Skip to content
×
Copyright

This online publication is intellectual property of Apptus Technologies. Its contents can be duplicated in part or whole, provided that a copyright label is visibly located on each copy and the copy is used in conjunction with the product described within this document.

All information found in these documents has been compiled with utmost attention to detail. However, this does not guarantee complete accuracy. Neither Apptus Technologies nor the authors shall be held liable for possible errors or the consequences thereof.

Software and hardware descriptions cited in these documents might be registered trademarks. All trade names are subject to copyright restrictions and may be registered trademarks. Apptus Technologies essentially adheres to the manufacturer’s spelling. Names of products and trademarks appearing in this document, with or without specific notation, are likewise subject to trademark and trade protection laws and may thus fall under copyright restrictions.

CLOSE

Email Recommendations

Email Recommendations is an add-on for Apptus eSales Fashion clients that provides personalised recommendations within emails to customers.

Features

Email Recommendations includes several powerful features for customisation in an easy to use interface in the Apptus Email app.

  • Create custom product display templates
  • Define products to be included or excluded for each campaign
  • Choose recommendation types per campaign
  • Test and preview templates and campaigns with real data
  • Smart image caching
  • View campaign statistics

Recommendations

The Email Recommendations support three different types of recommendation algorithms:

Top sellers

Uses aggregated data from all visitor interactions and purchases, product event data such as stock levels, product information such as newness and the selected business exposure strategy to produce the most relevant products. Typically used with filters.

Customer based

Uses the visitor's behaviour from a retailer's site to generate personalised recommendations. Typical data points include products that visitor has viewed, clicked or purchased.

Product based

Uses a list of base products and aggregated data from all visitor activity from a retailers site to recommend products which are likely to be bought along with the base products. Typically used with filters.


Last update: February 3, 2020