Introduction can power personalized recommendations on email for each shopper based on their preferences. It understands data signals collected on your site, so retailers can personalize every email with products handpicked for shoppers.

A/B tests for email marketing helps eCommerce marketing teams experiment, analyze, and implement solutions that can drive more traffic to the site. Understanding data about how shoppers look at products off-site and how they are responding to personalization in their inboxes can help both elevate shopper experience across channels and improve engagement and conversions. enables eCommerce marketers to side-step guesswork and easily A/B test how they apply email personalization in their business.


By making data-backed decisions using
A/B testing tool, retailers using’s recommendations have seen

Personalization Designed for Fashion

An uplift in
CTOR on emails

Personalization Designed for Fashion

An improvement in
engagement rate on emails

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A/B testing tool to help retailers get the best out of email personalization

Retailers asked for

What we built

Easy to use testing tool

Easy to use, low code tool, no expertise needed.

Flexible interface

Modify key parameters, tweak algorithms, adjust variations at scale.

Easy to understand impact

Track, measure and analyze metrics at the most granular level.

Scalable solutions

Multiple variations of recommendation tests can be scaled to understand shopper behavior across the site.

Easy method of acting on insights

Test, analyze, and implement solutions for recommendations across the shopper journey - all from one tool.

User Stories: Retailers and Email Personalization A/B Testing

Customer 1: Resale Marketplace

The resale customer wanted to understand how real-time personalization will make a difference to the product recommendations sent through emails to shoppers. They mainly wanted to know how it impacts conversion and engagement metrics.

A/B Test:

One set of shoppers received emails with personalized recommendations, while another set received emails with trending products that were not personalized.

The customers saw a 62% improvement in engagement rate and 53% uplift in click to open rate among users who clicked on recommendations.

Customer 2 - Diversified Global Fashion Retailer

The global retailer was looking to invest in personalization solutions that would extend the shopper experience off-site to emails. They wanted to assess how much impact mailing styling recommendations have on shopper engagement. Two A/B tests were conducted by the customer.

A/B Test 1:

  • One set of customers were sent emails with’s styling recommendations.
  • Another set of customers were sent in-house cross product recommendations.

Users engaged (44%) and converted (12%) better with’s recommendations.

A/B test 2:

  • One test group received emails with’s styling recommendations.
  • A second test group received emails with another vendor's styling recommendations.

Users converted better (48%) with’s styling recommendations.


A/B testing for email personalization understands data signals collected on your site, so you can personalize every email you send shoppers with products handpicked for them. These product recommendations are dynamically generated at the time that the shopper opens the email - helping avoid products that are unavailable or out of stock.

A/B testing helps in assessing the impact dynamic 1:1 personalization has in personalized email marketing. Data-backed solutions help make sense of shopper choices and intent, and strategies that deliver product recommendations, styling options, occasion or theme based ensembles help create an exceptional shopper experience every time. This provides the impetus for users to keep returning to their favorite sites.

A/B tests help you remove the guesswork and put in place solutions that are backed by data and can be used to understand every single shopper better.

By A/B testing on emails, retail marketing teams can match content to site intent and make sound decisions based on data and not just instinct. Trial and error based learning can be replaced with experiments that are designed well and measured. With A/B testing data-driven, informed decision-making can become one of the central tenants around which any personalization solution for your marketing and for your site is being built.


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