability to move the traffic more towards the category pages and has improved the performance of those homepages. We are not a price-oriented platform, but we are a brand forward platform."

Kishore Mardikar
CMO, Tata Cliq

The challenge

The marketplace’s CXO team aimed to design a ‘brand-led’ website, allowing user-brand affinities to lead the experience from the home page. The customer wanted to ensure that shoppers found brands they were looking for at the first touchpoint of their journey. They also wanted to ensure that a shoppers preferred brands surfaced on every visit to the site.
The customer was using recommendations based on Collaborative Filtering. This treated shoppers as segments, and did not consider individual shoppers’ intent or preferences.
There were irrelevant content recommendations, leading to low engagement.

Execution

Visual Intelligence across shopper journey

Vue.ai’s Personalization Suite uses customer intelligence to gain insight into every individual shopper’s preferences. With behavior tracking powered by proprietary technology as well as Amazon CloudFront, Vue.ai’s algorithms are able to pick up on shopper signals across the retail site and serve them the most compelling experiences.

Each and every click a shopper makes, the qualities and styles of the merchandise they are interacting with, specific attributes that they show affinity towards, intent of the shopper in any given session are all combined to provide a 360 degree view of the shopper. Intelligence at this scale for shopper preferences ensures that the on-site shopper journey is personalized in real-time across channels, while giving retailers the flexibility to apply specific business rules for their site.

Recommendations personalized to every shopper

Vue.ai marries Product Intelligence with Customer Intelligence to build Style Profiles which is unique to every shopper. Collaborative filtering cannot surface recommendations that are unique to every shopper. Vue.ai algorithms continually learn both from product data, individual shopper data, and specific attributes these shoppers look for in products. Shopper intelligence built on Style Profiles ensures 1:1 personalization at every touchpoint in the shopper journey.

Solution

Real-time dynamic personalization solution

No two shoppers have the same journey. Vue.ai’s Dynamic Personalization engine understands this. The image recognition based behavioral targeting engine, helps with:

real-time personalization, i.e. product recommendations that adapts to every click that the shopper makes. With Amazon Simple Queue Service powering real-time synchronization of catalog updates, Vue.ai is able to ensure that only the most recent version of product data is used by the automated product tagging engines, for data extraction and enrichment. Every product attribute is taken into consideration, and the products the shopper views are recommended based on specific attributes the shopper shows affinity towards. For eg, a color over a pattern, or a length over neckline.
product recommendations, based on shopper’s current journey as well as their historical data.

Key Takeaways

Vue.ai’s personalization solution ensures that

Every shopper has their journey tailored for them from the minute they come into the site.
User attributes are mapped meticulously to product preferences based on various signals.
Every shopper has access to products and styles that are unique to their on-site journey and their preferences.
Products with increased relevance are served to the shoppers. When shoppers reflected affinity to certain attributes, Vue.ai algorithms recognizes these affinities and displays relevant products.
These recommendations are also used to manage out-of-stock products on pages, so that shoppers can continue their journey even if a product they like is unavailable.
Visually similar product recommendations are sent in order cancellation emails to re-engage shoppers.

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Impact of Vue.ai’s personalization suite in Asia - A VueCommerce Case Study

One of Asia’s biggest marketplaces saw a 9.4% lift in overall revenue per visit after implementing personalization solutions.

Impact of Vue.ai’s personalization suite in Asia - A VueCommerce Case Study

One of Asia’s biggest marketplaces saw a 9.4% lift in overall revenue per visit after implementing personalization solutions.

Find out what Vue.aican do for you.

To get started, fill out the form.Our experts will get back to you shortly.

Find out what Vue.ai
can do for you.

To get started, fill out the form.
Our experts will get back to you shortly.