Prediggo vs. traditional personalization
The question is whether traditional solutions that rely heavily on transactional data can provide the level of personalization today's online shoppers expect? Prediggo does not think so. In order to have a comprehensive solution that can personalize not only recommendations but also content, search results, merchandising and customer relationship management, that requires a new approach.

Let's start with product recommendations because they are such a critical tool for sales conversion. Traditional recommendation systems, e.g. Amazon's "Customers Also Bought," have major limitations:
- Unable to recommend new items
- No diversity, biased to Top Sellers
- Require powerful and expensive servers
- Labor intensive for marketers to customize
Why use Prediggo Strategic Personalization?
Prediggo's exclusive new approach, Strategic Personalization, adresses the limitations of traditional solutions through innovation in the key components of effective targeting:
- Semantic modeling allows to enhance the customer purchase behavior with the semantic descriptors of items in order to make more precise recommendations without facing the limitations of traditional recommendation systems.
- Browsing Profile is built for every visitor that comes to the site in order to give him/her a unique experience and propose the hidden jewels.
- Ontology Filtering is our patented new technology that combines the semantic modeling and transactional data in advanced data structures called Ontologies. These ontologies are used along with the user's browsing profile to generate real time recommendations for every user action on the site.
- Marketer tools in our back office allows marketers to easily customize every single recommendation slot so that they meet the company business goals. This is the end of the black box!
Prediggo Strategic Personalization is so innovative and efficient that is was awarded 2 silver awards in Business Efficiency and Innovation at the Best of Swiss Web 2010. Check out our case study to see why >> | ![]() | ![]() |


