SALESmanago Marketing Automation

Deep Behavioural Profiling

Monitor and save information about all customer interactions with the store and a specific product. Measure interest and personalize communication with customers based on what they are actually looking for.

Deep Behavioural Profiling

Data obtained thanks to the user interaction tracking module is used to:

creating a unique analytics that fully exploits the specificity of the store and product characteristics,

identifying the attributes of the products most interesting to the customer in order to perfectly personalize offers and communication,

deeper understanding of users' needs and responding to them.

How does this work?

Deep Behavioural Profiling allows you to track not only the list of products viewed by the user, but also information on all parameters checked by him and displayed details about the product. On this basis, common features of the products are emerging, unique to each client, allowing for an accurate adjustment of the offer to his preferences.

Deep Behavioural Profiling

Thanks to this solution, it is possible to track various customer activities on the site, such as:

  • displaying a specific element on a website,
  • hovering over any element,
  • clicking on an interactive element, e.g. enlarged graphics, buttons, sliders,
  • change of product variant, color or size,
  • adding a product to the list (e.g. Favorites list)
  • start and end of basket checkout,
  • complaint, product return,
  • interaction with any configurators and calculators (e.g. credit),
  • degree of website scrolling,
  • integration with YouTube and Vimeo players embedded on the website to identify the minute / second to stop, restart and stop watching the movie.

Use Cases

Recommendations based on the characteristics of the products viewed

Website personalization

Displaying products (e.g. cosmetics) tailored to the user, based on previously selected product categories

Product recommendations and remarketing

The use of information about colors, sizes, product brands (clothes, shoes) most viewed by the user for remarketing and creating recommendations in all communication channels.

Direct customers to stationary stores

Online & offline data integration

Communicating the user with a specific stationary store, which he indicated when checking the availability of products offline.

Track search phrases and selected product filters

Personalization of offers

Preparation of an individual offer based on the parameters entered in the calculator or search engine on the website.
Offering products that meet the criteria set when filtering products in the store (e.g. price up to $ 1000, ore: gold, stone: diamond, rating: 5/5).

Build product scoring and encourage to buy favorite products

Matching discount coupons

Providing discount coupons for products saved on the favorites list, added to the clipboard.
Identification of products with which the user has made the most interaction (product scoring).

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