Why should you use Raptor personalization in combination with Clang?
Raptor use advanced algorithms, data mining and machine learning to create behavioral recommendations. Our platform learns and recognizes each individual user and their interactions – making marketers able to empower relevant communication based on consumer behavior through email.
Raptor’s personalization engine seamlessly integrates to Clang. There is no need to change email platform, Raptor can help you get the most out of Clang by personalizing your email campaigns and sending behavioral email triggers.
Use Raptor in combination with Clang to target the right people, at the right time and with the right products.
Right people: Track your subscribers online behavior and intent, that will initiate relevant emails
Right timing: Modify and split-test the timing of the send-outs that allows for iterations and optimization
Right products: Display relevant products and content to each individual subscriber based on their behavior
Easy Implementation
Setup product catalog import (shopping feed)
Build template(s)
Setup website tracking
Start sending
Leverage Automation
Save time and manual processes
Automatically increase the volume of emails with personalized communication
Deliver real-time & up-to-date emails automatically
Improve KPI’s & Drive Sales
Improve open rates with correctly timed emails
Increase click-through rates with dynamic and personalized product recommendations
Boost conversion rates by delivering personalized emails to subscribers with intent to buy
Product Recommendation
Send emails with dynamic product recommendations that are based on your subscriber’s website- and behavioral data which means that the emails are tailored to each of your subscribers.
Utilize 10 different machine-learning algorithms that are proven to increase sales, conversion rates, and overall engagement.
Abandoned Basket
Target subscribers who have placed products in the baskets but have not completed the purchase yet.
Remind subscribers and recover sales, before your subscribers decide to buy from your competitors.
Read more: Bon’A Parte Case – Behavioral Triggers
Product Interest
Target subscribers who have shown high interest in a specific product on your website, without adding the product to their basket.
Remind subscribers about a product they have shown high interest in, before they buy the product from a competitor.
Category Interest
Target subscribers who have shown high interest in a specific category on your website.
Show the most popular products within a category to inspire and engage your subscribers within the category they have shown interest in.
Price Drop
If a subscriber has shown interest in a product, and the product experience a price reduction, the subscriber will receive an automated email with the product.
Automatically remind subscribers about a product they have shown interest in after it has experienced a price drop.
Read more: Zizzi Case – Price Drop Trigger
Back in Stock
If a subscriber has shown high interest in a product that is out of stock, they will automatically be notified by email when the product is back in stock.
There is no need for the subscriber to sign up manually to be notified, as their behavior on the site will show if they have shown interest in a product that is out of stock.
DO YOU WANT TO KNOW MORE?
A complete suite of personalization solutions tailored to your business
Web Advisor offers advanced recommendation modules to drive sales, improve product selection, maximize cross-selling, increase conversion rates, and significantly enhance the user experience on your website.
Our personalized recommendations offer your visitors recommendations that are specifically targeted to their unique tastes and shopping behavior.
Ad Advisor use website data to deliver an advanced recommendation in banners and product ads. Use our products “Personalization for banners” or “Catalog Enhancement” to optimize sales, improve product selection, and increase conversion rates.
Our personalized recommendations offer users options that are specifically targeted to their unique tastes and shopping histories.
