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Multi-Level Ensemble Learning Based Recommendation System – Pinnacle of Personalized Marketing

Whitepaper

Multi-Level Ensemble Learning Based Recommendation System – Pinnacle of Personalized Marketing

By creating customer experience that is both personalized and comfortable, and by presenting the right set of products through the right channels according to the customers’ preferences, retailers can keep customers engaged and thus retained. This can be made possible through a revolutionized machine learning algorithm, Recommendation Engine. In this white paper we have built and deployed Recommendation Engines based on different algorithms such as Apriori algorithm and Generalized Low Rank Models (GLRM) for our clients in fashion and food.

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A Prudent Approach in Predicting Time to Churn for Fashion Retail Industry Using Accelerated Failure Time Models in A Non-Contractual Setting

Whitepaper

A Prudent Approach in Predicting Time to Churn for Fashion Retail Industry Using Accelerated Failure Time Models in A Non-Contractual Setting

With rapid growth in the market across all businesses, it is a mandate to focus on retaining the existing customers, failing which might result in massive profitability reduction across major perspectives. This white paper attempts to predict the time to churn of a customer in a non-contractual setting, thus providing a roadmap to model the said problem using Parametric Survival Analysis.

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Multinational Fashion Retailer

Case study

Multinational Fashion Retailer

A customer-led inside out behavior analytics approach was employed to ensure optimal impact, comprising:

  • Customer focus group led voucher planning, informed by insights and responsiveness to past behavior and predicted customer behaviour
  • Integration of personalization within Trade Promotion communications using Customer Intelligence
  • Rigorous and consistent measurement approach to establish true incremental sales baseline & evaluate effectiveness

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Multinational Food Retailer

Case study

Multinational Food Retailer

Through a collaborative effort between Customer Insights, Loyalty Operations & ITC Infotech’s CVM team the loyalty personalization program was redefined as:

  • Reset measurement and implement consistent reporting framework to assess short-term and longitudinal impact
  • Re-built an analytical model using Advanced Statistical / Machine Learning and Deep Learning algorithms to align to Customer Value Management principles
  • Evaluated , modified and expanded the voucher pool to align to customer objectives

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Contact Us | Sitemap | Corporate Identity Number: U65991WB1996PLC077341 | For any queries or grievances contact Mr. Jay Shah at contact.us@itcinfotech.com

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