Recommendation systems work on the premise that when past performance data is fed into a computer the resulting output is a prediction of the future performances. Developing an effective recommendation system is the biggest challenge in retargeting. The impetus lies on determining the most preferred product/service for an individual user and we often deal with such large and diverse amounts of data that the “one-size-fits-all” approach does not work.
Need for a recommendation system
During the initial days of retargeting, ads were customized based on a few fixed parameters that were common to the campaign and did not necessarily reflect the user’s interests and preferences. However, for effective retargeting, our ads have to be engaging and grab the user’s attention through product/service offerings in the ad that appeal to the user. With advancements in web technology, retargeting has evolved and it is possible today to customize our ads for an individual user based on his/her interests and preferences.
Implementation of recommended systems
Vizury has been experimenting with various recommendations systems for verticals like ecommerce, hotels, flights etc. The first experiment was developed on the principles of collaborative filtering (CF) and worked on the assumption that if there is a huge affinity for two products among users visiting an advertiser website, a user who has seen one of the products is likely to be interested in the second product as well. This is one of the most common algorithms developed in this field and it performs really well. There are other algorithms which are either variants of CF or are proprietary to Vizury. These algorithms work better than the previously used technique and have resulted in a boost in online sales for advertisers and at the same time have enhanced the user’s online experience.
Identifying the best performing experiment
With the recommendation system live on multiple campaigns it is extremely difficult to manually identify the best experiment for each campaign. We have developed the Homing Algorithm to automatically identify the experiments that perform well for a specific campaign. The algorithm takes into account the business model of the campaign, analyzes the data and changes the weights of each algorithm for an individual campaign. We are running the initial experiments with the Homing Algorithm and expect the results to flow in soon. With this development, we also hope to deliver better returns for our clients. Watch out this space for the results of our experimentations that we promise!
Nishant Kadian takes care of content marketing for mFaaS. He is passionate about sharing his learning on the ad technologies, mobile ad fraud preventions, and more. Drop him a 'Hello' on LinkedIn or Twitter to start a conversation with him.