CASE STUDY

How an eCommerce Giant Achieved Effective Targeting Strategy with the help of Tatvic’s Customer Lifetime Value Analysis using Google Analytics 360

Customer Lifetime Analysis
client background

Client Background

The client is India’s B2C frontrunner in the fashion and apparel domain. They have a huge customer base and like every eCommerce giant, they also run multiple marketing campaigns at any given point in time. Since their spend on these campaigns is so big, one of their pain points is achieving a higher ROI from better performances of the campaigns.

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    problem

    Challenge

    They were facing a difficulty in attracting high intent customers. Their requirement was to remarket to those customers who have a higher intent for making a purchase so as to increase their ROI through these marketing campaigns.
    Their marketing cost was touching the sky. They experienced the absence of a statistical or scientific model to target customers based on their behaviour for different offers. This shrunk their ROI on remarketing campaigns.

    approach to solution _ hypothesis

    Approach to Solution

    Our data science team has developed a Customer Lifetime Value model using which, we extracted users’ transactional data for three months – users acquired from 1st June 2017 to 15th June 2017 using Google Analytics 360.

    With this data set, Tatvic’s data science team ran a frequency, recency, and age based Customer Lifetime Value model to calculate CLV of users, probability of being alive and predicted purchases for next 1 month.

    Results

    Results

    Owing to Tatvic’s CLV analysis, the client saved money by not allocating their marketing spend on targeting the low CLV users.

    Read the full case study as to how the client achieved such a significant result using Tatvic’s Customer Lifetime Value Analysis solution.

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