Rajkumar Kaliyaperumal

Manager, Conde Nast

“I’m a Senior Data Science leader with over 15 years of experience in data sciences & data analytics, helping turn 1st party consumer data into digital assets through the application of machine learning & deep learning techniques at Conde Nast(Media). Experienced in building & implementing business use cases around consumer revenue, ad revenue businesses such as customer lifetime value, customer look alike models, subscription propensity etc. Set up & build datascience/Ml engineering teams from scratch.

As a seasoned analytics professional with prior experience in data warehousing & business intelligence, I think I have traversed the entire life cycle of data from inception till predictive insights giving me the experience across the entire spectrum of data analytics.”

Past sessions

Customer lifetime Value/Revenue(LTV/R) is the present value of the future profits/revenue from a customer. Estimating it, is important for businesses to optimise the marketing costs in acquiring and retaining the customers. Complex consumer behaviour and innumerable ways a consumer interacts with the business makes things challenging to estimate it. Years of ongoing research in this field has led to the development of various ML tools and techniques. We would like to take this opportunity to walkthrough some of these techniques and their applications in specific business contexts.


Condé Nast is a global media company that produces some of the world’s leading print, digital, video and social brands. These include Vogue, GQ, The New Yorker, Vanity Fair, Wired and Architectural Digest (AD), Condé Nast Traveler and La Cucina Italiana, among others. Subscription revenue is one of the major revenue streams for the organization and we'd like to demonstrate the implementation of LTV/R model for the subscription revenue for one of the brands using survival models and along with that illustrate the following.


Estimate the average lifetime (ALT) of a brand's subscriber.

  1. Estimate the average lifetime of various segments within the brand and identify the most valuable/least valuable segments, so marketing teams could device appropriate targeting strategies.
  2. Finally attempt to estimate the lifetime at a subscriber level.
  3. Key insights & findings through the analysis.
  4. Demo of sample code
  5. Leveraging databricks delta files for our big data processing needs.
In this session watch:
Rajkumar Kaliyaperumal, Manager, Conde Nast
Leeladhar Nagineni, Data Scientist, Condé Nast