Web Tracking Overview

The data collected by web tracking allows you to make communication with customers more personalized and offers more relevant to the needs of each user.

With web tracking, you get access to such information as:

  • categories of viewed/postponed/purchased goods;
  • their price segment;
  • number of purchases;
  • user device type, model, browser, location, and more.

By tracking the behavior of site visitors, you can:

  • create segments depending on events from your site;
  • send targeted omnichannel campaigns: email, SMS, Web push, App inbox, Mobile push, In-App, Telegram;
  • generate personalized product recommendations on the website, mobile app, and all message channels.

Setting Up Web Tracking

The setup takes place in 3 stages:

  1. Installing the web tracking script on the site.
  2. Uploading a product feed to your eSputnik account.
  3. Setting up events’ transmission.

1. Obtaining and installing the script on the site.

The web tracking script is a code snippet you need to get into your eSputnik account and install on your site. Script installation instructions >

2. Uploading a product feed to eSputnik.

A product feed is a list of products and their attributes in your online store, such as name, price, link to description and photo, etc. The system will extract data from the feed to form recommendations. Feed upload instructions >

3. Sending tracking events from the site to eSputnik.

Set up the transmission of the events you want to track: adding a product to a wishlist or cart, searches, sales, or any other user activity on your site. Web tracking setup instructions >



If you encounter difficulties at any of the configuration steps, please contact our support at [email protected]

After setting up web tracking, you can already build segments based on user behavior on your site and send targeted messages to them.

The step to the next marketing level is setting up product recommendations.

Our system builds recommendations based on

a) the history of views, orders, and other users’ actions;

b) predefined rules, such as products from bestsellers, related items, or regular demand goods;

c) machine learning algorithms, which analyze different behavior patterns and give the most relevant offers.

Due to high-level offer personalization, recommendations help to increase sales and improve buyers’ experience.

To add recommendation blocks, go to the following settings: